Proceedings of International Conference on Advancements in Engineering and Technology
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Load Stabilizing and Energy Conserving Routing Protocol for Wireless Sensor Networks Janakiraman V. M.E(Computer Science and Engineering) Sree Sowdambika College of Engineering Aruppukottai, Tamilnadu, India.
Mr. G.Vadivel Murugan M.E., Assistant Professor Dept of Computer Science and Engineering Sree Sowdambika College of Engineering Aruppukottai, Tamilnadu, India.
Abstract— Wireless sensor network (WSN) is a system consists of a massive collection of low-cost micro-sensors. WSN is used to gather and send various types of the messages to a base station (BS). WSN consists of low-cost nodes with limited battery capacity, and replacement of the battery is not easy for WSN with thousands of physically connected nodes, which means energy conserving routing protocol should be employed to offer a long-life work time. To accomplish the aim, we need not only to minimize total energy utilization but also to balance WSN load. Research workers have recommended many protocols such as LEACH, HEED, TBC, PEGASIS and PEDAP. In this paper, we suggest a Load Stabilizing Tree Based Energy Conserving Routing Protocol(LSTEC ) which constructs a routing tree using a process where as each round BS assigns a root node and transmits this selection to all sensor nodes. Eventually, each node chooses its parent by contemplating only itself and its neighbors' information, thus making LSTEC a dynamic protocol. Simulation results show that LSTEC has a better performance than other protocols in balancing energy utilization, thus extending the lifetime of WSN. Keywords—Energy utilization, Load balance, Network lifetime, Routing protocol, Tree based, Wireless Sensor Network.
I. INTRODUCTION Generally, wireless sensor nodes are located arbitrarily and tightly coupled in a target area, especially where the physical environment is too hard that the macrosensor counterparts cannot be deployed. After deployment, if there is no sufficient battery power, the network cannot work properly [1], [2], [3]. In general, WSN may generate quite a large amount of data, so if data fusion could be used, the throughput could be decreased [4]. Because sensor nodes are deployed densely, WSN might produce redundant data from multiple nodes, and the redundant data can be unified to reduce communication. Many familiar protocols execute data fusion, but almost all of them suppose that the length of the message dispatched by each relay node should be constant, i.e., each node sends the same amount of data no matter how much data it sustains from its child nodes[10]. PEDAP [7] and PEGASIS [8] are conventional protocols based on this supposition and perform far better than HEED and LEACH in this case. However, there are quite a few applications in which the length of the message transferred by a parent node depends not only on the length of its own, but also on the length of the messages received from its child nodes [10].
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Mr. M.Senthil Kumar M.Tech., Assistant Professor Dept of Computer Science and Engineering Sree Sowdambika College of Engineering Aruppukottai, Tamilnadu, India.
Energy consumption of a node is due to either “useful” or "wasteful" operations. The useful operations include transmitting or receiving data messages, and processing requests. On the other hand, the wasteful consumption is due to the operation of routing tree construction, overhearing, retransmitting because of rasping environment, and dealing with redundant broadcast overhead messages. In this paper, we propose Load Stabilizing Tree Based Energy Conserving Routing Protocol (LSTEC).We contemplates a situation in which the network collects data periodically from a location where each node continuously senses the environment and sends the data back to BS [9]. In General there are two definitions for network lifetime: a) The starting time of the network operation to the death of the first node in the network [8]. b) The starting time of the network operation to the death of the last node in the network. In this paper, we agree with the first definition. Moreover, we consider two exceptional cases in data fusion: Case (1) The data between any sensor nodes can be fused totally. Each node sends the same volume of data that does not matter how much data it receives from its child nodes. Case (2) The data cannot be fused. The length of the message transmitted by each relay node is the sum of its own sensed data and received data from its child nodes. The rest of the paper is categorized as follows: Section II related works. The radio models and network of our proposal are discussed in Section III. Section IV describes the detailed architecture of LSTEC. In Section V we show our simulations varied to the simulations of other protocols. Finally, Section VI conclusion. II. RELATED WORKS A major task of WSN is to collect information periodically from the preferred area and transmit the information to BS. A simple method to attain this task is that each sensor node transmits data directly to BS. However, when BS is sited a long away from the target area, the sensor nodes will die rapidly due to more energy utilization. On the other hand, since the gap between each node and BS are different, direct transmission may lead to unbalanced energy consumption. To solve these problems, many protocols have been proposed such as LEACH, PEGASIS, HEED, PEDAP.
