Meta-heuristic based optimization of WSNs Localisation Problem- a Survey

Sharma, Neha; Gupta, Vishal

doi:10.1016/j.procs.2020.06.006

Cited by 23 publications

(15 citation statements)

References 30 publications

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“…Symmetric nested array configuration can be used to find further nodes with a minimal number of antennas. Passive source position algorithms demonstrate accuracy degradation due to the presence of impulsive noise [52]. Some of the applications of localization are location based services, robotics, cellular networks and health care applications.…”

Section: Localizationmentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Review

Sneha¹,

Nagarajan²

2020

Cybernetics and Information Technologies

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Wireless Sensor Network (WSN) has been a source of attraction for many researchers as well as common people for the past few years. The use of WSN in various environmental applications like monitoring of weather, temperature, humidity, military surveillance etc. is not limited. WSN is built on hundreds to thousands of nodes where each node is a sensor whose main role is to sense data. These nodes are restricted to various constraints like power, energy, efficiency and deployment. The location of deployment influences the efficiency of data transmission. In this paper we briefly discuss on localization process in WSN and the classification of localization methodologies, namely centralized localization and distributed localization. The various techniques like ToA, TDoA, AoA and RSSI that are used to estimate the distance among the nodes are studied in detail. The localization issues categorized under proximity-based, range-based and range-free localization are discussed in detail. This paper also focuses on how the nodes with GPS can contribute to the localization process. The merits and demerits of using GPS have also been looked into. The various approaches of range-based techniques like Bounding box, SumDistMinMax, geometric methods, general techniques have been discussed briefly. We will also discuss on how the factors like path loss, noise, propagation, device measurements, connectivity, power control and tracking can influence the measurements in localization. In the tracking process we have briefly discussed about the variants of Kalman filter that can be used in detecting the direct path, strongest path and undirected path. This paper as a whole is just a brush up of the localization methodologies used in wireless sensor networks. This paper may give idea to the researchers to develop efficient algorithms to localize nodes with accuracy adapting to different techniques with respect to the environment and applications to be designed.

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Section: Localizationmentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Review

Sneha¹,

Nagarajan²

2020

Cybernetics and Information Technologies

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“…Meta-heuristic methods can be recommended for optimising the current localisation solutions. The positioning of sensor nodes is made easier by using this optimization strategy [1]. Using reusable communication protocols in a generic WSN architecture, the sensors are efficiently arranged.…”

Section: Introduction About Wsnmentioning

confidence: 99%

Energy-efficient PEGASIS Clustering based multipath routing with Lion Swarm and Chicken swarm optimization (PEGA-LSCS) for Wireless Sensor Network

Patra¹,

Mishra²,

Patra³

2022

INDJCSE

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Due to the popularity gain in the wide research area in different field of application wireless sensor network (WSN) have been subject to deployed in the last decades.WSN due to use in harsh environment difficult to exchange batteries physically. For that reason Energy efficiency acts as a major factor to restoration of battery power in sensor network. The proposed algorithm prolong the lifetime of the sensor network by enhancing load distributions in the WSN.This work used the chain-based routing PEGASIS (Power Energy Gathering in sensor information system.) as basis. In this approach optimal path model Lion swarm and chicken swarm in PEGASIS based chain clustering (PEGA-LSCS) has been used. The optimal path is chosen by using Lion Swarm Optimization (LSO) and to reduce the energy consumption of the network the Enhanced Clustering approach is initiated using Chicken Swarm Optimization (CSO).As a result the noticeably improvement of lifetime in this approach has been showing. Other results like residual energy, packet delivery ratio and end to end delay are also considered.

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“…The necessity of trustworthy, precise, and effective networks is most vital in this current communication age. The real‐time observation of the sensor nodes serves this purpose 1 . The sensors are well organized in an effective manner using reusable communication protocols in a generic WSN infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Cross‐layered energy optimization with MAC protocol based routing protocol in clustered wireless sensor network in internet of things applications

Ajmi

Msolli

Helali

et al. 2021

Int J Communication

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Summary In the present situation, there is a huge research gap and development present in the creation and utilization of smart environment due to the development of wireless sensor network (WSN) and Internet of Things (IoT). The disadvantage of the earlier models and present systems is it consumes more power and there is a presence of network monitoring issues. Here, there is a need of developing a model which solves both this issues for the real time environment. So, in this paper, we developed a novel model which is a cross‐layered energy optimization‐based Medium Access Control (CEO‐MAC) protocol to overcome those issues. Our model considers the optimal design of the network and MAC layer. Its focus is on the CEO‐MAC routing model, cross‐layer MAC model, cross‐layer energy optimization model, and intercluster/intracluster MAC operational model. This model is briefly analyzed and simulated using Network Simulator 2 (NS2) software, which is the discrete event simulator that works using C++ and Tcl languages. Our protocol is simulated and compared with the present models such as TDL, CMISO, and PCL‐IDA protocols.

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Meta-heuristic based optimization of WSNs Localisation Problem- a Survey

Cited by 23 publications

References 30 publications

Localization in Wireless Sensor Networks: A Review

Localization in Wireless Sensor Networks: A Review

Energy-efficient PEGASIS Clustering based multipath routing with Lion Swarm and Chicken swarm optimization (PEGA-LSCS) for Wireless Sensor Network

Cross‐layered energy optimization with MAC protocol based routing protocol in clustered wireless sensor network in internet of things applications

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