Self Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

Nighot, Mininath K.; Ghatol, Ashok A.; Thakare, V. M.

doi:10.9781/ijimai.2017.09.003

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…Onestage methods STM [5] Strong Low Pixel-wise and histogram similarities [6] Low Adaboost [7] Low Sparse Representation [8] Low Unknown target search in an unknown environment [10]…”

Section: Tradition Al Methodsmentioning

confidence: 99%

“…In [9], the object-based human identification stage and spatial feature-based human identification stage were fused using the fuzzy holoentropy for accurate human identification. [10] proposed the algorithms to find randomly moving target in unknown environment. Compared with these traditional methods, deep learning based visual inspection systems [11][12][13][14][15] showed their superiority after a long period training with a large dataset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cascade Network Based Bolt Inspection In High-Speed Train

2021

KSII TIIS

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“…Onestage methods STM [5] Strong Low Pixel-wise and histogram similarities [6] Low Adaboost [7] Low Sparse Representation [8] Low Unknown target search in an unknown environment [10]…”

Section: Tradition Al Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cascade Network Based Bolt Inspection In High-Speed Train

2021

KSII TIIS

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“…Longitudes and Latitudes can be received from GPS and distance between two nodes can be calculated. It is similar to calculation of two points in 2D plane by Euclidian distance formula [11].…”

Section: Distance Calculationmentioning

confidence: 98%

“…The accuracy of GPS based RTK is up to 2 centimetres. This technique can be used in the localization of sensor nodes and routing in WSN [11].…”

Section: Traditional Gpsmentioning

confidence: 99%

Design Issues of Wireless Sensor Network and Open Research Challenges

Nighot¹

2018

EAI Endorsed Transactions on Energy Web

Self Cite

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WSN is becoming more popular day by day among researchers due to its vast real-time applications. Various design parameters directly affect the overall performance and lifetime of the network. The design of WSN system is depends upon application. Many parameters need to consider to design WSN system such as number of nodes, types of nodes, routing strategies, way of communication among the nodes and in the network, etc. The network structure and its operations categorize the WSN into flat-based and cluster-based systems. At present, the major challenge is designing and deploying the WSN system. The objective of the paper is to study and analyze all the essential parameters to overcome the challenge. The paper also critically reviews the literature. Finally, the paper is summarized and concluded with open research issues to design robust, low cost, energy-aware WSN systems.

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“…Signal processing is very important during extracting valuable information from different detectors sensors, and a subfield of electrical engineering includes analyzing, modifying, and synthesizing signals such as sounds, chemical properties, and radio wave signals [ 81 ]. The introduction of a self-organized hybrid wireless sensor network for finding randomly moving targets in an unknown environment has been made by Nighot et al [ 43 ]. The proposed wireless sensor network used only mobile sensor nodes with a combination of static and mobile sensor nodes and the area is determined in advance.…”

Section: Research Data Analysismentioning

confidence: 99%

A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain

Hamami

Ismail

2022

Arch Computat Methods Eng

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Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments.

show abstract

Self Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

Cited by 8 publications

References 20 publications

Cascade Network Based Bolt Inspection In High-Speed Train

Cascade Network Based Bolt Inspection In High-Speed Train

Design Issues of Wireless Sensor Network and Open Research Challenges

A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain

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