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Proceedings of International Conference on Advancements in Engineering and Technology In LEACH [4], [5], for the entire network, nodes are selected according to a fraction p from all sensor nodes are fixed to serve as cluster heads (CHs), where p is a design parameter. The operations of LEACH are split into several rounds. Each round includes a design phase and a static-state phase. During the design phase, each node will decide whether to be a CH or not according to a predefined specification. After CHs are selected, each of other nodes will choose its own CH and join the cluster according to the power of many received messages. Each node will choose the nearest CH. During the static-state phase, CHs fuse the data received from their cluster nodes and send the fused data to BS by single-hop connection. LEACH uses randomization to turn around CHs for each round in order to consistently allocate the energy consumption. So LEACH can minimize the amount of data directly broadcasted to BS and balance WSN load, thus attaining a factor of 8 times growth compared with direct transmission. In [6], the authors proposed a hybrid, energyefficient, distributed clustering algorithm (HEED). HEED is an enhancement of LEACH on the method of CH choosing. In each round, HEED selects CHs according to the remaining energy of each node and a secondary parameter such as nodes proximity to their neighbors or nodes degrees. By iterations and competition, HEED ensures only one CH within a certain range, so uniform CHs distribution is achieved across the network. Compared with LEACH, HEED effectively prolongs network lifetime and is suitable for situations such as where each node has different initial energy. For Case1, LEACH and HEED greatly reduce total energy consumption. However, LEACH and HEED consume energy heavily in the head nodes, which makes the head nodes die quickly. S. Lindsey et al. proposed an algorithm related to LEACH and it is called PEGASIS [7]. PEGASIS is a nearly optimal power efficient protocol which uses GREEDY algorithm to make all the sensor nodes in the network form a chain. In PEGASIS, the (i mod N)th node is chosen to be a leader and the leader is the only one which needs to communicate with BS in round i. N is the total amount of nodes. Data is collected by starting from both endpoints of the chain, and transmitted along the chain, and fused each time it transmits from one node to the next until it reaches the leader. So PEGASIS sharply reduces the total amount of data for long-distance transmission and achieves a better performance than LEACH by 100% to 300% in terms of network lifetime. III. NETWORK AND RADIO MODEL In our work, we assume that the system model has the following properties: Sensor nodes are randomly distributed in the square field and there is only one BS deployed far away from the area. Sensor nodes are stationary and energy constrained. Once deployed, they will keep operating until their energy is exhausted. BS is stationary, but it is not energy constrained.
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All sensor nodes have power control capabilities; each node can change the power level and communicate with BS directly. Sensor nodes are location-aware. A sensor node can get its location information through other mechanisms such as GPS or position algorithms. Each node has its unique identifier (ID). For both cases, the medium is assumed to be symmetric so that the energy required for transmitting a message from node A to node B or from node B to node A is the same. IV. LOAD STABILIZING TREE BASED ENERGY CONSERVING PROTOCOL The main aim of LSTEC is to achieve a longer network lifetime for different applications. In each round, BS assigns a root node and broadcasts its ID and its coordinates to all sensor nodes. Then the network computes the path either by transmitting the path information from BS to sensor nodes or by having the same tree structure being dynamically and individually built by each node. For both cases, LSTEC can change the root and reconstruct the routing tree with short delay and low energy consumption. Therefore a better balanced load is achieved compared with the protocols mentioned in Section II. The operation of LSTEC is divided into Initial Phase, Tree Construction Phase, Data Collection and Transmission Phase, and Information Exchange Phase. A. Initial Phase In Initial Phase, the network parameters are initialized. Initial Phase is divided into three steps. Step 1: When Initial Phase begins, BS broadcasts a packet to all the nodes to inform them of beginning time, the length of time slot and the number of nodes N. When all the nodes receive the packet, they will compute their own energy-level (EL) using function: EL is a parameter for load balance. Step 2: Each node sends its packet in a circle with a certain radius during its own time slot after Step 1. For example, in the time slot, the node whose ID is I will send out its packet. This packet contains a preamble and the information such as coordinates and EL of node i. All the other nodes during this time slot will monitor the channel, and if some of them are the neighbors of node i, they can receive this packet and record the information of node i in memory. After all nodes send their information, each node records a table in their memory which contains the information of all its neighbors. Step 3: Each node sends a packet which contains all its neighbors’ information during its own time slot when Step 2 is over. Then its neighbors can receive this packet and record the information in memory. The length of time slots in Steps 2 and 3 is predefined,
