Efficient approach for maximizing lifespan in wireless sensor networks by using mobile sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms

LPTA: Location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks

This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.The work is supported by the Science and Technology Pillar Program of Changzhou (Social Development), no. CE20135052. Joel J. P. C. Rodrigues's work has been supported by the Fundamental Research Funds for the Central Universities (Program no. HEUCF140803), by Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Zhu, C.; Wang, Y.; Han, G.; Rodrigues, JJPC.; Lloret, J. (2014). LPTA: Location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks. Scientific World Journal. https://doi.org/10.1155/2014/476253SHan, G., Xu, H., Jiang, J., Shu, L., Hara, T., & Nishio, S. (2011). Path planning using a mobile anchor node based on trilateration in wireless sensor networks. Wireless Communications and Mobile Computing, 13(14), 1324-1336. doi:10.1002/wcm.1192Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619-632. doi:10.1016/j.jnca.2011.11.016Han, G., Xu, H., Duong, T. Q., Jiang, J., & Hara, T. (2011). Localization algorithms of Wireless Sensor Networks: a survey. Telecommunication Systems, 52(4), 2419-2436. doi:10.1007/s11235-011-9564-7Guoliang Xing, Tian Wang, Zhihui Xie, & Weijia Jia. (2008). Rendezvous Planning in Wireless Sensor Networks with Mobile Elements. IEEE Transactions on Mobile Computing, 7(12), 1430-1443. doi:10.1109/tmc.2008.58Basagni, S., Carosi, A., Melachrinoudis, E., Petrioli, C., & Wang, Z. M. (2007). Controlled sink mobility for prolonging wireless sensor networks lifetime. Wireless Networks, 14(6), 831-858. doi:10.1007/s11276-007-0017-xWang, G., Wang, T., Jia, W., Guo, M., & Li, J. (2008). Adaptive location updates for mobile sinks in wireless sensor networks. The Journal of Supercomputing, 47(2), 127-145. doi:10.1007/s11227-008-0181-5Shin, K., & Kim, S. (2012). Predictive routing for mobile sinks in wireless sensor networks: a milestone-based approach. The Journal of Supercomputing, 62(3), 1519-1536. doi:10.1007/s11227-012-0815-5Lee, K., Kim, Y.-H., Kim, H.-J., & Han, S. (2013). A myopic mobile sink migration strategy for maximizing lifetime of wireless sensor networks. Wireless Networks, 20(2), 303-318. doi:10.1007/s11276-013-0606-9Sheu, J.-P., Sahoo, P. K., Su, C.-H., & Hu, W.-K. (2010). Efficient path planning and data gathering protocols for the wireless sensor network. Computer Communications, 33(3), 398-408. doi:10.1016/j.comcom.2009.10.011Yang, Y., Fonoage, M. I., & Cardei, M. (2010). Improving network lifetime with mobile wireless sensor networks. Computer Communications, 33(4), 409-419. doi:10.1016/j.comcom.2009.11.010Liang, W., Luo, J., & Xu, X. (2011). Network lifetime maximization for time-sensitive data gathering in wireless sensor networks with a mobile sink. Wireless Communications and Mobile Computing, 13(14), 1263-1280. doi:10.1002/wcm.1179Kinalis, A., Nikoletseas, S., Patroumpa, D., & Rolim, J. (2014). Biased sink mobility with adaptive stop times for low latency data collection in sensor networks. Information Fusion, 15, 56-63. doi:10.1016/j.inffus.2012.04.003Liu, C. H., Ssu, K. F., & Wang, W. T. (2011). A moving algorithm for non-uniform deployment in mobile sensor networks. International Journal of Autonomous and Adaptive Communications Systems, 4(3), 271. doi:10.1504/ijaacs.2011.040987Shi, L., Zhang, B., Mouftah, H. T., & Ma, J. (2012). DDRP: An efficient data-driven routing protocol for wireless sensor networks with mobile sinks. International Journal of Communication Systems, n/a-n/a. doi:10.1002/dac.2315Liu, X., Zhao, H., Yang, X., & Li, X. (2013). SinkTrail: A Proactive Data Reporting Protocol for Wireless Sensor Networks. IEEE Transactions on Computers, 62(1), 151-162. doi:10.1109/tc.2011.207Aioffi, W. M., Valle, C. A., Mateus, G. R., & da Cunha, A. S. (2011). Balancing message delivery latency and network lifetime through an integrated model for clustering and routing in Wireless Sensor Networks. Computer Networks, 55(13), 2803-2820. doi:10.1016/j.comnet.2011.05.023Liu, D., Zhang, K., & Ding, J. (2013). Energy-efficient transmission scheme for mobile data gathering in Wireless Sensor Networks. China Communications, 10(3), 114-123. doi:10.1109/cc.2013.648883