Lifetime Increase for Wireless Sensor Networks Using Cluster-Based Routing Hana Khamfroush1 1
Engineering Faculty of Porto University-FEUP
[email protected]
Abstract. Life time limitation in wireless sensor networks is a big problem. Energy limitation of sensor nodes is the basic factor for lifetime limitation. In this paper, we propose a new routing algorithm which can increase the network lifetime. We assume that each node can estimate its residual energy and then a new clustering method will be proposed for increase of network lifetime. In the new method, the predefined numbers of nodes which have the maximum residual energy are selected as cluster heads (CHs) first and then the members of each cluster are determined based on the distances between the node and the cluster head and also between the cluster head and BS. All cluster heads use a multi-hop chain structure for transmission of data packets from themselves to BS. We compare our method with ERA routing algorithm in terms of network life-time by simulation. Experiment results show that new method improved network lifetime in comparison to other methods like ERA. Also energy consumption will be distributed more evenly among nodes in our new method. Key Words. Wireless Sensor Networks, Network lifetime, Energy-Aware Clustering
1. Introduction Wireless Sensor Networks (WSNs), are set of many sensor nodes that have some limitations of energy and dimension. Because of the energy limitation for these nodes, we must use their energy very usefully such that the network lifetime increases. Therefore, the role of the routing which is one of the most important causes for energy consumption in WSN will be important. Up until now, many routing algorithms for network life-time increase in WSNs was proposed. We can classify them based on two parameters. First classification is based on the network structure that classifies all routing algorithms to flat, hierarchical, and location-based algorithms. Second classification is based on the protocol operation that classify all routing algorithms to multi-path based routing, negotiation-based routing, query-based routing, QOS-based routing and coherent-based routing algorithms[1]. The evaluation results in terms of network lifetime and the energy consumption over these networks show that hierarchical routing algorithms or cluster-based algorithms have the best work in contrast to other algorithms. Since our proposed algorithm attempt to increase the network lifetime, therefore it works based on the nodes 1
clustering. In this method, nodes clustering are done based on the residual energy for nodes and the distances between them and BS. The network lifetime meaning in this case is the round numbers that the BS can receive data packets from all nodes in the network, or simply, the time when the first node is died. The rest of this paper is organized as follows: related works is introduced in section2, and then in section 3, energy model is proposed. Section4 will propose our new method, and the last section shows the evaluation and simulation results.
2. Related Works LEACH, is one of the most popular hierarchical routing algorithms for WSNs that classify all sensor nodes in forms of clusters [3]. In this method, the time of network performance is divided to some rounds. Each round includes two stages: First, the clustering of nodes Second, Information transmission In the clustering stage, first the specific number of nodes is selected as cluster heads (CHs) which the basic function of them is to collect the data packets from the other nodes and transmission of them toward the BS directly. Therefore, the energy consumption of CHs is much more than the other nodes. The CH selection process in LEACH is done randomly. When the CH selection was done, each node selects a cluster head which has the minimum distance from it as its cluster head. When the cluster formation phase finished, the information transmission phase is started. During this phase, each normal node (nodes that are not cluster head) collects information and uses its allocated slot to transmit the collected data to the cluster head directly. Then each cluster head combines its data packet with received data packets and send the aggregated data packet toward the BS directly. One of the other proposed algorithms for energy reduction in these networks is PEGASIS [4]. This algorithm was decreased the energy consumption in contrast to LEACH by creation of a chain structure comprised of all nodes and continually data aggregation across the chain. In this method, the duration of the network lifetime is divided to some rounds. In the beginning of each round, the farthest node to the BS is selected as the first node in the chain structure. Then the nearest node to this node is selected as the second node in the chain. Other nodes are added to the chain structure based on the minimization of distance to the last node in the chain. On the other hand, nodes that are currently outside the chain are added to the chain in a greedy fashion, the closest neighbor to the top node in the current chain first, until all nodes are included. Therefore, each node communicates only with its nearest neighbor node in the chain structure. In each round, a node is selected as the chain-leader and it must transmit all received data packets from other nodes to the BS directly. The data packets are transmitted hop by hop and are aggregated continually across the chain. Chain leader is selected randomly in each round and so the energy consumption is evenly distributed among all nodes. One of the most important drawbacks of the LEACH and PEGASIS, is that the role of all nodes is determined without take their residual energy into account and so this may cause to network lifetime decrease. To solve these problems, many routing algorithms are proposed up to now such as X-LEACH and ERA and so on [5,6]. In ERA, all nodes are classified to some clusters similar to LEACH. The process of CH 2
selection in ERA is exactly identical to LEACH. But unlike LEACH, ERA selects the path with maximum residual energy to transmit data packets toward the BS and so it can increase the network lifetime. In fact, since the basic goal of this method is network lifetime increase, therefore the evenly distribution of energy consumption between all nodes is more important than the average energy consumption reduction. In ERA method, cluster heads are selected similar to LEACH and then, each cluster head estimates its equation (1): ECHres ECHrem EtoBS