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Proceedings of International Conference on Advancements in Engineering and Technology thus when time is up, each node has sent its information before Initial Phase ended. After Initial Phase, each node records two tables in memory which contain the information of all its neighbors and its neighbors’ neighbors. These two tables are defined as Table I and Table II. B. Tree Construction Phase Within each round, LSTEC performs the following steps to build a routing tree. Between Case1 and Case2 there are some differences in the steps of routing tree constructing: Step 1: BS assigns a node as root and broadcasts root ID and root coordinates to all sensor nodes. TABLE I NETWORK LIFETIMES OF DIFFERENT SCHEMES
For Case1, because data fusion technique is implemented, only one node which communicates directly with BS can transmit all the data with the same length as its own, which results in much less energy consumption. In order to balance the network load for Case1, in each round, a node with the largest residual energy is chosen as root. The root collects the data of all sensors and transmits the fused data to BS over long distance. For Case2, because data can’t be fused, it will not save the energy for data transmitting by making fewer nodes communicate directly with BS. When one of the sensor nodes collects all the data and sends it to BS, it would deplete its energy quickly. In this case BS always assigns itself as root. Step 2: Each node tries to select a parent in neighbors using EL and coordinates which are recorded in Table I. The selection criteria are: 1) For both Case1 and Case2, for a sensor node, the distance between its parent node and the root should be shorter than that between itself and the root. 2) For Case1, each node chooses a neighbor that satisfies criterion 1 and is the nearest to itself as its parent. And if the node can’t find a neighbor which satisfies criterion 1, it selects the root as its parent.
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3) For Case2, the process of Tree Constructing Phase can be regarded as an iterative algorithm. Besides criterion 1, for a sensor node, only the nodes with the largest EL of all its neighbors and itself can act as relay nodes. If the sensor node itself has the largest EL, it can also be considered to be an imaginary relay node. Choosing the parent node from all the relay nodes is based on energy consumptions. Any of these consumptions is the sum of consumption from the sensor node to a relay node and that from the relay node to BS. The relay node which causes minimum consumption will be chosen as the parent node. It is true that this relay node should choose its parent node in the same way. So a path with minimum consumption is found by iterations. And by using EL, LSTEC chooses the nodes with more residual energy to transmit data for long distance. If the sensor node cannot find a suitable parent node, it will transmit its data directly to BS. Step 3: Because every node chooses the parent from its neighbors and every node records its neighbors’ neighbors’ information in Table II, each node can know all its neighbors’ parent nodes by computing, and it can also know all its child nodes. If a node has no child node, it defines itself as a leaf node, from which the data transmitting begins. As discussed above, for Case1, because each packet
sent to the parent nodes will be fused, the minimum energy consumption can be achieved if each node chooses the node nearest to it. But if all nodes choose their nearest neighbors, the network may not be able to build a tree. Fig. 1 shows a network of 100 nodes in this situation. We can find that some
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Proceedings of International Conference on Advancements in Engineering and Technology clusters are formed, but they cannot connect with others. Thus in LSTEC, we use criterion 1 in Case1 to limit the search direction. By this approach, a routing tree is constructed and some nodes still have the possibility of connecting to their nearest neighbors. For Case2, criterion 1 should also be obeyed and this criterion helps to save the energy for data transmitting to a certain extent. To build a routing tree, for Case1, each node follows the steps. But for Case2, we use BS to compute the topography. C. Data Collection and Transmission Phase After the routing tree is constructed, each sensor node collects information to generate a DATA_PKT which needs to be transmitted to BS. For Case1, TDMA and Frequency Hopping Spread Spectrum (FHSS) are both applied. This phase is divided into several TDMA time slots. In a time slot, only the leaf nodes try to send their DATA_PKTs. After a
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node receives all the data from its child nodes, this node itself serves as a leaf node and tries to send the fused data in the next time slot. Each TDMA time slot is divided into three segments as follows (see Fig. 2). Segment1: The first segment is used to check if there is communication interference for a parent node. In this segment, each leaf node sends a beacon which contains its ID to its parent node at the same time. Three situations may occur and they divide all the parent nodes into three kinds. For the first situation, if no leaf node needs to transmit data to the parent node in this time slot, it receives nothing. For the second situation, if more than one leaf node needs to transmit data to the parent node, it receives an incorrect beacon. For the third situation, if only one leaf node needs to transmit data to the parent node, it receives a correct beacon.
