Solving uniform coverage problem with a modified Remez-algorithm

2015 ◽  
Vol 65 (2) ◽  
pp. 477-491
Author(s):  
D. Nowak ◽  
K. H. Küfer
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74315-74325 ◽  
Author(s):  
Manju ◽  
Samayveer Singh ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Fadi Al-Turjman ◽  
...  

2014 ◽  
Vol 02 (03) ◽  
pp. 243-248 ◽  
Author(s):  
Cheng Song ◽  
Gang Feng

This paper investigates the coverage problem for mobile sensor networks on a circle. The goal is to minimize the largest distance from any point on the circle to its nearest sensor while preserving the mobile sensors' order. The coverage problem is translated into a multi-agent consensus problem by showing that the largest distance from any point to its nearest sensor is minimized if the counterclockwise distance between each sensor and its right neighbor reaches a consensus. Distributed control laws are also developed to drive the mobile agents to the optimal configuration with order preservation. Simulation results illustrate the effectiveness of the proposed control laws.


2005 ◽  
Vol 24 (11) ◽  
pp. 883-898 ◽  
Author(s):  
Prasad N. Atkar ◽  
Aaron Greenfield ◽  
David C. Conner ◽  
Howie Choset ◽  
Alfred A. Rizzi

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1457
Author(s):  
Dieyan Liang ◽  
Hong Shen

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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