An endocrine-based intelligent distributed cooperative algorithm for target tracking in wireless sensor networks

Ye, Jin; Ding, Yongsheng; Jin, Yaochu

doi:10.1007/s00500-014-1352-3

Cited by 18 publications

(2 citation statements)

References 32 publications

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“…When there are no targets in the detection area, a large number of sensors are put to sleep to reduce the power consumption. Furthermore, to save energy, a novel intelligent distributed cooperative method is designed, inspired by the regulating mechanisms of the human hormone system [ 17 ]. The algorithm enables the sensor nodes to self-organize themselves autonomously without a centralized control.…”

Section: Related Studiesmentioning

confidence: 99%

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

Zou

et al. 2017

Sensors

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Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

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Section: Related Studiesmentioning

confidence: 99%

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

Zou

et al. 2017

Sensors

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“…Wireless sensor networks are widely applied in military monitoring, disaster rescue, environmental monitoring, intelligent transportation, medical treatment, and health. As a basic research aspect on wireless sensor networks, coverage technology fundamentally reflects a sensor network's perception of the physical world with the help of sensor perception, node configuration, and target monitoring to completely and effectively acquire the needed information [1].…”

Section: Introductionmentioning

confidence: 99%

A Novel Threshold - Controllable Algorithm for Moving Target Coverage

Zhao¹,

Heng-chuan²,

Sun³

et al. 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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Target Tracking Scheme Using Multi-Objective Differential Evolution for Underwater Wireless Sensor Networks

Kumari

Mishra

Anand

2021

Lecture Notes in Electrical Engineering

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An endocrine-based intelligent distributed cooperative algorithm for target tracking in wireless sensor networks

Cited by 18 publications

References 32 publications

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

A Novel Threshold - Controllable Algorithm for Moving Target Coverage

Target Tracking Scheme Using Multi-Objective Differential Evolution for Underwater Wireless Sensor Networks

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