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Proceedings of International Conference on Advancements in Engineering and Technology
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Segment 2: During the second segment, the leaf nodes which can transmit their data are confirmed. For the first situation, the parent node turns to sleep mode until next time slot starts. For the second situation, the parent node sends a control packet to all its child nodes. This control packet chooses one of its child nodes to transmit data in the next segment. For the third situation, the parent node sends a control packet to this leaf node. This control packet tells this leaf node to transmit data in the next segment. Segment 3: The permitted leaf nodes send their data to their parent nodes, while other leaf nodes turn to sleep mode. The process in one time slot is shown in Fig. 2. For Case2, each node chooses its parent by considering not the distance but the total energy consumption. In our simulation results, we will show that there may be many leaf nodes sharing one parent node in one time slot. If all the leaf nodes try to transmit their data at the same time, the data messages sent to the same parent node may interfere with each other. By applying Frequency Division Multiple Access (FDMA) or Code Division Multiple Access (CDMA), the schedule generated under competition is able to avoid collisions. However, the accompanying massive control packets will cause a large amount of energy to be wasted. Thus the process may be much simpler. At the beginning of each round, the operation is also divided into several time slots. In the ith time slot, the node whose ID is i turns on its radio and receives the message from BS. BS uses the same approach to construct the routing tree in each round, and then BS tells sensor nodes when to send or receive the data. D. Information Exchange Phase For Case1, since each node needs to generate and transmit a DATA_PKT in each round, it may exhaust its energy and die. The dying of any sensor node can influence the topography. So the nodes that are going to die need to inform others. The process is also divided into time slots. In each time slot, the nodes whose energy is going to be exhausted will compute a random delay which makes only one node broadcast in this time slot. When the delay is ended, these nodes are trying to broadcast a packet to the whole network. While all other nodes are monitoring the channel, they will receive this packet and perform an ID check. Then they modify their tables. If no such packet is received in the time slot, the network will start the next round. For Case2, BS can collect the initial EL and coordinates information of all the sensor nodes in Initial Phase. For each round, BS builds the routing tree and the schedule of the network by using the EL and coordinates information. Once the routing tree is built, the energy consumption of each sensor node in this round can be calculated by BS, thus the information needed for calculating the topology for the next round can be known in advance. However, because WSN may be deployed in an unfriendly environment, the actual EL of each sensor node may be different from the EL calculated by BS. To cope with this problem, each sensor node calculates its EL and
ISBN NO : 978 - 1502893314
detects its actual residual energy in each round. We define the calculated EL as EL1 and the actual EL as EL2. When the two ELs of a sensor node are different, the sensor node generates an error flag and packs the information of actual residual energy into DATA_PKT, which needs to be sent to BS. When this DATA_PKT is received, BS will get the actual residual energy of this sensor node and use it to calculate the topology in the next round. V. COMPARATIVE ANALYSIS AND SIMULATION RESULTS A MATLAB simulation of LSTEC is done for both Case1 and Case2 to evaluate the performance. For Case1, we first compare LSTEC with PEGASIS and use the same network model as PEGASIS. We generate a randomly distributed 100 to 400 nodes network of square area 100 m Ă— 100 m with BS located at (50 m, 175 m) and use DATA_PKT length of 2000 bits and
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Proceedings of International Conference on Advancements in Engineering and Technology CTRL_PKT length of 100 bits. We let each node have 0.25 J initial energy. Fig. 3 and Fig. 4 show the routing tree generated by LSTEC and PEGASIS for exactly the same 100 node topology. In Fig. 3 the triangle is root node and in Fig. 4 the triangle is head node and the rectangle is tail node. As seen, the routing tree generated by LSTEC is better. Since PEGASIS uses GREEDY algorithm to form a chain, long links may exist between parent nodes and child nodes, which will cause an unbalanced load. As for LSTEC, each node tends to choose the nearest neighbor to avoid long links. Fig. 5 shows that the time when the first node dies changes within a range from 100 nodes to 400 nodes in the network. We can find that LSTEC performs much better than PEGASIS and prolongs network lifetime by about 100% to 300% in Fig. 5.
