IntroductionResearch in wireless sensor network (WSN) is receiving lot of attention from the academia, as well as from industries, because of the enormous scope of its application in numerous areas. In many applications of WSN, it may be needed to randomly deploy the sensor nodes in a given region and responses from these nodes must be sent to the base station quickly and reliably. Such applications include forest fire detection, disaster relief, emergency rescue operation etc. In most of the above mentioned applications, two things are important:

a) Fast and reliable delivery of the information.b) Optimum energy consumption within the network.

Delayed and erroneous delivery of information in a disaster situation can lead to an enormous calamity which is undesirable. Secondly, energy constraint in individual sensor node may shorten the lifetime of the sensor node itself, as well as the lifetime of the entire network. This paper recommends use of multiple sinks in disaster management applications in order to ensure fast and reliable delivery of information and optimum energy consumption. A network partitioning algorithm based onk-Nearest Neighbor Graph is proposed in this paper. A restricted flooding routing algorithm is used to demonstrate that data transmission can be made more efficient in a partitioned network. A simulation of the proposed algorithm is carried out in the Matlab environment and it is shown that the performance is much better in a partitioned network.

Topology PartitioningIn a large scale WSN, fast and energy efficient routing of sensed data is always a challenge. To meet this challenge, deployment of multiple sinks is proposed. Routing algorithms are designed in such a way that a source node sends the sensed data only to its nearest sink. Here a partitioning scheme is proposed that partitions the network into small sized sub-networks.Each sub-network contains a sink. Fig.1 shows data transmission in non-partitioned and partitioned network.

AcknowledgmentsThe first author would like to thank School of Mobile Computing and Communication, Jadavpur University and UGC for being a recipient of the Fellowship in Science for Meritorious Students.

FN_NNG PartitioningThis proposed algorithm is based on K-Nearest Neighbor Graph (K-NNG).

NNG: Let be a set of points in The nearest neighbor of is a point , with minimum Euclidean distance from .

K-NNG: We can generalize NNG (V) to K-NNG (V), the k-nearest-neighbors graph of V, by introducing k-edges from a vertex to its k-nearest neighbors.

The proposed wireless sensor network partitioning algorithm is based on selection of Farthest Node from K-Nearest Neighbor Graph (FN_NNG) of G. After the deployment of sinks and source nodes, our FN_NNG partitioning algorithm is run initially from all the sinkssimultaneously.

Algorithm FN_NNG The algorithm is run in two phases: Initial Phase and Incremental phase. Initially, there is p-number of sink nodes. At the end, the algorithm outputs p-number of partitions.

In the initial phase, each sink generates its kNNG. From k-NNG it finds its neighbor nodes and stores them in its own Neighbor_list as well as in the Partition_list associated with it. A Flag is associated with each sensor node which avoids visiting the same node twice.

Performance Metrics(i) Average consumed energy for a single data packet

(ii) Average number of hops needed to reach a single data packet from source node to sink

Topology Partitioning in Wireless Sensor Networks Using Multiple Sinks

498410053 姜俞任 , 602410013 曾彥雄School of Mobile Computing and Communication, Jadavpur University, Kolkata, India

For further informationPlease contact s2233447@gmail.com or ss42199@hotmail.com for further information. You can also get more information about this paper at www.cs.ccu.edu.tw/~1234567 .

In the Incremental phase, the farthest neighbor node for each sink is found from the Neighbor_list. These nodes are set as NextNode and their neighbor nodes are found in the next step. The k-NNGs of the set of NextNodes are generated and these neighbor nodes are stored in the Neighbor_list, as well as in the Partition_list. The farthest neighbor node for each NextNode is found from the Neighbor_list. This phase is repeated until the union of all the Partition_lists equals total number nodes deployed. Using the Flag it is ensured that each Partition_list contains disjoint set of sensor nodes. Whenever a sensor node is included in a partition, it stores the id of the sink in its own partition as the destination address.

P. A. Forero, A. Cano and G. B. Giannakis, "Consensus-based k-means algorithm for distributed learning using wireless sensor networks," in the Workshop on Sensors, Signal and Info Process., Sedona, AZ, May 2008.

E. I. Oyman, and C. Ersoy, "Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks," in the International Conference on Communications (ICC 2004), Paris, France, June 20-24, 2004.

M. A. M. Vieira, L. F. M. Vieira, and L. B. Ruiz, "Scheduling Nodes in Wireless Sensor Networks: A Voronoi Approach," in 28th Annual IEEE International Conference on Local Computer Networks (LCN), 2003.

W. R. Heinzelman et a!., "Energy-Efficient Communication Protocol for Wireless Micro sensor Networks," in 33rd Hawaii International Conference on System Sciences 2000.

Literature citedB. Elbhiri, S. EI. Fkihi, R. Saadane, and D. Aboutajdine, "Clustering in Wireless

Sensor Networks Based on Near Optimal Bi-partitions," in Next Generation Internet (NGI), 2010.

R. Kawano, T. Miyazaki, "Distributed Data Aggregation in Multi sinks sensor networks using a Graph Coloring Algorithm," in 22nd International Conference on Advanced Information Networking and Applications (AINA W), 2008.

I. Slama, B. Jouaber, and 0.1. Zeghlache, "Energy Efficient Scheme for Large Scale Wireless Sensor Networks with Multiple Sinks," in Wireless Communications and Networking Conference (WCNC-IEEE) 2008.

Performance EvaluationSimulation has been done in Matlab environment. We consider three cases. In order to compare the performances among all the three cases, we use flooding and restricted flooding routing protocol. The three cases are explained below:Case 1: Single sink without partitionAn entire large network (without partitioning) is considered. Here only single sink node is used and fixed at one comer of the square region.Case 2: Multiple sinks without partitionHere also an entire network (without partitioning) with multiple sinks is considered. Four sinks are deployed at each comer of the square area.Case 3: Multiple sinks with partitionThe entire network is partitioned into four smaller sub-networks. Each sub-network contains one single sink at each comer.

Fig.4 Average energy usage for transmitting data packet

Fig.5 Number of hops traveled by a data packet to reach sinks

Fig.6 Average Energy usage with number of hops

Fig.7 Lifetime of the Network

ConclusionUsing multiple sinks in a wireless sensor network can significantly decrease the amount of energy spent on communication. Partitioning the entire network intosmall size sub-networks further decreases energy consumption than a non-partitioned network. Energy consumption is reduced by shortening the distancebetween sensor nodes and sinks. The results included in this paper demonstrate that energy consumption is reduced during data transmission by using multiplesinks and partitioning the network. Although the functioning of the algorithm has been explained, the proposed algorithm must be compared with other partitioning algorithms in order to demonstrate its effectiveness.

D. Eppstein, M. S. Paterson, and F. F. Yao, "On Nearest-Neighbor Graphs," in Discrete and Computational Geometry 17, 1997, pp. 263-282.

1. Chlebikova, "Approximating the Maximally Balanced Connected Partition Problem in graphs,“ in Information Processing Letters 60, 1996.