A near optimal algorithm for lifetime optimization in wireless sensor networks

International audienceA problem that has received a lot of interest in wireless sensor networks (WSN) is lifetime optimization. Indeed, in WSN each sensor node is battery powered and it is not convenient to recharge or replace the batteries in many cases, especially in remote and hostile environments. In this paper, we introduce an efficient energy-aware algorithm to enhance the lifetime of WSN by i) organizing/clustering the sensor nodes into disjoint cover sets where each cover set is capable of monitoring all the targets of the region of interest and ii) scheduling these cover sets successively/periodically. This study differs from previous works for the following reasons: i) it achieves near optimal solutions compared to the optimal ones obtained by the exact method and ii) unlike existing algorithms that construct gradually cover sets one after the other, our algorithm builds the different sets in parallel. Indeed, at each step of the clustering process, the algorithm attempts to add to each cover set a sensor capable of monitoring the most critical target (a critical target is defined to be the one covered by the smallest set of sensors). The choice of a sensor to be placed/clustered in each cover set is based on solving a linear assignment problem. The proposed algorithm provides a lower bound Kmin of the optimal number of disjoint cover sets Kopt . Intuitively, the upper bound Kmax of the optimal value is given by the size of the smallest set of sensors covering a target. We deduce Kopt by performing a binary search procedure. At each step of the binary search process, we check if there exists a partition of the sensors in K cover sets by solving an integer programming problem. Simulation results show the efficiency of our algorithm

A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this pa-per, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the pro-posed scheduling method can better prolong the lifetime of the network in comparison to similar existing method

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

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

Multihop clustering algorithm for load balancing in wireless sensor networks

The paper presents a new cluster based routing algorithm that exploits the redundancy properties of the sensor networks in order to address the traditional problem of load balancing and energy efficiency in the WSNs.The algorithm makes use of the nodes in a sensor network of which area coverage is covered by the neighbours of the nodes and mark them as temporary cluster heads. The algorithm then forms two layers of multi hop communication. The bottom layer which involves intra cluster communication and the top layer which involves inter cluster communication involving the temporary cluster heads. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing and is also more efficient in terms of energy consumption from Leach and the enhanced version of Leach

Maximum precision-lifetime curve for joint sensor selection and data routing in sensor networks

In many classes of monitoring applications employing battery-limited sensor networks, periodic sampling of an area with a given precision level is required. For such applications, we provide mathematical programming formulations for deriving the optimal trade-off curve between network lifetime and data precision, and design a practical heuristic for near-optimal operation. The properties of our models and the effectiveness of our heuristic are demonstrated by computational experiments