Mobile sensors networks: a distributed solution to the area coverage problem

2008 ◽  
Author(s):  
Paolo Di Giamberardino ◽  
Simone Gabriele
Keyword(s):  
Area Coverage ◽  
Mobile Sensors ◽  
Coverage Problem

Coverage Control for Mobile Sensor Networks on a Circle

2014 ◽  
Vol 02 (03) ◽  
pp. 243-248 ◽  
Author(s):  
Cheng Song ◽  
Gang Feng
Keyword(s):  
Sensor Networks ◽  
Mobile Agents ◽  
Mobile Sensor Networks ◽  
Mobile Sensors ◽  
Mobile Sensor ◽  
Coverage Problem ◽  
Control Laws ◽  
Coverage Control ◽  
Order Preservation ◽  
Multi Agent

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.

scholarly journals Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1457
Author(s):  
Dieyan Liang ◽  
Hong Shen
Keyword(s):  
Optimal Algorithm ◽  
Approximate Solutions ◽  
Approximation Ratio ◽  
Mobile Sensors ◽  
Np Hard ◽  
Coverage Problem ◽  
Polynomial Time Approximation Algorithm ◽  
Weighted Point ◽  
Eulerian Graph ◽  
Set Of Points

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.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2020 ◽  
pp. 1580-1600
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2016 ◽  
pp. 441-461
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

scholarly journals Approximation Algorithms for Barrier Sweep Coverage

2019 ◽  
Vol 30 (03) ◽  
pp. 425-448 ◽  
Author(s):  
Barun Gorain ◽  
Partha Sarathi Mandal
Keyword(s):  
Energy Source ◽  
Approximation Algorithm ◽  
Finite Length ◽  
Optimal Solution ◽  
Mobile Sensors ◽  
Continuous Curve ◽  
Mobile Sensor ◽  
Coverage Problem ◽  
Line Segments ◽  
Approximation Factor

Time-varying coverage, namely sweep coverage is a recent development in the area of wireless sensor networks, where a few mobile sensors sweep or monitor a comparatively large number of locations periodically. In this article, we study barrier sweep coverage with mobile sensors where the barrier is considered as a finite length continuous curve on a plane. The coverage at every point on the curve is time-variant. We propose an optimal solution for sweep coverage of a finite length continuous curve. Usually, energy source of a mobile sensor is a battery with limited power, so energy restricted sweep coverage is a challenging problem for long running applications. We propose an energy-restricted sweep coverage problem where every mobile sensor must visit an energy source frequently to recharge or replace its battery. We propose a [Formula: see text]-approximation algorithm for this problem. The proposed algorithm for multiple curves achieves the best possible approximation factor 2 for a special case. We propose a 5-approximation algorithm for the general problem. As an application of the barrier sweep coverage problem for a set of line segments, we formulate a data gathering problem. In this problem a set of mobile sensors is arbitrarily monitoring the line segments one for each. A set of data mules periodically collects the monitoring data from the set of mobile sensors. We prove that finding the minimum number of data mules to collect data periodically from every mobile sensor is NP-hard and propose a 3-approximation algorithm to solve it.

DISTRIBUTED ENERGY-EFFICIENT ALGORITHMS FOR COVERAGE PROBLEM IN ADJUSTABLE SENSING RANGES WIRELESS SENSOR NETWORKS

2009 ◽  
Vol 01 (03) ◽  
pp. 299-317 ◽  
Author(s):  
CHINH VU ◽  
ZHIPENG CAI ◽  
YINGSHU LI
Keyword(s):  
Network Lifetime ◽  
Complexity Analysis ◽  
Boundary Effect ◽  
Area Coverage ◽  
Distributed Scheduling ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Continuous Space ◽  
Algorithm Efficiency ◽  
Wide Range

Due to wide range of applications of Wireless Sensor Network (WSN), lots of effort has been dedicated to solve its various issues. Among those issues, coverage is one of the most fundamental ones of which a WSN has to watch over the environment such as a forest (area coverage) or set of subjects such as collection of precious renaissance paintings (target of point coverage) and collect environment parameters and maybe, further monitor the environment. With variable sensing range, the difficulties to cover a continuous space (where number of points is infinity) in the area coverage problem becomes somewhat harder than covering limited number of discrete points in the target coverage problem. Very few papers have paid effort for the former problem. In this paper, we consider the area coverage problem for WSN where sensors can arbitrarily change their sensing ranges under some upper bound. We first improve the work in [1] so that the boundary effect is ruled out and the monitored area can be completely covered at all cases. Next, we extend that improved algorithm by introducing two distributed scheduling algorithms which are trade-off in term of network lifetime and algorithms efficiency. The major objective of each of our 3 proposed algorithms in this paper is to balance energy consumption and to maximize network lifetime. Our proposed algorithm efficiency is shown by algorithms complexity analysis and extensive simulation. In compared with the work in [1], our proposed algorithms are not only better in providing coverage quality, they could also greatly lengthen network lifetime and greatly reduce the unnecessary coverage redundancy.

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