Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

Qi, Guangping; Song, Ping; Li, Kejie

doi:10.1016/s1672-6529(08)60025-6

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…ACO has been used for maximizing the network lifetime in WSN. The autonomy and dynamic deployment of mobile sensor networks was effectively solved using a blackboard mechanism based ant colony theory by Qi and Li in [17]. The algorithm reduced the power consumption by 13%, enhanced the efficiency of path planning and deployment of WSN by 15%.…”

Section: B Aco and Network Lifetimementioning

confidence: 99%

Evaluation of Hybrid Trust Models Using Ant Colony Optimization in Wireless Sensor Networks

Baskaran¹,

Blessing

Lydia

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Wireless sensor networks (WSNs) are prone to various kinds of threats and are subjected to several constraints like energy, communication overhead and lifetime. Application of bio-inspired algorithms based trust models has shown significant improvement in the security mechanism of the wireless sensor networks. This paper presents a brief survey on application of Ant Colony Optimization (ACO) and significance of trust models in WSNs. ACO application in routing, increase in lifetime, energy efficiency, intrusion detection and security has been presented. The performance of three hybrid trust models is evaluated based on the path length, trust calculation and energy consumption using ACO.

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Section: B Aco and Network Lifetimementioning

confidence: 99%

Evaluation of Hybrid Trust Models Using Ant Colony Optimization in Wireless Sensor Networks

Baskaran¹,

Blessing

Lydia

2016

International Journal on Smart Sensing and Intelligent Systems

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“…and parameters have been taken as 7 meters and 2 (14 meters), respectively. Two scenario were done for each case study, and the OPT LOWERBOUND DIST, OPT UPPERBOUND DIST and MAX CONVERGENCE DIST parameters have been taken as √ 3 , √ 3.2 , and √ 1 in the 1st scenario and as [] then (5) for j = 1 to OPT SENSORS COUNT do (6) if ( , ) between OPT LOWERBOUND DIST and OPT UPPERBOUND DIST then (7) OPT NEIGHBOUR COUNT ← OPT NEIGHBOUR COUNT + 1 (8) else if ( , ) < MAX CONVERGENCE DIST (9) NONOPT NEIGHBOUR COUNT ← NONOPT NEIGHBOUR COUNT + 1 (10) end if (11) end for (12) if (NONOPT NEIGHBOUR COUNT == 0 AND OPT NEIGHBOUR COUNT >= 1) then (13) OPT SENSORS COUNT ← OPT SENSORS COUNT + 1 (14) OPT √ 3 , √ 3.5 , and √ 1.3 in the 2nd scenario, respectively. The OPT NEIGHBOUR COUNT parameter has been taken as 1 to be common for both the scenarios in the case studies except for the case study 3.…”

Section: Parameter Selection For the Suggested Algorithm Based On Emmentioning

confidence: 99%

“…Blackboard mechanism-based ant colony theory [10] which is a novel bionic swarm intelligence algorithm has been applied to the dynamic deployment problems of mobile sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

Ozdag

Karcı

2015

International Journal of Distributed Sensor Networks

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The dynamic deployment of sensors in wireless networks significantly affects the performance of the network. However, the efficient application of dynamic deployments which determines the positions of the sensors within the network increases the coverage area of the network. As a result of this, dynamic deployment increases the efficiency of the wireless sensor networks (WSNs). In this paper, dynamic deployment was applied to WSNs which consist of mobile sensors by aiming at increasing the coverage area of the network with electromagnetism-like (EM) algorithm which is a population-based optimization algorithm. A new approach has been improved in calculating the coverage rate of the sensors by using binary detection model so as to carry out the dynamic deployments of sensors and it has been thought to reach realistic results efficiently. Simulation results have shown that the EM algorithm can be preferred in the dynamic deployment of mobile sensors within the wireless networks.

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“…Therefore, the active deployment of the sensors in the area is possible by performing the deterministic deployment of nodes. The node deployment made by using meta-heuristic algorithms [6,7], which is also the subject of this article, serves as an example of the deterministic deployment in WSNs.…”

Section: Introductionmentioning

confidence: 99%

A New Dynamic Deployment Approach Based on Whale Optimization Algorithm in the Optimization of Coverage Rates of Wireless Sensor Networks

Ozdag¹,

Canayaz²

2017

EJT

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Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

Cited by 17 publications

References 11 publications

Evaluation of Hybrid Trust Models Using Ant Colony Optimization in Wireless Sensor Networks

Evaluation of Hybrid Trust Models Using Ant Colony Optimization in Wireless Sensor Networks

Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

A New Dynamic Deployment Approach Based on Whale Optimization Algorithm in the Optimization of Coverage Rates of Wireless Sensor Networks

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