wireless sensor networks

View with images and charts Routing Protocol of Wireless Sensor Network Chapter 1 Introduction A wireless sensor network (WSN) consists of spatially distributed autonomous devices called sensors, and one or more base stations to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The research in sensor networks received a big boost with a number of funding initiatives by US military, NSF and DARPA SENSIT [17]. Many novel sensor based applications have emerged in recent past. We may classify the sensor applications into following classes [6]: •

Monitoring spaces: This class refers to passive data gathering recognizing occurrence of some events or conditions. The gathered data are typically inputs to a number of target applications. These target applications includes habitat monitoring, monitoring of crops (failure, pest attack), climate control, security surveillance, intelligent alarms (fire, flash flood, volcanic eruption), etc.

•

Monitoring things: This class refers to gathering data to recognize occurrence of specific states of a system. On occurrence of these states the system may execute a sequence of internal transitions to get into a desirable state. The target applications could be structural monitoring (bridge health monitoring), equipment maintenance, medical diagnostics, etc.

•

Monitoring complex interactions: It involves monitoring of complex interactions of systems and things. For example, wild-life habitat monitoring, disaster management, emergency response, smart environments, surface and sea navigation, health care, manufacturing process flow, etc., would require complex interactions of systems with objects or things.

Such wide-ranging applications requiring WSNs, make them candidates for intense research.

The research spans hardwares, systems, networking, and programming methodologies.

Considering ubiquity of applications, one of the crucial design decisions for sensor nodes has been to settle for a small form factor. The advantage of small form factor is that these miniature devices are inexpensive. Thousands of sensor nodes can be deployed with a small cost. Therefore, the key to success of sensor based applications is to network sensors in an efficient way for gathering sensory data from their respective deployed environments.

Every sensor has three basic units, namely, sensing, radio, and battery. The major constraint being limited energy (small battery unit). Consequently, a large number of sensor nodes can be networked to gather sensory data and each sensor performs two main responsibilities, namely, (i) sensing activities, and (ii) routing the sensed data to the base station or a controller. The base station is a master node which is generally fixed and assumed to have uninterrupted power supply or accessible for maintenance (such as replacement of battery). It

acts as an interface of the WSN for complex interactions with other objects. The main responsibility of base station is to collect information from various sensor nodes and process it for further dissemination/actions.

Energy dissipation at sensor node is a major concern, as in many applications sensor have to be deployed in inaccessible environments. Sensing alone is not an energy consuming activity, but networking and programming certainly are. So, the major problem lies in activities like routing, addressing and support for different class of programming services. In this thesis our focus will be on routing problems.

The topology of a WSN determined by 1. a set of external parameters such as node mobility, weather interference and noise, and 2. Also by a set controllable parameters like transmission power, antenna type and direction, etc.

Normally the sensor nodes are uniformly spread over the whole of physical space to get the best coverage. The nodes are mostly static unless they are either mounted on moving vehicles, robots, or tagged to live animals.

Therefore, topology control is mainly possible through adjustment of transmission power, and associated radio adjustments. Thus, careful design of routing protocols that combine well with topology control mechanisms is extremely important for working of WSNs. The design of routing protocols for WSN are influenced by many factors including hardware constraints, network topology, and power consumption. The sensor networks are mainly of two types - event-driven and time-driven (or continuous dissemination networks). In the event driven networks the sensor nodes sense the data and transmit it only if the data is considered critical enough to be communicated. In the time driven networks sensors sense the data and transmit it to the central controller periodically. The periodicity of relaying data packets is application dependent.

The two broad categorization of routing protocols are: (i) proactive, and (ii) reactive. In a proactive protocol, the routes are discovered and maintained using periodic messaging. Each node is expected to send periodic packets to the neighboring nodes. The packets contain the routing information of the sender node. The recipient nodes configure their routing tables based on received packets or make necessary updates as required. These periodic packets known as Hello packets indicate that the sender can still take part in routing process. As topology information is exchanged on the regular basis it causes unwanted overhead. On the one hand, a route to base station will always be available on request; but on the other hand the nodes unnecessarily update routing tables on periodic basis even for the routes which may not be required at all.

The reactive protocol discovers the route once and then maintains it reactively. Every time a path is unable to deliver a message, that path is rendered invalid and new path is discovered.

Though the reactive protocols are quick to respond to the nodes moving in and out of the network, the latency for discovering a path could be long and may be inadmissible for certain applications. Both proactive and reactive protocols have their pros and cons. So, a protocol can best work mid way between the two.

The coverage area of a sensor node (or the reach ability of a node) depends on its transmission range. If the transmission range of all the nodes is high enough to reach the base station, then it is considered one hop network. Such networks do not incur overhead of additional control packets for route discovery and maintenance. However, as the wireless is a shared medium, one hop networks lead to densely connected networks and suffer from severe congestion. In other words, there is a trade off in selecting a suitable transmission range for the nodes and severity of congestion. The range should be chosen optimally to eliminate congestion and to retain desired network connectivity.

A network in which all the nodes have same transmission range, as in the example depicted

by Fig 1.1(b) is called a Symmetric Network. In a symmetric network as indicated by Fig 1.1(b), if node A is in the transmission range of node B, then B must also be within transmission range A. If the transmission ranges of nodes are configured at the network start-up time and all the nodes do not have same transmission range, then the network is considered to be an Asymmetric Network. Fig 1.1(a) provides an example where a pair of asymmetric links is shown to exist between nodes A and B. Node B is in range of A, but A is not in range of B

Figure1.1: a.) Asymmetric Link b.) Symmetric Link 1.1 Motivation As stated earlier, the sensor nodes due to their small form factor have limited power. In order to prolong the life of the wireless sensor networks, the routing protocols apart from being robust and scalable, needs to be highly energy efficient. A lot of research [7, 8, 23, 30] has taken place in this direction and various routing protocols are proposed to achieve these objectives.

The work reported in this thesis aims at designing of a multihop energy efficient, reliable and fault-tolerant routing protocol. In a fully connected network, all nodes can directly access the base station. However, wireless being a broadcast medium, the congestion [13] in such a network is very high. Typically, each node in a multihop WSN would discover a path to the base station and route its data through this path. This causes the nodes near the base station to be used more frequently than the nodes away from the base station. The reason is the former set of nodes not only send their own sensed data, but are also responsible for forwarding the packets from the far off nodes in the network. This results in a bottleneck around the base

station. If the nodes around the base station go dead, then the nodes away from base station will be unable to send the data unless they increase their transmission ranges.

Figure 1.2: Example Network Fig 1.2 shows an example of a typical sensor network. The filled black node is the base station. The lines depict the connectivity and the filled gray nodes are the normal sensing nodes. In this example node-2 and node-3 are one hop nodes. Node-2 is responsible not only for sending its own data but also for forwarding the data from nodes-4, 5, 6, 9 and 10.

Similarly, node-3 is responsible for sending its own data and as well as forwarding data of nodes-7, 8, 11 and 12. Thus the nodes situated at a distance of one hop from base station are used more often than the other nodes. It causes such nodes to dissipate energy at a substantially higher rate than the rest of the nodes in the network. Consequently, the network becomes dead very soon. The residual energy in the nodes near the base station may be sufficient to sense, but may not be sufficient communicate the sensed data to the base station.

This observation led us to think how a routing protocol may avoid the formation of transmission bottleneck around the base station. The underlying idea is to ensure that the energy dissipation at each node is more or less same over the entire network. The approach we devised is to adjust transmission range of individual nodes to provide connectivity of base station without involving the nodes near the base station as and when desired. It led to dynamicity in the sensor network environment. We can view the dynamicity as insertion of new nodes and deletion of nodes (node failure) at random time.

Our second observation is that WSNs experience high packet losses. So, reliability is also important for WSN. Transmission reliability can be achieved through an acknowledgment based protocol. It helps in deciding the requirement of retransmissions if the packet loss occurs and thus increases the reliability.

An usual trend in protocol design suggests that the location parameters play crucial role for the routing purposes [12]. The determination of position parameters requires use of embedded GPS [4] receivers. But with embedded GPS receivers, will make the sensor nodes substantially expensive. Additionally, also the nodes will consume more power. Therefore, our aim is also to ensure come up with routing protocols which avoids use of location based information.

1.2 Problem statement and the Challenges Involved The investigation in this thesis is oriented towards the design a multi hop routing protocol for time driven WSN which achieves following objectives.

•

Avoid the formation of the congestion bottleneck around the base station

•

Handle dynamic changes in topology caused by transmission power adjustments.

•

Handle node failures on existing paths by a novel route repair procedure that leverages ability to adjust node transmission range.

•

Ensure reliability through use of acknowledgements and limited retransmission of lost packets.

Improve the network lifetime by considering residual node energy during route discovery.

• •

Analyze and compare the new protocol with the existing ones.

The objectives listed at items 1 and 5 are achieved by better utilization of the resources and treating the network as a dynamically changing asymmetric network. The topology change in the network is mainly determined by adjustment of transmission range from time to time. The resulting change topology modifies path from every node to base station. The routing protocol is, therefore, is a mix of both proactive and reactive protocol. The tasks of route discovery and route maintenance become more challenging in an asymmetric network, as Hello messages can not be used for such purposes in asymmetric links. The latency of route discovery also has to be minimized.

Balancing distribution of load for routing (forwarding packets) among the nodes shall ensure the uniform and better resource utilization. The concept of asymmetric network shall allow the nodes to use appropriate transmission ranges, which is more suitable for its longer lifetime depending on its position in the network.

Fault tolerance in routing protocols for WSN is a novel idea. It has been possible to tolerate node failure simply by eliminating failed nodes through a route maintenance step. It involves the adjustment of transmission power at some nodes for establishing connectivity with the base station when the routes are found to be broken due to node failures. We experimented with up to 5% node failures.

The objective concerning reliability in transmission is achieved by acknowledgments for the data packets. The protocol supports at most three retransmissions of the data packets to handle packet loss.

The routing metric is chosen by optimizing both latency of routes and residual energies at the nodes. The other desirable property of a protocol which is addressed is load sharing. The load for routing data packets is distributed over the multiple alternate paths available at a node. It ensures overall better utilization of energy resources and effectively extends lifetime of the network.

Finally, we performed simulation over OMNet++ [1] and compared our results with other notable time driven WSN routing protocols such as LEACH [12] and SPIN [11]. Our results show performance enhancement over symmetric routing protocols and over both SPIN and LEACH even without data aggregation.

