Energy Optimized Routing Algorithm in Multi-sink Wireless Sensor Networks

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2 350 : denotes the set of wireless links among nodes which can communicate directly. V = V N V S, where V N represents the set of sensor nodes and V S represents the set of sinks. E = {(i, j) i, j V N } {(i, j) i V N, j V S }, the distance of node i and j is less than the maximum communication distance R. Neighbor. The neighbor set of node i is defined as N(i) = { j j V,d i j < R}, where d i j denotes the distance between node i and j. 3 Virtual Potential Field Fig. 1 shows the topology of a typical multi-sink wireless sensor network. The data collected by sensor nodes is transmitted to the sink through the intermediate nodes with hop-by hop manner. Data transmission in WSNs is the many-to-one traffic pattern and the spatial distribution of the data shows obviously centripetal feature, which has similarities with the potential field in physics [14, 15]. In this paper, we will borrow the concept of potential in classical physics and construct multiple potential fields using different network state variables, and then superpose them into a hybrid virtual potential field. Field strengths are used to drive data packets toward the sink along the direction of the great change of the potential gradient. sink(4) A F E B sink(1) C D sink(2) message. Then, the other nodes will also obtain their own hop by receiving update message from its neighbors which already have its hop just in the same way as the nodes one hop away. For example, the hop of sensor node i to sink v can be expressed as H(i,v). When the hop of sensor nodes to all sinks are determined, the minimum will be chosen as the hop of senor nodes, which can be expressed as H(i)=min{H(i,v) i V N, v V S }. (1) To provide the basic routing function, namely to relay packets toward the sink, we define the hop of sensor i as the potential value in hop virtual potential field P h (i): p h (i)=h(i). (2) Set node j as node i s neighbor, in accordance with the definition of the potential field, the virtual force between node i and j is given by F h (i, j)= P h(i) P h ( j) C i j,i V N, j N(i), (3) where C i j denotes the cost of link between these two nodes, which is expressed by the distance between nodes. Considering that forwarding data is between one hop neighbor nodes, so the distance between sending and receiving nodes can be unified set as 1. The virtual force can be expressed as follows: F h (i, j)=p h (i) P h ( j). (4) If only to build hop potential field, the data will be transmitted to the sink along the shortest path in the role of the maximum virtual force. Nodes on the forwarding path, especially near the sink, will suffer heavy forwarding task and cause fast energy consumption, easy to premature failure. In order to prolong network lifetime, it should be rational and practical to make an appropriate trade-off between energy efficiency and balanced energy consumption. 3.2 Residual Energy Virtual Potential Field We define the potential value of node i in residual energy virtual potential field as: sink(3) Fig. 1: Topology of wireless sensor networks 3.1 Hop Virtual Potential Field At the beginning, the sinks in turn broadcast the update message, nodes one hop away from the sink will get their own hop count by adding 1 to the hop in the update P e (i)= RE(i) E 0, (5) where, RE(i) denotes the residual energy of node i and E 0 denotes its initial energy. The virtual force between node i and j is defined as follows: F e (i, j)=p e (i) P e ( j),i V N, j N(i). (6) From (5) and (6), we can get F e (i, j)= RE( j) RE(i) E 0 (7)

4 352 : sink(1) A B sink(4) F C sink(2) E D sink(3) Fig. 2: Routing after hop adjustment Fig. 3: Distribution of nodes in wireless sensor network 5 Results and Discussion In this section, we evaluate the performance of our proposed Energy Optimized Routing Algorithm () for multi-sink wireless sensor networks via matlab. For simplicity, an ideal Media Access Control (MAC) layer and error-free communication links are assumed. We calculate the energy consumption of each node from data packet transmission and reception. We define the lifetime of the wireless sensor network as the time when the residual energy of the sensor node becomes zero firstly, which is counted by round. We compare the performance of with Energy Level-Based Routing () [9] and Shortest Path Based Routing () [7]. In our simulations, sensor nodes are randomly and uniformly deployed over the square monitoring area. Sinks are uniformly distributed at the outside of the monitoring area. Other simulation parameters are given in Table 1. area, the distribution of nodes is shown in Fig. 3. In the initial stage of the network, the potential value of sensor nodes in the hybrid virtual potential field is shown in Fig. 4. In the subsequent simulation, the number of sinks changes from 1 to 8. For further evaluating the performance of, the following of this paper will give the behavior of this algorithm with different number of sinks. will be compared with and on network lifetime, residual energy of nodes, the average hop of packets and the variance of data received by sinks. Table 1: Simulation parameters Parameter Value Network coverage / m 2 100*100 Number of sensors Initial Energy /J 0.5 E elec /(nj bit 1 ) 50 ε amp /(pj bit 1 m 2 ) 10 Data packet size/b 500 Control packet size/b 12 Maximum transmission range If the network contains 4 sinks which are uniformly distributed at peripheral 10m outside of the monitoring Fig. 4: Potential value Fig. 5 gives the network lifetime when the number of sinks changes from 1 to 8. It can be seen from the figure that has extended the network lifetime compared with and. only considers the node hop when making routing decisions and has the shortest network lifetime. The routing path once be constructed

