Dynamic Coverage of Mobile Sensor Networks (1101.0376v1)

Abstract: In this paper we study the dynamic aspects of the coverage of a mobile sensor network resulting from continuous movement of sensors. As sensors move around, initially uncovered locations are likely to be covered at a later time. A larger area is covered as time continues, and intruders that might never be detected in a stationary sensor network can now be detected by moving sensors. However, this improvement in coverage is achieved at the cost that a location is covered only part of the time, alternating between covered and not covered. We characterize area coverage at specific time instants and during time intervals, as well as the time durations that a location is covered and uncovered. We further characterize the time it takes to detect a randomly located intruder. For mobile intruders, we take a game theoretic approach and derive optimal mobility strategies for both sensors and intruders. Our results show that sensor mobility brings about unique dynamic coverage properties not present in a stationary sensor network, and that mobility can be exploited to compensate for the lack of sensors to improve coverage.

• The paper examines how sensor mobility enhances coverage over time in mobile sensor networks (MSNs) compared to static networks.
• Analysis shows dynamic coverage metrics like area and interval coverage improve with mobility, with expected coverage approaching unity.
• Using game theory, the study identifies optimal random directional movement strategies for sensors in intrusion detection scenarios to minimize detection times.

Dynamic Coverage of Mobile Sensor Networks

The paper "Dynamic Coverage of Mobile Sensor Networks" offers a comprehensive examination of dynamic coverage aspects in mobile sensor networks (MSNs) influenced by sensor mobility. The investigation, conducted by Benyuan Liu, Olivier Dousse, Philippe Nain, and Don Towsley, presents both a theoretical and computational framework for understanding the nuances of sensor network coverage when the nodes are in motion, a considerable deviation from the traditional fixed sensor networks.

The core premise of this paper is the exploration of how sensor mobility enhances network coverage over time. In contrast to static networks where coverage is predetermined by initial deployment, MSNs can significantly increase their coverage area as time progresses due to the continuous movement of the sensors. This provides a fundamental advantage in environments that require temporal monitoring, enabling previously uncovered areas to be surveyed without deploying additional sensors.

Coverage Analysis

The paper presents an analytical characterization of two critical coverage metrics: area coverage at a given time and interval coverage. The authors derive expressions for the fraction of an area covered at specific instants and over intervals, demonstrating that sensor mobility can lead to a more extensive cumulative coverage when compared to stationary networks. Such dynamics imply that intruders undetectable in a stationary network can, with time, be identified by mobile sensors. The results indicate that the expected fraction of the area covered approaches unity as time progresses, particularly under straight-line movement patterns.

Intrusion Detection and Game Theory Applications

Intrusion detection is a pivotal application of sensor networks, and the authors make significant contributions by quantifying the detection time for a stationary intruder. The findings reveal that the introduction of mobility reduces detection times when sensor density is limited, hence improving the operational efficacy of the network. The paper further explores more complex scenarios where mobile intruders are accounted for. Employing a game-theoretic approach, the authors determine optimal strategies for both sensor movement and intruder evasion. They conclude that sensors should adopt random directional movements to maximize coverage and minimize detection times, forming a Nash equilibrium.

Implications and Future Directions

The implications of this research are profound, particularly in surveillance, security, and environmental monitoring applications. MSNs are shown to provide robust solutions in situations with constrained sensor resources or challenging deployment terrains. The authors suggest that the optimal balance of sensor speed and mobility can significantly influence detection capabilities, marking a critical consideration for network design.

The paper also paves the way for future exploration into optimizing sensor mobility patterns for specific applications. While straight-line motion offers improved coverage, potential exists in exploring other movement paradigms that might yield similar benefits under different constraints. Moreover, the extension of these principles to three-dimensional spaces could have significant utility in aerial and underwater monitoring systems.

This paper constitutes an essential resource for researchers focused on enhancing sensor network design and deployment. The fusion of stochastic modeling with game theory provides a robust analytical toolset for evaluating and optimizing coverage in dynamic environments, ultimately contributing to the foundational knowledge required to advance the capabilities and application scope of mobile sensor networks.