A Stochastic <i>k</i>-Coverage Scheduling Algorithm in Wireless Sensor Networks

Yu, Jiguo; Ren, Shengxiang; Wan, Shengli; Yu, Dongxiao; Wang, Guanghui

doi:10.1155/2012/746501

Cited by 18 publications

(10 citation statements)

References 22 publications

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“…iii) Extensive experiments are performed using the C++ custom simulator. The conducted simulation results prove that the proposed CSA technique can improve the lifespan of the network and maintaining an acceptable level of coverage in the area of interest in comparison with some existing methods such as DESK [26], GAF [25], and PeCO [49].…”

Section: Introductionmentioning

confidence: 89%

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

Hameed

Idrees

2021

Inventive Systems and Control

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The network topology control represents an essential factor during designing the Wireless Sensor Networks (WSNs) due to its primary role in the lifetime optimization of the WSNs. This paper proposes a Cuckoo Scheduling Algorithm (CSA) for lifetime optimization in Sensor Networks of Internet of Things (IoT). First, the sensor devices have clustered using the DBSCAN method, and then the CAS technique is applied at each cluster head. The CSA provides the best schedule of the sensor nodes inside each cluster to monitor the cluster region of with a minimum number of nodes while keeping a suitable level of coverage. The cluster head pooling and the CSA have executed periodically. The experimental results confirm that the CSA technique enhances the network lifespan and whilst preserving an acceptable ratio of coverage in WSNs.

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Section: Introductionmentioning

confidence: 89%

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

Hameed

Idrees

2021

Inventive Systems and Control

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“…They provided a generic greedy heuristic algorithm to solve the problem. In [ 19 ], Yu et al studied the k -coverage scheduling problem to guarantee k -coverage sensing and network connectivity under both deterministic and stochastic sensing models of the sensors. In [ 20 ], Liu et al addressed the problem of achieving energy conservation, coverage and connectivity requirements together in WSNs for vehicular applications.…”

Section: Related Workmentioning

confidence: 99%

On Connected Target k-Coverage in Heterogeneous Wireless Sensor Networks

Chen

et al. 2016

Sensors

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Coverage and connectivity are two important performance evaluation indices for wireless sensor networks (WSNs). In this paper, we focus on the connected target k-coverage (CTCk) problem in heterogeneous wireless sensor networks (HWSNs). A centralized connected target k-coverage algorithm (CCTCk) and a distributed connected target k-coverage algorithm (DCTCk) are proposed so as to generate connected cover sets for energy-efficient connectivity and coverage maintenance. To be specific, our proposed algorithms aim at achieving minimum connected target k-coverage, where each target in the monitored region is covered by at least k active sensor nodes. In addition, these two algorithms strive to minimize the total number of active sensor nodes and guarantee that each sensor node is connected to a sink, such that the sensed data can be forwarded to the sink. Our theoretical analysis and simulation results show that our proposed algorithms outperform a state-of-art connected k-coverage protocol for HWSNs.

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“…Extensive studies have been done on the connection problem of networks. Many of them focus on how many neighbors or network density is needed so that a network connects with high probability, such as [15]; some construct network to satisfy connectivity [16,17]; some works try to develop algorithms to preserve network connectivity or coverage, for example, [18][19][20], while some other works study other aspects of network connectivity, such as [21] which evaluates the quality of connectivity by measuring the reliability of link; it shows that the largest eigenvalue of the probabilistic connectivity matrix can serve as a good measure of the quality of network connectivity. When all the nodes of a region fail, [22] measures the number of connected components.…”

Section: Related Workmentioning

confidence: 99%

The Influence of Communication Range on Connectivity for Resilient Wireless Sensor Networks Using a Probabilistic Approach

Huang

Martínez-Ortega

Sendra

et al. 2013

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) consist of thousands of nodes that need to communicate with each other. However, it is possible that some nodes are isolated from other nodes due to limited communication range. This paper focuses on the influence of communication range on the probability that all nodes are connected under two conditions, respectively: (1) all nodes have the same communication range, and (2) communication range of each node is a random variable. In the former case, this work proves that, for 0 < < −1 , if the probability of the network being connected is 0.36 , by means of increasing communication range by constant ( ), the probability of network being connected is at least 1 − . Explicit function ( ) is given. It turns out that, once the network is connected, it also makes the WSNs resilient against nodes failure. In the latter case, this paper proposes that the network connection probability is modeled as Cox process. The change of network connection probability with respect to distribution parameters and resilience performance is presented. Finally, a method to decide the distribution parameters of node communication range in order to satisfy a given network connection probability is developed.

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A Stochastic k-Coverage Scheduling Algorithm in Wireless Sensor Networks

Cited by 18 publications

References 22 publications

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

On Connected Target k-Coverage in Heterogeneous Wireless Sensor Networks

The Influence of Communication Range on Connectivity for Resilient Wireless Sensor Networks Using a Probabilistic Approach

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