5 Appl. Math. Inf. Sci. 8, No. 1L, (2014) / Network lifetime(round) Average residual energy of sensor nodes(j) Fig. 5: Network lifetime Fig. 6: Average residual energy of sensor nodes Average residual energy of sink neighbors(j) Average hop of data packets(hop) Fig. 7: Average residual energy of Sink s neighbors Fig. 8: Average hop of data packets will no longer change, so the nodes on the forwarding path suffer heavy load and has serious impact on network lifetime. has considered the residual energy of sensor nodes in routing, but more energy is consumed in the process to obtain the energy level of the path. So its network lifetime is between and. Fig. 6 and 7 show the average residual energy of sensor nodes and the average residual energy of sink s neighbors when the first node is failure in the network. The figures show that the average residual energy of sensor nodes and the average residual energy of sink s neighbors in is less than and. In, residual energy potential field is superimposed on hop potential field and the avoid strategy for low-energy nodes is adopted, so the energy consumption of sensor nodes is balanced. As the result of load balancing strategy for multiple sinks, has balanced the energy consumption of sink s neighbors which play a decisive role on the network lifetime. has not taken any measures to balance node s energy consumption, so the average residual energy of nodes is the most. Fig. 8 describes the average hop when data packets are transmitted to sink in different routing algorithms. In this figure, the average hop decreases with the increasement of the number of sinks, because the average distance from sensor nodes to sinks is decreased. Comparing the average hop of data packets in different routing algorithms, the average hop in is the least. In, the residual energy of sensor nodes is considered when making routing decisions and some sensor nodes select long path to transmit data, so its average hop of data packets is the most. 6 Conclusion Energy is one of the most critical resources for WSNs. It should make an appropriate trade-off between energy efficiency and energy balance. With this mind, in this

6 354 : paper we construct hybrid virtual potential field based on the hop of nodes to sinks and the residual energy of nodes and design a virual potential field based multi-sink routing algorithm, according to the feature of concentric in data transmission. The avoid strategy for low-energy nodes and load balancing strategy for multiple sinks are adopted to achieve effective and balanced energy consumption. Simulation results show that the routing algorithm proposed in this paper has extended the network lifetime and balanced energy consumption, compared with and. In our future work, we will deploy sinks within the monitoring region and research the optimization of multi-sink deployment and data routing with the goal of maximizing network lifetime. Acknowledgement Financial support for this work, provided by the National Natural Science Foundation of China (No , ), the Fundamental Research Funds for the Central Universities (No. JGD101671) is gratefully acknowledged. References [1] J. Evans, D. Raychaudhuri, and S. Paul. GENI Design Document, (2006). [2] Y. Mao. Applied Mathematics & Information Sciences, 5, (2011). [3] Q. Huang, Y. Bai, and L. Chen. In Vehicular Technology Conference, VTC2007-Spring. IEEE 65th, IEEE, (2007). [4] K. Kweon, H. Ghim, J. Hong, and H. Yoon. In Wireless Pervasive Computing, ISWPC th International Symposium on, IEEE, 1 5 (2009). [5] H. Kim, Y. Seok, N. Choi, Y. Choi, and T. Kwon. In Information Networking. Convergence in Broadband and Mobile Networking, Springer, (2005). [6] K. Haeyong and M. Pyeongsoo. IEICE transactions on communications, 91, (2008). [7] H. Lee, A. Klappenecker, K. Lee, and L. Lin. In Mobile Adhoc and Sensor Systems Conference, IEEE International Conference on, IEEE, 7 (2005). [8] M. Soyturk and T. Altilar. In Wireless and Microwave Technology Conference, WAMICON 06. IEEE Annual, IEEE, 1 5 (2006). [9] Y. Kiri, M. Murata, and M. Sugano. In Self- Adaptive and Self-Organizing Systems, SASO 07. First International Conference on, IEEE, (2007). [10] Z. Wu, C. Zhang, and H. Chen. In Networking, Sensing and Control, ICNSC IEEE International Conference on, IEEE, (2008). [11] C. Wang and W. Wu. In Communication Software and Networks, ICCSN 09. International Conference on, IEEE, (2009). [12] M. Kalantari and M. Shayman. In Wireless Communications and Networking Conference, WCNC IEEE, 1, (2006). [13] M. Kalantari and M. Shayman. In Information Sciences and Systems, th Annual Conference on, IEEE, (2006). [14] T. He, F. Ren, C. Lin, and S. Das. In Sensor, Mesh and Ad Hoc Communications and Networks, SECON 08. 5th Annual IEEE Communications Society Conference on, IEEE, (2008). [15] F. Ren, J. Zhang, T. He, C. Lin, and S. K. Ren. Parallel and Distributed Systems, IEEE Transactions on, 22, (2011). Haifeng Jiang is currently a associate professor in China University of Mining and Technology. He received the MS degree in Circuits and Systems from China University of Mining and Technology in 2005, and the PhD degree in Communication and Information System from China University of Mining and Technology in His research interests are in the areas of wireless sensor networks, routing systems, and information systems. Renke Sun is currently a lecturer in China University of Mining and Technology.He received the MS degree in application of computer technology from China University of Mining and Technology in 2004, and the PhD degree in Communication and Information System from China University of Mining and Technology in His research interests are in the areas of network architecture, routing systems, and information systems.

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