A New Self-Management Model for Large-Scale Event-Driven Wireless Sensor Networks

Vajdi, Ahmadreza; Zhang, Gongxuan; Wang, Yongli

doi:10.1109/jsen.2016.2598832

Cited by 15 publications

(9 citation statements)

References 19 publications

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“…In such scenarios, there is a possibility that the nodes in the edges of the holes may not capture the event and report it to the cluster head in the correct time, which may lead to a huge disaster. Vajdi et al [37] proposed an approach to detect the holes in the network. Here, the authors used the Delaunay triangulation approach to detect the holes in the network and applied a virtual gridding scheme to form grids that represent a cluster.…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

“…All the protocols discussed in Table 1 are cluster-based protocols implemented with the fuzzy-based approach. According to Table 1, self-management model [37] performs better than the other mechanisms as it is highly reliable because the authors proposed a novel hole detection mechanism along with the fuzzy-based approach. One more vital factor to be noted in the comparison chart is that the self-management model is not round-based, and hence the network operation is never affected due to reconfiguration.…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

“…Figure 4 depicts a diagram summarizing the different parameters discussed in this paper used in cluster formation utilizing fuzzy logic. Furthermore, in most of the fuzzy-based clustering algorithms discussed in the literature, the metrics such as first node dies (FND), half of the nodes dies (HND), and last node dies (LND) were used to evaluate the network lifetime [33,35,37]. The authors validated the performance of the proposed techniques under different network densities and for different locations of base station.…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

“…The projected data demonstrate the variations in the performance on the basis of the fuzzy descriptors utilized in the fuzzy-based clustering algorithms. Fuzzy logic for clustering scheme [35] densities, distances, residual 675 Fuzzy logic-based clustering algorithm for WSN [36] battery power, mobility, centrality 300 Self-management model [37] residual energy, radio energy dissipation, centrality 500…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.

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Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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“…The signal is transmitted over the air and received at the sensor nodes [17,18] within the monitoring area. During the transmission, the signal is attenuated by environmental factors such as physical obstacles, which would lead to uncertainty for the received signal at the sensor nodes [19][20][21]. Furthermore, this will cause errors in the calculation results.…”

Section: Methodsmentioning

confidence: 99%

Controllable Effective Threshold Based Fusion Coverage Algorithm in Mobile Sensor Networks

Sun

2018

Wireless Communications and Mobile Computing

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The coverage quality and network lifetime are two key parameters in the research of sensor networks. The coverage quality shows direct influences on the network lifetime. Meanwhile, it is influenced by many other factors such as physical parameters and environmental parameters. To reveal the connection between the coverage quality and the parameters of target node concerned, a fusion coverage algorithm with controllable effective threshold is proposed based on the sensing probability model. We give the model for the membership function of coverage intensity as well as the prediction model for the fusion operator. The range for the effective threshold is presented according to the membership function model. Meanwhile, the maximum of the effective coverage intensity for the target nodes within the monitoring area is derived. The derivation of the maximal fusion coverage intensity is elaborated utilizing a processing function on the distances from the target node to the ones in the sensor node set. Furthermore, we investigate different network properties within the monitoring area such as network coverage quality, the dynamic change of parameters, and the network lifetime, based on the probability theory and the geometric theory. Finally, we present numerical simulations to verify the performances of our algorithm. It is shown under different settings that, compared with the demand coverage quality, the proposed algorithm could improve the network coverage quality by 15.66% on average. The simulation experiment results show that our proposed algorithm has an average improvement by 10.12% and 13.23% in terms of the performances on network coverage quality and network lifetime, respectively. The research results are enlightening to the edge coverage and nonlinear coverage problems within the monitoring area.

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Deployment of a Wireless Sensor Network in the Presence of Obstacle and Its Performance Evaluation

Nayak

Nashine

Kumar

2018

Lecture Notes in Electrical Engineering

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A New Self-Management Model for Large-Scale Event-Driven Wireless Sensor Networks

Cited by 15 publications

References 19 publications

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

Controllable Effective Threshold Based Fusion Coverage Algorithm in Mobile Sensor Networks

Deployment of a Wireless Sensor Network in the Presence of Obstacle and Its Performance Evaluation

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