(1)
In this equation, ECHrem is the residual energy of the cluster head node in the current round and EtoBS is the required energy for the transmission of data packets between this cluster head and BS. Then each cluster head put this residual energy into cluster head advertisement packet and broadcasts it to the all other nodes. During the cluster head election phase, each non-CH node receives all advertisement messages and extracts all energy residue data of CHs from advertisement messages. Furthermore, each non-CH node calculates the energy residues to send data packets toward every cluster heads respectively from the equation (2): EnonCHres EnonCHrem EtoCH
(2)
Which EnonCH-rem, is the residual energy of one normal node in the current round and EtoCH is the required energy for data transmission between the normal node and the desired cluster head. Each normal node must calculate these for all cluster heads and eventually selects one of the cluster heads that maximizes the equation (3) as its CH: E ECHres EnonCHres
(3)
In fact, each node is searching to find a cluster head that can maximize the residual energy of the path which is built from the node to cluster head and then to the BS. Therefore, the ERA algorithm improved some traditional algorithms such as LEACH and X-LEACH in terms of network lifetime. Also some other energy-aware routing algorithms was proposed for energy consumption reduction in WSNs [7,8]. In this paper we improved the performance of ERA and will increase the network lifetime using the modification of cluster head selection method and the method of data transmission from cluster heads to BS. Since, ERA improved the performance of LEACH; therefore our new method will improve the performance of network in contrast to LEACH.
3. Energy and Network Model In all simulations, we assume a network comprised of N sensor nodes which are stationary and homogeneous. All nodes are deployed in a square area randomly. The initial energy of all nodes is equal and they have equal processing and communication abilities. We also assume that each node can estimate its residual energy in each time similar to ERA and X-LEACH. All nodes can approximate their distances to other nodes and BS through the comparison between strength of the received signals from them. The energy model which is used for computation of energy consumption in all simulations is similar to energy model in [3,4,5,6,7]. In this model, the total energy consumption for transmission of L bit data between node i and node j that have distance d from each other, is calculated from (4). 3
Etotal (L, d) L(2Ec e.ds )
(4)
In this equation, e and s are calculated from the following equation. e1 s 2 d d 0 e e2 s 4 d d 0
(5)
As we can see from the equation (5), if the distance between the transmitter and the receiver nodes be smaller than d0, we use the free space model (s=2) and otherwise use the multi-path model (s=4). The typical value for d0, is 86.7 meter. Also Ec is the base energy required to run the transmitter or receiver circuitry and e1 or e2 is the unit energy required for the transmitter amplifier. The typical values for all variables are shown in the table 1. TABLE 1,THE TYPICAL VALUES FOR TWO COMMUNICATION MODELS
Free Space Model
Multi-Path Model ----------------------
e1
10 pJ / bit.m 2
e2
---------------------------------
0.0013 pJ / bit.m 4
d0
86.7m
86.7m
S
2
4
4. Proposed Algorithm As we said in the last sections, the ERA algorithm maximizes the residual energy of the traversed paths for increase of network lifetime. One of the most important drawbacks of this method is that the selection of cluster heads are done without take the residual energy and distance to BS of the nodes into account. In fact, the cluster head selection phase is done similar to LEACH. This kind of cluster head selection, may lead to the death of CHs very soon because the cluster head selection do not care to the residual energy of nodes and so in the higher rounds, it may lead to selection of a node that has a large distance from BS and also has the low residual energy for data sending. We change the way of cluster head selection for solving of this problem. In fact, the specific numbers of nodes which have the maximum energy in contrast to other nodes are selected as cluster head in each round similar to [7]. Therefore, we can sure that the cluster heads have the needed energy to data transmission toward the BS and the network lifetime is increased. The second drawback of ERA is directly data packet transmission toward the BS. Since in some cases, BS is very far from the sensor deployment area, therefore the energy consumption for directly data transmission is very high and so it can lead to depletion of all energy savings for cluster heads. We propose a new multi-hop method for transmission of data packets by means of one of the cluster heads that has maximum residual energy and minimum distance from BS in each round. Our new method can reduce the energy consumption and so it can help us to increase the network lifetime. In 4
this method, when cluster heads receive data packets from their cluster members, one of the cluster heads is selected as leader for transmission of data toward BS directly. We select one of the cluster heads that maximizes the equation (6), as the leader. it must transfer all data packets from other cluster heads to the BS directly. EresCH Erem EtoBS
(6)
In this equation, Erem is the residual energy of that cluster head in the current time, EtoBS is the required energy for transmission of "L" bit data toward the BS. Therefore, we can sure that the nearest CH to BS which has maximum residual energy is selected in each round to transmit all aggregated data packets to BS. In fact, similar to PEGASIS, all cluster heads build a chain structure and aggregate their data packets continually. In the chain structure, the distances between neighboring nodes are minimum and only a selected cluster head (leader) must transmit final data packets to the BS. The chain structure is building by means of CHs and causes to continually data aggregation and so, it can increase the network lifetime and reduce the energy consumption in each round.
