An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks

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1 An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks Ayon Chakraborty 1, Swarup Kumar Mitra 2, and M.K. Naskar 3 1 Department of CSE, Jadavpur University, Kolkata, India 2 Department of ECE, MCKV Institute of Engineering, Howrah, India 3 Department of ETCE, Jadavpur University, Kolkata, India {jucse.ayon,swarup.subha}@gmail.com, mrinalnaskar@yahoo.co.in Abstract. For time-sensitive applications requiring frequent data gathering from a remote wireless sensor network, it is a challenging task to design an efficient routing scheme that can minimize delay and also offer good performance in energy efficiency and network lifetime. In this paper, we propose a new data gathering scheme which is a combination of clustering and shortest hop pairing of the sensor nodes. The cluster heads and the super leader are rotated every round for ensuring an evenly distributed energy consumption among all the nodes. We have implemented the proposed scheme in nesc and performed simulations in TOSSIM. Successful packet transmission rates have also been studied using the interference-model. Compared with the existing popular schemes such as PEGASIS, BINARY, LBEERA and SHORT, our scheme offers the best energy delay performance and has the capability to achieve a very good balance among different performance metrics. Keywords: Data Gathering, Network Lifetime, Interference Model, Energy x Delay. 1 Introduction Wireless Sensor Networks (WSNs) are usually self-organized wireless ad hoc networks comprising of a large number of resource constrained sensor nodes. One of the most important tasks of these sensor nodes is systematic collection of data and transmit gathered data to a distant base station(bs) where the data is processed. But once the nodes are deployed it is often undesirable or infeasible to replace or recharge them. Hence network lifetime becomes an important parameter for efficient design of data gathering schemes for sensor networks. Each node is provided with transmit power control and omni directional antenna and therefore can vary the areas of its coverage [1]. Since communication requires significant amount of energy compared to computations, sensor nodes must collaborate in an energy-efficient manner for transmitting and receiving data so that not only the lifetime is enhanced but also a better energy x delay performance is achieved. We propose and analyze in this paper a new cluster-based routing scheme called Hybrid Data gathering Scheme(HDS), which can ensure the best energy delay performance while, at the same time, achieve a good balance among other performance T. Janowski, H. Mohanty, and E. Estevez (Eds.): ICDCIT 2010, LNCS 5966, pp , Springer-Verlag Berlin Heidelberg 2010

2 An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks 99 metrics such as energy efficiency and network lifetime. We divide the network into clusters and subsequently SHORT [2] is applied for data gathering in each cluster as well as among the cluster heads. The data gathering scheme HDS is coded in nesc for TinyOS[3] software platform. This not only signifies the coding feasibility of the scheme, but also verifies it for running in real hardware platforms (like Micaz or Mica2). The TOSSIM radio interference model has been used in simulating the packet reception ratio. 2 Related Works Several cluster based and chain based algorithms have been proposed for efficient data gathering. PEGASIS scheme proposed in [1] is based on a chain, which starts from the farthest node from the BS. By connecting the last node on the chain to its closest unvisited neighbor, PEGASIS greatly reduces the total communication distance and achieves a very good energy and lifetime performance for different network sizes and topologies. CDMA capable and non- CDMA-capable sensor nodes, the chain-based BINARY and 3-Level Hierarchy schemes were proposed respectively in [4] to achieve better energy delay performance than PEGASIS. In [5], a clusterbased Load Balance and Energy Efficient Routing Algorithm (LBEERA) is presented. LBEERA divides the whole network into several equal clusters and every cluster works as in PEGASIS. To reduce energy consumption, a new algorithm to construct the lower chain in each cluster is also proposed. A tree-structure routing scheme called Shortest HOP Routing Tree (SHORT) [2] offers a great improvement in terms of Energy x Delay with a good performance for network lifetime. LEACH [6] rotates the roles of cluster heads among all the sensor nodes. In doing so, the energy load is distributed evenly across the network and network lifetime (in unit of data collection rounds) becomes much longer than the static clustering mechanism. 3 The System Model We consider a field containing N randomly deployed sensor nodes, divided into M geographic clusters. Without any loss of generality, we assume that Cluster 1 contains N 1 nodes, Cluster 2 contains N 2 nodes and so on, Cluster M containing N M nodes. Data aggregation is performed at intermediate nodes by generating single k-bit packet from multiple incoming k-bit packets. The position information of all the nodes is known to the BS by using the Global Positioning Systems (GPS) or other techniques[6]. For wireless communication, the simple first-order radio model is used to calculate the energy consumption for transmitting and receiving data packets. Let ξ elec = 50nJ/bit and ξ amp = 100 pj/bit/m 2 denote the energy consumption rates for operating the electronics in radio transceiver and transmitter amplifier, respectively. We assume ξ elec also take into account the energy consumption for aggregating multiple incoming data packets and generating a single same sized outgoing packet which is known as data fusion. For receiving a k-bit packet, a sensor node consumes E rx (k) Joule of energy, or, E rx (k)= ξ elec * k (1)