1.3 Organization of Thesis The rest of the thesis is organized as follows.

Chapter 2 discusses the related work and lays the background further discussion. The important existing protocols and the improvements subsequently proposed upon them are also discussed. It also indicates how these solutions differ from the new protocol proposed in this thesis.

In Chapter 3, the new protocol is described. We focus on robustness, network lifetime, routing latency to show how the proposed algorithm has potentials to outperform other existing protocols. The protocol is discussed in details by dwelling on its features, various phases of operation and implementation aspects.

Chapter 4 throws light on the actual logic of the proposal by presenting the algorithms. An analysis of performance parameters is also presented to provide an insight into its performance. The chapter also describes simulation software and the simulation environment.

The details of the radio characteristics used for the purpose of empirical evaluation.

The results duly supported by the relevant plots for performance characteristics and related analysis are presented in Chapter 5.

Chapter 6 concludes the thesis with some directions for future work.

Chapter 2 Related Work As explained in chapter 1, the design of routing protocols for WSN is a subject of intense research as both quality and quantity of information delivered to the end-users is very important for the applications centered on WSN. It has been observed that different protocols work better in different environments/applications. The issue of the effective utilization of energy resource has also been addressed in extensively in the literature. This chapter deals with related work and the underlying concepts which form the basis of energy aware routing protocols for WNSs. Section 2.1 explains how simple routing ideas could lead to unnecessary wastage of precious energy resources. Section 2.2 deals with some initial thinking done by researchers on the ways to avoid broadcast storm which appear to be the main reason behind excessive energy wastage in routings ideas based on flooding and gossiping. Section 2.3 describes a number of routing protocols, namely, LEACH [12], PEGASIS [19] TEEN [20] and APTEEN [21] which try to eliminate redundant data broadcast and conserve the crucial energy resource by aggregation in some way or other. Later, section 2.4 which are relevant in context to this thesis. The subject of Discussion in section 2.5 is the quality and reliability of data delivered by the sensors.

2.1 Flooding and Gossiping The conventional mechanism for relaying data from individual sensors to base station is Flooding or Gossiping [10]. The underlying idea of flooding is that each sensor on receiving a data packet broadcasts the same to its every neighbor. This packet is further re-broadcast until it arrives at the destination or the maximum number of hops for the packet is reached.

Quite obviously, flooding raises unmanageable broadcast storms as depicted in Fig 2.1.

Figure 2.1: Broadcast storm It may be appropriate for networks which experience quick topology changes. WSN mainly consists of static sensors nodes. So, topology remains more or less static for a considerable period of time. Of course, it is possible to control topology by adjusting transmission power of sensor motes. But such power adjustments can only be realized by program controls and hence, predictable. The other serious problems with broadcast storm are redundancies, contentions and collisions which result in wasteful consumption of node energies. Since sensor nodes have only limited energy reserves in the form of small batteries, they can illafford such associated problems arising out of broadcast storms. Therefore, flooding does not seem to be an appropriate approach of design of routing protocols in WSNs. The deficiencies of data reporting by flooding can be viewed under three categories [11], namely, •

Implosion: it refers to nodes disseminating data to all of their neighbors regardless of whether the data has already been received by these neighbors. It results in wastage of energy across several nodes, since the same piece of data arrives at every node via multiple paths.

•

Overlap: it is not possible to have a precise deployment whereby sensor could cover disjoint spatial regions. So nodes often have overlapping spatial coverage. Data dissemination by flooding, therefore, results in data from overlapping regions to be routed by different alternative sensors and flooded over the network.

•

Resource ignorance: flooding does not require the nodes to modify their activities on the basis of energy threshold. Consequently, a node may run out of energy too soon to perform any meaningful data sensing or reporting. For example, nodes near the base station could run out of their batteries being over used while forwarding data

from distant nodes. This may result in network going dead too soon even when many distant nodes have sufficient energy to carry out their reporting and sensing activities.

Gossiping [9] is just a slightly enhanced version of flooding wherein the recipient node sends the data packet to a randomly selected neighbor. That neighbor in turn picks another random neighbor to forward the packet to and so on. Another usual variation of gossip protocol is that the recipient could rebroadcasts or discard the data with probabilities p and (1ďż˝ p) respectively. By controlling the fraction of executions where gossip die out relatively low and keeping the gossip probability low it is possible to save up to 35% message overhead compared to flooding [9]. But gossip based protocol exhibit bimodal behavior in the sense that in almost all executions either most of the nodes receive a message or hardly any of them do. Furthermore, in many applications gossip delay may be unacceptable.

2.2 SPIN and Directed Diffusion The focus Sensor Protocols for Information via Negotiation (SPIN) [11] was to work around the problems of simple flood based protocol mentioned in the previous section.

SPIN was successful in addressing all the problems existing in the flood based protocols.

It labeled the data with the high level data descriptors, called metadata. The metadata was used to negotiate between the nodes, eliminating the redundant data from being transmitted to the nodes. As the metadata was supposed to be the compressed form of the original data so, the network congestion could be avoided. So, the SPIN mainly comprises of three phases.

STEP 1: Advertisement of the metadata STEP 2: Request for the data STEP 3: Actual data transmission.

Fig 2.2 depicts the stepwise process.

Figure 2.2: SPIN

There is also an energy efficient version of the SPIN in which a node enters into this three way handshake only if it has sufficient energy to complete it till the end. SPIN not only achieved a considerable improvement over flooding, but also instrumental to open up the existing gaps in the research on routing protocols. It is observed that the data actually traverses along multiple paths but gets finally accepted from just one path, and discarded from remaining others. SPIN’s implementation did not talk about the reliability which may be one of the important issues in reliability considering that the wireless networks are highly error-prone.

Subsequently, a new protocol called Directed Diffusion [14] was proposed for event driven Applications. Directed diffusion concentrated on reducing the number of multiple paths along which data traverses. It also incorporates some novel features - data-centric dissemination, Reinforcement based adaptation to the best path, in-network data aggregation, and caching. A central controller called Sink injects its interest in the network by normal flooding with a large update interval. Sensors report data if they match with the interest received from the sink node. A sensor sends to the interested node through multiple paths. The neighboring node establishes a gradient towards each other based on the direction from which they have received interest. This way the interested data finds its path to the sink. Apart from being unsuitable for continuous data delivery, it incurs extra overhead for data matching and interest injection.

2.3 Aggregation Protocols Considering that the sensors are resource constraint, and the existing inherent redundancies which characterize data reporting in SPIN as well as directed diffusion, a lot of scope for improvements was there. LEACH [12] is another protocol in line to throw some light on the same issues and propose some more improvements. The basic idea employed in LEACH is cut down the overhead by reduce volume of data reportage. LEACH is an energy efficient, aggregation protocol. It includes cluster formation and local processing to reduce global communication by collecting the packets of all the nodes in the cluster in one place and aggregating the information contained. The randomized rotation of the cluster-heads helps in implementing the load sharing property.

The LEACH protocol involves the following steps: STEP 1: Status collection of all nodes by the base station.

STEP 2: Selection of the cluster heads by the base station.

STEP 3: Choosing of the best cluster head by the node STEP 4: Data dissemination to the cluster head STEP 5: Aggregated data dissemination to the base station by the cluster heads.

The problem with LEACH is that it considers all the nodes to have accessibility to the base station when it becomes the cluster head or when sending the status message to the base station. According to the MICAz [2] specifications, the maximum outdoor transmission range which is allowed for a node is 100m. The indoor transmission range is even less, it is 30m at the maximum. That means, to provide one hop connectivity the maximum distance of a node

from the base station can be at most 100m.If the base station is placed at edge of the network area then the maximum area which can be allowed in LEACH is (_ _ 502=2) m2.

In LEACH each node decides its cluster head based on which of the heads is closest to it.

It means that the location parameter plays an important role here. The nodes need to know the position coordinates of all the cluster heads chosen for that round. LEACH needs GPS enabled node, which requires considerable amount of energy apart from making the sensor motes quite expensive.

The original LEACH proposal provisions for 5% of the total nodes to be selected as the cluster head. With 100 nodes, the number of clusters formed will be 5. Given that the maximum area which can be covered is (_ _ 502=2) m2, then each cluster will cover about 785m2. Therefore, even after using so many nodes in such a small area, the actual data transferred to the base station is not much. If the volume of data to be transferred to the control-center is considerable then either the number of nodes must be increased or the percentage of the total nodes selected as the cluster heads should be increased.

The energy dissipation at nodes is proportional to the square of the distance from the destination, therefore, the node which dies first is always the farthest nodes.

LEACH is not an acknowledgement based protocol. So, the nodes receive no information of data being lost or corrupted on the way to the destination. The protocol, therefore, lacks reliability. The /wireless networks are full of noisy interferences and LEACH does allow any redundancy in data, as the aggregation removes redundancy, so non-reliable nature can be considered as one of the major drawbacks.

Although the LEACH does not talks directly about asymmetric links, nor does introduce the concept, it is implicit from the explanation that the nodes adjust their transmission range every time they need to communicate, based on their distance from the node with which they want to communicate. In a sense, the network can be considered to be asymmetric. The asymmetric nature of the network here does help in avoiding the wastage of energy and congestion, but as the protocol is not implemented in a distributed way, the asymmetric characteristic is under emphasized. Rather it is considered a network with nodes having adjustable transmission range.

Environmental energy harvesting has also been considered for improving the sustainable lifetime of the resource constraint network. Numerous harvesting modalities have been suggested including solar, vibration, biochemical, and light. A successful demonstration of the solar energy harvesting has been shown in [28]. The SOLAR-aware LEACH [28] aims at extending the lifetime of the sensor network by choosing solar-aware nodes as the preferable cluster heads.

While the harvesting energy provides the ability to extract energy from the environment, it must be efficiently integrated into an embedded system to translate that harvested energy into increased application performance and the system lifetime. This shows that harvesting itself is very complex. The other drawback being that the solar energy harvesting requires outdoor system deployment, thus does not work for the indoor networks.

Lindsay and Raghavan [19] proposed Power Efficient Gathering in Sensor Information Systems (PEGASIS) which achieved about 100-300% improvement over LEACH over a

range Of percentages of nodes dying out in different network sizes. In PEGASIS sensor nodes form a chain to transmit and receive the data. Each node transmits or receives data from a neighbor and only one node from the chain is selected to send the data to the base station. Each node on receiving the data aggregates it to its own data and then transmits further. The aggregated data is eventually sent to the base station. The chain construction is done in a greedy way.

