Agile Sensor Networks: Adaptive Coverage via Mobile Nodes (original) (raw)

Mobile Sensor Coverage

2010

Abstract—Mobile sensors can be used to effect complete coverage of a surveillance area for a given threat over time, thereby reducing the number of sensors necessary. The surveillance area may have a given threat profile as determined by the kind of threat, and accompanying meteorological, environmental, and human factors. In planning the movement of sensors, areas that are deemed higher threat should receive proportionately higher coverage. We propose a coverage algorithm for mobile sensors to achieve a coverage that will match – over the long term and as quantified by an RMSE metric – a given threat profile. Moreover, the algorithm has the following desirable properties: (1) stochastic, so that it is robust to contingencies and makes it hard for an adversary to anticipate the sensor’s movement; (2) efficient; and (3) practical, by avoiding movement over inaccessible areas. Further to matching, we argue that a fairness measure of performance over the shorter time scale is also impo...

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.

Deploying sensors for maximum coverage in sensor networks

2007

Abstract Sensing coverage is an important issue in wireless mobile sensor networks. The strategy of how to deploy sensor nodes in an environment, especially in unknown large environment, will affect the utility of the network just like the quality of communication. We present an efficient method for sensor deployment assuming that global information is not available. Our algorithm (Self-Deployment by Density Control, SDDC), uses density control by each node to deploy sensor nodes concurrently.

Coverage Strategies in Wireless Sensor Networks

International Journal of Distributed Sensor Networks, 2006

An energy efficient cover of a region using Wireless Sensor Networks (WSNs) is addressed in this paper. Sensor nodes in a WSN are characterized by limited power and computational capabilities, and are expected to function for extended periods of time with minimal human intervention. The life span of such networks depends on the efficient use of the available power for sensing and communication. In this paper, the coverage problem in a three dimensional space is rigorously analyzed and the minimum number of sensor nodes and their placement for complete coverage is determined. Also, given a random distribution of sensor nodes, the problem of selecting a minimum subset of sensor nodes for complete coverage is addressed. A computationally efficient algorithm is developed and implemented in a distributed fashion.

Collaborative Area Monitoring Using Wireless Sensor Networks with Stationary and Mobile Nodes

Eurasip Journal on Advances in Signal Processing, 2009

Monitoring a large area with stationary sensor networks requires a very large number of nodes which with current technology implies a prohibitive cost. The motivation of this work is to develop an architecture where a set of mobile sensors will collaborate with the stationary sensors in order to reliably detect and locate an event. The main idea of this collaborative architecture is that the mobile sensors should sample the areas that are least covered (monitored) by the stationary sensors. Furthermore, when stationary sensors have a "suspicion" that an event may have occurred, they report it to a mobile sensor that can move closer to the suspected area and can confirm whether the event has occurred or not. An important component of the proposed architecture is that the mobile nodes autonomously decide their path based on local information (their own beliefs and measurements as well as information collected from the stationary sensors in a neighborhood around them). We believe that this approach is appropriate in the context of wireless sensor networks since it is not feasible to have an accurate global view of the state of the environment.

Mobility improves coverage of sensor networks

Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '05, 2005

Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.

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.

Dynamic Coverage in Ad-Hoc Sensor Networks

Mobile Networks and Applications, 2000

Ad-hoc networks of sensor nodes are in general semi-permanently deployed. However, the topology of such networks continuously changes over time, due to the power of some sensors wearing out, to new sensors being inserted into the network, or even due to designers moving sensors around during a network re-design phase (for example, in response to a change in the requirements of the network).

The Sentinel Algorithm: Distributed Dynamic Coverage

Sensor networks are predicted to revolutionize the world as they draw us increasingly closer to the Holy Grail of ubiquitous computing. However, sensor networks also introduce new challenges. In our project we will focus on addressing the fundamental issue of coverage. The coverage problem deals with how well a target region is monitored or tracked by sensors. To date work in this field has focused on algorithms for node deployment to statically cover an area. We introduce the concept of dynamic coverage, based on the ideas of exploration and achieving a blanket coverage over time. We then present the Sentinel algorithm for dynamic coverage, which allows a limited number of nodes to efficiently explore and provide coverage of an unknown environment.