26,155 research outputs found

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

    Full text link
    [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

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

    Full text link
    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

    A game theory approach for cooperative control to improve data quality and false data detection in WSN

    Get PDF
    [EN] New solutions are required for the management of heterogeneous distributed sensor networks in order to address the problem of data quality and false data detection in wireless sensor networks (WSNs). In this paper, we present a nonlinear cooperative control algorithm based on game theory. Here, a new model is proposed for the automatic processing and management of information in heterogeneous distributed WSNs. We apply our algorithm to a case study with the aim of improving the quality of temperature data collected from indoor surfaces by a WSN. Unlike the classic unsupervised methods, in the proposed algorithm, it is not necessary to define the number of clusters beforehand. Once the game reaches the game equilibrium, the resulting number of clusters can be used as input for the unsupervised classification analysis. Anomalous temperature values are corrected according to their neighborhood, without modifying the temperature clusters

    Deploying Wireless Sensor Devices in Intelligent Transportation System Applications

    Get PDF
    As future intelligent infrastructure will bring together and connect individuals, vehicles and infrastructure through wireless communications, it is critical that robust communication technologies are developed. Mobile wireless sensor networks are self-organising mobile networks where nodes exchange data without the need for an underlying infrastructure. In the road transport domain, schemes which are fully infrastructure-less and those which use a combination of fixed (infrastructure) devices and mobile devices fitted to vehicles and other moving objects are of significant interest to the ITS community as they have the potential to deliver a ‘connected environment’ where individuals, vehicles and infrastructure can co-exist and cooperate, thus delivering more knowledge about the transport environment, the state of the network and who indeed is travelling or wishes to travel. This may offer benefits in terms of real-time management, optimisation of transportation systems, intelligent design and the use of such systems for innovative road charging and possibly carbon trading schemes as well as through the CVHS (Cooperative Vehicle and Highway Systems) for safety and control applications. As the wireless sensor networks technology is still relatively new and very little is known about its real application in the transport domain. Our involvement in the transport-related projects provides us with an opportunity to carry out research and development of wireless sensor network applications in transport systems. This chapter outlines our experience in the ASTRA (ASTRA, 2005), TRACKSS (TRACKSS, 2007) and EMMA (EMMA, 2007) projects and provides an illustration of the important role that the wireless sensor technology can play in future ITS. This chapter also presents encouraging results obtained from the experiments in investigating the feasibility of utilising wireless sensor networks in vehicle and vehicle to infrastructure communication in real ITS applications

    Wireless industrial monitoring and control networks: the journey so far and the road ahead

    Get PDF
    While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

    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

    An Outline of Security in Wireless Sensor Networks: Threats, Countermeasures and Implementations

    Full text link
    With the expansion of wireless sensor networks (WSNs), the need for securing the data flow through these networks is increasing. These sensor networks allow for easy-to-apply and flexible installations which have enabled them to be used for numerous applications. Due to these properties, they face distinct information security threats. Security of the data flowing through across networks provides the researchers with an interesting and intriguing potential for research. Design of these networks to ensure the protection of data faces the constraints of limited power and processing resources. We provide the basics of wireless sensor network security to help the researchers and engineers in better understanding of this applications field. In this chapter, we will provide the basics of information security with special emphasis on WSNs. The chapter will also give an overview of the information security requirements in these networks. Threats to the security of data in WSNs and some of their counter measures are also presented
    • …
    corecore