An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks

Mahboubi, Hamid; Masoudimansour, Walid; Aghdam, Amir G.; Sayrafian-Pour, Kamran

doi:10.1109/tcyb.2016.2519939

Cited by 34 publications

(18 citation statements)

References 26 publications

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“…The work focuses on selecting cluster heads and forming energy-efficient paths between cluster heads and sink nodes. An energyefficient target tracking algorithm is proposed in [14]. The algorithm has translated the minimum energy problem into a constrained shortest path problem.…”

Section: A Related Workmentioning

confidence: 99%

Path-Planning-Enabled Semiflocking Control for Multitarget Monitoring in Mobile Sensor Networks

Yuan

Ganganath

Cheng

et al. 2020

IEEE Trans. Ind. Inf.

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Mobile sensor networks (MSNs) are good candidates for large-scale unattended surveillance applications. However, it is challenging to track moving targets due to their complex dynamic behaviors. Semi-flocking algorithms have been proven to be efficient in controlling MSNs in both area coverage and target tracking applications. While many existing literatures on the study of semi-flocking algorithms often assume an area of interest (AoI) to be regular and with unified traversal cost, the uneven and rough landscapes in real-life applications have imposed extra challenges and raised demands for new management strategies. In this paper, a mobility map is used to incorporate different costs associated with irregular terrains which results in different maximum allowed speeds on nodes in different regions. In order to reduce target detection time and node energy consumption, a heuristic search algorithm is developed to find time-efficient and feasible paths between nodes and sensing targets. Under the proposed algorithm, nodes can effectively select a target to track or search for new targets in the AoI. Results of extensive experiments show that semi-flocking-controlled nodes together with path planning can reach their targets faster with lower energy consumption compared to three exiting flocking-based algorithms.

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Section: A Related Workmentioning

confidence: 99%

Path-Planning-Enabled Semiflocking Control for Multitarget Monitoring in Mobile Sensor Networks

Yuan

Ganganath

Cheng

et al. 2020

IEEE Trans. Ind. Inf.

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“…Apart from sensing the AoI, MSNs controlled by the proposed algorithm can detect and follow multiple targets in the AoI. Multiple targets tracking is a non-trivial task comparing to single target tracking [10]. In this work, an information list is introduced to keep track of locations and movements of each target.…”

Section: B Information Maps and Information Listsmentioning

confidence: 99%

“…Apart from area coverage, accurate information on the status of targets, such as their current locations, moving directions, and speeds, is vital to the tracking performance. In [10], Mahboubi et al proposed an energyefficient strategy for tracking a single moving target with MSNs. Their strategy is based on graph theory and a shortestpath finding algorithm.…”

Section: Introductionmentioning

confidence: 99%

Semi-Flocking-Controlled Mobile Sensor Networks for Dynamic Area Coverage and Multiple Target Tracking

et al. 2018

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Mobile sensor networks (MSNs) can provide sensing coverage to large areas of interest (AoIs). Area coverage and target tracking capabilities of MSNs are heavily depending on their motion control and coordination mechanisms. Many existing MSN motion control algorithms ignore or poorly utilize available information from their operating environment, thus lead to unsatisfactory monitoring performances. This paper proposes a fully distributed semi-flocking algorithm which enables mobile nodes to self-organize themselves based on mobility and sensing information via information exchanges among nearby nodes. A distributed mechanism is designed to maximize area coverage and target tracking performances of MSNs. Mobile nodes perform evaluations based on received information and switch between searching and tracking modes. Behaviors of MSNs controlled by the proposed algorithm are studied under different levels of information exchanges. Our study shows that the proposed semi-flocking algorithm is capable of delivering desirable area coverage and target tracking performances in MSNs.

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“…As shown in Figure 1 , a lot of static sensor nodes are deployed randomly and unevenly in a area of interest, and some MNs, also called mobile sinks, which could move anywhere in a random way locate initially in the area boundary [ 19 ]. The network consists of

cheap and low-power SNs

and a few MNs

.…”

Section: Problem Formulation and System Modelsmentioning

confidence: 99%

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

Qian

Liu

2018

Sensors

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Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.

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An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks

Cited by 34 publications

References 26 publications

Path-Planning-Enabled Semiflocking Control for Multitarget Monitoring in Mobile Sensor Networks

Path-Planning-Enabled Semiflocking Control for Multitarget Monitoring in Mobile Sensor Networks

Semi-Flocking-Controlled Mobile Sensor Networks for Dynamic Area Coverage and Multiple Target Tracking

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

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