Figure 2.3: PEGASIS Fig 2.3 illustrates that node-0 transmits data to node-1. Node-1 aggregates it to its own data and transmits it to the node-2. Similarly, node-4 transmits its data to node-3. Node-3 aggregates it to its own data and transmits it to the node-2. The node to finally aggregates all the received data along with its own and relays it to the base station.

Unlike LEACH, PEGASIS uses multi hop routing and only one node sends the data to the base station. Although, PEGASIS shows 100 ďż˝ 300% improvements over LEACH for different network and topologies, there are many issues which cannot be neglected when analyzing a protocol. There is only one packet which is reported to the base station every second, no matter how large a network may be. The nodes are supposed to be power adjustable do that if they are eventually selected to the send the final packet to the base station, then many must be able to do it in one hop. This again brings to us the constraint of maximum power which a node can possess. Given that the maximum outdoor range is 100m, restricts the area which can be covered by PEGASIS. As PEGASIS reduces the redundancy to zero in the network, there is expected to be some scheme to ensure reliability which is again absent.

Threshold sensitive Energy Efficient sensor Network (TEEN) proposed by Manjeshwar and Agrawal [20] is actually the modified version of LEACH. The modification proposed was to convey two attributes to the nodes from the cluster heads, namely, (i) Hard Threshold and (ii) Soft Threshold. Hard threshold was the absolute value of the sensed parameter beyond which the value must be t ransmitted to the cluster head. Soft threshold was the value of the sensed parameter beyond which the node must activate its transmitter.

As the data was expected to be transmitted less frequently than being sensed, so the protocol is much more energy efficient then LEACHES. But there are drawbacks which cannot be neglected. If the threshold is not reached then data is not communicated to the base station. There were no messages to inform the base station that whether the node has gone dead or the data is not crucial enough to be reported. Thus, the reliability of data reportage becomes an important drawback.

Adaptive Periodic Threshold sensitive Energy Efficient sensor Network APTEEN [21] was designed to remove the drawbacks of TEEN. APTEEN adds an extra attribute to the packet sent by the cluster heads to the cluster members. It not only includes the thresholds but also includes the maximum interval between two packets. This modification makes the protocol

usable even by time driven networks. As the data get sent periodically, it is easy for base station to know that the node is dead or has failed to send data.

2.4 Other Related Works The problem of bottleneck around the base station was previously addressed in [24]. It proposed to increase the density of nodes as we move towards the base station. So, more nodes are available near the base station to share the responsibility of relaying the data packets. The proposal recommends certain nodes as relay nodes which act only as forwarding nodes and not as sensors. One of the problems faced with such an approach is that if sensor nodes are dropped from a height with possible error in location, so, having certain nodes in predetermined coordinates is not possible. Furthermore, interferences also increase with increase in density of the nodes. An energy efficient probabilistic scheme has also been proposed in [27].

Parameters

Flooding

Energy Efficient Redundancy Reliability

No

NO

YES IMPLICI T HIGH

YES IMPLICT

HIGH

HIGH

LOW

LOW

Reported Data Message Complexity BS Responsibility

Gossiping

SPI LEAC PEGASIS TEEN N H YES YES YES YES NO NO

HIGH

NO NO

APTEEN

AROS

YES

YES

NO NO

NO NO

NO NO

NO NO

HIG Medium

LOW

LOW

LOW

Medium

LO Medium

LOW

LOW

LOW

Medium

H W LO Medium Medium

Medium Medium

HIGH

W

Table 2.1: Summary of Some Well known Routing Protocols for Wireless Sensor Networks The author the view that always using lowest energy path may not be optimal for the lifetime and the long-term connectivity of the network. So, they propose to use probabilistic forwarding. The paper concentrates on the importance of energy but only in a symmetric network. The weight age to be given to the residual energy factor is also not clearly justified.

The paper compares the proposed protocol over existing directed diffusion protocol.

Not much work has been done on the sensor networks with asymmetric links. Asymmetric communication and Routing in Sensor networks (AROS) protocol [22] proposes improvements over the existing LEACH protocol. In AROS, all nodes are not at one hop from the base station. The cluster heads which does not have direct reach ability to the base station uses a path through other cluster heads. AROS was compared to LEACH and showed 97% improvement in the lifetime of the network. AROS loads base station with most of the responsibility. The path discovery from a particular cluster head to the base station is actually considered to be a centralized process, to be handled by the base station. This way it becomes very difficult to account for any dynamicity in the network, i.e., addition of new nodes and the failure of any existing one are difficult to handle.

The protocol which overloads the base station with high responsibilities is not always suited for the applications where the base station may also run on battery, e.g., a laptop. The bridge monitoring application can be considered as one such example.

2.5 Quality and Reliability in Sensor Networks As the wireless sensor networks have found their applications in many crucial domains like medical, defense and navigation system, it is important to lay due emphasis on the reliability and quality of the data transferred. The traditional protocols paid least attention towards the reliability issue. The delivery of the data packets was expected to be positive, as there was much redundancy in the network. Later the protocols [11, 12, and 14] reduced redundancy to conserve energy but the reliability issue was left untouched.

The middleware quality attributes [26] has always been an area of interest. But the researchers were of the view that, to achieve these quality attributes there has to be trade-off in performance. The major attributes which attracted attention includes, Scalability, Modifiability, Availability (fault tolerance) and Maintainability. Currently, there is lot of interest on ensuring quality attributes with high performance guarantee.

The work in this thesis is motivated by all the properties which are desirable considering the sensor network applications and are found lacking in existing protocols. In particular, we try to address the following issues.

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Load distribution among the nodes which are near and far-off from the base station.

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Dynamic enough to handle the insertion and deletion of the nodes in a distributed way.

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Route discovery and maintenance also through a distributed algorithm.

•

The redundancy of the data packets is reduced to zero and the protocol is made reliable through the presence of the acknowledgements.

•

Energy is considered as one of the most crucial resources and invested with care.

The aim of this work is to propose a design with all the above properties and performs better compared to the other exiting protocols.

Chapter 3 Proposed Methodology Wireless Sensor Networks (WSNs) like mobile ad hoc Networks (MANETs), are selfconfiguring networks. But unlike MANETs, they are statically deployed and each WSN has a central authority, known as base station. Each sensor performs the sensing task independent of others, but the routing of the sensed data to the base station needs communication among the nodes. Sensor nodes do not communicate except for forwarding data to the base station or helping in disseminating data from the base station. Therefore, when determining the routes in a WSN, single destination shortest paths are needed. Sensing requires very little energy as compared to communication.

Typically, the major physical layer property which is assumed to be fixed in design of a routing protocol for WSNs is the node transmission power. The network with nodes having uniformly fixed transmission power is referred to symmetric networks. The traditional protocol stack independently models the physical layer and the higher layers. The abstraction of one la1yer is added to the other layer to provide power of multi-layering. Power control is very important in designing of any protocol for wireless sensor networks as the nodes are battery dependent. To control the energy consumption at the nodes, the transmission range could be varied. But, the variation should be done considering the fact that the nodes have at least one path to the base station and at the same time do not increase their respective transmission ranges so as to give rise to a densely connected graph. The configuration of the physical layer properties at the startup time needs hardware instructions. The transmission range is adjusted thereby to get the best results at the network layer.

When working with asymmetric networks the physical layer properties come into picture very often, as transmission range is required to be adjusted from time to time. The current chapter of this thesis deals with a proposal for a new routing protocol based on the above principle of cross layer optimization [18]. Extensive simulations were carried out to compare the proposed protocol with some existing protocols. The simulated protocol includes SPIN [11], LEACH [12] and the two versions of symmetric protocols. The symmetric protocol versions are based on DSDV [25], so minimum hop count has been used as routing metric.

The rest of the chapter organized as follows. Section 3.1 gives a brief statement of the problems discussed in the chapter. Section 3.3 presents the various technicalities of the newly proposed protocol describing each phase and its features and the reason for the same. In the end, section 3.4 concludes the chapter highlighting the properties of the proposed design.

3.1 Energy-aware Protocols The research on energy-aware protocols for WSN has so far focused on two major strategies to optimize energy usage, viz., (i) reducing transmission of redundant data, and (ii) confining communication to local sub-regions (clusters) by partitioning areas of sensing environment.

Two well known protocols, namely SPIN and LEACH which we chose to simulate are the representative cases of the above two strategies. We also simulated two versions of symmetric protocols. The characteristics of symmetric protocol versions are as follows: •

The basic protocol is a modification over Destination Sequenced Distance Vector (DSDV) [25] protocol for ad hoc networks. The routing metric used is minimum hop count. The routing in the first version does not have any energy optimization criterion.

•

The second version attempts to make the first version energy efficient by introducing residual energy consideration.

The newly proposed asymmetric routing protocol has been simulated with the purpose of comparing it with the existing work. We looked at the following three versions of the proposed protocol. •

The first version is just a simple (no energy consideration) routing protocol that works for asymmetric network. It uses minimum hop count as the routing metric.

•

The second version proposes to make the above protocol energy efficient by introducing the consideration of residual energy while relaying the data.

•

The third version adds an aggregation protocol to combine residual energy consideration with the idea of confining communication among nodes locally.

3.2 Routing Protocols for Symmetric Networks Since, WSNs are resource constraint, the most important thing to be kept in view when designing a routing protocol for WSN is that it should be (i) resource aware, and (ii) should not be too complex to implement. The symmetric routing protocol has been designed with these two constraints in mind. The underlying idea is to avoid flooding or injecting redundant data packets while the data is being transmitted, a route discovery is initiated or a route maintenance event occurs.

3.2.1 Multihop Minimum Hopcount Protocol Our first attempt is to find the shortest path between the nodes and the base station and to be able to maintain it in a distributed way. We have employed acknowledgements for the data packets to ensure data transmission reliable.

Route Discovery Normally, route discovery in symmetric networks is performed in top-down fashion. The base station initiates a route discovery process by sending a broadcast message to all the neighbor nodes. Each node caches a routing table which contains the preferred neighbor and the hop count of the path. Fig 3.1 illustrates the fields of routing table. The receiving node updates its routing table and rebroadcasts the request packet including its own details downstream towards destination. The updates are performed only if the hops reported by the route information packets are less than the hop count of the path cached.

