Mobility improves coverage of sensor networks (original) (raw)

On the Self-Organization of Mobile Agents to Ensure Dynamic Multi-level Coverage in Sensor Networks

A critical issue for the k-coverage problem in wireless sensor networks is how efficiently deploying sensors to cover an area of interest. In many critical scenarios such as in the military field, ensuring that each point in the monitored area of interest is sufficiently covered can guaranty the effectiveness of intrusion detection for both monitoring and tracking applications. Prior research indicated that Mobile Sensor Networks (MSNs) are capable of acting with great flexibility to enhance and cover holes appeared in certain regions when a sensor died due to limited energy and battery lifetime. In this paper, we consider the use of a strategy based on the collective motion mechanisms to relocate sensors nodes to achieve a higher k-coverage level. Each sensor node is able to compare its current k-coverage level with a predefined threshold so as to react dynamically by enabling a specific mobility behavior with a high priority. Based on this mobility behavior, a sensor node can move towards other sensors in its local neighborhood, and it would then be closer enough to them in order to enhance its k-coverage level and then it participates in achieving a higher k-coverage level for the whole group. Simulation results show the effectiveness of our considered approach in terms of the k-coverage level of 30 % as well as a significant improvement in energy consumption.

Improving Target Coverage and Network Connectivity of Mobile Sensor Networks

2015

Mobile sensor network is the collection of independent and scattered sensors with capacity of mobility. In recent years, Mobility of Sensors has been utilized to improve the target coverage quality and network connectivity in randomly deployed mobile sensor networks. Target coverage and Network connectivity are two main challenging issues of mobile sensor networks. Target coverage covers a set of specified points of interest in the randomly deployed MSNs. Target coverage is usually interpreted as how well a sensor network will cover an area of interest. Network Connectivity is defined as the ability of the sensor nodes to collect data and report data to the sink node. Target Coverage and Network Connectivity may also affect the quality of network. In this paper, for target coverage, two algorithms i.e. basic algorithm based on clique partitions and TV Greedy algorithm based on voronoi diagrams of target are proposed. For network Connectivity, an optimal solution based on Steiner tre...

Coverage planning of wireless sensors for mobile target detection

2008

We consider surveillance applications through wireless sensor networks (WSNs) with fully accessible areas to be monitored. In this context, the WSN topology can be planned a priori to maximize application efficiency. We propose an optimization framework for selecting the positions of wireless sensors to detect mobile targets traversing a given area. By leveraging the concept of exposure as a measure of coverage quality, we propose two problem versions: the minimization of the sensors installation cost while guaranteeing ...

Exploiting Mobility for Efficient Coverage in Sparse Wireless Sensor Networks

Wireless Personal Communications, 2010

Reliable monitoring of a large area with a Wireless Sensor Network (WSN) typically requires a very large number of stationary nodes, implying a prohibitive cost and excessive (radio) interference. Our objective is to develop an efficient system that will employ a smaller number of stationary nodes that will collaborate with a small set of mobile nodes in order to improve the area coverage. The main strength of this collaborative architecture stems from the ability of the mobile sensors to sample areas not covered (monitored) by stationary sensors. An important element of the proposed system is the ability of each mobile node to autonomously decide its path based on local information (i.e. a combination of self collected measurements and information gathered by stationary sensors in the mobile’s communication range), which is essential in the context of large, distributed WSNs. The contribution of the paper is the development of a simple distributed algorithm that allows mobile nodes to autonomously navigate through the field and improve the area coverage. We present simulation results based on a real sparse stationary WSN deployment for the coverage improvement scenario.

DPRMM: A novel coverage-invariant mobility model for wireless sensor networks

2008 IEEE Global Telecommunications Conference, GLOBECOM 2008, 2008

Existing mobility models for wireless sensor networks generally do not preserve a uniform scattering of the sensor nodes within the monitored area. This paper proposes a coverage-preserving random mobility model called DPRMM. Direction and velocity are randomly chosen according to the local information about sensor density. Our mobility model is devised in a manner that sensors move towards the least covered regions within their neighborhood. We show that this guarantees a rapid convergence to the steady state while preserving a uniform coverage degree on the monitored region. Throughout the simulations we have carried out, we find that the analytical study is corroborated by practical experiments. Our experiments show that the average distance made by a target without being detected is approximately enhanced at least by a factor of 2 using DPRMM. http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4697820

Wireless sensor network redeployment under the target coverage constraint

2011

A critical problem of wireless sensor networks is the efficient handling of the nodes energy under the target coverage constraint. The sensors are randomly deployed in the field covering a set of targets. Due to the randomness of the deployment some targets are covered by a few only sensors. Thus, the maximum achieved network lifetime is upper bounded by the energy of the sensors that cover the most poorly covered target. To tackle this problem one could move some sensors to these poorly covered areas from areas that are covered by many sensors. In this paper we present centralised and localised solutions that can be used to redeploy the sensing nodes and balance the amount of energy between the sensors that cover each target. We simulate our algorithms and our findings show an over 100% increase of the amount of energy of the sensors that cover the most poorly covered target in the network.

Coverage in Wireless Sensor Networks

Computer Communications and Networks, 2009

Ad-hoc networks of devices and sensors with (limited) sensing and wireless communication capabilities are becoming increasingly available for commercial and military applications. The first step in deploying these wireless sensor networks is to determine, with respect to application-specific performance criteria, (i) in the case that the sensors are static, where to deploy or activate them; and (ii) in the case that (a subset of) the sensors are mobile, how to plan the trajectory of the mobile sensors. These two cases are collectively termed as the coverage problem in wireless sensor networks. In this book chapter, we give a comprehensive treatment of the coverage problem. Specifically, we first introduce several fundamental properties of coverage that have been derived in the literature and the corresponding algorithms that will realize these properties. While giving insights on how optimal operations can be devised, most of the properties are derived (and hence their corresponding algorithms are constructed) under the perfect disk assumption. Hence, we consider in the second part of the book chapter coverage in a more realistic setting, and allow (i) the sensing area of a sensor to be anisotropic and of arbitrary shape, depending on the terrain and the meteorological conditions, and (ii) the utilities of coverage in different parts of the monitoring area to be non-uniform, in order to account for the impact of a threat on the population, or the likelihood of a threat taking place at certain locations. Finally, in the third part of the book chapter, we consider mobile sensor coverage, and study how mobile sensors may navigate in a deployment area in order to maximize threat-based coverage.

Distributed Coverage Games for Energy-Aware Mobile Sensor Networks

2013

Inspired by current challenges in data-intensive and energy-limited sensor networks, we formulate a coverage optimization problem for mobile sensors as a (constrained) repeated multiplayer game. Each sensor tries to optimize its own coverage while minimizing the processing/energy cost. The sensors are subject to the informational restriction that the environmental distribution function is unknown a priori.

Uniform coverage control of mobile sensor networks for dynamic target detection

2010

In surveillance problems, the uncertainty in the position of a target can be specified in terms of a probability distribution. To reduce the average search times to detect a target using mobile sensors, it is desirable to have the trajectories of the sensors sample the probability distribution uniformly. When the target is moving, the initial uncertainty in the position of the target evolves forward in time according to the target dynamics. We assume a model for the dynamics of the target and build upon our previous work for stationary targets to define appropriate metrics for uniform coverage of the evolving probability distribution. Using these metrics, we derive centralized feedback control laws for the motion of the sensors so that they achieve uniform coverage of the moving target distribution. We demonstrate the performance of the algorithm on various scenarios.