3 100 A. Chakraborty, S.K. Mitra, and M.K. Naskar While for transmitting a k-bit packet to another node over a distance of d meters, the energy consumption is given by, E tx (k, d) =( ξ elec + ξ amp * d 2 ) *k (2) The packet reception ratio in this scheme was simulated by the radio interference model in TOSSIM which is based on the empirical data. The loss probability captures transmitter interference using original trace that yielded the model. More detailed measurements would be required to simulate the exact transmitter characteristics; however experiments have shown the model to be very accurate. 4 Proposed HDS Algorithm The key idea of our approach is to divide the whole field into a number of Clusters as in LBEERA. The applied SHORT scheme in each of the cluster adopts centralized algorithms and requires the powerful BS, rather than the sensor nodes with limited resources, to take the responsibility to manage the network topology and calculate the routing path and time schedule for data collection. The cluster-head in each of the cluster acts as a leader. HDS operates in three phases: (i) Cluster and group formation phase: In each round one leader for each cluster will be elected based on the residual energy of the cluster members and their distances from the BS. (ii) Leader and super leader selection phase: Initially in each cluster the nearest node to the BS is selected as the cluster-head and among the cluster-heads the nearest to the BS is selected as the super leader. From the 2 nd round to select cluster-heads as well as the super leader, BS considers two important parameters. The first parameter is the distance between the node and BS, denoted by D. The remaining energy of a 2 node denoted by E residual, where P i = E residual i / D i For a particular round the cluster member with the maximum P will be selected as the cluster-head and the cluster-head with the maximum P as the super leader. Super leader and leader are rotated in every round according to criterion for evenly distributing the energy load among all the nodes. (iii) Data transmission phase: After the creation of the clusters and selection of cluster-heads and super leader, sensors start data gathering and transmission operation. 4.1 Calculation of Delay, Message Complexity, Energy* Delay Product and Mean Delay i) Delay calculation: In each cluster delay for data gathering in the individual cluster heads is log 2 N i, for the i th cluster. After the data is accumulated in the M cluster heads, it takes another log 2 M + 1 time slots to gather the data in the base station. The plus one factor is for transmitting the final data packet from the super leader to the base station. Since, the algorithm is applied in parallel among all the clusters, data gathering tasks to the cluster heads occur simultaneously. Thus the delay for data

