A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints of cost and power consumption make it infeasible to equip each sensor node in the network with a global position system(GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use several mobile anchors which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. This paper proposes a mobile anchor assisted localization algorithm based on regular hexagon (MAALRH) in two-dimensional WSNs, which can cover the whole monitoring area with a boundary compensation method. Unknown nodes calculate their positions by using trilateration. We compare the MAALRH with HILBERT, CIRCLES, and S-CURVES algorithms in terms of localization ratio, localization accuracy, and path length. Simulations show that the MAALRH can achieve high localization ratio and localization accuracy when the communication range is not smaller than the trajectory resolution.The work is supported by the Natural Science Foundation of Jiangsu Province of China, no. BK20131137; the Applied Basic Research Program of Nantong Science and Technology Bureau, no. BK2013032; and the Guangdong University of Petrochemical Technology's Internal Project, no. 2012RC0106. Jaime Lloret's work has been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516. Joel J. P. C. Rodrigues's work has been supported by "Instituto de Telecomunicacoes," Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, by national funding from the Fundacao para a Ciencia e a Tecnologia (FCT) through the Pest-OE/EEI/LA0008/2013 Project.Han, G.; Zhang, C.; Lloret, J.; Shu, L.; Rodrigues, JJPC. (2014). A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks. Scientific World Journal. https://doi.org/10.1155/2014/219371SLiu, Y., Yang, Z., Wang, X., & Jian, L. (2010). Location, Localization, and Localizability. Journal of Computer Science and Technology, 25(2), 274-297. doi:10.1007/s11390-010-9324-2Akcan, H., Kriakov, V., Brönnimann, H., & Delis, A. (2010). Managing cohort movement of mobile sensors via GPS-free and compass-free node localization. Journal of Parallel and Distributed Computing, 70(7), 743-757. doi:10.1016/j.jpdc.2010.03.007Akyildiz, I. F., Weilian Su, Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102-114. doi:10.1109/mcom.2002.1024422Vupputuri, S., Rachuri, K. K., & Siva Ram Murthy, C. (2010). Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints. Journal of Parallel and Distributed Computing, 70(7), 767-778. doi:10.1016/j.jpdc.2010.03.010Zeng, Y., Cao, J., Hong, J., Zhang, S., & Xie, L. (2010). Secure localization and location verification in wireless sensor networks: a survey. The Journal of Supercomputing, 64(3), 685-701. doi:10.1007/s11227-010-0501-4Han, G., Xu, H., Duong, T. Q., Jiang, J., & Hara, T. (2011). Localization algorithms of Wireless Sensor Networks: a survey. Telecommunication Systems, 52(4), 2419-2436. doi:10.1007/s11235-011-9564-7Al-Fuqaha, A. (2013). A Precise Indoor Localization Approach based on Particle Filter and Dynamic Exclusion Techniques. Network Protocols and Algorithms, 5(2), 50. doi:10.5296/npa.v5i2.3717Chaurasiya, V. K., Jain, N., & Nandi, G. C. (2014). A novel distance estimation approach for 3D localization in wireless sensor network using multi dimensional scaling. Information Fusion, 15, 5-18. doi:10.1016/j.inffus.2013.06.003Diallo, O., Rodrigues, J. J. P. C., & Sene, M. (2012). Real-time data management on wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1013-1021. doi:10.1016/j.jnca.2011.12.006Amundson, I., & Koutsoukos, X. D. (2009). A Survey on Localization for Mobile Wireless Sensor Networks. Lecture Notes in Computer Science, 235-254. doi:10.1007/978-3-642-04385-7_16Ding, Y., Wang, C., & Xiao, L. (2010). Using mobile beacons to locate sensors in obstructed environments. Journal of Parallel and Distributed Computing, 70(6), 644-656. doi:10.1016/j.jpdc.2010.03.002Chenji, H., & Stoleru, R. (2010). Mobile Sensor Network Localization in Harsh Environments. Lecture Notes in Computer Science, 244-257. doi:10.1007/978-3-642-13651-1_18Campos, A. N., Souza, E. L., Nakamura, F. G., Nakamura, E. F., & Rodrigues, J. J. P. C. (2012). On the Impact of Localization and Density Control Algorithms in Target Tracking Applications for Wireless Sensor Networks. Sensors, 12(6), 6930-6952. doi:10.3390/s120606930Ou, C.-H., & He, W.-L. (2013). Path Planning Algorithm for Mobile Anchor-Based Localization in Wireless Sensor Networks. IEEE Sensors Journal, 13(2), 466-475. doi:10.1109/jsen.2012.2218100Koutsonikolas, D., Das, S. M., & Hu, Y. C. (2007). Path planning of mobile landmarks for localization in wireless sensor networks. Computer Communications, 30(13), 2577-2592. doi:10.1016/j.comcom.2007.05.048Cui, H., & Wang, Y. (2012). Four-mobile-beacon assisted localization in three-dimensional wireless sensor networks. Computers & Electrical Engineering, 38(3), 652-661. doi:10.1016/j.compeleceng.2011.10.012Ssu, K.-F., Ou, C.-H., & Jiau, H. C. (2005). Localization With Mobile Anchor Points in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 54(3), 1187-1197. doi:10.1109/tvt.2005.844642Guo, Z., Guo, Y., Hong, F., Jin, Z., He, Y., Feng, Y., & Liu, Y. (2010). Perpendicular Intersection: Locating Wireless Sensors With Mobile Beacon. IEEE Transactions on Vehicular Technology, 59(7), 3501-3509. doi:10.1109/tvt.2010.2049391Bin Xiao, Hekang Chen, & Shuigeng Zhou. (2008). Distributed Localization Using a Moving Beacon in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 19(5), 587-600. doi:10.1109/tpds.2007.70773Lee, S., Kim, E., Kim, C., & Kim, K. (2009). Localization with a mobile beacon based on geometric constraints in wireless sensor networks. IEEE Transactions on Wireless Communications, 8(12), 5801-5805. doi:10.1109/twc.2009.12.090319Han, G., Choi, D., & Lim, W. (2009). Reference node placement and selection algorithm based on trilateration for indoor sensor networks. Wireless Communications and Mobile Computing, 9(8), 1017-1027. doi:10.1002/wcm.65

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

Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

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

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial