Effect of the number of participating nodes on recovery of WSN coverage holes

Rafiei, Ali; Franklin, Daniel; Safaei, Farzad; Smith, Stephen; Ni, Wei

doi:10.1109/atnac.2017.8215433

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…LeDiR algorithm autonomously repositions a subset of the sensor nodes to restore connectivity with minimal topology changes. Rafiei et al [26] devise a node relocation algorithm to recover large-scale coverage holes. e effects of the number of participating sensor nodes and movement iterations are examined in Voronoi-based node 2…”

Section: Related Workmentioning

confidence: 99%

A Novel Trust Model Based on Node Recovery Technique for WSN

Fucheng

Hong

2019

Security and Communication Networks

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With the rapid development of sensor technology and wireless network technology, wireless sensor network (WSN) has been widely applied in many resource-constrained environments and application scenarios. As there are a large number of sensor nodes in WSN, node failures are inevitable and have a significant impact on task execution. In this paper, considering the vulnerability, unreliability, and dynamic characteristics of sensor nodes, node failures are classified into two categories including unrecoverable failures and recoverable failures. Then, the traditional description of the interaction results is extended to the trinomial distribution. According to the Bayesian cognitive model, the global trust degree is aggregated by both direct and indirect interaction records, and a novel trust model based on node recovery technique for WSNs is proposed to reduce the probability of failure for task execution. Simulation results show that compared with existing trust models, our proposed TMBNRT (trust model based on node recovery technique) algorithm can effectively meet the security and the reliability requirements of WSN.

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Section: Related Workmentioning

confidence: 99%

A Novel Trust Model Based on Node Recovery Technique for WSN

Fucheng

Hong

2019

Security and Communication Networks

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“…A large number of studies have been devoted to detecting and recovering coverage holes [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, most existing approaches [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] are based on an assumption that the sensing range of a sensor node is like a disk (as shown in Figure 1 a) with the centre of the disk as a sensor.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery

Zhai

Tang

Wang

et al. 2018

Sensors

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In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage.

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