Energy efficient cooperative computing in mobile wireless sensor networks

Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution

Stochastic Cooperative Decision Approach for Studying the Symmetric Behavior of People in Wireless Indoor Location Systems

[EN] Nowadays, several wireless location systems have been developed in the research world. The goal of these systems has always been to find the greatest accuracy as possible. However, if every node takes data from the environment, we can gather a lot of information, which may help us understand what is happening around our network in a cooperative way. In order to develop this cooperative location and tracking system, we have implemented a sensor network to capture data from user devices. From this captured data we have observed a symmetry behavior in people's movements at a specific site. By using these data and the symmetry feature, we have developed a statistical cooperative approach to predict the new user's location. The system has been tested in a real environment, evaluating the next location predicted by the system and comparing it with the next location in the real track, thus getting satisfactory results. Better results have been obtained when the stochastic cooperative approach uses the transition matrix with symmetry.This work is supported by the "Universitat Politecnica de Valencia" through "PAID-05-12".Tomás Gironés, J.; García Pineda, M.; Canovas Solbes, A.; Lloret, J. (2016). Stochastic Cooperative Decision Approach for Studying the Symmetric Behavior of People in Wireless Indoor Location Systems. Symmetry (Basel). 8(7):1-13. https://doi.org/10.3390/sym8070061S11387Gu, Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials, 11(1), 13-32. doi:10.1109/surv.2009.090103Maghdid, H. S., Lami, I. A., Ghafoor, K. Z., & Lloret, J. (2016). Seamless Outdoors-Indoors Localization Solutions on Smartphones. ACM Computing Surveys, 48(4), 1-34. doi:10.1145/2871166Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012). A reliable and accurate indoor localization method using phone inertial sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp ’12. doi:10.1145/2370216.2370280Zheng, Y., Shen, G., Li, L., Zhao, C., Li, M., & Zhao, F. (2014). Travi-Navi. Proceedings of the 20th annual international conference on Mobile computing and networking - MobiCom ’14. doi:10.1145/2639108.2639124Sendra, S., Lloret, J., Turró, C., & Aguiar, J. M. (2014). IEEE 802.11a/b/g/n short-scale indoor wireless sensor placement. International Journal of Ad Hoc and Ubiquitous Computing, 15(1/2/3), 68. doi:10.1504/ijahuc.2014.059901Farid, Z., Nordin, R., & Ismail, M. (2013). Recent Advances in Wireless Indoor Localization Techniques and System. Journal of Computer Networks and Communications, 2013, 1-12. doi:10.1155/2013/185138Jain, A. K., Duin, P. W., & Jianchang Mao. (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4-37. doi:10.1109/34.824819Fitzek, F. H. P., & Katz, M. D. (Eds.). (2006). Cooperation in Wireless Networks: Principles and Applications. doi:10.1007/1-4020-4711-8Nosratinia, A., Hunter, T. E., & Hedayat, A. (2004). Cooperative communication in wireless networks. IEEE Communications Magazine, 42(10), 74-80. doi:10.1109/mcom.2004.1341264Ammari, H. M. (2010). Coverage in Wireless Sensor Networks: A Survey. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.276Hsiao-Wecksler, E. T., Polk, J. D., Rosengren, K. S., Sosnoff, J. J., & Hong, S. (2010). A Review of New Analytic Techniques for Quantifying Symmetry in Locomotion. Symmetry, 2(2), 1135-1155. doi:10.3390/sym2021135Nunes, B. A. A., & Obraczka, K. (2011). On the symmetry of user mobility in wireless networks. 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks. doi:10.1109/wowmom.2011.5986146Deng, Z., Yu, Y., Yuan, X., Wan, N., & Yang, L. (2013). Situation and development tendency of indoor positioning. China Communications, 10(3), 42-55. doi:10.1109/cc.2013.6488829Lloret, J., Tomas, J., Garcia, M., & Canovas, A. (2009). A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments. Sensors, 9(5), 3695-3712. doi:10.3390/s90503695Feng, C., Au, W. S. A., Valaee, S., & Tan, Z. (2012). Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing. IEEE Transactions on Mobile Computing, 11(12), 1983-1993. doi:10.1109/tmc.2011.216Wang, J., Hu, A., Liu, C., & Li, X. (2015). A Floor-Map-Aided WiFi/Pseudo-Odometry Integration Algorithm for an Indoor Positioning System. Sensors, 15(4), 7096-7124. doi:10.3390/s150407096Dhruv Pandya, Ravi Jain, & Lupu, E. (s. f.). Indoor location estimation using multiple wireless technologies. 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003. doi:10.1109/pimrc.2003.1259108Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Garcia, M., & Lloret, J. (2009). A Cooperative Group-Based Sensor Network for Environmental Monitoring. Cooperative Design, Visualization, and Engineering, 276-279. doi:10.1007/978-3-642-04265-2_41Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54-69. doi:10.1109/msp.2005.1458287Conti, A., Guerra, M., Dardari, D., Decarli, N., & Win, M. Z. (2012). Network Experimentation for Cooperative Localization. IEEE Journal on Selected Areas in Communications, 30(2), 467-475. doi:10.1109/jsac.2012.120227Xuyu Wang, Hui Zhou, Shiwen Mao, Pandey, S., Agrawal, P., & Bevly, D. M. (2015). Mobility improves LMI-based cooperative indoor localization. 2015 IEEE Wireless Communications and Networking Conference (WCNC). doi:10.1109/wcnc.2015.7127811Cooperative Decision Making in a Stochastic Environment (No. urn: nbn: nl: ui: 12-76799)http://EconPapers.repec.org/RePEc:tiu:tiutis:a84d779a-d5a9-48e9-bfe7-46dea6f1de69Krishnan, P., Krishnakumar, A. S., Ju, W.-H., Mallows, C., & Gamt, S. (2004). A system for LEASE: Location estimation assisted by stationary emitters for indoor RF wireless networks. IEEE INFOCOM 2004. doi:10.1109/infcom.2004.1356987WANG, H., & Jia, F. (2007). A Hybrid Modeling for WLAN Positioning System. 2007 International Conference on Wireless Communications, Networking and Mobile Computing. doi:10.1109/wicom.2007.537Roos, T., Myllymäki, P., Tirri, H., Misikangas, P., & Sievänen, J. (2002). International Journal of Wireless Information Networks, 9(3), 155-164. doi:10.1023/a:1016003126882Xie, H., Tanin, E., & Kulik, L. (2007). Distributed Histograms for Processing Aggregate Data from Moving Objects. 2007 International Conference on Mobile Data Management. doi:10.1109/mdm.2007.30Krishnamachari, B., & Iyengar, S. (2004). Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers, 53(3), 241-250. doi:10.1109/tc.2004.1261832Nguyen, X., Jordan, M. I., & Sinopoli, B. (2005). A kernel-based learning approach to ad hoc sensor network localization. ACM Transactions on Sensor Networks, 1(1), 134-152. doi:10.1145/1077391.107739

