Novel DV-Hop localization algorithm for wireless sensor networks

Kumar, Shrawan; Lobiyal, D. K.

doi:10.1007/s11235-016-0189-8

Cited by 64 publications

(26 citation statements)

References 37 publications

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“…The locations of target nodes can be estimated with the help of anchor nodes [18]. However, the distances estimated based on the DV-Hop algorithm tend to be imprecise due to the anchor's hop size error, which in turn, reduces the localisation accuracy [22]. Consequently, many attempts have been made to enhance the DV-Hop algorithm [23][24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Wireless sensor nodes localiser based on sine–cosine algorithm

Hamouda

Abohamama

2020

IET wirel. sens. syst.

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Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the authors propose a node localisation scheme based on a sine-cosine algorithm. The proposed scheme addresses the flip ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and the computation time.

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Section: Related Workmentioning

confidence: 99%

Wireless sensor nodes localiser based on sine–cosine algorithm

Hamouda

Abohamama

2020

IET wirel. sens. syst.

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“…The emergence IoT-based applications are revolutionizing the current concept of industry, transportation systems, energy grids, cities and society (Mukherjee et al 2017). To archive this transformation, many daily applications based on IoT require an efficient nodes position, and localization mechanism (Kumar and Lobiyal 2017;Bradai et al 2015). Localization technology has been a core component for Internet of Things, especially for Wireless Sensor Network.…”

Section: Computational Intelligence Applied In Localization Optimizatmentioning

confidence: 99%

Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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“…The local-hop-based method only requires unknown nodes to collect information from neighbour nodes, but this method requires a sufficient number of anchor nodes. The global-hopbased method generally uses the weighted algorithm [8,9] to reduce the influence of distance estimation error in irregular networks. This method does not require the support of a large number of anchor nodes (only three for 2D in theory), but the selection and setting of weight functions are subjective.…”

Section: Introductionmentioning

confidence: 99%