10. EEE - IJEEER - Implementation - Pankaj Govindrao V

International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol.2, Issue 3 Sep 2012 106-120 © TJPRC Pvt. Ltd.,

IMPLEMENTATION AND PERFORMANCE EVALUATION OF NEW ROUTING PROTOCOL IN WIRELESS SENSOR NETWORKS 1

PANKAJ GOVINDRAO VISPUTE & 2 R. S. KAWITKAR

1

Research Scholar, JJT University, Shatabdi Institute of Engineering, and Research, Agaskhind ,Nasik, MH. India 2

Department of E and TC Engineering, Sinhgad College of Engineering, Pune, India

ABSTRACT Energy consumption is the major issue in wireless sensor networks (WSN). To provide the solution for minimum energy consumption because WSN’s are battery operated and till energy conservation is under research and this not possible in every scenario because WSN’s are randomly deployed to observed and monitor practical scenarios such as military application, Environmental application, agriculture application and many more, so energy utilization is important factor. Energy consumed in WSN’s during sensing, processing and communication. In our proposed algorithm we design single bit transmission to minimize the energy consumption. To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. Aim of this paper is to evaluate the performance of AODV, DSR, DSDV with proposed routing protocol with possible information such as Node Id, Source Node, Destination Node, Next Hop, Packet Id, Packet size, Routing table information, position of node from sink and many more with minimum energy consumption to increase network lifetime as well as node lifetime. We are getting the some result which compare with the exiting protocols and found that our proposed work is done good job in terms of minimum energy consumption

KEYWORDS: WSN’s, AODV, DSR, DSDV, Energy Consumptions, Cluster Head.

INTRODUCTION The increasing miniaturization of electronic components and the advances in wireless technologies has fostered researches on sensor networks and systems. Individual sensor nodes are lowpower devices that integrate computing, wireless communication, and sensing capabilities. They are able to sense physical environmental information such as temperature, humidity, light intensity, etc., and to process these information locally, or send it to one or more collection points (usually referred to as sinks) typically through wireless communications. In important application scenarios a massive deployment of sensor nodes is required, in the order of thousands or tens of thousands. The aggregation of such a multitude of sensor nodes into a computing and communication infrastructure forms what is called a sensor network. Potential applications of sensor networks includes a large number of fields ranging from

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Implementation and Performance Evaluation of New Routing Protocol in Wireless Sensor Networks

military, to scientific, to industrial, to health-care, to domestic, etc. Sensor nodes forming a sensor network are densely (and randomly) deployed inside the area in which a phenomenon is being monitored. Each sensor node delivers the collected data to one (or more) neighbor node, one hop away.

By following a multi-hop communication paradigm data are routed to the sink and through this to the users. Therefore, multi-hop ad hoc techniques constitute the basis also for wireless sensor networks.

Routing is a process of determining a path between source and destination upon request of data transmission. In WSNs, the layer that is mainly used to implement the routing of the incoming data is called as network layer. When the sink is far away from the source or not in the range of source node, multi-hop technique is followed. So, intermediate sensor nodes have to relay their packets.

The rest of the paper is organized as follows: The work contributed in this area is provided in section II. The proposed architecture, sequence diagram and algorithm are explained in section III. The simulation environment details and nodes parameters are described in Section IV .The simulation results described in section V. The performance evaluation in terms of Packet Delivery Ratio (PDR) and Energy Consumption are plotted by using xgraph command in NS2 simulator.

RELATED WORKS Wireless sensor networks play a major role in environmental monitoring, military, health, and other commercial applications. A sensor network is composed of a large number of small low-cost sensor nodes, which are typically densely and randomly deployed either inside the area in which a phenomenon is being monitored or very close to it. The sensor nodes, which consist of sensing, data processing, and communicating components, gather information about the physical world and communicate unattended in short distances. One or more data collection points (sinks), either static or mobile, have the responsibility of collecting the information gathered by the sensors for further processing or making decisions based on the observations and performing appropriate actions. The special constraints and technical challenges that arise because of the unique characteristics of sensing devices pose many new problems and issues that have to be addressed when designing a wireless sensor network [2], [3], [4].

