A new energy efficient management approach for wireless sensor networks in target tracking

Pang, Chengzong; Xu, Guizhi; Shan, Ganlin; Zhang, Yunpu

doi:10.1016/j.dt.2020.05.022

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…The suggested power regulator is unique in that it combines many approaches, including time-out Dynamic Power Management, Dynamic Voltage and Frequency Scaling, and the Global Earliest Deadline First (GEDF) scheduler, to achieve greater scheduling efficiency. Pang et al [29] investigated to save energy in WSNs for object tracking. This study included three types of precautionary measures to reduce energy consumption: Ensuring optimal management system, prolonging the elapsed time between two neighboring occurrences, which could save detecting and interacting energy, and preferentially conveying findings, potentially saving interacting energy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

Sharma¹,

Khan²,

Batra³

et al. 2023

Computer Systems Science and Engineering

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The amount of needed control messages in wireless sensor networks (WSN) is affected by the storage strategy of detected events. Because broadcasting superfluous control messages consumes excess energy, the network lifespan can be extended if the quantity of control messages is decreased. In this study, an optimized storage technique having low control overhead for tracking the objects in WSN is introduced. The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List (OBLL). Whenever an object passes over the sensor area, the recognizing sensor can immediately produce an OBLL along the object's route. To retrieve the entire information, the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures. Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.

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Section: Literature Reviewmentioning

confidence: 99%

“…In existing literature there are techniques that save energy optimizing various parameters of the WSN [28][29][30]. The current research targets to conserve energy through an optimized data storing approach to be used in WSN applications that require historical data related to monitor object activity.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

Sharma¹,

Khan²,

Batra³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…This method can overcome some problems of the KF scheme, such as non-availability of target data at regular intervals, missing of packets and identifying the target rather than just detecting the target. Energy-efficient management approach of target tracking for WSNs is proposed in [32]. After comprehensive analyses of the structure of WSNs and energy consumption sources, two indicators, including target detection probability and tracking accuracy, are combined to be regarded as the constraints of the energy conserving objective function.…”

Section: Related Workmentioning

confidence: 99%

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Zhu

Zhang

et al. 2022

J Wireless Com Network

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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“…This method can overcome some problems of KF scheme, such as non-availability of target data at regular intervals, missing of packets and identifying the target rather than just detecting the target. Energy efficient management approach of target tracking for WSNs is proposed in [26]. After comprehensive analyses for structure of WSNs and energy consumption sources, two indicators, including target detection probability and tracking accuracy, are combined to be regarded as the constraints of the energy conserving objective function.…”

Section: Related Workmentioning

confidence: 99%

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Zhu

Zhang

2022

Preprint

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (Measurement Compensation based Mixture Population Monte Carlo) method which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee the sample diversity. And also, extra measurements or observations generating method is introduced to compensate missing predict locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, the HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

show abstract

A new energy efficient management approach for wireless sensor networks in target tracking

Cited by 20 publications

References 25 publications

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

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