Figure 3.1: Routing Table The route discovery process is a one time task which is performed at the network startup.

Route Maintenance Symmetric network uses periodic Hello packets to maintain network topology. A Hello packet tells a node about the status its neighbors. These packets contain routing table information of the neighbors. When relaying any data packet along the preferred neighbor, the node checks whether it has received Hello message from that neighbor or not. If no hello packet is received from that neighbor for a long time then that neighbor is considered to be dead or out of service for the time being.

If the frequency of these packets is low then it takes long time to determine a nonfunctioning route. Consequently, all the packets routed through existing routes may either be

dropped or have to be retransmitted. Therefore, it necessitates frequency of Hello packets sending to be substantial in the case of time-driven networks.

Reliability The wireless medium is not a reliable medium, as it experiences interferences and noises from various sources. The reliability has to achieve by designing a robust protocol. Thus, our design is an acknowledgement based protocol. The acknowledgement is sent by base station on receiving a data packet. The sender node caches the send packet till it receives an acknowledgement. A data packet may be retransmitted three times in the case of subsequent failures. If failure occurs even after the three successive retransmission, then the packet is discarded and the cached neighbor is invalidated. The node is then left with no valid path to relay the data packets. But as it keeps on receiving the periodic Hello packets from its neighbors, it may cache the sender of the next Hello packet as the preferred neighbor. Later, it can update its cache if any other Hello packet offers a better path in terms of the hop count Evaluation of the Protocol Although the above routing protocol perform better than the traditional protocols like flooding gossiping, SPIN, still there are certain problems. In the routing tree the nodes near the base station are loaded with the responsibility of relaying the data packets of the far-off nodes as well. Therefore, the nodes near the base station exhaust their energy much before the far-off nodes. With this, even if the far-off nodes have sufficient energy to sense, they are unable to send data to the base station because of the absence of a valid routing path.

As the links are required to be symmetric, all nodes need to maintain same transmission power. Each node i select the respective minimum level li at which they have at least one path to the base station. The transmission range is the maximum of the power level selected by each node, i.e., y = max { l1, l2, ...., ln}. So, a node has to maintain higher power level even when it can possibly work well with lower power.

As already explained above, the Hello packets are required to be more frequent than the data packets in case of time-driven networks.

3.2.2 Energy Efficient, Multihop Minimum Hopcount Protocol This protocol present a modification over the simple protocol mentioned in the previous subsection. The routing table is enlarged to cache three preferred neighbors all with the minimum hop count. Fig 3.2 shows the structure of the routing table. It contains three fields the new field is introduced for caching the residual energy parameter. This version aims at providing load balancing by caching alternate paths and selecting the best out of them for each turn.

Energy Consideration The energy updates in symmetric network are sent along with the periodic Hello packets. As such no extra energy updates are required to be sent.

Figure 3.2: Extended Routing Table Every node caches three preferred neighbors (all of them lead to the minimum hop count path to the base station). One of them is chosen to send the data to the base station. The choosing of the path has to be done in a way to utilize the resources best and balance the load. If the same neighbor is used often then it will exhaust its energy before other node does and the area being covered by that node could be left unsensed. Furthermore, it might even leave certain other nodes disconnected. So it is advisable to balance the load. The best path is selected comparing the residual energy E of the three cached neighbor nodes. The node with the maximum E is selected as the neighbor for relaying the data for that turn. This accounts for the change in the routing tree every turn and thus exploits the resources best.

3.3 Routing Protocols for Asymmetric Networks This work, as already stated, aims at proposing a multihop energy efficient, reliable routing protocol for wireless sensor networks. The idea is to retain the positive characteristics of the existing protocols and overcome the drawbacks. The protocol also targets to be fault tolerant as well as scalable.

3.3.1 Energy Efficiency and Reliability Our first target is to eliminate the bottleneck around base station by sharing the responsibilities of data transmission equitably among the near as well as far-off nodes. It involves adjustment of per node transmission power. The periodic variation in the transmission power changes the network topology, and gives rise to existence of asymmetric links in the network. It maintains multiple optimal paths for communication, so that an alternate path is available if one being used fails.

Maintaining Discrete Levels Our protocol maintains discrete levels of the transmission range. The transmission ranges changes in steps. Fig 3.3 depicts the changes in network topology when the transmission range of the nodes changes in step of 5. It may be observed that by adjusting node transmission power, the number of nodes at one hop distance from the base station changes. •

Initially, as Fig 3.3 (a) shows, the transmission range is 10m and the there are 2 nodes namely, node-2 and node-3 at distance one hop to the base station. So, the data transmission load from all the nodes gets distributed among these two nodes.

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Fig 3.3 (b) depicts the change in topology when the transmission range is 15m. There are four nodes, viz., node-2, node-3, node-4 and node-5 at one hop distance to the base station. With this new configuration, the load of data transmission can now be distributed among four nodes, implying lesser loads on node-2 and node-3.

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Fig 3.3 (c) gives the topology when the transmission range is raised 20m. Now five nodes, viz., node-2, node-3, node-4, node-5, node-6 are at one hop distance from the base station. As a result, the load of data transmission can be distributed among five nodes.

Figure 3.3: Topology variation: a) Transmission Range=10m b) Transmission Range=15m c) Transmission Range=20m Thus, by regulating between the three transmissions ranges periodically, we can avoid the bottleneck around the base station.

The lowest level transmission range of different nodes may be different. It depends on the position of a node in the network, so that the node remains connected with the rest of the network, where connectivity means existence of a route to the base station. At a particular time instant two nodes can be at a different level of transmission range. The maximum level of transmission range is fixed based on two conditions: •

It should be large enough so that no node should be left disconnected with this Transmission range when considering a symmetric network.

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It should not be very large so as to make the graph very densely connected and lead to wastage of energy resources.

The transmission range of the node increases in levels. But there is no point in increasing the transmission range of a node further once it has reached the level when it is at one hop distance away from the base station. This way the maximum transmission range level attained by different nodes is different.

Transmission Range Period The period for which a particular level should be maintained is same for all nodes. This period is decided based on number of nodes in the network, the application and the area to be covered. The reason is: sufficient time should be allowed for the newly formed topology to converge.

The time required to converge the topology depends on the RTT (round trip time), since the route discovery takes time proportional to RTT (when done in bottom up fashion). The round trip time is different for different nodes. It depends on the number of hops (counted on the basis of initial transmission range) between the node and the base station. As the number of

hops increases the round trip time increases. The period of change of transmission range is required to be large enough to allow the topology convergence.

Suppose the time taken to cover one hop distance is x seconds. For N nodes the worst topology possible is linear topology and so in worst case the hop count of a node to reach the destination (base station) is N. In this scenario the round trip time to discover the route is 2N x seconds. Therefore, to provide enough time to a topology to converge and use the discovered path, the transmission range period _ should be greater than 2N x seconds, i.e., _ > 2N x.

Route Discovery The periodic changes in transmission ranges of nodes create asymmetric networks. The process of route discovery in such networks is different from that in symmetric networks. In an asymmetric network a path which may be valid from the base station to a node may not be valid when reversed.

The reason is some node-A on the one way may be in range of other node-B, but node-B might not be in the range of node-A. Fig 3.4 illustrates an example where there is a valid path 1-2-5-9 from base station to node-9, but this path is not valid in the reverse direction. In other words, node-9 can not reach 1 via the reverse path 9-5-2-1. The data packets from node-9 can be transmitted along the path 9-4-2-1. Such a path can be discovered only by bottom-up approach and not in top-down manner.

The node which needs to discover the route initiates a route request which is broadcast to all the neighbor nodes. A node receiving the route request packet checks for the staleness of the packet and on finding it fresh rebroadcasts it further, till the request reaches the base station.

Assuming that the base station is a powerful node (connected to stable power supply) so it can reach all the nodes through one hop. The base station sends back the reply which includes the preferred neighbor of the requesting node. The base station avoids sending the complete path to the requesting node. It has three advantages.

Figure 3.4: Asymmetric Network

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The sending complete path would unnecessarily increase the size of the reply packet.

If the number of nodes is N, assuming the worst case topology (linear topology) the maximum number of hop-counts to reach the destination (base Station) can be as large as N. So, the size of the reply packet may increase by a factor of N.

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The requesting node caches only the preferred neighbor’s id. If the complete path was to be cached then memory (an important resource) would be wasted for no fruitful reasons.

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If complete path were cached then the nodes caching the three paths to the base station has an option of using one out of three alternate paths available to a node. On the other hand, by caching preferred neighbors, potentially more alternate paths can be stored. In Fig 3.5, the number in curly braces is the cached ids of the preferred neighbors, and arrows represent the connectivity. Consider for example node-11, if the complete paths were cached then the number of alternate paths available is 3, i.e., {11-5-2-1, 11-6-3-1, 11-7-3-1}. Whereas, if only the neighbors are cached then the available number of alternates increase to 5, namely, {11-5-2-1, 11-5-3-1, 11-6-2-1, 11-6-3-1, 11-7-3-1}. Actually, if all the three neighbors lead to three valid paths each, then the potential number of number of alternate paths could be as many as 3k, where k is the number of hops from node to the base station.

To avoid the same request packet from being rebroadcast by the same node, route request packets are numbered. This sequence number is of the form < node_id, request_no >. If node receives a request packet with a repeated sequence number then that packet is discarded. This helps in avoiding the loop formation in the paths also removes unnecessary congestion in the network.

Figure 3.5: Wireless Sensor Network Acknowledgement Based Protocol

The protocol retains the reliable nature of symmetric protocols by inheriting their acknowledgement characteristic. The base station sends the acknowledgements for the received data packets. Like the previous case, there can be at most three retransmissions in the case of delivery failures. If the failure occurs for the fourth time, then the cached neighbor is declared as invalid. After invalidating the neighbor, if no cached neighbor is available, then reactive route discovery takes place.

Route Maintenance In asymmetric networks hello packets can serve no purpose. Suppose node-A and node-B share an asymmetric link. Even it a node A has direct access to another node B, B may not have direct access A. A could cache B as its neighbor for routing the data packets to the destination. Since the hello packets from B can not reach A, the use of these packets does not work for maintaining routes in asymmetric networks.

Consequently, discovered routes in asymmetric network are maintained in reactive manner.

The initial route discovery is done in a proactive way at the start of each transmission period.

