This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Adequate coverage is very important for sensor networks to fulfill the issued sensing tasks. In many working environments, it is necessary to make use of mobile sensors, which can move to the correct places to provide the required coverage. In this paper, we study the problem of placing mobile sensors to get high coverage. Based on Voronoi diagrams, we design two sets of distributed protocols for controlling the movement of sensors, one favoring communication and one favoring movement. In each set of protocols, we use Voronoi diagrams to detect coverage holes and use one of three algorithms to calculate the target locations of sensors if holes exist. Simulation results show the effectiveness of our protocols and give insight on choosing protocols and calculation algorithms under different application requirements and working conditions.

The effectiveness of cluster-based distributed sensor networks depends to a large extent on the coverage provided by the sensor deployment. We propose a virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. For a given number of sensors, the VFA algorithm attempts to maximize the sensor field coverage. A judicious combination of attractive and repulsive forces is used to determine virtual motion paths and the rate of movement for the randomly-placed sensors. Once the effective sensor positions are identified, a one-time movement with energy consideration incorporated is carried out, i.e., the sensors are redeployed to these positions. We also propose a novel probabilistic target localization algorithm that is executed by the cluster head. The localization results are used by the cluster head to query only a few sensors (out of those that report the presence of a target) for more detailed information. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

We introduce a framework, called "physicomimetics," that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an effective basis for self-organization, fault-tolerance, and self-repair. Three primary factors distinguish our framework from others that are related: an emphasis on minimality (e.g., cost effectiveness of large numbers of agents implies a need for expendable platforms with few sensors), ease of implementation, and run-time efficiency. Examples are shown of how this framework has been applied to construct various regular geometric lattice configurations (distributed sensing grids), as well as dynamic behavior for perimeter defense and surveillance. Analyses are provided that facilitate system understanding and predictability, including both qualitative and quantitative analyses of potential energy and a system phase transition. Physicomimetics has been implemented both in simulation and on a team of seven mobile robots. Specifics of the robotic embodiment are presented in the paper.

We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) is needed to cover the environment. We present an e#cient minimalist algorithm which assumes that global information is not available (neither a map, nor GPS). Our algorithm deploys a network of radio beacons which assists the robot(s) in coverage. This network is also used for navigation. The deployed network can also be used for applications other than coverage. Simulation experiments are presented which show the collaboration between the deployed network and mobile robot(s) for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot(s) recovery (homing behavior). We present a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.

We present a resource-bounded optimization framework for sensor resource management under the constraints of sufficient grid coverage of the sensor field. We offer a unique "minimalistic" view of distributed sensor networks in which sensors transmit/report a minimum amount of sensed data. The proposed theory is aimed at optimizing the number of sensors and determine their placement to support such minimalistic sensor networks. We represent the sensor field as a grid (two- or three-dimensional) of points. The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections. The proposed algorithm addresses coverage optimization under constraints of imprecise detections and terrain properties. The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled. Experimental results for an example sensor field with obstacles demonstrate the application of our approach.

Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The sensor coverage problem has received increased attention recently, being considerably driven by recent advances in affordable and efficient integrated electronic devices. This problem is centered around a fundamental question: How well do the sensors observe the physical space? The coverage concept is subject to a wide range of interpretations due to a variety of sensors and their applications. Different coverage formulations have been proposed, based on the subject to be covered (area versus discrete points) and sensor deployment mechanism (random versus deterministic) as well as on other wireless sensor network properties (e.g. network connectivity and minimum energy consumption). In this article, we survey recent contributions addressing energy-efficient coverage problems in the context of static wireless sensor networks. We present various coverage formulations, their assumptions, as well as an overview of the solutions proposed.

Introduction Wireless sensor networks (WSN) [1] are an increasingly attractive means to monitor environmental conditions and to bridge the gap between the physical and the virtual world. A WSN consists of large numbers of cooperating small-scale nodes capable of limited computation, wireless communication, and sensing. By correlating sensor output of multiple nodes, the WSN as a whole can provide functionality that an individual node cannot. Application areas for WSNs include geophysical monitoring (seismic activity) , precision agriculture (soil management), habitat monitoring (tracking of animal herds), transportation (traffic monitoring), military systems, business processes (supply chain management), and in the future, possibly cooperating smart everyday things. The basic mode of operation of WSNs is significantly different from traditional computer networks, due to their tight integration with the physical world. Additionally, sensor networks have some unique characteristics tha

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.

Abstract — Recently there has been a great deal of research on using mobility in sensor networks to assist in the initial deployment of nodes. Mobile sensors are useful in this environment because they can move to locations that meet sensing coverage requirements. This paper explores the motion capability to relocate sensors to deal with sensor failure or respond to new events. We define the problem of sensor relocation and propose a two-phase sensor relocation solution: redundant sensors are first identified and then relocated to the target location. We propose a Grid-Quorum solution to quickly locate the closest redundant sensor with low message complexity, and propose to use cascaded movement to relocate the redundant sensor in a timely, efficient and balanced way. Simulation results verify that the proposed solution outperforms others in terms of relocation time, total energy consumption, and minimum remaining energy. I.