Data fusion based coverage optimization in heterogeneous sensor networks: A survey

Deng, Xianjun; Jiang, Yalan; Yang, Laurence T.; Lin, Man; Yi, Lingzhi; Wang, Minghua

doi:10.1016/j.inffus.2018.11.020

Cited by 86 publications

(33 citation statements)

References 117 publications

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“…In [26], the artificial bee colony algorithm and PSO are combined to optimize the efficient coverage strategy of sensor nodes, including the rotation working, and the multi-objective function about the final coverage rate and the number of sensors. How to use the collaboration of data fusion and deployed sensors to enhance the performance of coverage is ignored in most existing surveys and analysis studies, the authors of [27] present a comprehensive summary and classification of coverage optimization strategies based on data fusion. Based on the learning automata and cellular learning automata models, an overall research framework is proposed, including the confident information coverage (CIC)-based sensor scheduling for CIC-based deployment for minimizing network deployment cost, maximizing network lifetime, and CIC-based coverage hole detection and healing.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Zhao

Cui

Guo

et al. 2020

Sensors

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Sensor nodes perform missions based on the effectual invariable coverage of events, and it is commonly guaranteed by the determinate deployment for sensor nodes who deviate from the optimum site frequently. To reach the optimal coverage effect with the lowest costs is a primary goal of wireless sensor networks. In this paper, by splicing the sensing area optimally with cellular grids, the best deployment location for sensors and the required minimum number of them are revealed. The optimization problem of coverage rate and energy consumption is converted into a task assignment problem, and a dynamic partition algorithm for cellular grids is also proposed to improve the coverage effect when the number of sensors is variable. Furthermore, on the basis of solving the multi-objective problem of reducing and balancing the energy cost of sensors, the vampire bat optimizer is improved by introducing virtual bats and virtual preys, and finally solves the asymmetric assignment problem once the number of cellular grids is not equal to that of sensors. Simulation results indicate that the residual energy of sensors during redeployment is balanced notably by our strategy when compared to three other popular coverage-enhancement algorithms. Additionally, the total energy cost of sensor nodes and coverage rate can be optimized, and it also has a superior robustness when the number of nodes changes.

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Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Zhao

Cui

Guo

et al. 2020

Sensors

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“…The inclusion of object detection in the environment map adds a difficulty and forces the use of advanced sensors. Consequently, when there are many sensors, the quality of data fusion is dependent on the fusion mechanism [24]. Once a certain precision in the detection of the object and its characteristics has been achieved, it should be possible to classify the object [25].…”

Section: Related Workmentioning

confidence: 99%

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

Poza-Lujan

Posadas-Yagüe

Simó-Ten

et al. 2019

Sensors

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Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2–4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.

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“…Proposed DT managed to get the coverage hole area with number of holes. The mitigation of these sensor nodes considered the redundant nodes [1] which remain idle or in sleep mode while other sensor mates work. These redundant nodes add into vertices of sensor tree as a new subset ∈ 1 , 2 , … .…”

Section: Coverage Hole Healingmentioning

confidence: 99%

“…In previous years many researchers have suggested solutions on coverage holes detection and restoration [1][2][3][4][5][6][7][8][9][10][11]. The coverage optimization problem discussed with different coverage models which provided the sensor sensing capacity [1]. An RSSI approach was proposed for the coverage holes detection and recovery process in [2].…”

Section: Introductionmentioning

confidence: 99%

A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

Mehta¹,

Malik

2018

ICST Transactions on Scalable Information Systems

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In the WSN network, nodes are always deprived of battery and can't be in operation for a long time. Some nodes may die sooner than others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid optimization algorithm. Our algorithm makes the locally converging particle swarm optimization to a global optimization algorithm by ensembling it with the Gravitational search algorithm. The perturbation in this algorithm is also reduced by logistic chaotic mapping. The current solution has detected the holes and healed them with additional redundant nodes. The Delaunay triangulation method detected the number of holes with the improved algebraic approach. The results are validated by extensive experimentation with different sensing range and node's density. Our method seems to perform well than state of the art schemes. We have also shown that increasing the redundant nodes, more coverage can be achieved.

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Data fusion based coverage optimization in heterogeneous sensor networks: A survey

Cited by 86 publications

References 117 publications

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

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