Our design combines the advantages of both proactive and reactive protocols. A proactive protocol has low latency but discovers and rediscovers routes which are not required, whereas a reactive protocol discovers only the required routes but has higher latency. In the proposed approach, the proactive discovery of the routes is done when the node changes its transmission range (change in transmission range is periodic). The maintenance of routes is done in reactive way. In case the acknowledgement is not received for any data packet even after three retransmissions, the path invalidated and a reactive route discovery takes place.

3.3.2 Energy-Aware Routing The second version of our proposed protocol is designed to uncover the importance of residual node energy in data transmission. It provides an integrated approach to cache the residual energy of the preferred neighbors and choose the best alternate available for relaying the data packets.

Residual Energy Consideration In the protocol for symmetric networks, the energy updates were provided by the Hello packets. However, in the light of the discussions in section 3.3.1, Hello packets cannot help.

Therefore, each node sends a periodic energy update to the neighbors informing the maximum level of the transmission power with which it can operate. If the sender of the Energy Update packet is cached as the preferred neighbor at the receiver, the recipient also stores the updated residual energy value. Since, energy update packets have no other purpose like route maintenance, they can be sent less frequent than route update packets (Hello packets) in symmetric networks.

3.3.3 Role of Aggregation Protocol We also integrated an aggregation protocol for data transmission in the proposed energy aware routing protocol the asymmetric network. There are many applications where the precise location of data is not important. In other words, the spatiality of data can be extended to include data from any point within a sub-region. So, it we may aggregate data from the cluster members by either finding the mean, median, or mode, then transmit a single value to

base station. It helps to eliminate spatial redundancy in the sensor data. An example application can be temperature monitoring in an area.

Cluster Formation Typically, the cluster formation all the clustering protocols is performed by the base station.

But our design this unwanted overhead on the base station has been eliminated by the distributed formation of the clusters.

Fig 3.6 explains the clustering process. The nodes sharing the same preferred neighbor for a particular round forms a cluster. For example, as node-2, 3, and 4 all have node-1 as their preferred neighbor, so these nodes belong to the same cluster. Similarly, node-5, and 6 cache node-2 as their preferred neighbor implying that {5, 6} form a cluster. In all seven clusters can be identified in Fig 3.6. These are: {(2,3,4), (5,6), (7,8), (9,10), (11,12), (13,14)}

Figure 3.6: Aggregation in Asymmetric Network 3.4 Evaluation of the Proposed Protocol Before carrying out empirical evaluation of the proposed protocol through simulation, it is instructive to identify the basic parameters on which an evaluation should be based, and to qualitatively argue out the reasons behind expecting improvements compared to existing routing protocols for WSNs. In this section we discuss about these parameters.

3.4.1 Improved Lifetime The proposed protocol eliminates bottleneck around the base station by periodic change of topology through per node power adjustment. The protocol is resource-aware as the preferred path is selected on the basis of the maximum residual energy. Therefore, the proposed protocol when compared with the existing protocols should results in better network lifetime.

3.4.2 Resource-awareness

The design is resource-aware as is chooses the path with maximum residual energy from among the existing alternatives, for relaying the data packets to the base station.

3.4.3 Reduced Message Complexity and Zero Redundancy There exists no redundancy in transmission of the data packets. The redundancy in the route request packets has been avoided by using the request ids in the respective packets.

Every node caches at most three neighbors to be used as the preferred next hops for relaying the data. Once three neighbors have been discovered, further discovery is considered redundant. The base station uses a REQ_CAN packet, which contains the id of the requesting node and the sequence number of the request. It is broadcast by the base station once three neighbors for any node are discovered. Any node on receiving this packet discards the route request quoted by the REQ_CAN packet. Thus, this packet is used as a death certificate for the route request packets from a particular node and a specific sequence number.

3.4.4 Reliability As the redundancy is reduced to the minimum level, the overhead due to reliability is required to be considered. There are acknowledgements for the data packets. The protocol design supports at max three retransmissions of a data packet. There is trade-off concerning reliability versus redundancy. But reliability in a way helps to eliminate requirement for redundancy.

3.4.5 Scalability The protocol puts no constraint of the deployment area. New nodes can be injected to the network. A new node performs a reactive route discovery for a path to the base station. It can be used as a forwarding node only after the next proactive discovery takes place, or if there is any reactive discovery before the next proactive schedule.

3.4.6 Fault Tolerance The protocol is dynamic enough to handle the node failures. If a node fails, the packets relayed along that path will result in no acknowledgements, so the paths will be invalidated after three retransmissions. If on invalidating the path, a node is left with no valid path, it reactively starts a new route discovery.

3.4.7 Load Balancing Each node maintains three alternate preferred neighbors to be used for relaying the data to the base station. As the best is chosen based on the maximum residual energy, the load is balanced among the three neighbors.

3.4.8 Requirements for Specialized Support Parameters Some routing protocols like LAR [15] or GAF [29] make use of position vectors of the nodes to eliminate flooding during route discovery. However, our proposed protocol does not require any such additional support. The protocol works independent of the location of the

individual nodes. Therefore, suited both for indoor and outdoor networks. The routing algorithm is simple to implement.

Chapter 4 Implementation Details Considering applications for which WSN are usually deployed, some of the routine maintenance requirements like changing battery could be difficult to perform. So, in chapter 3, we discussed about various routing protocols for WSN with approaches to make these resource aware. The underlying motivation is to extend the lifetime of WSN as much as possible. Our approach was to use cross layer optimization to make the node aware of their own energy level and operate cooperatively to transmit data in way that per-node energy dissipation is more or less uniform. The purpose of this chapter is to find out if our approach is feasible in terms of real implementation. So, in this chapter, we present the nuts and bolts for implementing the proposed routing protocol for asymmetric WSNs. In chapter 3, we presented an overview of the proposed protocol outlining the ideas behind function of the proposed protocol in terms of route discovery, route maintenance, reliability of data transmission, etc. In this chapter our aim is to examine how these ideas can indeed be realized algorithmically and programmed for simulation using standard simulation tools such OMNeT++, TOSSIM, ns-2, etc. We also discuss about the reasons for our choice of OMNeT++ for simulation of the protocol.

The chapter is organized as follows. Section 4.1 presents the algorithms specifying the actions of individual sensor nodes on receiving a particular packet or when a timeout occurs.

We emphasize on the fact that the protocol is as easy to implement as to understand. Section 4.2 deals with the analysis of the various important parameters which might help us in judging the performance of the protocol. It also describes simulation environment used for evaluating the performance of the proposed protocol. Section 4.3 talks about the network configuration used. It speciďŹ es radio characteristics of sensor nodes and also describes other parameters in some details. Section 4.4 concentrates on the simulation tool used for the purpose of creating the required network prototype.

4.1 Algorithms We have two classes of entities in any routing protocol for WSN, namely, (i) ordinary sensor nodes, and (ii) a base station. Broadly, the task of every sensor node is identical. Each node senses some data and transmits the sensed data to base station. The job of the base station mainly passive except for acknowledging the receipt of the sensor data, and assisting sensor nodes to find and maintain routes to it. So, we can specify the protocol in terms of the actions at these two entities.

Lets first examine the tasks performed by a sensor node. It has to 1. Participate in route discovery.

2. Take various actions on certain timeout events. The timeout actions are as follow:

(a) Acknowledgement timeout: Since the proposed protocol is an acknowledgement protocol, the retransmission of a data item should be initiated if an acknowledgement is not received from the base station within a finite time interval. (b) Route request timeout: The base station is expected to send the reply once it receives the route request packet from a sensor node. The initiating sensor node would try to increase its transmission power, if possible, to establish a route in case there is no reply within a finite interval. (c) Transmission period timeout: Though increase of transmission range could help to eliminate congestion near the base station, there are occasions when a route becomes invalid due to low energy levels at intermediate nodes on the path to base station. In this situation, a reactive discovery may be necessitated to establish a new path.

3. Participate in aggregation protocol for reducing the volume of application specific data transmission requirements.

Algorithm 4.1: Algorithm executed at sensor node on receiving a packet /* ON Receiving Route Request Packet by sensor node */ if RouteRequest Packet then if ReqNotCancelled AND noloop then Rebroadcast the Route Request; else Discard the Route Request; /* Data Packet Received by a sensor node */ if Data Packet then if no neighbor node available then Initiate route request; Start the route request timer; Put the data packet in the buffer; else Select the neighbor with the maximum Residual Energy E; Forward the data packet to the selected neighbor; /* ON Receiving Route Reply Packet */ if RouteReply Packet then if hops < hop_count then Invalidate the cached Neighbors; Set hop_count to the new value; Cache the new neighbor; else if hops = hop count then if no duplicates then Cache new neighbor; else Discard the Reply; else Discard the Reply; if buffer is not empty then Transmit the data packets with the interval of 1 sec each; Start the data packet timer;

/* On Receiving Request cancel packet and Acknowledgment packet */ if Request Cancel Packet then cache the node id and the request id included in the request cancel packet; if Acknowledgment Packet then Delete the packet for which acknowledgment has been received; /* On Receiving Energy Update Packet */ if Energy Update Packet then if source node is cached as a neighbor then update the cached neighbors Residual Energy E; Algorithm 4.2: Algorithm executed at sensor node on Timeout /* Acknowledgment Timeout */ if Acknowledgment Timeout then if Retransmission_Count < 3 then Increment Retransmission_Count; Retransmit packet along the same neighbor; else Discard the data packet; Invalidate the neighbor used for the transmission; if neighbor cache is empty then Initiate Route Request; Start the route request timer; /* Route Request Timeout */ if Route Request Timeout then if Transmission_Range == Min_Transmission_Range then Increment the transmission power T by the step size s; Set the Min_Transmission_Range to Current_Transmission_Range; else Increment the transmission power T by the step size s; if T > Max_Transmission_Range then Set Transmission_Range to Min_Transmission_Range; Initiate the route request; Start the route request timer; /* Transmission Period Timeout */ if Transmission Period Timeout then if hop_count == 1 then Set Transmission_Range to Min_Transmission_Range; else Increment the transmission power by the step size s; if T > Max_Transmission_Range then Set Transmission_Range to Min_Transmission_Range; Invalidate all the neighbors; Start the route request timer; Algorithms 4.1 and 4.2 present the actions of sensor node for ďŹ rst two tasks listed above. The base station is primarily passive. It reacts when it either receives a route request from a sensor node or it receives a sensed data packet from sensor node. On receiving a sensed data item it needs to send an acknowledgement to the sender. In case of route discovery it needs to send

id of preferred neighbor to the requesting node. Algorithm 4.3 describes the actions of the base station.