Such an issue is the efficient management of the finite amount of energy provided by the batteryoperated sensor nodes. In the sensor network, sensor node can communicate with the base station directly or through the cluster head, or through other relaying nodes. In a direct communication, each node communicates directly with the base station. When the sensor network is large, the energy for communicating with the base station is correspondingly large. Hence, some nodes far apart from the base station will quickly run out of energy [2]. The other scheme is the clustering; where the nodes are grouped into clusters and one node of the cluster send all gathered data from the nodes in its cluster to the sink. The problem of maximum lifetime routing in wireless sensor networks has received significant attention over the last few years. In the work by authors [5], [6], [7], [8], the information obtained by the monitoring sensors needs to be routed in an energy-efficient way to a set of static designated gateway nodes. Energy-aware routing has received attention in the recent few years, motivated by advances in wireless mobile devices. Since the overhead of maintaining the routing table for wireless mobile

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networks is very high, the stability of a route becomes of a major concern. The main operation of wireless sensor network is to collect and process data at the network nodes, and transmit the necessary data to the sink for further analysis and processing. Currently there are several energy efficient communication models and protocols that are designed for specific applications, queries, and topologies.

The problem of efficiently positioning the data collection points (sinks) in a wireless sensor network is addressed in [9], [10]. In [9] it is shown that the choice of positions has a marked influence on the data rate, or equivalently, the power efficiency of the network. In [10] multiple sinks are used not only to increase the manageability of the network, but also to reduce the energy dissipation at each node.

The Flooding Protocol In flooding [11], the source node floods all events to every node in the network. Whenever a sensor receives a data message, it keeps a copy of the message and forwards the message to every one of its neighboring sensors and the cycle repeats.

The Directed Diffusion Protocol Direct Diffusion [12, 13] is the data centric protocol. It is the first proposed protocol for the wireless sensor network scenarios. If directed diffusion does not perform better than flooding, it cannot be considered viable for sensor networks. It consists of several elements: interests, data messages, gradients, and reinforcements. First, sink node requests data by sending interests. An interest message is a query or an interrogation, which specifies what a user wants to its neighbors for named data. The data is named using attribute-value pairs and it is the collected or processed information of a phenomenon that matches an interest of a user. The interests are flooded over the whole network by the sink.

Ad-hoc On-demand Distance Vector (AODV) Protocol AODV [14] is the simplest and widely used algorithm either for wired or wireless network. It is one of the most efficient routing protocols in terms of establishing the shortest path and lowest power consumption. It is mainly used for ad-hoc networks but also in wireless sensor networks. It uses the concepts of path discovery and maintenance. However, AODV builds routes between nodes on-demand i.e. only as needed.

The Destination Sequenced Distance Vector Protocol (DSDV) [15] DSDV is a proactive, distance vector protocol which uses the Bellmann -Ford algorithm. DSDV is a hop-by hop distance vector routing protocol, wherein each node maintains a routing table listing the “next hop” and “number of hops” for each reachable destination. This protocol requires each mobile station to advertise, to each of its current neighbors, its own routing table (for instance, by broadcasting its entries). The entries in this list may change fairly dynamically over time, so the advertisement must be made often enough to ensure that every mobile computer can almost always locate every other mobile computer of the collection. In addition, each mobile computer agrees to relay data packets to other computers upon request. This agreement places a premium on the ability to determine the shortest number of hops for a route to a destination we would like to avoid unnecessarily disturbing mobile hosts

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if they are in sleep mode. In this way a mobile computer may exchange data with any other mobile computer in the group even if the target of the data is not within range for direct communication.

Dynamic Source Routing (DSR) Protocol The Dynamic Source Routing [16] (DSR) protocol is an on demand routing protocol based on source routing. DSR Protocol is composed by two “on-demand” mechanisms, which are requested only when two nodes want to communicate with each other. Route Discovery and Route Maintenance are built to behave according to changes in the routes in use, adjusting them-selves when needed. Along with those mechanisms, DSR allows multiple routes to any destination, thus can lead easily to load balancing or increase robustness .In the source routing technique, a sender determines the exact sequence of nodes through which to propagate a packet. The list of intermediate nodes for routing is explicitly contained in the packet’s header. In DSR, every mobile node in the network needs to maintain a route cache where it caches source routes that it has learned. When a host wants to send a packet to some other host, it first checks its route cache for a source route to the destination. In the case a route is found, the sender uses this route to propagate the packet. Otherwise the source node initiates the route discovery process.

