Distributed lifetime-maximized target coverage game

Yen, Li-Hsing; Lin, Che-Ming; Leung, Victor C. M.

doi:10.1145/2489253.2489263

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…Since then, a variety of centralized and discrete algorithms have been used to solve the target coverage problem. Most of these methods are based on greedy algorithm [31], linear algorithm [32] and game theory [33].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids

Fan

et al. 2018

Sensors

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The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.

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Section: Related Workmentioning

confidence: 99%

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids

Fan

et al. 2018

Sensors

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“…According to Yen et al, 13 this coverage problem can be formulated as an integer programming problem as follows…”

Section: Description Of Our Problemmentioning

confidence: 99%

“…The area is named as the ε -coverage. When ε e K R j and ε -coverage theory 13 are used, the problem of how area coverage becomes equivalent to finite point coverage can be solved well. If the entire area needs to meet coverage ratio ε , target points within area must be extensively distributed at a probability of not less than ε e K R j .…”

Section: Introductionmentioning

confidence: 99%

“…To address the second issue, we can employ any of the algorithms presented in Chen et al, 11 Yen et al, 13 Usman et al, 14 and Qin et al 15,16 to deal with the point coverage problem. The literature 13 reported lifetime-maximized target coverage on the basis of game theory. However, the number of targets was only 10–60, which could not satisfy the number level of target points.…”

Section: Introductionmentioning

confidence: 99%

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An area coverage algorithm for wireless sensor networks based on differential evolution

Qin

Chen

2018

International Journal of Distributed Sensor Networks

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Lifetime requirements and coverage demands are emphasized in wireless sensor networks. An area coverage algorithm based on differential evolution is developed in this study to obtain a given coverage ratio e. The proposed algorithm maximizes the lifetime of wireless sensor networks to monitor the area of interest. To this end, we translate continuous area coverage into classical discrete point coverage, so that the optimization process can be realized by wireless sensor networks. Based on maintaining the e-coverage performance, area coverage algorithm based on differential evolution takes the minimal energy as optimization objective. In area coverage algorithm based on differential evolution, binary differential evolution is redeveloped to search for an improved node subset and thus meet the coverage demand. Taking into account that the results of binary differential evolution are depended on the initial value, the resulting individual is not an absolutely perfect node subset. A compensation strategy is provided to avoid unbalanced energy consumption for the obtained node subset by introducing the positive and negative utility ratios. Under the helps of those ratios and compensation strategy, the resulting node subset can be added additional nodes to remedy insufficient coverage, and redundancy active nodes can be pushed into sleep state. Furthermore, balance and residual energy are considered in area coverage algorithm based on differential evolution, which can expand the scope of population exploration and accelerate convergence. Experimental results show that area coverage algorithm based on differential evolution possesses high energy and computation efficiencies and provides 90% network coverage.

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“…In this case, not all sensor nodes have to be active to monitor, and hence scheduling the sleep and activation periods of sensor nodes can provide significant energy benefits. For instance, in sensor coverage problems, such mechanisms are used to maximize network lifetime while guaranteeing that all target demands are covered [20]. Besides, scheduling the activation/sleeping periods has been also considered to maximize the lifetime of a query-based WSN in [21].…”

Section: A Wsns For Water Monitoringmentioning

confidence: 99%

Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing

Gkatzikis

Fischione

et al. 2015

IEEE J. Select. Areas Commun.

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The recent development of low cost wireless sensors enables novel Internet-of-Things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamicprogramming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.Index Terms-Energy balancing, energy efficiency, water distribution networks, compressive sensing 0733-8716 (c)

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Distributed lifetime-maximized target coverage game

Cited by 8 publications

References 22 publications

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids

An area coverage algorithm for wireless sensor networks based on differential evolution

Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing

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