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

Review on Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Localization in Wireless Sensor Networks

Wireless sensor network (WSN) is an evolving research topic with potential applications. In WSN, the nodes are spatially distributed and determining the path of transmission high challenging. Localization eases the path determining process between source and destination. The article, describes the localization techniques based on wireless sensor networks. Sensor network has been made viable by the convergence of Micro Electro- Mechanical Systems technology. The mobile anchor is used for optimizing the path planning location-aware mobile node. Two optimization algorithms have been used for reviewing the performacne. They are Grey Wolf Optimizer(GWO) and Whale Optimization Algorithm(WOA). The results show that WOA outperforms in maximizing the localization accuracy

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. 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Review on Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Localization in Wireless Sensor Networks

Wireless sensor network (WSN) is an evolving research topic with potential applications. In WSN, the nodes are spatially distributed and determining the path of transmission high challenging. Localization eases the path determining process between source and destination. The article, describes the localization techniques based on wireless sensor networks. Sensor network has been made viable by the convergence of Micro Electro- Mechanical Systems technology. The mobile anchor is used for optimizing the path planning location-aware mobile node. Two optimization algorithms have been used for reviewing the performacne. They are Grey Wolf Optimizer(GWO) and Whale Optimization Algorithm(WOA). The results show that WOA outperforms in maximizing the localization accuracy

GCP: Gossip-based Code Propagation for Large-scale Mobile Wireless Sensor Networks

Wireless sensor networks (WSN) have recently received an increasing interest. They are now expected to be deployed for long periods of time, thus requiring software updates. Updating the software code automatically on a huge number of sensors is a tremendous task, as ''by hand'' updates can obviously not be considered, especially when all participating sensors are embedded on mobile entities. In this paper, we investigate an approach to automatically update software in mobile sensor-based application when no localization mechanism is available. We leverage the peer-to-peer cooperation paradigm to achieve a good trade-off between reliability and scalability of code propagation. More specifically, we present the design and evaluation of GCP ({\emph Gossip-based Code Propagation}), a distributed software update algorithm for mobile wireless sensor networks. GCP relies on two different mechanisms (piggy-backing and forwarding control) to improve significantly the load balance without sacrificing on the propagation speed. We compare GCP against traditional dissemination approaches. Simulation results based on both synthetic and realistic workloads show that GCP achieves a good convergence speed while balancing the load evenly between sensors

Mobile Wireless Sensor Networks: An Overview

Mobile wireless sensor networks (MWSNs) have emerged and shifted the focus from the typical static wireless sensor networks to networks with mobile sensor nodes that are capable to sense the various types of events. Also, they can change their position frequently in a specific sensing area. The applications of the MWSNs can be widely divided into time-driven, event-driven, on-demand and tracking based applications. Mobile sensor node architecture, residual energy utilization, mobility, topology, scalability, localization, data collection routing, Quality of Service (QoS), etc., are the key factors to design an energy efficient MWSNs for some specific purpose. This chapter deals with an overview of the MWSNs and a few significant phenomena to design an energy efficient MWSNs to the large-scale environment

Localization Algorithms of Underwater Wireless Sensor Networks: A Survey

In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs

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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