Localization in wireless sensor networks: A dimension based pruning approach in 3D environments

Raguraman, Preeth; Ramasundaram, Mohan; Balakrishnan, V.

doi:10.1016/j.asoc.2018.03.039

Cited by 25 publications

(15 citation statements)

References 14 publications

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“…In addition, metaheuristic optimisation algorithms have been validated in other areas of knowledge, such as industrial soft sensor modelling [61], wireless sensor networks [62,63,64,65,66], privacy-preserving data mining [67] and RFID network planning [68,69]. A brief description of main population-based optimisation metaheuristics is summarised below.…”

Section: Materials and Methodsmentioning

confidence: 99%

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Crespo-Cano

Cuenca-Asensi

Fernández

et al. 2019

Sensors

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A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics—genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)—are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.

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Section: Materials and Methodsmentioning

confidence: 99%

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Crespo-Cano

Cuenca-Asensi

Fernández

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Reference [40] proposed dimension-based PSO (DPSO) and hybrid DPSO (HDPSO) for node positioning in a 3D environment. The DPSO adopts dimension-based optimization technology to determine the location of the target node by considering each dimension, which works well in all applications.…”

Section: Si Applications In Node Localizationmentioning

confidence: 99%

A Survey of Using Swarm Intelligence Algorithms in IoT

Sun

Tang

Zhang

et al. 2020

Sensors

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With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

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“…Localization techniques are evaluated for accuracy and success percentage at different node densities and reference node counts. These techniques are also evaluated for memory requirements, communication costs, time taken for communication and complexity of algorithms [47]. Localization accuracies and other characteristics are evaluated in different irregular shapes of fields with network holes and obstacles.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Bhat

Santhosh

2020

Wireless Pers Commun

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Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.

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Localization in wireless sensor networks: A dimension based pruning approach in 3D environments

Cited by 25 publications

References 14 publications

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

A Survey of Using Swarm Intelligence Algorithms in IoT

Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

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