PROPOSED ARCHITECTURE OF WSN’s Clustering is the method by which sensor nodes in a network organize themselves into hierarchical structures. By doing this, sensor nodes can use the scarce network resources such as radio resource, battery power more efficiently. Within a particular cluster, data aggregation and fusion are performed at cluster-head to reduce the amount of data transmitting to the base station. Node deployment in WSNs is either fixed or random depending on the application. In fixed deployment the nodes are deployed on predetermined locations whereas in random deployment the resulting distribution can be uniform or non uniform. In such a case careful management of the network is necessary in order to ensure maximum area coverage and also to ensure uniform energy consumption across the network.

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Figure 1: Architecture of Efficient Energy Management in Wireless Sensor Network Cluster based routing in WSNs comes under the category of hierarchal routing. Hierarchal routing involves the formation of clusters where nodes are assigned the task of sensing which have low energy and transmission task to nodes which have higher energy. The purpose is to perform energy efficient routing. The cluster heads may be special nodes with higher energy or normal nodes depending on the algorithm and application. The cluster head also performs computational functions such as data aggregation and data compression in order to reduce the number of transmission to the sink there by saving energy. One of the basic advantages of the clustering is that latency is minimized compared to flat base routing and also flat based routing nodes that are far away from the base station lack the power to reach the base station. During the creation of network topology, the process of setting up routes in WSNs is usually influenced by energy considerations. Because the power attenuation of a wireless link is proportional to square or even higher order of the distance between the sender and the receiver, multihop routing is assumed to use less energy than direct communication. However, multi-hop routing introduces significant overhead to maintain the network topology and medium access control. In the case that all the sensor nodes are close enough to the BS, direct communication could be the best choice for

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routing since it reduces network overhead and have a very simple nature. Many research projects and papers have shown that the hierarchical network routing and specially the clustering mechanisms make significant improvement in WSNs in reducing energy consumption and overhead. In this architecture we consider vibration as application. One geographical area is divided into number of clusters each clusters having its cluster head. Election of cluster head is based on maximum energy available, each node send one bit information to cluster head and cluster head is also send one bit information to sink to increase node lifetime

Figure 2: Sequence Diagram As per as architecture is concern in our work we divide total area into the clusters and how sink, cluster heads and nodes are work that is shown in sequence diagram. Flow of sequence diagram is Sink send Query message to cluster heads for data availability. Cluster heads forward this query message to all sensor nodes. Sensor nodes sends data to cluster head in one bit information If event is occur data to cluster head from node is high (1), if event is not occur data to the cluster head is low (0).All data is collected by cluster head suppose in each cluster heads number of nodes are 100 all nodes are not active at a time some are in sleeping mode to increase nodes lifetimes. If 51 nodes send high (1) to the cluster head suppose cluster head Id= 00, then cluster head send high (1) to sink else if 49 nodes send high (1) to cluster head then cluster head send low (0) to sink. Sink send information to data processing unit, in

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which particular area event is occur that is suppose in our example vibration with cluster Id and all node ids.

ALGORITHM 1) Define the geographical area for all sensor nodes.

2) Divide this area into number of sub-groups.

3) Each sub-group has n nodes and its cluster head.

4) Cluster head selection using maximum energy in sensor node.

5) Assign initial power to nodes as well as transmitting and receiving power.

6) Define Domain name, Cluster Id, Node Id. 7) sink as a data collector unit that is base station 8) Sink send Query message to cluster heads for data availability.

9) Cluster heads forward this query message to all sensor nodes.

10) Sensor nodes sends data to cluster head in one bit information 11) If event is occur data to cluster head from node is high (1), if event is not occur data to the cluster head is low (0).

12) All data is collected by cluster head suppose in each cluster heads number of nodes are 100 13) All nodes are not active at a time some are in sleeping mode to increase nodes lifetimes.

14) If 51 nodes send high(1) to the cluster head suppose cluster head Id= 00, then cluster head send high(1) to sink else if 49 nodes send high(1) to cluster head then cluster head send low(0) to sink 15) Sink send information to data processing unit, in which particular area event is occur that is suppose in our example vibration with cluster Id and all node ids.

