6,997 research outputs found

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

    Full text link
    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

    An objective based classification of aggregation techniques for wireless sensor networks

    No full text
    Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

    Get PDF
    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Collaborative Storage Management In Sensor Networks

    Full text link
    In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example, a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource: the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage technique to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering, allowing the network to run for a longer time without running out of storage space and reducing the amount of data that will eventually be relayed to the observer. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network.Comment: 13 pages, 7 figure

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

    Full text link
    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

    Reliable routing scheme for indoor sensor networks

    Get PDF
    Indoor Wireless sensor networks require a highly dynamic, adaptive routing scheme to deal with the high rate of topology changes due to fading of indoor wireless channels. Besides that, energy consumption rate needs to be consistently distributed among sensor nodes and efficient utilization of battery power is essential. If only the link reliability metric is considered in the routing scheme, it may create long hops routes, and the high quality paths will be frequently used. This leads to shorter lifetime of such paths; thereby the entire network's lifetime will be significantly minimized. This paper briefly presents a reliable load-balanced routing (RLBR) scheme for indoor ad hoc wireless sensor networks, which integrates routing information from different layers. The proposed scheme aims to redistribute the relaying workload and the energy usage among relay sensor nodes to achieve balanced energy dissipation; thereby maximizing the functional network lifetime. RLBR scheme was tested and benchmarked against the TinyOS-2.x implementation of MintRoute on an indoor testbed comprising 20 Mica2 motes and low power listening (LPL) link layer provided by CC1000 radio. RLBR scheme consumes less energy for communications while reducing topology repair latency and achieves better connectivity and communication reliability in terms of end-to-end packets delivery performance
    corecore