A novel heuristic algorithm for node localization in anisotropic wireless sensor networks with holes

Shi, Z.; Er, Meng Joo; Zhang, Baihai; Naderahmadian, Yashar

doi:10.1016/j.sigpro.2017.03.010

Cited by 31 publications

(32 citation statements)

References 22 publications

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“…Table II shows the evaluation of existing localization techniques. The work presented in [5] - [6], [18], [37] describe range free algorithms. Generally, range free algorithms do toil very proficiently in unfriendly locations.…”

Section: Resultsmentioning

confidence: 99%

“…Generally, range free algorithms do toil very proficiently in unfriendly locations. Although precision may be high, scalability is low, and cost greater [5] [6]. Energy efficiency is missing both in [18] [37], Localization error is low in [5][18] [37].…”

Section: Resultsmentioning

confidence: 99%

“…Experimental results unfold that this anticipated algorithm excels other range-free methods in homogeneous and heterogeneous WSNs. For performing with high precision of node localization in anisotropic WSNs with holes, Shi et al [6] presented a Heuristic Multi-Dimensional Scaling (HMDS) algorithm. The Euclidean distance between nodes found by the virtual node and creating the shortest paths.…”

Section: Comparative Studymentioning

confidence: 99%

See 2 more Smart Citations

Retrospection on Localization Techniques for Positioning Nodes in Wireless Sensor Networks

Kathiroli¹,

Kanmani²

2020

IJEAT

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Wireless Sensor Networks have highly scattered, self-organized nodes that can detect, compute, and transmit the information collected at different nodes in the network. These nodes spread over a specific topographical zone. They enhance the instantaneous formation of the network. Knowing the current location of a node is a crucial and cardinal requirement for any application promulgated in WSN. Once the locations of the sensor nodes can be precisely positioned, there are ample of probabilities for the data transmission of the network to be excelling inefficiency. Location responsiveness enables essential network features such as coverage, routing, deployment, topology control, clustering, boundary discovery, target tracking, rescue, and other location services. Hence, WSN localization has become a breath and backbone arena that ostentatiously attracted significant research interest. Our work traces a compilation of all the dynamic research in sensor networks on localization techniques and emanates eminent understanding of it.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

See 1 more Smart Citation

Retrospection on Localization Techniques for Positioning Nodes in Wireless Sensor Networks

Kathiroli¹,

Kanmani²

2020

IJEAT

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“…The findings were far greater than trilateration in multilateralisation. By addressing the linear system, its mean square error of multilateration was reduced [19]. Assumption of having knowing the position of the three anchors, i.e., (xi, yi) for i = 1, 2 and 3.…”

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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“…It is known that range-based techniques are generally based on some physical range metrics like signal strength or angular orientation or distance gap. Some examples of range-based techniques include time of arrival (TOA) method, time difference of arrival (TDOA) method, the received signal strength indicator (RSSI) method and angle of arrival (AOA) method [4]. On the other hand, range-free techniques are based on abstract parameters like hop count and other such hop based information and do not rely on physical range metrics [5].…”

Section: Introductionmentioning

confidence: 99%

Node localization for indoor tracking using artificial neural network

Samanta

Kumari

Deb

et al. 2018

2018 Third International Conference on Fog and Mobile Edge Computing (FMEC)

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Wireless sensor network (WSN) always comes up with the need of deploying either mobile or immobile sensor nodes or both. Wireless communication among these nodes is crucial and it requires identifying the location of these nodes within a specific region. Global positioning system (GPS) is widely used for location tracking. However, when it comes to WSN, GPS has its limitations, due to its high power consumption and the overhead of additional hardware cost. The research challenge here lies in the efficient location tracking of wireless sensor nodes, especially in closed indoor and outdoor environments. This paper comes up with a simple and easy-to-implement technique using artificial neural networks (ANNs) to manipulate the location of the sensor nodes. In this paper, the backpropagation network training algorithm for providing supervised learning to multilayer perceptron is generalized to synthesize the WSN and gives out 2D Cartesian coordinates of the nodes. The technique is both cost-efficient and achieves 98% accuracy.

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A novel heuristic algorithm for node localization in anisotropic wireless sensor networks with holes

Cited by 31 publications

References 22 publications

Retrospection on Localization Techniques for Positioning Nodes in Wireless Sensor Networks

Retrospection on Localization Techniques for Positioning Nodes in Wireless Sensor Networks

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

Node localization for indoor tracking using artificial neural network

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