SIMULATION DETAILS In this paper the simulation tool used for analysis is NS-2 which is highly preferred by research communities. NS is a discrete event simulator targeted at networking research. Ns provides substantial support for simulation of TCP, routing, and multicast protocols over wired and wireless (local and satellite) networks [17]. NS2 is an object oriented simulator, written in C++, with an OTcl interpreter as a frontend. This means that most of the simulation scripts are created in Tcl(Tool Command Language).

If the components have to be developed for ns2, then both tcl and C++ have to be used. The flow diagram given in figure4 shows the complete working of NS2 for Analysis.

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SIMULATION PARAMETER The performance analysis is done on Red Hat Linux Operating System. Ns –allinone-2.34 was installed on the platform.

Table 1: Simulation Parameters Parameter

Value

Simulation Area

800mx800m

Simulator

Ns-allinone-2.34

Number of nodes

50

Simulation Time

200 Sec.

Energy Model

Energy Model

Initial Energy

10J

Transmitting Power

0.6mw

Receiving Power

0.3mw

Transmission Range

250m

Nodes distribution

Nodes are randomly distributed

Traffic type

CBR

Packet size

230 bytes

Pause time

100s

Maximum speed

10,20, 30, 40, 50 (m/s)

Table 2: Node configuration parameters Parameter

Value

Channel Type

WirelessChannel

Radio Propagation Model

TwoRayGround

Antenna Model

OmniAntenna

Network interface type

WirelessPhy

MAC Type

802.11

Interface Queue Type

PriQueue/CMUPriQueue

Buffer size of IFq

50

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SIMULATION RESULTS The simulation results are shown in the following section from Network Simulator 2 and some graphs using xgraph command. The performance of AODV, DSDV, DSR compare with proposed routing protocol based on change in mobility that is speed of nodes in meter per second and energy consumption in the node, packet delivery ratio. Figure 3 to 7 shows NS2 implementation with energy status of the nodes that is energy remaining in the node after transmission. Figure 7 and 8 shows the information of node Id, position of nodes from sink, sequence number, route table information, current hop, next hop, etc. Packet Delivery Ratio (PDR) is as the ratio between the numbers of packets sent by Constant Bit Rate (CBR) at application layer and the number of received packets by the CBR sink at destination. Remaining energy is the available energy after the simulation completed. Energy consumption is the energy used for various node density and speed For those purpose, we use formulas to calculate these performance indicators.

1 .Packet delivery ratio is defined as Σ Number of Packets received / Σ Number of Packets sent 2. Average Energy Consumption is defines as follows: Σ Percentage Energy Consumed by all Nodes/Number of Node

Figure 3: Initial stages all nodes with full energy.

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Figure 4: Node energy get decrease as time progress.

Figure 5: Random movement of nodes and yellow color indicate energy loss

Pankaj Govindrao Vispute & R. S. Kawitkar

Figure 6: Transmission of data from node to sink

Figure 7: Final position of nodes with energy remaining in the nodes

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Figure 8: Position of nodes from sink.

Figure 9: Information of Node Id, Source Id, Hop, etc.

PERFORNMACE EVALUATION Figure 10 shows how the packet delivery ratio is affected by number of node.

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Figure 10: Number of nodes v/s Packet Delivery Ratio.

Figure 11 shows the result of the evaluation of energy consumption versus maximum speed of nodes. We consider 50 nodes with maximum speed of 10 m/s, 20 m/s, 30 m/s, 40 m/s, 50 m/s the energy consumption after 200 seconds of simulation. However, when nodes move with 10 m/s. 20 m/s, and 30 m/s of maximum speed, we obtain the similar results in terms of energy consumption of nodes. By using setdest command in NS2

setdest -n 50 –p 100 –M 10 –t 200 –x 800 –y 800 >scen-50-10 setdest -n 50 –p 100 –M 20 –t 200 –x 800 –y 800 >scen-50-20 setdest -n 50 –p 100 –M 30 –t 200 –x 800 –y 800 >scen-50-30 setdest -n 50 –p 100 –M 40 –t 200 –x 800 –y 800 >scen-50-40 setdest -n 50 –p 100 –M 50 –t 200 –x 800 –y 800 >scen-50-50

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Figure 11: Mobility v/s Energy consumption.

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Nsnam web pages: http://www.isi.edu/nsnam/ns/