A competent fault tolerant system using simulated annealing approach for target tracking in wireless sensor networks

Venkatesh, S.; Mehata, K. M.

doi:10.1109/iccci.2014.6921823

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…In [19,20,21], the fault tolerance capability, although partially, was increased only at the level of the network nodes; this is because the proposed fault-tolerant architectures only cover the CH nodes and there is no strategy to increase the fault tolerance capability of the NCH nodes. The reason for this difference is that some backup nodes were selected for CH in [19], while in [20], only one CH node was considered as a backup node.…”

Section: A Review Of the Clustering Protocol Based On Fault-tolerancementioning

confidence: 99%

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A New Fault-Tolerant Clustering Protocol for Wireless Sensor Networks

Cheraghlou¹,

Zadeh²,

Haghparast³

2018

IJSIA

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The major restriction of wireless sensor networks is represented by the energy requirements of the nodes. Also, the main challenge of these networks is their faulttolerance feature since the nodes are scattered in remote, inaccessible areas. Therefore, these issues must be the main concern of the management of the network nodes and all the proposed protocols must be evaluated based on a fault-tolerance capacity and handling basic network restrictions.In

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Section: A Review Of the Clustering Protocol Based On Fault-tolerancementioning

confidence: 99%

“…The reason for this difference is that some backup nodes were selected for CH in [19], while in [20], only one CH node was considered as a backup node. Despite the proposed protocol in [21], the backup CH selection is not mandatory in [20].…”

Section: A Review Of the Clustering Protocol Based On Fault-tolerancementioning

confidence: 99%

A New Fault-Tolerant Clustering Protocol for Wireless Sensor Networks

Cheraghlou¹,

Zadeh²,

Haghparast³

2018

IJSIA

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“…In the first step, for the given nodes and cluster center, the improved k-means clustering algorithm being used [13].…”

Section: Problem Definitionmentioning

confidence: 99%

Particle Swarm Optimization for Gene cluster Identification

Manasi¹,

Kamlapur²

2014

IJCATR

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Abstract-In this paper, we explored the possibility of using Genetic Algorithm (GA) being used in Wireless Sensor Networks in general with specific emphasize on Fault tolerance. In Wireless sensor networks, usually sensor and sink nodes are separated by long communication distance and hence to optimize the energy, we are using clustering approach. Here we are employing improved K-means clustering algorithm to form the cluster and GA to find optimal use of sensor nodes and recover from fault as quickly as possible so that target detection won't be disrupted. This technique is simulated using Matlab software to check energy consumption and lifetime of the network. Based on the simulation results, we concluded that this model shows significant improvement in energy consumption rate and network lifetime than other method such as Traditional clustering or Simulated Annealing.

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Elite Adaptive Particle Swarm Optimization for Target Coverage Problem in High-density Wireless Sensor Networks

Zhou

2019

J. Phys.: Conf. Ser.

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The rapid development of intelligent sensing, micro electro-mechanical systems and communication has made it feasible to equip low computational complexity, low energy consumption, autonomous, and intelligent sensor nodes. High-density wireless sensor networks (HDWSNs) have information acquisition and communication abilities. HDWSNs are widely used in a number of areas including traffic avoidance, homelands security, target monitoring and so on. One of the major challenges in HDWSNs is to maximize the point coverage percentage. It is important since it is known that obtaining an optimal coverage target for HDWSNs is an NP-hard problem. In this paper, we use an elite adaptive particle swarm optimization (EAPSO) to solve the issue of target coverage in HDWSNs. In order to improve the effectiveness of system, a system model is provided to evaluate the monitored rate for HDWSNs. The proposed EAPSO with an efficient particle swarm optimization in discrete mode has the advantages of both adaptive as well as elite strategy. Numerical simulations are conducted with a number of nodes and targets using EAPSO, evolutionary algorithm (EA) and simulated annealing (SA). In the simulations, a better performance of EAPSO is given when it is compared with EA and SA with the same computational complexity.

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A competent fault tolerant system using simulated annealing approach for target tracking in wireless sensor networks

Cited by 4 publications

References 15 publications

A New Fault-Tolerant Clustering Protocol for Wireless Sensor Networks

A New Fault-Tolerant Clustering Protocol for Wireless Sensor Networks

Particle Swarm Optimization for Gene cluster Identification

Elite Adaptive Particle Swarm Optimization for Target Coverage Problem in High-density Wireless Sensor Networks

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