A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments

Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

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

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

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

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. 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Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85