Nonlinear Trajectory Discovery of a Moving Target by Wireless Sensor Networks

Target tracking is an important cooperative sensing application of wireless sensor networks. In these networks energy, computing power and communication bandwidth are scarce. In this paper, we consider a randomly deployed sensor network with sensors acting as a set of distributed datasets. Each dataset is assumed to have its local temporal dataset, along with spatial data and the geographical coordinates of a given object. An approach towards mines global temporal patterns from these datasets and to discovers nonlinear trajectories of a moving object is proposed. It is tested in a simulation environment and compared with straightforward method. The results of the experiments clearly show the benefits of the new approach in terms of energy consumption

A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring

[EN] Sensor networks can be used in many sorts of environments. The increase of pollution and carbon footprint are nowadays an important environmental problem. The use of sensors and sensor networks can help to make an early detection in order to mitigate their effect over the medium. The deployment of wireless sensor networks (WSNs) requires high-energy efficiency and secures mechanisms to ensure the data veracity. Moreover, when WSNs are deployed in harsh environments, it is very difficult to recharge or replace the sensor's batteries. For this reason, the increase of network lifetime is highly desired. WSNs also work in unattended environments, which is vulnerable to different sort of attacks. Therefore, both energy efficiency and security must be considered in the development of routing protocols for WSNs. In this paper, we present a novel Secure and Low-energy Zone-based Routing Protocol (SeLeZoR) where the nodes of the WSN are split into zones and each zone is separated into clusters. Each cluster is controlled by a cluster head. Firstly, the information is securely sent to the zone-head using a secret key; then, the zone-head sends the data to the base station using the secure and energy efficient mechanism. This paper demonstrates that SeLeZoR achieves better energy efficiency and security levels than existing routing protocols for WSNs.Mehmood, A.; Lloret, J.; Sendra, S. (2016). A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring. Wireless Communications and Mobile Computing. 16(17):2869-2883. https://doi.org/10.1002/wcm.2734S286928831617Sendra S Deployment of efficient wireless sensor nodes for monitoring in rural, indoor and underwater environments 2013Javaid, N., Qureshi, T. N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). EDDEEC: Enhanced Developed Distributed Energy-efficient Clustering for Heterogeneous Wireless Sensor Networks. Procedia Computer Science, 19, 914-919. doi:10.1016/j.procs.2013.06.125Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Garcia, M., Lloret, J., Sendra, S., & Rodrigues, J. J. P. C. (2011). Taking Cooperative Decisions in Group-Based Wireless Sensor Networks. Cooperative Design, Visualization, and Engineering, 61-65. doi:10.1007/978-3-642-23734-8_9Garcia, M., & Lloret, J. (2009). A Cooperative Group-Based Sensor Network for Environmental Monitoring. Cooperative Design, Visualization, and Engineering, 276-279. doi:10.1007/978-3-642-04265-2_41Jain T Wireless environmental monitoring system (wems) using data aggregation in a bidirectional hybrid protocol In Proc of the 6th International Conference ICISTM 2012 2012Senouci, M. R., Mellouk, A., Senouci, H., & Aissani, A. (2012). Performance evaluation of network lifetime spatial-temporal distribution for WSN routing protocols. Journal of Network and Computer Applications, 35(4), 1317-1328. doi:10.1016/j.jnca.2012.01.016Heinzelman WR Chandrakasan A Balakrishnan H Energy-efficient communication protocol for wireless microsensor networks In proc of the 33rd Annual Hawaii International Conference on System Sciences 2000 2000Xiangning F Yulin S Improvement on LEACH protocol of wireless sensor network In proc of the 2007 International Conference on Sensor Technologies and Applications SensorComm 2007 2007Tong M Tang M LEACH-B: an improved LEACH protocol for wireless sensor network In proc of the 6th International Conference on Wireless Communications Networking and Mobile Computing WiCOM 2010 2010Mohammad El-Basioni, B. M., Abd El-kader, S. M., Eissa, H. S., & Zahra, M. M. (2011). An Optimized Energy-aware Routing Protocol for Wireless Sensor Network. Egyptian Informatics Journal, 12(2), 61-72. doi:10.1016/j.eij.2011.03.001Younis O Fahmy S Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach In proc of the Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies INFOCOM 2004 2004Noack, A., & Spitz, S. (2009). Dynamic Threshold Cryptosystem without Group Manager. Network Protocols and Algorithms, 1(1). doi:10.5296/npa.v1i1.161Nasser, N., & Chen, Y. (2007). SEEM: Secure and energy-efficient multipath routing protocol for wireless sensor networks. Computer Communications, 30(11-12), 2401-2412. doi:10.1016/j.comcom.2007.04.014Alippi, C., Camplani, R., Galperti, C., & Roveri, M. (2011). A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring. IEEE Sensors Journal, 11(1), 45-55. doi:10.1109/jsen.2010.2051539Parra L Sendra S Jimenez JM Lloret J Smart system to detect and track pollution in marine environments, in proc. of the 2015 2015 1503 1508Atto, M., & Guy, C. (2014). Routing Protocols and Quality of Services for Security Based Applications Using Wireless Video Sensor Networks. Network Protocols and Algorithms, 6(3), 119. doi:10.5296/npa.v6i3.5802Liu, Z., Zheng, Q., Xue, L., & Guan, X. (2012). A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks. Future Generation Computer Systems, 28(5), 780-790. doi:10.1016/j.future.2011.04.019Bri D Sendra S Coll H Lloret J How the atmospheric variables affect to the WLAN datalink layer parameters 2010Ganesh, S., & Amutha, R. (2013). Efficient and secure routing protocol for wireless sensor networks through SNR based dynamic clustering mechanisms. Journal of Communications and Networks, 15(4), 422-429. doi:10.1109/jcn.2013.000073Amjad M 2014 Energy efficient multi level and distance clustering mechanism for wireless sensor networksMeghanathan, N. (2015). A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks. Network Protocols and Algorithms, 7(3), 18. doi:10.5296/npa.v7i3.796

