Layer-Based Data Aggregation and Performance Analysis in Wireless Sensor Networks

Due to the similarity and correlation among sensed data in wireless sensor network, it is an important way to reduce the number of packets transmitted with data aggregation technology so as to prolong the network lifetime. However, data aggregation is still a challenging issue since quality-of-service, such as end-to-end delay, is generally considered as a severe criterion required in many applications. We focus on the minimum-latency data aggregation problem and proposed a new efficient scheme for it. The basic idea is that we first build an aggregation tree by ordering nodes into layers, and then we proposed a scheduling algorithm on the basis of the aggregation tree to determine the transmission time slots for all nodes in the network with collision avoiding. We have proved that the upper bound for data aggregation with our proposed scheme is bounded by (15R+Δ-15) for wireless sensor networks in two-dimensional space. Extensive simulation results have demonstrated that the proposed scheme has better practical performance compared with related works

Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.This work is supported by the National Science Foundation of China under Grand nos. 61370210 and 61103175, Fujian Provincial Natural Science Foundation of China under Grant nos. 2011J01345, 2013J01232, and 2013J01229, and the Development Foundation of Educational Committee of Fujian Province under Grand no. 2012JA12027. It has also been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary Projects.Cheng, H.; Su, Z.; Zhang, D.; Lloret, J.; Yu, Z. (2014). Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks. International Journal of Distributed Sensor Networks. 2014:1-14. https://doi.org/10.1155/2014/576573S1142014Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292-2330. doi:10.1016/j.comnet.2008.04.002Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Diallo, O., Rodrigues, J. J. P. C., Sene, M., & Lloret, J. (2015). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 26(2), 604-620. doi:10.1109/tpds.2013.207Oliveira, L. M. L., Rodrigues, J. J. P. C., Elias, A. G. F., & Zarpelão, B. B. (2014). Ubiquitous Monitoring Solution for Wireless Sensor Networks with Push Notifications and End-to-End Connectivity. Mobile Information Systems, 10(1), 19-35. doi:10.1155/2014/270568Diallo, O., Rodrigues, J. J. P. C., & Sene, M. (2012). Real-time data management on wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1013-1021. doi:10.1016/j.jnca.2011.12.006Boyinbode, O., Le, H., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2/3), 130. doi:10.1504/ijssc.2011.040339Aslam, N., Phillips, W., Robertson, W., & Sivakumar, S. (2011). A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks. Information Fusion, 12(3), 202-212. doi:10.1016/j.inffus.2009.12.005Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847-860. doi:10.1007/s11276-012-0438-zNaeimi, S., Ghafghazi, H., Chow, C.-O., & Ishii, H. (2012). A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks. Sensors, 12(6), 7350-7409. doi:10.3390/s120607350Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Rajagopalan, R., & Varshney, P. (2006). Data-aggregation techniques in sensor networks: a survey. IEEE Communications Surveys & Tutorials, 8(4), 48-63. doi:10.1109/comst.2006.283821Al-Karaki, J. N., Ul-Mustafa, R., & Kamal, A. E. (2009). Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms. Computer Networks, 53(7), 945-960. doi:10.1016/j.comnet.2008.12.001Tan, H. O., Korpeoglu, I., & Stojmenovic, I. (2011). Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 22(3), 489-500. doi:10.1109/tpds.2010.68Gao, Q., Zuo, Y., Zhang, J., & Peng, X.-H. (2010). Improving Energy Efficiency in a Wireless Sensor Network by Combining Cooperative MIMO With Data Aggregation. IEEE Transactions on Vehicular Technology, 59(8), 3956-3965. doi:10.1109/tvt.2010.2063719Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793-802. doi:10.1016/j.comcom.2010.10.003Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. doi:10.1109/sahcn.2011.5984932Xu, Y., & Choi, J. (2012). Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields. Automatica, 48(8), 1735-1740. doi:10.1016/j.automatica.2012.05.029Min, J.-K., & Chung, C.-W. (2010). EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations. Journal of Systems and Software, 83(2), 271-282. doi:10.1016/j.jss.2009.08.004Jianzhong Li, & Siyao Cheng. (2012). (ε, δ)-Approximate Aggregation Algorithms in Dynamic Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(3), 385-396. doi:10.1109/tpds.2011.193Hung, C.-C., Peng, W.-C., & Lee, W.-C. (2012). Energy-Aware Set-Covering Approaches for Approximate Data Collection in Wireless Sensor Networks. IEEE Transactions on Knowledge and Data Engineering, 24(11), 1993-2007. doi:10.1109/tkde.2011.224Liu, C., Wu, K., & Pei, J. (2007). An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation. IEEE Transactions on Parallel and Distributed Systems, 18(7), 1010-1023. doi:10.1109/tpds.2007.1046Xiaobo Zhang, Heping Wang, Nait-Abdesselam, F., & Khokhar, A. A. (2009). Distortion Analysis for Real-Time Data Collection of Spatially Temporally Correlated Data Fields in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 58(3), 1583-1594. doi:10.1109/tvt.2008.928906Karasabun, E., Korpeoglu, I., & Aykanat, C. (2013). Active node determination for correlated data gathering in wireless sensor networks. Computer Networks, 57(5), 1124-1138. doi:10.1016/j.comnet.2012.11.018Gupta, H., Navda, V., Das, S., & Chowdhary, V. (2008). Efficient gathering of correlated data in sensor networks. ACM Transactions on Sensor Networks, 4(1), 1-31. doi:10.1145/1325651.1325655Campobello, G., Leonardi, A., & Palazzo, S. (2012). Improving Energy Saving and Reliability in Wireless Sensor Networks Using a Simple CRT-Based Packet-Forwarding Solution. IEEE/ACM Transactions on Networking, 20(1), 191-205. doi:10.1109/tnet.2011.2158442Tseng, L.-C., Chien, F.-T., Zhang, D., Chang, R. Y., Chung, W.-H., & Huang, C. (2013). Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning. IEEE Communications Letters, 17(12), 2304-2307. doi:10.1109/lcomm.2013.102113.131876Rodrigues, J. J. P. C., & Neves, P. A. C. S. (2010). A survey on IP-based wireless sensor network solutions. International Journal of Communication Systems, n/a-n/a. doi:10.1002/dac.1099Aziz, A. A., Sekercioglu, Y. A., Fitzpatrick, P., & Ivanovich, M. (2013). A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 15(1), 121-144. doi:10.1109/surv.2012.031612.00124Mehlhorn, K. (1988). A faster approximation algorithm for the Steiner problem in graphs. Information Processing Letters, 27(3), 125-128. doi:10.1016/0020-0190(88)90066-xCheng, H., Liu, Q., & Jia, X. (2006). Heuristic algorithms for real-time data aggregation in wireless sensor networks. Proceeding of the 2006 international conference on Communications and mobile computing - IWCMC ’06. doi:10.1145/1143549.1143774Cheng, H., Guo, R., & Chen, Y. (2013). Node Selection Algorithms with Data Accuracy Guarantee in Service-Oriented Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(4), 527965. doi:10.1155/2013/52796

Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks

This chapter discusses the need of security and privacy protection mechanisms in aggregation protocols used in wireless sensor networks (WSN). It presents a comprehensive state of the art discussion on the various privacy protection mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA protocol and proposes a mechanism to plug that vulnerability. To demonstrate the need of security in aggregation process, the chapter further presents various threats in WSN aggregation mechanisms. A large number of existing protocols for secure aggregation in WSN are discussed briefly and a protocol is proposed for secure aggregation which can detect false data injected by malicious nodes in a WSN. The performance of the protocol is also presented. The chapter concludes while highlighting some future directions of research in secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table