A Novel Approach to Reliable Sensor Selection and Target Tracking in Sensor Networks

Anvaripour, Mohammad; Saif, Mehrdad; Ahmadi, M.

doi:10.1109/tii.2019.2916091

Cited by 29 publications

(9 citation statements)

References 33 publications

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“…Classically, the need for selection emerges from maximization of a performance metric subject to limited resource budget, being it of economical, functional (e.g., weight of autonomous platforms), spatial (e.g., locations to place sensors), or other nature. Typical works in this field [25]- [33] focus on such budget-related constraints and pay little attention to impact on system dynamics. For example, [25] proposes selection strategies based on coverage probability and energy consumption for a target tracking problem, [33] studies a clustering-based selection to address communication constraints in underwater environments, and [31] tackles placement of cheap and expensive sensors to optimize reconstruction of dynamical variables.…”

Section: A Related Literaturementioning

confidence: 99%

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Ballotta,

Peserico,

Zanini

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off : raw measurements are inaccurate but timely, whereas accurate processed updates are available after processing delay. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize network monitoring performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds both computation and communication latency, and propose a Reinforcement Learning-based approach that dynamically allocates computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical experiments motivated by smart sensing for the Internet of Drones and self-driving vehicles. In particular, we show that, under constrained computation at the base station, monitoring performance can be further improved by an online sensor selection.

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Section: A Related Literaturementioning

confidence: 99%

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Ballotta,

Peserico,

Zanini

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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“…Finally, the target node is fused using fuzzy data fusion and fuzzy fusion rules to acquire the judgment criterion, and the wireless sensor network node deployment is completed within bounds. The authors [19][20][21] used a stepwise location technique to provide an acceptable coordinate value for the target node depending on its location by locating the triangle or tetrahedron that surrounded it.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Deployment Model for Target Coverage in Wireless Sensor Network

Subramanian¹,

Shanmugavel²

2023

Intelligent Automation &Amp; Soft Computing

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Target coverage and continuous connection are the major recital factors for Wireless Sensor Network (WSN). Several previous research works studied various algorithms for target coverage difficulties; however they lacked to focus on improving the network's life time in terms of energy. This research work mainly focuses on target coverage and area coverage problem in a heterogeneous WSN with increased network lifetime. The dynamic behavior of the target nodes is unpredictable, because the target nodes may move at any time in any direction of the network. Thus, target coverage becomes a major problem in WSN and its applications. To solve the issue, this research work is motivated to design and develop an intelligent model named Distributed Flexible Wheel Chain (DFWC) model for efficient target coverage and area coverage in WSN applications. More number of target nodes is covered by minimum number of sensor nodes that can improve energy efficiency. To be specific, DFWC motivated at obtaining lesser connected target coverage, where every target is available in the monitoring area is covered by a smaller number of sensor nodes. The simulation results show that the proposed DFWC model outperforms the existing models with improved performance.

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“…Several similar targets include five identical red targets and similar RGB colors and five similar green targets. Continuous tracking means that in an infinitely long video clip, when the tracked target is stuck or the tracking fails, the system can redetect the target and continue tracking [27].…”

Section: Uav Target Tracking Systemmentioning

confidence: 99%

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Tang

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

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A Novel Approach to Reliable Sensor Selection and Target Tracking in Sensor Networks

Cited by 29 publications

References 33 publications

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Intelligent Deployment Model for Target Coverage in Wireless Sensor Network

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

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