Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors

Gao, Xiaofeng; Fan, Jia-Hao; Wu, Fan; Chen, Guihai

doi:10.1109/tnet.2018.2815630

Cited by 44 publications

(15 citation statements)

References 37 publications

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“…Gao et al. studied Min-peroid Sweep Coverage (MPSC) problem when the sensors did not cooperate with each other [ 29 ], in which, they proposed a nearly 5-appr algorithm for MPSC when the sensors with the same velocity covered target points on 2-D plane and a

-appr algorithm when the sensors have different velocities, where

was the ratio of the maximum velocity to the minimum one. They also had extended it to the scene where a graph needed to be covered.…”

Section: Related Workmentioning

confidence: 99%

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Liang

Shen

2021

Sensors

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As an important application of wireless sensor networks (WSNs), deployment of mobile sensors to periodically monitor (sweep cover) a set of points of interest (PoIs) arises in various applications, such as environmental monitoring and data collection. For a set of PoIs in an Eulerian graph, the point sweep coverage problem of deploying the fewest sensors to periodically cover a set of PoIs is known to be Non-deterministic Polynomial Hard (NP-hard), even if all sensors have the same velocity. In this paper, we consider the problem of finding the set of PoIs on a line periodically covered by a given set of mobile sensors that has the maximum sum of weight. The problem is first proven NP-hard when sensors are with different velocities in this paper. Optimal and approximate solutions are also presented for sensors with the same and different velocities, respectively. For M sensors and N PoIs, the optimal algorithm for the case when sensors are with the same velocity runs in O(MN) time; our polynomial-time approximation algorithm for the case when sensors have a constant number of velocities achieves approximation ratio 12; for the general case of arbitrary velocities, 12α and 12(1−1/e) approximation algorithms are presented, respectively, where integer α≥2 is the tradeoff factor between time complexity and approximation ratio.

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-appr algorithm when the sensors have different velocities, where

was the ratio of the maximum velocity to the minimum one. They also had extended it to the scene where a graph needed to be covered.…”

Section: Related Workmentioning

confidence: 99%

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Liang

Shen

2021

Sensors

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“…The coverage problem in sensor networks has been the subject of extensive interest (e.g., see [28,39,16,5,2,4,13,3,29,38,3,10,25,15,40]). The theoretical foundations for k-barrier coverage were developed in [28], while [16] presents and compares several state-of-the-art algorithms and techniques to address the integrated coverage-connectivity issues in WSNs.…”

Section: Related Workmentioning

confidence: 99%

“…Proof. There are two cases to consider Case 1: Inequality (15) We know that the random variable X i , i.e., the random position of i-th sensor is the sum of i independent and identically distributed exponential random variables with parameter λ and obeys Gamma distribution with parameters i ∈ N \ {0}, λ > 0 (see [26,37]). Notice that,…”

Section: )mentioning

confidence: 99%

On the Robot Assisted Movement in Wireless Mobile Sensor Networks

Das¹,

Kapelko²

2021

Preprint

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This paper deals with random sensors initially randomly deployed on the line according to general random process and on the plane according to two independent general random processes. The mobile robot with carrying capacity k placed at the origin point is to move the sensors to achieve the general scheduling requirement such as coverage, connectivity and thus to satisfy the desired communication property in the network. We study tradeoffs between the energy consumption in robot's movement, the numbers of sensors n, the sensor range r, the interference distance s, and the robot capacity k until completion of the coverage simultaneously with interference scheduling task. In this work, we obtain upper bounds for the energy consumption in robot's movement and obtain the sharp decrease in the total movement cost of the robot so as to provide the coverage simultaneously with interference requirement.

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“…Wireless mobile sensors network (WMSN) (e.g. see [1], [16], [28], [31], [36] and [38]) are being deployed for detecting and monitoring events which occur in many instances of every day life. However, it is often their case that monitoring may not be as effective due to external factors such as harsh environmental conditions, sensor faults, geographic obstacles, etc.…”

Section: Introductionmentioning

confidence: 99%

On the Energy in Displacement of Random Sensors for Connectivity and Interference

Kapelko

2020

Proceedings of the 21st International Conference on Distributed Computing and Networking

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This paper investigates the problem of the minimilization of energy consumption in reallocation of wireless mobile sensors network (WMSN) to assure good communication without interference.Fix d ∈ N \ {0}. Assume n sensors are initially randomly placed in the hyperoctant [0, ∞) d according to d identical and independent Poisson processes each with arrival rate λ > 0.Let 0 < s ≤ v be given real numbers. We are allowed to move the sensors, so that every two consecutive sensors are placed at distance greater than or equal to s and less than or equal to v.Fix a ≥ 1. Assume that i−th sensor is displaced a distance equal to m(i). The cost measure for the displacement of the team of sensors is the sum n i=1 d a i (a−total movement). In this work, we discover and explain a sharp decline and a sharp increase (a threshold phenomena) in the expected minimal a−total movement around the interference-connectivity distances s, v equal to 1 λ .

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Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors

Cited by 44 publications

References 37 publications

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

On the Robot Assisted Movement in Wireless Mobile Sensor Networks

On the Energy in Displacement of Random Sensors for Connectivity and Interference

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