Algorithm 4.3: Algorithm executed at Base Station on receiving a packet /* ON Receiving Route Request Packet by base station */ if RouteRequest Packet then if hop_count > hops then Invalidate all the replies send; Set the reply_count to Zero; Cache the new reply and the new hop_count; Send the reply; Increment the reply_count; else if hop_count = hops then if reply_count < 3 AND no duplicates then Cache the Reply; Send the reply; Increment the reply_count; if reply_count = 3 then Send the REQ_CAN message; else Discard the message; else Discard the Reply; /* Data Packet received by Base Station */ if Data Packet then Send the Ack; The remaining part of the protocol is concerned with aggregation protocol. Though aggregation protocol is executed by a sensor node, the node may have to play dual role of sensing its own data, and collecting sensed data from its neighbors. Algorithm 4.4 described the actions of a sensor node when it executes aggregation.

Algorithm 4.4: Algorithm for the Aggregation Protocol Initialize data_counter, neighbor_list to Zero; Periodically send data packet (DATA_PKT) to the preferred neighbor; Send no data signal (NO_DATA_PKT) to the other cached neighbors; /* ON Receiving Route Reply Packet by Sensor Node */ if RouteReply Packet then if hops < hop_count then Invalidate the cached Neighbors; Set hop_count to the new value; Cache the new neighbor; Send the Neighbor information packet (NEIGH_INFO) to the cached neighbor; else if hops = hop_count then if no duplicates then Cache new neighbor; Send the information to the cached neighbor;

else Discard the Reply; else Discard the Reply; /* Data Packet Received by a sensor node */ if Data Packet then Decrement the data_counter; if data_counter == 0 then Aggregate the collected data packets; if no neighbor node available then Initiate route request; Start the route request timer; Put the data packet in the buffer; else Select the neighbor with the maximum Residual Energy E; Forward the data packet to the selected neighbor; else Collect the data packet; /* ON Receiving No data signal */ if NO_DATA_PKT then Decrement the data_counter; if data_counter == 0 then Aggregate the collected data packets; if no neighbor node available then Initiate route request; Start the route request timer; Put the data packet in the buffer; else Select the neighbor with the maximum Residual Energy E; Forward the data packet to the selected neighbor; /* ON Receiving Neighbor Information Packet by Sensor Node */ if NEIGH_INFO Packet then Increment the neighbor_list; Initialize data_counter to neighbor_list; /* TIMEOUT for data packets */ if data packet timeout then if data_counter < nei ghbour_l ist then Aggregate the collected data; Send the data to the preferred neighbor; Algorithm:Algorithm for the Aggregation Protocol Contd..

4.2 Analysis The protocol analysis involves the time to converge the topology, total buffer required, percentage of packet loss, and the delay parameter. â&#x20AC;˘

The time to converge the topology is the maximum RTT of the network.

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The maximum buffer space requirement is 5x y, where x is the RTT and y is packets send per second. The protocol involves maintenance of two buffers.

1. Buffer to cache the packets send but waiting for acknowledgments. The space requirement for this buffer is computed as follows. Let RTT be x seconds, packets sent per second be y. So, packets sent per RTT = x y. Since there can be at most 3 re-transmissions of a packet before it is discarded, the buffer size to cache waiting packets is 3x y + x y = 4x y.

2. Buffer to cache the packets which are delayed due to non-availability of path and will be transmitted on receiving the route reply. Route discovery takes one RTT time (assuming the case when a path is always available). Therefore, the maximum local buffer size needed is xy •

The worst case loss percentage is 20%. We again base our computation on the same parameter values for RTT and packets sent per second. Since the packets lost due in 5x time could be x y packets, x y/5x , i.e., at most 1/5 th packets are lost per second.

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The Maximum delay in sending a packet is 3 RTT + k (otherwise the packet will be dropped), where k is a parameter introduced due to congestion

4.3 Simulation Details Now, let us discuss about the simulation tools we used to carry out the experiments. There are two important issues to be settled even before we can discuss about the appropriateness of the choice of simulation tool. These are the radio model of individual node and the network configuration.

4.3.1 Radio Model For the purpose of comparisons among different protocols, it is very important to adhere to specific radio characteristics. In this work, we assume a simple model where radio dissipates Ee = 50nJ/bit to run the transmitter or receiver circuitry and E amp = 100pJ=bi t=m2 for the transmitter amplification.

The formulas used are as follows: •

The energy Required to transmit a data packet of size l bits from a node i to node j is given by: Tij= lEe + lEamp(dij)2 , where dij is the distance between the node i and j.

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The energy required to receive a l bit packet for any node i is given by: Ri = Eel

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The energy required to aggregate i data packets is given by is given by: Ai= Eamp * i

4.3.2 Network Configuration

The simulation environment constructs a prototype area of (125X190)m2. The nodes are uniformly randomly distributed over this entire area. The number of nodes taken is 80.

Although, certain experiments have also been done to see the effect of change in the network density over the lifetime.

The prototype created strongly adheres to the details provided by the MICAz motes. The MICAz motes is a 2.4 GHz, IEEE 802.15.4 complaint, Mote module used for enabling low power, wireless, sensor networks. It clearly speciďŹ es that the indoor range of for the sensor networks is 20m As specified in the IEEE 802.15.4, the standard data packet size taken is 128 bytes. The simulation assumes the base station to be fixed at the origin and is a powerful node.

4.4 Simulation Tools WSNs have the potential to become significant subsystems of engineering applications. The simulation software helps in accurately modeling the real world scenario. The practical testing of the design in wireless sensor networks requires large quantity of hardware and involves high cost. Using simulators is an intelligent way to determine whether testing of the proposed methodology can be fruitful or not. In summary, simulation software provide us a low cost way to test the scalability, dynamicity, and ease of implementation of the proposed design.

There are many simulation tools for WSNs. These include Network Simulator-2 (ns-2) [3], OMNeT++ [1], OPNET [5], GloMoSim [31], etc.

NS-2 [3] being the most popular among them. It uses C++ and OTcl, and follows an object oriented approach. But this simulator does not scale well. So, ns-2 is not advisable for simulation of large networks. There is also lack of customization.

GloMoSim [31] was developed in 1998 for mobile ad hoc networks. It is written in Parsec, which is an extension of C for parallel programming. To run GloMoSim, we need to have the latest Parsec compiler. So, familiarity with Parsec is one of the basic requirements for developing a protocol in GloMoSim. Parsec code is used extensively in the GloMoSim kernel. Therefore, to understand the GloMoSim kernel, extensive knowledge of Parsec is needed.

OPNET [5] is another discrete event, object oriented, general purpose network simulator. It uses a hierarchical model to define each aspect of the system. OPNET suffers from the same object-oriented scalability problems as ns-2. It is not as popular as ns-2 or GloMoSim, at least in research being made publicly available, and thus does not have the high number of protocols available to those simulators. Additionally, OPNET is only available in commercial form.

TOSSIM [16] was developed to provide a scalable, high fidelity simulation of a complete TinyOS (operating system) for sensor network. Instead of compiling a TinyOS application for a mote, users can compile it into the TOSSIM framework, which runs on computer system. This allows users to debug, test, and analyze algorithms in a controlled and repeatable environment. The TOSSIM and TinyViz, the GUI for TOSSIM, simulation capabilities are anyhow constrained to TinyOS based applications (protocols and modules

already implemented in TinyOS). More-over, they can be seen more as emulators, rather than simulators. TOSSIM’s debugging aid tools are inadequate. Therefore, only way to detect bugs is to inspect large log files, which is tedious and time consuming. Simulations can be carried out for hours instead of minutes. TOSSIM still has lot undetected bugs as of date.

OMNeT++ [1], was designed with a view to provide the researchers with a better discrete event simulation environment. Its primary application area is the simulation of communication networks, but because of its generic and flexible architecture, is successfully used in other areas like the simulation of complex IT systems, queuing networks or hardware architectures as well.

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OMNeT++ allows the design of modular simulation models, which can be combined and reused flexibly.

It is possible to compose models with any granular hierarchy.

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The object-oriented approach of OMNeT++ allows the flexible extension of the base classes provided in the simulation kernel of the Simulator.

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OMNeT++ offers an extensive simulation library that includes support for input/output, statistics, data collection, graphical presentation of simulation data, random number generators and data structures.

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OMNeT++ simulation kernel uses C++ which makes it possible to be embedded in larger applications.

•

OMNeT++ models are built with NED and omnetpp.ini and do not use scripts which makes it easier for various simulations to be configured.

4.4.1 Reasons for Choosing OMNeT++ In view of the preceding discussion on simulation tools , the choice of simulation was between ns-2 and OMNeT++. Both are open source tools and available for unrestricted download. We chose OMNeT++. OMNeT++ offers following advantages over ns-2: •

Flexibility:

OMNeT++ is a flexible and generic simulation framework. It is possible to simulate anything that can be mapped to active components that communicate by passing messages. Some good example systems include queuing networks, multiprocessor systems, hardware architectures (routers, optical switches, file servers etc.), or business processes. There are model frameworks available for different problem domains, some popular ones include, INET Fw, Mobility Fw, OverSim, NesCT, MACSimulator, etc.

Ns-2 is basically a (TCP/IP) network simulator, and it is generally found difficult to simulate things other than packet-switching networks and protocols with it. It has highly hard coded concepts about the defined components nodes, agents, protocols, links, packet representation, and network addresses etc, which may be found good to work in traditional ways, but makes it very hard if one wants to do things a little differently. In summary ns-2 are more appropriate simulation experiments over wire network for which it is originally designed.

•

Programming Model

Omnet++ is an object-oriented, event-driven simulator, written in C++. Topology descriptions are either written as text files (NED language), or can be dynamically created at run-time. This makes it scalable and robust for testing the design for different configurations. There is also a graphical interface (GNED) for creating and editing the topologies, which automatically creates the topology file.

In ns2, there is mixed-mode: OTcl (Object-Tcl) with underlying C++ classes. OTcl is also used for creating and configuring networks, recording results etc.

