LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications. In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution. Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices. In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed. The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions. Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms. With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.Junta de Andalucía P07-TIC-0247

Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

[EN] In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node's location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m.This work was fully supported by the Vice Chancellor Doctoral Scholarship at Auckland University of Technology, New Zealand.Ahmad, T.; Li, XJ.; Seet, B.; Cano, J. (2020). Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks. Electronics. 9(5):1-19. https://doi.org/10.3390/electronics9050738S11995Zhou, B., Yao, X., Yang, L., Yang, S., Wu, S., Kim, Y., & Ai, L. (2019). Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station. Electronics, 8(6), 622. doi:10.3390/electronics8060622Ahmad, T., Li, X., & Seet, B.-C. (2017). Parametric Loop Division for 3D Localization in Wireless Sensor Networks. Sensors, 17(7), 1697. doi:10.3390/s17071697Kaur, A., Kumar, P., & Gupta, G. P. (2019). 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Energy Aware Iterative Source Localization for Wireless Sensor Networks. IEEE Transactions on Signal Processing, 58(9), 4824-4835. doi:10.1109/tsp.2010.2051433Yang, X., Kong, Q., & Xie, X. (2009). One-Dimensional Localization Algorithm Based on Signal Strength Ratio. International Journal of Distributed Sensor Networks, 5(1), 79-79. doi:10.1080/15501320802571822Xie, S., Wang, T., Hao, X., Yang, M., Zhu, Y., & Li, Y. (2019). Localization and Frequency Identification of Large-Range Wide-Band Electromagnetic Interference Sources in Electromagnetic Imaging System. Electronics, 8(5), 499. doi:10.3390/electronics8050499Zhu, X., Wu, X., & Chen, G. (2013). Relative localization for wireless sensor networks with linear topology. Computer Communications, 36(15-16), 1581-1591. doi:10.1016/j.comcom.2013.07.007Meng, W., Xiao, W., & Xie, L. (2011). An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks. 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Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wireless Networks, 13(2), 221-235. doi:10.1007/s11276-006-5064-1Singh, M., & Khilar, P. M. (2015). An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network. Wireless Networks, 22(8), 2537-2550. doi:10.1007/s11276-015-1116-8Yiqiang Chen, Qiang Yang, Jie Yin, & Xiaoyong Chai. (2006). Power-efficient access-point selection for indoor location estimation. IEEE Transactions on Knowledge and Data Engineering, 18(7), 877-888. doi:10.1109/tkde.2006.112Alzoubi, K., Li, X.-Y., Wang, Y., Wan, P.-J., & Frieder, O. (2003). Geometric spanners for wireless ad hoc networks. IEEE Transactions on Parallel and Distributed Systems, 14(4), 408-421. doi:10.1109/tpds.2003.1195412Safa, H. (2014). A novel localization algorithm for large scale wireless sensor networks. Computer Communications, 45, 32-46. doi:10.1016/j.comcom.2014.03.020Kaemarungsi, K., & Krishnamurthy, P. 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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

Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art