5. Simulation and Results We consider many conditions for performance comparison of proposed algorithm and ERA by MATLAB simulator. We suppose the following assumptions for network modeling: a) All sensor nodes are homogeneous; on the other hand, all characteristics of them are identical. The initial energy of all sensor nodes is equal and the typical value for it is 2 joule. b) All sensor nodes are deployed in a square area randomly and the dimension of the square area in all simulations is 100*100. The data packets length is 525 bytes and the Meta data or control packet length is 100 bits. We define two parameters for better comparison of network performance to two methods. The first parameter for network lifetime evaluation, FND, is defined as the time when the first node is died in the network and second parameter, LND, is defined as the time when all nodes died in the network. BS is located at (50,200) in all simulations. The figures show the average results of 100 time program execution. 3500
network life-time
ERA-ME-LND ERA-ME-FND 3000
2500
2000
1500 100
200
300 400 500 the number of node
600
700
Figure5-1,The comparison of network lifetime in terms of two parameters, LND and FND for ERA-ME. 2500 ERA-LND ERA-FND
network life-time
2000 1500 1000 500 0 100
200
300 400 500 the number of node
600
700
Figure5-2, The comparison of network lifetime in terms of two parameters, LND and FND for ERA.
As we can see from the figures, plot 5-1 for proposed method is more evenly in contrast 5
to plot 5-2 and the difference between time which first node died and time which last node died is lesser for figure 5-1. These observations state that the proposed method (ERA-ME) distributes energy consumption more evenly among all nodes and all networks. Also we can understand that the proposed method can increase network lifetime in terms of both parameters, FND and LND. The difference between LND plots is lower than FND plots and improvement of network performance is very salient for FND parameter. We can conclude from these observations, our method distributes the energy consumption more flat among all nodes in contrast to ERA. We proved our claim by figure 5-1 and 5-2 which shows the difference between first node die and last node die duration for two methods separately. The nodes in ERA-ME, almost die in the same duration time but the difference between two plots in ERA is much more and so the distribution of energy consumption between nodes is not flat.
6. Conclusion and Future works In this paper, we proposed a new routing algorithm for network lifetime increase through the modification of the ERA algorithm by means of some corrections in final data transmission method to BS and cluster head selection method. Simulation results state that our new method works better than ERA in all cases and increases the network lifetime. Although in new method, the number of control packets is more in contrast to ERA, but because these packets are transmitted intra the deployment area of sensors, the energy consumption for transmission of them is lower in contrast to data packet transmission from cluster heads to the BS in ERA. Since both ERA and ERA-ME methods, do not care about distances between cluster head and BS in cluster head selection phase, so we can change the cluster head selection method such that both residual energy and distance to BS take into account and so increase the network lifetime much more. Also we can combine the ERA method with other routing methods such as negotiation-based methods. Also we can reduce the number of control packets by means of changing of the CHs selection method.
7. References [1] [2] [3] [4] [5] [6] [7] [8]
Al-Karaki J.N, A.E.Kamal, " Routing Techniques in Wireless Sensor Networks: A Survey," IEEE Wireless Communications, vol.11, No. 6, Dec. 2004, pp. 6-28. Akyildiz I.F, Su W., Sankarasubramaniam Y., Cayirci E., " A Survey on Sensor Networks", IEEE Communications, Aug.2002, pp. 102-114. Heinzelman W., Chandrakasan, A., and Balakrishnan, H., " Energy-Efficient Communication Protocol for Wireless Microsensor Networks", Pro. of the 33 rd IEEE Int. Conf. on System Siences, Honolulu, USA,Jan 2000, pp. 1-10. Lindsey S, Raghavendra C., "PEGASIS: Power Efficient Gathering in Sensor Information Systems", IEEE Aerospace Conference Proceedings, 2002, Vol.3. No. 9-16, pp. 1125-1130. Handy M., Haase M., Timmermann D., " Low Energy Adaptive Clustering Hierarchy with Deterministic ClusterHead Selection", IEEE MWCN, Stockholm, Sweden, Sep.2002. Chen H., Wu C., Chu Y., Cheng C., Tsai L., " Energy Residue Aware (ERA) Clustering Algorithm for Leach-based Wireless Sensor Networks", Second International Conference on Systems and Networks Communications (ICSNC2007). Xiangning F., Yulin S., " Improvement on LEACH Protocol of Wireless Sensor Network", Proceeding of the International Conference on Sensor Technologies and Applications2007, SensorComm2007.14-20 Octobr 2007,pp.260-264. Younis O., Fahmy S., " Heed: A hybrid,energy-efficient, distributed clustering approach for adhoc sensor networks", IEEE trans. Mobile Comput., vol.3, no.4, Oct,.-Dec,2004.
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