•

Hierarchical Models (Reusability)

The OMNeT++ simulation kernel is a class library, it can be easily linked to the user defined components (simple modules), dynamically at the execution time. No need to modify OMNeT++ sources anywhere. This enforces reusability. A complex model can be formed from self-contained building blocks (i.e. simple modules and compound modules) which are reusable in other simulation.

In contrast, ns-2 tends to be a bit monolithic. Implementing a complex protocol as a composition of several independent procedures (that appear as one unit) is not possible in ns-2.

•

Scalability

OMNeT++ can simulate very large scale network topologies. The limit is the virtual memory capacity of the computer used.

In ns-2 there are scalability problems on simulating large network topologies. It has been observed that with the increase in number of nodes the run-time of the network increases exponentially.

•

Experiment Design

In OMNeT++, the parameters required by a simulation experiment are mentioned in the configuration file omnetpp.ini, which enforces the concept of separating model from experiments.

Whereas both models and experiments are usually interwoven in ns-2: topology, parameters, model customizations, result collection, etc., usually in the same Tcl script.

Although ns-2 seems to be more popular among researcher, OMNeT++ is also catching up due to fore mentioned advantages over ns-2. OMNeT++ has been used with success in a wide range of research areas, from various network simulations (optical, wireless, ad-hoc, IP, etc.) to performance analysis of complex software systems (at Siemens) and hardware design (at networking equipment vendor companies).

Chapter 5 Results of Simulation Experiments

In this chapter we present the results of experiments carried out for evaluating the performance of the proposed energy efficient routing protocol. For an evaluation to be meaningful, the performance of the proposed protocol should be compared with the performances of certain well known existing energy aware protocols. We base our evaluation primarily by comparing with the performance of two other protocols, namely, SPIN [11] and LEACH [12]. The choice of these two protocols for performance comparison is guided by two important reasons. SPINâ&#x20AC;&#x2122;s approach is based on eliminating data redundancies due to implosion and overlaps exhibited by plain flooding. On the other hand, LEACHâ&#x20AC;&#x2122;s approach is to form random clusters in each round so that the load of data transmission borne by the cluster heads can be spread uniformly over all nodes. So, LEACH cuts back overheads due communication by localizing most of the communication through data aggregation. Since, the proposed protocol leverages cross layer optimization for energy efficiency in WSN routing, it should independently show performance enhancement over symmetric routing protocols and over both SPIN and LEACH even without data aggregation. The results are discussed through plots accompanied by explanation as needed.

The simulation environment consists of 80 nodes uniformly randomly distributed over a space of (125X190)m2. Certain experiments have also been done to see the effect of change in the network density over the lifetime.

The node attributes chosen to strongly resemble MICAz motes. A MICAz mote is a 2.4GHz, IEEE 802.15.4 complaint chip. The mote module used for enabling low power wireless interface. Since the indoor range of for the sensor networks is 20m-30m, the transmission range variations are applied within this range. As specified in the IEEE 802.15.4, we have used the standard data packet size of 128b y tes. The simulation assumes the base station to be fixed at the origin and is a powerful node.

The chapter is organized as follows. Section 5.1 gives the prototype of the node distribution which is used for the purpose of evaluation. Section 5.2 compares the Lifetime of the various protocols with the proposed asymmetric protocol. Section 5.3 analyses the effect various parameters on the lifetime of the network in proposed protocol. Section 5.4 evaluates the introduction of clustering in the proposed protocol comparing the improvement in lifetime, number of delivered packets and the area covered by a single cluster.

5.1 Node Placement

Figure 5.1: Node Placement The plot in Fig. 5.1 shows the distribution of the nodes. The base station or the control center is placed at the origin, i.e., (0,0). The position of the other nodes is randomly generated by the simulating software. The distribution followed is the random uniform distribution. A black dot displays the position of a node, the id of the node is written alongside the dot. The base station id is 1, and the 80 sensor nodes are allocated ids from 2-81 5.2 Network Lifetime As, already mentioned, one of the major objective of this work has been to improve the lifetime of the sensor networks. So we compare the lifetime in the proposed methodology with the existing protocols. Typically, there are three different measures for the lifetime of a WSN, namely, 1. The time till the first node exhaust its energy, 2. The time till the last node exhaust its energy, and 3. The time till half the nodes in the network exhausts their energy. In this work, The first parameter has been used for the purpose of comparison of different protocols

Figure 5.2: Lifetime The plot in Fig. 5.2 shows the improvement in the lifetime obtained with the proposed methodology over the existing symmetric protocol semantics. The five protocols compared in this section are •

SPIN: The transmission range of the nodes is taken to be 29m, as this is the minimum range when all nodes get at least one path to the base station.

•

Symmetric Protocol: The transmission range used is same as above.

•

Symmetric protocol with Residual Energy Consideration: The transmission ranged used is same as above.

•

Asymmetric Network: In this all nodes change their transmission range between a fixed range in steps.

Parameter values:: – – –

•

Transmission Range: 20m – 32m Transmission Period: 12Nsec Step Size: 3m

Asymmetric protocol with Residual Energy Consideration: All the parameters are kept unchanged as above. The Energy update packets are broadcast to the neighbors every 5secs.

5.3 Effects of Different Parameters on the Lifetime In this section, the effect of various parameters has been evaluated on the asymmetric protocol with residual energy consideration.

5.3.1 Variation in Number of Nodes The plot in Fig. 5.3 provides comparison of the lifetime of the network with change in network density.

It is observed that with the increase in network density, the lifetime of the network decreases.

This happens because as the density increase the network becomes densely connected. The more densely connected network is victim to congestion as wireless is a broadcast medium.

Figure 5.3: Lifetime Comparison Varying Number of nodes

5.3.2 Variation in Transmission Range Period

Figure 5.4: Lifetime Comparison Varying Transmission Range Period The next plot in Fig. 5.4 compares the lifetime of the network with change in transmission range period. As already mentioned in chapter 3, the transmission range period has to be

greater than 2N x, where N is the number of nodes and x is the time take to cover one hop distance by a node.

•

The transmission range period changes over 4N – 20 N

•

The transmission range varies over 20m – 32m with step size of 3m

It is observed that the lifetime increases up to a certain level and then starts decreasing. It exhibits that there is one optimal value of the period for a particular network configuration.

5.3.3 Variation in Node Transmission Power

Figure 5.5: Lifetime Comparison Varying Range of the Transmission Power The plot of Fig. 5.5 compares the lifetime of the network with varying range of transmission power.

•

The period of the transmission range is 12Nsec

•

The transmission power is varied over the range 20m – 30m, 20m – 32m and 20m – 35m with step size of 3m.

The lifetime is observed to be maximum for the node ranges between 20m – 32m this range is found to provide better balancing of load between the nodes near to the base station and the nodes away from the base station. But the lifetime decreases for 20m – 35, because 35m is a high enough range to create additional congestion in the network when network becomes very densely connected.

5.3.4 Variation in Transmission Range Steps The graph shown in Fig. 5.6 compares the lifetime of the network with varying transmission range step size. The specific parameter values for experiment are: •

The period of the transmission range: 12Nsec.

•

The range between which transmission power is varied: 20m – 32m.

•

The steps in which transmission range is varied: 2m, 3m, 4m, and6m.

Figure 5.6: Lifetime Comparison Varying Transmission Range Step size It is observed that the maximum lifetime is attained for step size 3m. With much larger step size the needed granularity is lost. The minimum transmission range is set by the node at the execution time. If the step size is large then for some nodes the minimum value set is very large which leads to wastage of energy and increases congestion as well. If the step Size is small then, it may happen that the two different transmission ranges may lead to the same topology. In that case it just leads to doubling of transmission period, and consequently, leading to decrease in lifetime.

5.3.5 Effect of Node Failure The plots in this subsection are used to observe the effect of node failure on the lifetime of the network when the nodes near the base station fail. The network configuration used for the purpose of evaluation is as follows: •

The period of the transmission range: 12Nsec.

•

The range between which transmission power is varied: 20m - 32m.

•

The steps in which transmission range is varied: 6m.

•

The variation in percentage of node failures: 1%-5%.

The plot in Fig. 5.7 shows the effect on the lifetime of the network when nodes near the base station fails. It was observed that the lifetime in this case decreases by about 6% with 5% increase in the failure percentage. This can be explained as follows. If the nodes near the

Figure 5.7: Effect of node failure on Lifetime base station fails then the total load of data packet in the network is required to be distributed among reduced number of nodes. As such, the near nodes exhaust their energy much faster leading to decrease in the lifetime.

Figure 5.8: Effect of node failure on Lifetime Our second graph in Fig. 5.8 presents the effect on the lifetime of the network when nodes lying in the middle of the region being sensed, fail. It was observed that the increase in the percentage of the dying nodes in the central region does not exhibit any specific pattern. The explanation for this observation is as follow. There is a trade-off between the following: 1. The total energy drained out of the network because of the packets generated by centrally located nodes, and 2. The total energy saved by the far-off nodes when they use those centrally located for routing data packets.

The plot in Fig. 5.9 shows the effect on the lifetime of the network when nodes far-off from the base station fail. If the far-off nodes die then the lifetime shows an improvement of about 7% with 5% nodes going dead. It happens because the load due to the route discovery and the data packet of the far-off nodes reduces. The nodes near the base station are now less loaded and last longer.

Figure 5.9: Effect of node failure on Lifetime 5.4 Effect of Aggregation The aggregation version of the proposed protocol has been compared with the well known aggregation protocol LEACH [12]. The lifetime obtained with proposed approach was 150400sec which shows an improvement of more than 400% is observed over LEACH (27099sec). As already stated in chapter 2, LEACH is not suited for such large area. But still if we remove the constraint of the transmission range and assume to use nodes with very high transmission ranges, that are maximum of approximately 250m, the proposed protocol outperforms LEACH. 5.4.1 Clusters in LEACH

Figure 5.10: a) Clusters in LEACH b)Clusters in Asymmetric Network with Residual Energy Consideration Although as mentioned in the chapter 2, LEACH can work in this configuration with the assumption that the nodes are able to increase their transmission range to around 250m, the number of clusters to be formed are taken to be 20% of the number of nodes. If the number is higher then the performance goes down further. Since, the total number of nodes is 80, the highest number of clusters formed at any particular instant of the simulation was 16. The total area covered by each cluster is approximately 1500m2, which is large enough.