4 An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks 101 gathering in each cluster is lower bounded by log 2 N max, where N max = max of (N 1, N 2 N M ). So overall delay will be, ceil( log 2 N max ) + ceil( log 2 M ) + 1 ceil( log 2 (N max M ) ) = ceil( log 2 N ) + k (3) k is a constant which decreases as the distribution of the sensor nodes is more uniform, and becomes zero when N 1 = N 2 =.. = N M = N max.. Here ceil(x) is the ceiling of x, denoting the least integer, greater than or equal to x. So, we see the delay to have a complexity be O(logN). ii) Message Complexity: In the HDS scheme, in each round, there is a single cluster and group formation phase and a single leader and super leader election phase. Assuming, each node has a packet to send in every round, total number of messages passed is N. Thus message passing complexity is linear, i.e. O(N). iii) Energy Delay product: As we can, reasonably say that the radio transmission and reception energy is greater than CPU or processing energy by several orders of magnitude, we take message passing as a rough measure of energy consumption in the nodes. Thus energy x delay product have a complexity of O(N logn). iv) Mean Delay: We define the mean delay as the average of the delay to the BS from each of the nodes. The network has a total of N 1 +N N M (=N) nodes. In the first slot they are divided in (N 1 /2 + N 2 /2 + + N M /2) groups. Now each of the (N 1 /2 + N 2 /2 + + N M /2) transmitter nodes in the first slot ( 1 from each group) will have a delay of (log(mn i ) + 1) time slots to the base station, where N i denotes the nodes in the i th cluster. Similarly calculating for the t th slot each of the (N 1 /2 t + N 2 /2 t + + N M /2 t ) transmitter nodes have a delay of (log(mn i ) t + 1) time slots to the base station. So, for calculating the mean delay, we go for the weighted mean, MD = log N log M i { ( Nj[log Ni i+ 2]/ 2 )} i= 1 j= 1 N (4) 5 Simulation Results Our proposed scheme is validated by extensive computer simulations. A network consisting of 100 homogeneous sensor nodes deployed randomly in a field of size 50m x 50m is considered for the simulation model. The BS is fixed and located x=50m and y=150m. The network is geographically divided into 5 equal sized clusters. HDS is compared with the classical data gathering schemes in literature like the PEGASIS, LBERRA, SHORT and BINARY. It not only shows a very good network lifetime as compared to these schemes but also has a better energy-delay product. The simulation results are as follows.

5 102 A. Chakraborty, S.K. Mitra, and M.K. Naskar Fig. 1. Comparison of Network Lifetime and Energy-Delay Product vs Number of Nodes From Figure 1, we see that as the number of nodes increases the network lifetime falls. But among others HDS shows the best performance. So it is energy efficient. The better performance in the Energy-delay product signifies better throughput on top of energy efficiency. The detailed results about the simulation are given in Table 1. Fig. 2. Fraction of Packets successfully reaching the base station with retransmission Attempts the upper dark portion of the bar shows the range of the fraction, the tips indicating maximum and minimum fractions (simulated in TOSSIM) The packet reception ratio is calculated as the ratio of the packets received successfully to the total packets transmitted. In HDS we calculated the fraction of packets reaching the BS successfully, by varying the number of retransmission attempts. As the number of retransmission attempts increase the packet reception ratio also increases. The Figure 2 depicts the packet loss in HDS. This simulation introduces the interference model in our simulation making it more realistic.

6 An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks Performance Comparison Table 1. Comparison of the various schemes FND: First Node Dies, HND: Half Nodes Die, LND: Last Node Dies, Delay calculated in average slots per round Performance Metrics PEGASIS BINARY SHORT LBEERA HDS Network Lifetime (rounds) Energy Consumption (mj) FND HND LND Delay Mean Conclusions Our proposed algorithm overcomes the losses incurred from all other data gathering schemes proposed in literature. HDS makes a good harmony among network lifetime, energy costs and network throughput. It not only reduces the network lifetime but also guarantees the best energy-delay product. The coding of HDS in nesc deserves a special mention as it proves the scheme to be feasible on real hardware platforms. Also the radio interference model used for simulation purposes helped us to study the problem from the perspective of a more realistic physical layer. References 1. Lindsey, S., Raghavendra, C.S.: PEGASIS: Power Efficient Gathering in Sensor Information Systems. In: Proceedings of IEEE ICC 2001, pp (2001) 2. Yang, Y., Wu, H.H., Chen, H.H.: SHORT: Shortest Hop Routing Tree for Wireless Sensor Networks. In: IEEE ICC 2006 proceedings (2006) 3. Levis, P.: TinyOS Programming (2006) 4. Lindsey, S., Raghavendra, C.S., Sivalingam, K.: Data Gathering in Sensor Networks using energy*delay metric. In: Proceedings of the 15th International Parallel and Distributed Processing Symposium, pp (2001) 5. Yu1, Y., Wei, G.: Energy Aware Routing Algorithm Based on Layered Chain in Wireless Sensor Network, /07/$ IEEE (2007) 6. Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy- Efficient Communication Protocol for Wireless Microsensor Networks. In: IEEE Proceedings of the Hawaii International Conference on System Sciences (2000)