A Non-Cooperative Game Theoretical Approach For Power Control In Virtual MIMO Wireless Sensor Network

Power management is one of the vital issue in wireless sensor networks, where the lifetime of the network relies on battery powered nodes. Transmitting at high power reduces the lifetime of both the nodes and the network. One efficient way of power management is to control the power at which the nodes transmit. In this paper, a virtual multiple input multiple output wireless sensor network (VMIMO-WSN)communication architecture is considered and the power control of sensor nodes based on the approach of game theory is formulated. The use of game theory has proliferated, with a broad range of applications in wireless sensor networking. Approaches from game theory can be used to optimize node level as well as network wide performance. The game here is categorized as an incomplete information game, in which the nodes do not have complete information about the strategies taken by other nodes. For virtual multiple input multiple output wireless sensor network architecture considered, the Nash equilibrium is used to decide the optimal power level at which a node needs to transmit, to maximize its utility. Outcome shows that the game theoretic approach considered for VMIMO-WSN architecture achieves the best utility, by consuming less power.Comment: 12 pages, 8 figure

Lifetime Improvement in Wireless Sensor Networks via Collaborative Beamforming and Cooperative Transmission

Collaborative beamforming (CB) and cooperative transmission (CT) have recently emerged as communication techniques that can make effective use of collaborative/cooperative nodes to create a virtual multiple-input/multiple-output (MIMO) system. Extending the lifetime of networks composed of battery-operated nodes is a key issue in the design and operation of wireless sensor networks. This paper considers the effects on network lifetime of allowing closely located nodes to use CB/CT to reduce the load or even to avoid packet-forwarding requests to nodes that have critical battery life. First, the effectiveness of CB/CT in improving the signal strength at a faraway destination using energy in nearby nodes is studied. Then, the performance improvement obtained by this technique is analyzed for a special 2D disk case. Further, for general networks in which information-generation rates are fixed, a new routing problem is formulated as a linear programming problem, while for other general networks, the cost for routing is dynamically adjusted according to the amount of energy remaining and the effectiveness of CB/CT. From the analysis and the simulation results, it is seen that the proposed method can reduce the payloads of energy-depleting nodes by about 90% in the special case network considered and improve the lifetimes of general networks by about 10%, compared with existing techniques.Comment: Invited paper to appear in the IEE Proceedings: Microwaves, Antennas and Propagation, Special Issue on Antenna Systems and Propagation for Future Wireless Communication