5.4.2 Effects of Aggregation in Proposed Protocol The graph in Fig. 5.10 b) shows that by introducing aggregation in the proposed asymmetric protocol, around 40 clusters are formed at any particular instant of the simulation. Therefore, the number of packets delivered is much more than in the case LEACH as depicted in Fig. 5.10 a). The total area covered by each cluster in case of the asymmetric network is approximately 600m2 which leads to better localization of communication.

Chapter 6 Application of Wireless Sensor Network 6.1 Structural Health Monitoring Smart Structures Sensors embedded into machines and structures enable condition-based maintenance of these assets [32]. Typically, structures or machines are inspected at regular time intervals, and components may be repaired or replaced based on their hours in service, rather than on their working conditions. This method is expensive if the components are in good working order, and in some cases, scheduled maintenance will not protect the asset if it was damaged in between the inspection intervals. Wireless sensing will allow assets to be inspected when the sensors indicate that there may be a problem, reducing the cost of maintenance and preventing catastrophic failure in the event that damage is detected. Additionally, the use of wireless reduces the initial deployment costs, as the cost of installing long cable runs is often prohibitive.

In some cases, wireless sensing applications demand the elimination of not only lead wires, but the elimination of batteries as well, due to the inherent nature of the machine, structure, or materials under test. These applications include sensors mounted on continuously rotating parts [33], within concrete and composite materials [34], and within medical implants [35, 36]

Industrial Automation In addition to being expensive, lead wires can be constraining, especially when moving parts are involved. The use of wireless sensors allows for rapid installation of sensing equipment and allows access to locations that would not be practical if cables were attached. An example of such an application on a production line is shown in Figure 22.6.1. In this application, typically ten or more sensors are used to measure gaps where rubber seals are to

be placed. Previously, the use of wired sensors was too cumbersome to be implemented in a production line environment. The use of wireless sensors in this application is enabling, allowing a measurement to be made that was not previously practical [37]

Figure 6.1: Industrial application of wireless sensors Other applications include energy control systems, security, wind turbine health monitoring, environmental monitoring, location-based services for logistics, and health care.

6.2 Application Highlight â&#x20AC;&#x201C; Civil Structure Monitoring One of the most recent applications of todayâ&#x20AC;&#x2122;s smarter, energy-aware sensor networks is structural health monitoring of large civil structures, such as the Ben Franklin Bridge (Figure 22.6.2), which spans the Delaware River, linking Philadelphia and Camden, N.J [38, 39]. The bridge carries automobile, train and pedestrian traffic. Bridge officials wanted to monitor the strains on the structure as high-speed commuter trains crossed over the bridge.

A star network of ten strain sensors were deployed on the tracks of the commuter rail train.

The wireless sensing nodes were packaged in environmentally sealed NEMA rated enclosures. The strain gauges were also suitably sealed from the environment and were spot welded to the surface of the bridge steel support structure. Transmission range of the sensors on this star network was approximately 100 meters.

The sensors operate in a low-power sampling mode where they check for presence of a train by sampling the strain sensors at a low sampling rate of approximately 6 Hz. When a train is present the strain increases on the rail, which is detected by the sensors. Once detected, the system starts sampling at a much higher sample rate. The strain wave form is logged into local Flash memory on the wireless sensor nodes. Periodically, the waveforms are downloaded from the wireless sensors to the base station. The base station has a cell phone attached to it which allows for the collected data to be transferred via the cell network to the engineersâ&#x20AC;&#x2122; office for data analysis.

Figure 6.2: Ben Franklin Bridge This low-power event-driven data collection method reduces the power required for continuous operation from 30 mA if the sensors were on all the time to less than 1 mA continuous. This enables a lithium battery to provide more than a year of continuous operation.

Resolution of the collected strain data was typically less then 1 micro strains. A typical waveform downloaded from the node is shown in Figure 22.6.3. Other performance specifications for these wireless strain sensing nodes have been provided in an earlier work [40]

Figure 6.3: Bridge strain data Chapter 7 Limitations

A number of security issues exist in WSN and need to be analyzed in detail in order to design appropriate security mechanisms and overcome security problems that arise in the sensor environment. However, designing new security protocols and mechanisms is constrained by the capabilities of the sensor nodes. This section discusses the limitations that complicate the security design and deployment in sensor networks. It is important to understand the constrained capabilities of sensor nodes if you wish to develop proper security that balances demanding security performance against sensor nodes' limitations [41] 7.1 Hostile Environment Sensor networks can be deployed in remote or hostile environments such as battlefields. In these cases, the nodes cannot be protected from physical attacks, since anyone could have access to the location where they are deployed. An adversary could capture a sensor node or even introduce his own malicious nodes inside the network. If the latter is the case, the adversaryâ&#x20AC;&#x2122;s aim is to trick the network into accepting his nodes as legitimates.

In either case, the adversary can compromise sensitive information, which is either stored on the compromised nodes or is forwarded through the adversaryâ&#x20AC;&#x2122;s nodes to the next hop; the sensitive information that is collected could be used for illegal purposes. The challenge here for researchers and developers is to design resilient security protocols and solutions offering security, even if a subset of sensor nodes are compromised. It is important to ensure that, if a node is compromised, sensitive information stored on the node cannot be taken off with ease.

7.2 Random topology Most of the time, deploying a sensor network in a hostile environment is done by random distribution, i.e. from an airplane. Therefore, it is difficult to know the topology of sensor networks a priori. In these situations, it is hard to store various encryption keys on nodes in order to establish encryption among a group of neighbors, since the neighborhood cannot be known a priori.

The challenge is to design key agreement protocols that do not require certain nodes to be neighbors of some other nodes, and also do not require encryption keys to be stored on sensors before deployment. Appropriate key distribution algorithms must be designed along a flexible WSN architecture to securely provide encryption keys in real time.

7.3 Power restrictions The power restrictions of sensor nodes are raised due to their small physical size and lack of wires. Since the absence of wires results in lack of a constant power supply, not many power options exist. Sensor nodes are typically battery-driven. However, because a sensor network contains hundreds to thousands of nodes, and because often WSN are deployed in remote or hostile environments, it is difficult to replace or recharge batteries. The power is used for various operations in each node, such as running the sensors, processing the information gathered and data communication.

Keep in mind that communication between sensor nodes consumes most of the available power, much more than sensing and computation. Power limitations greatly affect security, since encryption algorithms introduce a communication overhead between the nodes; more messages must be exchanged, i.e. for key management purposes, but also messages become larger as authentication, initialization and encryption data must be included.

7.4 Limited Computational power In the case of computational power, computations are linked with the available amount of power. As you may understand, since there is a limited amount of power, computations are constrained also. Although it is acknowledged that sensors are not expected to have the computing power of workstations or even mobile handheld devices, researchers and developers are greatly concerned with the issue.

As I mentioned in the previous section, more power is used for communication than computations. Therefore, since the power for computations is even more constrained than the total quantity of power, complex security solutions are prohibited. The limitation of computational power limits the adoption of strong cryptographic algorithms such as the RSA public key algorithm, which is computationally expensive.

Instead, symmetric encryption algorithms are used to secure sensor nodes' communication, since symmetric encryption doesnâ&#x20AC;&#x2122;t have as demanding computational requirements as asymmetric encryption. However, with asymmetric encryption, features like digital signatures are not supported. Therefore, another challenge for researchers and developers is to design appropriate algorithms to establish and verify trust among the nodes participating in a communication. Furthermore, other security solutions must be adopted to cover the weaknesses of symmetric encryption; when an adversary compromises a node, he could retrieve the shared key used to encrypt the messages and then compromise the entire communication of the sensor network.

7.5 Storage Restrictions The limited capability for storage affects the storage of cryptographic keys as well.

According to the encryption scheme used, each sensor node may need to know a number of keys for each other node in the network to secure communication, and thus store the keys in the nodesâ&#x20AC;&#x2122; storage space. However, the large number of sensor nodes requires a lot of memory, which may not be provided. As I mentioned previously, having a single encryption key common to all nodes allows an adversary to compromise the whole network by compromising only a single node. The challenge of storage restriction is for researchers to design security protocols in a way that a minimum number of encryption keys must be used to provide adequate protection to the network. [41] Chapter 8 Conclusion and Future Work 8.1 Conclusion The work in this thesis is focused towards designing, implementation and evaluation of energy efficient routing protocols for wireless sensor networks. The elimination of bottleneck around the base station is a matter of major concern. The effort was directed towards uniform distribution of data transmission and dissemination load among the nodes across the network.

We realized that only way to achieve this is to develop a methodology for distributed topology control. We studied the specification of MICAz motes and came to conclusion that by per-node transmission power adjustment, it is possible to control topology and thus eliminate the bottle- neck around the base station. It resulted in increase in the lifetime of the network. Through a series of simulation experiments using different network configurations, we observed that the approach is justified and result in substantial performance enhancements

over existing energy-aware protocols. We addressed the following issues through our experiments.

Energy efficiency: The lifetime of the network increased by about 155% in comparison to the symmetric network.

Scalability: The protocol is dynamic enough to handle the failures and new insertions.

Low latency: As the protocol is a mixture of both proactive and reactive protocols, the total delay factor is 3RT T + k, where k is a parameter introduced due to congestion.

Reliability: It is an acknowledgment based protocol. If acknowledgment is not received for a packet till acknowledgment timeout, retransmissions take place.

Ease of implementation: The implementation details presented, clearly shows that the protocol is easy to implement.

The proposed protocol when compared to the symmetric multihop protocol was found to attain 155% improvement in the lifetime. The aggregation version of the proposed protocol showed an improvement of 400% over the existing aggregation protocol LEACH [12].

8.2 Future Work There are several opportunities for future work. It might be interesting to study the pattern of the nodes dying due to exhaustion of their energy. This pattern might give some insight into the proposal and may actually lead to performance enhancements. In fact, the distribution pattern of routing load could be important starting point of applicability of cross layer optimization.

The study of the percentage of packet loss may also lead to some important conclusions like effect of interferences and noise brought about by range adjustment.

Extending the proposed protocol for the real life event driven networks might be fruitful. The protocol is expected to perform better for event driven networks with some minor modifications.

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41. Limitations : 1. http://webhosting.devshed.com/c/a/Web-Hosting-Articles/WirelessSensor-Networks-part-2-Limitations/ Articles/Wireless-

2. http://webhosting.devshed.com/c/a/Web-HostingSensor-Networks-part-2-Limitations/1/