Fig. 5. For Case1, we compare the time when first node dies for GSTEB and PEGASIS for the number of nodes from 100 to 400.
Fig. 6. For Case1, we compare the time when first node dies for GSTEB and HEED for the number of nodes from 100 to 400.
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In Fig. 6 shows that the time when the first node dies changes within a range from 100 nodes to 400 nodes in the network. Clearly, LSTEC performs better than HEED and prolongs the network lifetime by more than 100%. VI. CONCLUSIONS In this work, we introduce LSTEC. Two definitions of network lifetime and two extreme cases of data fusion are proposed. The simulations show that when the data collected by sensors is strongly correlative, LSTEC outperforms LEACH, PEGASIS and HEED. Because LSTEC consumes only a small amount of energy in each round to change the topography for the purpose of balancing the energy consumption. All the leaf nodes can transmit data in the same TDMA time slot so that the transmitting delay is short. When lifetime is defined as the time from the start of the network operation to the death of the first node in the network, LSTEC prolongs the lifetime by 100% to 300% compared with PEGASIS. In some cases, we are more interested in the lifetime of the last node in the network. Some slight changes are made to make the performance of LSTEC similar to that of PEDAP. So LSTEC is nearly the optimal solution in Case1. When the data collected by sensors cannot be fused, LSTEC offers another simple approach to balancing the network load. In fact, it is difficult to distribute the load evenly on all nodes in such a case. Even though LSTEC needs BS to compute the topography, which leads to an increase in energy waste and a longer delay, this kind of energy waste and longer delay are acceptable when compared with the energy consumption and the time delay for data transmitting. Simulation results show that when lifetime is defined as the time from the start of the network operation to the death of the first node in the network, LSTEC prolongs the lifetime of the network by more than 100% compared with HEED. REFERENCES [1] K. Akkaya and M. Younis, “A survey of routing protocols in wireless sensor networks,” Elsevier Ad Hoc Network J., vol. 3/3, pp. 325–349, 2005. [2] I. F. Akyildiz et al., “Wireless sensor networks: A survey,” Computer Netw., vol. 38, pp. 393–422, Mar. 2002. [3] K. T. Kim and H. Y. Youn, “Tree-Based Clustering(TBC) for energy efficient wireless sensor networks,” in Proc. AINA 2010, 2010, pp. 680–685. [4] M. Liu, J. Cao, G. Chen, and X.Wang, “An energy-aware routing protocol in wireless sensor networks,” Sensors, vol. 9, pp. 445–462, 2009. [5] W. Liang and Y. Liu, “Online data gathering for maximizing network lifetime in sensor networks,” IEEE Trans Mobile Computing, vol. 6, no. 1, pp. 2–11, 2007. [6] O. Younis and S. Fahmy, “HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,” IEEE Trans. Mobile Computing, vol. 3, no. 4, pp. 660–669, 2004. [7] S. Lindsey and C. Raghavendra, “Pegasis: Power-efficient gathering in sensor information systems,” in Proc. IEEE Aerospace Conf., 2002, vol. 3, pp. 1125–1130. [8] H. O. Tan and I. Korpeoglu, “Power efficient data gathering and Aggregation in wireless sensor networks,” SIGMOD Rec., vol. 32, no. 4, pp. 66–71, 2003. [9] G. Mankar and S. T. Bodkhe, “Traffic aware energy efficient routing protocol,” in Proc. 3rd ICECT, 2011, vol. 6, pp. 316–320, . [10] R. Szewczyk, J. Polastre, A.Mainwaring, and D. Culler, “Lessons from sensor network expedition,” in Proc. 1st European Workshop on Wireless Sensor Networks EWSN ‘04, Germany, Jan. 19-21, 2004.
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