Optimizing Localization Route Using Particle Swarm-a Genetic Approach

Lakshmanan, L.; Tomar, D. C.

doi:10.3844/ajassp.2014.520.527

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…The authors in [ 88 ] use a two tier particle swarm algorithm to configure a network for achieving an optimal energy consumption and packet delivery rate performance. A hybrid technique is proposed in [ 89 ], which integrates both the PSO algorithm and the GA algorithm together. In this algorithm, the constraint HC (Hybridization Cofficient) is used to express the population percentage.…”

Section: The Cross-layer Design Methodsmentioning

confidence: 99%

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Martínez-Ortega

Chaus

et al. 2016

Sensors

183

113

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Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

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Section: The Cross-layer Design Methodsmentioning

confidence: 99%

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Martínez-Ortega

Chaus

et al. 2016

Sensors

183

113

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“…The estimated location accuracy is affected by the size of reference points and k-value. In Lakshmanan and Tomar (2014) and Sun et al (2015) used particle swarm algorithm (PSO) to improve the localisation accuracy in wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

An enhanced localisation system for indoor environment using clustering technique

Sapumohotti

Tan

Alhammadi

et al. 2017

IJCVR

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Recently, indoor localisation techniques that use wireless local area network (WLAN) be a-con signals have gained much attention by the research communities. Many localisations methods are used to estimate the user of mobile device in indoor environments. However, the accuracy of these methods is affected by the nature of the test-bed environment. In this paper, we introduce an experimental test-bed in a typical indoor environment. We used a finger printing-based localisation algorithm to estimate the user location. The fingerprinting technique consists of two phases: offline phase and online phase. In the offline phase, calibration points are collected at certain places in floor to build a radio map. In the online phase, deterministic and probabilistic approaches are applied in order to get the correct estimated position of a mobile device. In deterministic approach, the position of mobile device estimated by K-nearest neighbour (KNN). In probabilistic approach, the position of mobile device estimated by Bayesian network (BN). Clustering technique is proposed to improve the system's accuracy and reduce the radio map size in the offline phase. We present experimental results that improved the system accuracy and reduce the size of radio map by using the proposed clustering technique.

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