Deployment and coverage maintenance in mobile sensor networks

Deployment of mobile nodes in a region of interest is a critical issue in building a mobile sensor network because it affects cost and detection capabilities of the system. The deployment of mobile sensors in essence is the movement of sensors from an initial position to a final optimal location. Considerable attention has recently been given to this deployment issue. Many of the distributed deployment schemes use the potential field method. In most cases, the negative gradient of the potential function becomes the feedback control input to a node. This assumes that the potential function is differentiable over the entire region. This assumption is valid primarily when the topology of the network is fixed.; In this research, we analyze the stability of a network that uses piecewise smooth potential functions. A gravitation-like force is proposed to deploy a group of agents and to form a certain configuration. We use a nonsmooth version of the Lyapunov stability theory and LaSalle's invariance principle to show asymptotic stability of the network which is governed by discontinuous dynamics.; We propose a hierarchical structure using potential fields for mobile sensor network deployment. A group of mobile nodes first form a cluster using a potential field method and then cluster heads are used to establish a hexagonal structure that employs a higher level potential field.; We consider specifically the problem of deploying a mobile sensor network so that a certain area coverage is realized and maintained. And we propose an algorithm for maintaining the desired coverage that assumes the availability of a stochastic sensor model. The model reflects the decline of the sensor accuracy as the distance increases from the sensor. It is further assumed that each node's sensor has a different sensing range to represent sensor performance deterioration due to power decay. The network deployment scheme combines artificial forces with individual sensor ranges. The validity and the effectiveness of the proposed algorithm are compared to the conventional methods in simulations. Simulation results confirm the effectiveness of the proposed algorithms with respect to a defined performance metric.