Multiple Sources Localization With WSN, Eliminating the Direction-of-Arrival Ambiguity Symmetric With Respect to the Planar Array

Yu-hua, WU

doi:10.1109/access.2018.2866311

Cited by 4 publications

(19 citation statements)

References 43 publications

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“…The range-based algorithms reach a higher accuracy in the localization of the NOI than the range-free algorithms; but these require extra hardware in the NOI estimation [21], [31]. In many studies, the RSS is used to estimate the distance between the RNs and the NOI; since this technique can be easily implemented in hardware, but its accuracy in the NOI localization is lower than that when the ToA, TDoA and AoA techniques are used, [21], [32]. ToA presents the syncing nodes issue, TDoA has limited coverage and AoA implies computationally expensive hardware and it also requires an antenna array [21].…”

Section: Related Workmentioning

confidence: 99%

Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks

et al. 2020

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Localization is one of the main problems in Mobile Wireless Sensor Networks, since it provides the location of an event occurrence. This paper presents a performance evaluation of the localization algorithms: Multilateration Algorithm, Weighted Multilateration Algorithm and Probabilistic Multilateration Algorithm (PMA). In addition, we propose an Improved Probabilistic Multilateration Algorithm that decreases the localization error of the interest node by using an approach that computes iteratively the position of a node of interest until it reaches the solution that minimizes the localization error. The proposed approach regards the noisy environment by its impact on a correlation matrix that involves the variance of the separation distance between the node of interest and the respective reference nodes (RNs). Furthermore, we also introduce a constant parameter called damping factor; which enhances the convergence of the localization algorithm providing the solution that minimizes the localization error. In this study, we evaluate localization algorithms in a single-hop and multi-hop scenarios considering a distribution with solid geometry of the RNs and randomly distributed RNs in both scenarios. The results we obtained show that our proposed algorithm Improved PMA presents a better performance according to the Normalized Root Mean Squared Error varying the number of reference nodes and noise proportion.

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Section: Related Workmentioning

confidence: 99%

Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks

et al. 2020

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“…The trilateration, triangulation and multilateration are amon g the distance/ angle measurements mixing approaches. a) Trilateration: Geometrically, trilateration is the method using the understanding about range between both the goal and the three nearby anchor nodes to acquire the comparative position of the destination node [15]- [18]. Target node 2D co-ordinates are acquired by crossing the three rings.…”

Section: B Computation Stagementioning

confidence: 99%

H-Best Particle Swarm Optimization Based Localization Algorithm for Wireless Sensor Network

kamal,

Sharma,

Vandana

2019

IJEAT

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Discussion of the work, which proposed the idea of virtual anchor nodes for the localization of the sensor nodes, with having the movement of single sensor node in the circular movement with being optimized by the HPSO. For the ranging the RSSI model has been proposed in the algorithm. As a reference node, single anchor node has been used for the localization of whole network. As of the random deployment of the sensor nodes (target nodes), when the target nodes fall under the range of the mobile anchor node, the Euclidean distance between the target node and mobile anchor node is being calculated. After the calculation of the Euclidean distance the two anchor nodes are being deployed with a difference of 600 angle. Using the directional information the projecting of virtual anchor nodes is done, to which the virtual anchor nodes helps in the calculation of the 2D coordinates. While the calculation the mobile sensor node follow ups the circular path. The mobile sensor node considering at a center of the area marks up distance of its maximum range, and with that distance as a radius its goes for other circular path movement if all sensor nodes don’t fell to its range. With its movement at constant velocity the algorithm runs again and again. The performance of the algorithm are done on the factors of the average localization error and convergence time. The problem as of the LoS, with the virtual anchor nodes have been minimized.

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“…Depending on the measurements, the conventional localization algorithms for multiple sources in the literature can be roughly classified into the following categories: (1) the Global Positioning System (GPS) [4], (2) the directly received data [2], [6], [22], [26], [28], (3) the received signal strength (RSS) [10], [8], [17], [23], [29], (4) the time-ofarrival (TOA) or the time-difference-of-arrival (TDOA) [30], [35] and (5) the direction-of-arrival (DOA) [18], [36]. For a more detailed survey on the existing methods, one may refer to [12], [14], and [32].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, an efficient multiple sources localization scheme is proposed in [36]. With all the DOA estimates obtained at the sensor nodes, a classification process is proposed to associate each estimate with the corresponding source.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Zhang

Yu-hua

2019

IEEE Access

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Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources' positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy.

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Multiple Sources Localization With WSN, Eliminating the Direction-of-Arrival Ambiguity Symmetric With Respect to the Planar Array

Cited by 4 publications

References 43 publications

Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks

Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks

H-Best Particle Swarm Optimization Based Localization Algorithm for Wireless Sensor Network

Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

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