A sensor node scheduling algorithm for heterogeneous wireless sensor networks

Wang, Zhangquan; Chen, Yourong; Liu, Banteng; Yang, Haibo; Su, Ziyi; Zhu, Ye

doi:10.1177/1550147719826311

Cited by 16 publications

(8 citation statements)

References 11 publications

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“…The sensing range varies from 1 m to 5 m and the ratio of battery/network lifetime is ν that varies from 1/5 to 3/5. We conduct experiments by changing the number of sensor nodes, the number of targets, sensing range and the ratio of battery/network lifetime to compare the performance of TSCM, Distributed ECT [4], STCOS algorithm [26], MinRedancy algorithm [25] and RndScheduling algorithm [19].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In order to extend the network lifetime while satisfying the coverage requirement, the authors of Reference [12] propose an improved genetic algorithm based scheduling for wireless sensor networks. However, in mission-driven applications, the network has constrained lifetime and the objective is to improve the network coverage while meeting the lifetime constraint [17,18,19]. Thus, according to different application scenarios, the goals of the sensor node scheduling can be roughly divided into the following two categories: the network lifetime extension with coverage requirement and the network coverage maximization with a lifetime requirement [20,21].…”

Section: Related Workmentioning

confidence: 99%

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Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

Xiong

2019

Sensors

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Coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to meet both coverage and network lifetime requirements. Therefore, in order to satisfy the network lifetime constraint, sometimes the coverage needs to be traded for network lifetime. In this paper, we study how to schedule sensor nodes to maximize the spatial-temporal coverage of the critical locations under the constraint of network lifetime. First, we analyze the sensor node scheduling problem for the spatial-temporal coverage of the critical locations and establish a mathematical model of the node scheduling. Next, by analyzing the characteristics of the model, we propose a Two-phase Spatial-temporal Coverage-enhancing Method (TSCM). In phase one, a Particle Swarm Optimization (PSO) algorithm is employed to organize the directions of sensor nodes to maximize the number of covered critical locations. In the second phase, we apply a Genetic Algorithm (GA) to get the optimal working time sequence of each sensor node. New coding and decoding strategies are devised to make GA suitable for this scheduling problem. Finally, simulations are conducted and the results show that TSCM has better performance than other approaches.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

Xiong

2019

Sensors

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“…The algorithm considers the distance between the virtual repair node and the mobile node, the energy consumption during the mobile repair process, and the confidence that the node can be repaired. To improve the regional coverage rate and network lifetime of HWSNs, a sensor node scheduling algorithm for HWSNs was proposed by [12]; the proposed algorithm was found to improve the network lifetime, increase the number of living sensor nodes, and maintain the average node energy consumption at a low level. In [13], the author proposed a multi-objective deployment strategy (MODS), which uses multi-objective evolutionary algorithms to achieve near-optimal solutions for WSN deployment problems.…”

Section: Related Workmentioning

confidence: 99%

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

et al. 2021

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To overcome the problems of coverage blind areas and coverage redundancy when sensor nodes are deployed randomly in heterogeneous wireless sensor networks (HWSNs). An optimal coverage method for HWSNs based on an improved social spider optimization (SSO) algorithm is proposed, which can reduce the energy consumption and improve the network coverage. First, a mathematical model of HWSN coverage is established, which is a complex combinatorial optimization problem. To improve the global convergence speed of the proposed algorithm, a chaotic initialization method is used to generate the initial population. In addition, the SSO algorithm has a poor convergence speed and search ability, which is enhanced by improving the neighborhood search, global search, and matching radius. In the iterative optimization process, the optimal solution is ultimately obtained by simulating the movement law of the spider colony, i.e., according to the cooperation, mutual attraction, and mating process of female and male spiders. An improved SSO algorithm based on chaos, namely the CSSO algorithm, is proposed to apply to the optimal deployment of sensory nodes in HWSNs. On this basis, the optimization goals are to improve the network coverage and reduce network costs. The optimal deployment plan of nodes is searched via the proposed CSSO algorithm, which effectively prevents coverage blind spots and coverage redundancy in the network.

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“…These contradictory objectives are maximizing the coverage, optimizing the energy consumption, and many more. In [24], authors have proposed a scheduling algorithm for increasing the life expectancy of wireless sensor networks. At the same time, this algorithm ensures the coverage of discrete set of points lying over field.…”

Section: Related Workmentioning

confidence: 99%

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Kumar

AlShboul

et al. 2021

Sensors

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Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

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A sensor node scheduling algorithm for heterogeneous wireless sensor networks

Cited by 16 publications

References 11 publications

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

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