Localization method for low power consumption systems

Locating nodes is a fundamental problem in wireless networks with hundreds of devices deployed in a wide area. This is especially relevant for mobile nodes. Wireless sensor nodes are usually powered by small batteries, solar panels or piezoelectric generators, so that, and consequently, power consumption is the main constraint to deal with. But classic localization techniques do not consider the problem of energy consumption as a key point. This paper presents a novel low power and range-free localization technique based on distributed fuzzy logic and cooperative processing among a set of fixed nodes and its neighbours. This feature permits better accuracy with less power consumption than most relevant localization techniquesJunta de Andalucía P07-TIC-0247

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

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments

We consider the problem of sensor localization in a wireless network in a multipath environment, where time and angle of arrival information are available at each sensor. We propose a distributed algorithm based on belief propagation, which allows sensors to cooperatively self-localize with respect to one single anchor in a multihop network. The algorithm has low overhead and is scalable. Simulations show that although the network is loopy, the proposed algorithm converges, and achieves good localization accuracy