ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

Sun, Zeyu; Zhao, Guozeng; Xing, Xiaofei

doi:10.1186/s13638-018-1023-7

Cited by 18 publications

(16 citation statements)

References 45 publications

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“…All the nodes are isomorphic, i.e., all the nodes share the same sensing radius and communication radius. The nodes employ the directional sensing model in paper [ 20 ].…”

Section: Network Modelmentioning

confidence: 99%

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

Sun

Liu

et al. 2018

Sensors

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When the nodes in the network are deployed in the target area with an appropriate density, the effective aggregation and transmission of the data gathered in the monitoring area remain to be solved. The existing Compressed Sensing (CS) based on data aggregation schemes are accomplished in a centralized manner and the Sink node achieves the task of data aggregation. However, these existing schemes may suffer from load imbalance and coverage void issues. In order to address these problems, we propose a Compressed Sensing based on Fault-tolerant Correcting Data Aggregation (CS-FCDA) scheme to accurately reconstruct the compressed data. Therefore, the network communication overhead can be greatly reduced while maintaining the quality of the reconstructed data. Meanwhile, we adopt the node clustering mechanism to optimize and balance the network load. It is shown via simulation results, compared with other data aggregation schemes, that the proposed scheme shows obvious improvement in terms of the Fault-tolerant correcting capability and the network energy efficiency of the data reconstruction.

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“…All the nodes are isomorphic, i.e., all the nodes share the same sensing radius and communication radius. The nodes employ the directional sensing model in paper [ 20 ].…”

Section: Network Modelmentioning

confidence: 99%

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

Sun

Liu

et al. 2018

Sensors

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“…Network deployment should optimize existing network resources to obtain the highest and longest network life in future applications with its network. In WSNs, nodes have both information collection and information transmission functions, network deployment must meet two aspects: (1) under the premise of ensuring perceptual coverage, communication coverage, and connectivity coverage, the maximum monitoring area should be covered with the fewest sensor nodes (2) On the premise of ensuring the smooth transmission of information and control commands in the network, the delay should be shortened as much as possible to improve the network lifetime. In WSNs, the maximum physical space that each sensor node can sense (the sensing range) is limited.…”

Section: Introductionmentioning

confidence: 99%

CoC-SCS: Cooperative-Optimization Coverage Algorithm Based on Sensor Cloud Systems in Intelligent Computing

Sun

2020

IEEE Access

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the coverage of traditional Wireless Sensor Networks (WSNs) is limited by node energy and data redundancy, which forces WSNs to be interrupted abnormally. Therefore, Cooperative-optimization Coverage Algorithm based on Sensor Cloud Systems in Intelligent Computing (CoC-SCS) is proposed. First, the algorithm determines the location information of the Focus Target Nodes (FTNs), and uses the Genetic Algorithm (GA) to give the node path planning. Second, the mutation parameters and the controllable threshold parameters are used in order to control the event domain nodes. Optimizing clustering makes the clustering of the nodes to be more uniform, so that it can improve the search ability of the global target nodes and even reduce the nodes energy consumption. Third, the adaptation function covers the continuity of the covered target positions and the monitoring range of the nodes. Optimization is also performed to achieve the goals of increasing network coverage and extending the network lifetime. Finally, simulation results show that the proposed CoC-SCS algorithm compared with other three algorithms in this paper has improved 11.27% and 16.35% on average in terms of network coverage, network lifetime, etc., and 13.95% in terms of network energy overhead, thereby we can further verify that CoC-SCS algorithm has strong stability and effectiveness.

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“…At present, the coverage algorithm for static wireless sensor networks is relatively mature, whereas that for moving targets is less developed [2,3]. In practice, wireless sensor networks are mainly used in the dynamic monitoring of sensor nodes about moving targets within a coverage area; examples of such applications include vehicle tracking in military monitoring and wildlife tracking in habitat monitoring [4]. The coverage of a moving target must be capable of effectively aiming at the target perception, location, and tracking, in addition to flexible scheduling of nodes for the moving target at different speeds and different locations.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

JESTR

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The uncertainty of a moving target's speed and direction can lead to inaccurate target location and large energy consumption of network nodes when the target is covered and controlled by the sensor node in the wireless sensor network. This study proposed a method of threshold-controllable moving target coverage to accurately predict the target movement location, improve the energy consumption rate of the network, and clarify the main factors affecting the coverage of the moving target. First, a node participation threshold was introduced in the area of the moving target, and a scheduling mechanism of the node state was established. Second, the location prediction model of the moving target and the self-adjusting mechanism of the node data reporting frequency were established in accordance with the historical location information, movement direction, and speed of the moving target. Finally, the effect of node participation threshold on target location accuracy and network energy consumption during monitoring of the moving target was analyzed. Results show that when the participation threshold is set to decrease from 5 to 2, the target location error at different speeds decreases by 56.7%. When the participation threshold is equal to 3, the average energy consumption at different speeds decreases by 55% compared with consumption without a present threshold. Energy consumption decreases by an average of 41% in the model of moving target location prediction. Therefore, the threshold-controllable algorithm for moving target coverage shows excellent performance in terms of location accuracy and network energy utilization efficiency. The study can be utilized for sensor network environments with limited node energy to provide theoretical guidance for the dynamic coverage optimization of moving targets.

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ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

Cited by 18 publications

References 45 publications

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

CoC-SCS: Cooperative-Optimization Coverage Algorithm Based on Sensor Cloud Systems in Intelligent Computing

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