Cooperative Control And Sensing For Mobile Sensor Networks PDF Download

Abstract: In this dissertation, we focus on some cooperative system problems by applying control and optimization approaches. We identify key cooperative system design and operational control problems and present solution approaches which include deployment, routing, coverage control, and power control. In the case of a static sensor network where nodes are incapable of moving, we concentrate on the problem of minimum-power node deployment, which is crucial, for example, in extending the lifetime of a wireless sensor network with limited energy capacity. To avoid combinatorial complexity that is common to current approaches, we put forward an incremental self-deployment algorithm to approximately solve this problem. In the case of mobile nodes, a cooperative system is called upon to perform a "mission". We present solution approaches to two types of missions, both involving stochastic mission spaces and cooperative control: reward maximization missions, and coverage control missions. In the reward maximization mission, we consider a setting where multiple vehicles form a team cooperating to visit multiple target points and collect rewards associated with them. The team objective is to maximize the total reward accumulated over a given time interval. We present a Cooperative Receding Horizon (CRH) control scheme that dynamically determines vehicle trajectories by solving a sequence of optimization problems over a planning horizon and executing them over a shorter action horizon. We subsequently develop a distributed cooperative controller which does not require a vehicle to maintain perfect information on the entire team and whose computational cost is scalable and significantly lower than the centralized case, malting it attractive for applications with limited computation capacity. In the coverage control mission, the mission space is modeled using a density function representing the frequency of random events taking place, with mobile sensors operating over a limited range defined by a probabilistic model. A gradient-based algorithm is designed requiring local information at each sensor and maximizing the joint detection probabilities of random events. The solution also incorporates communication costs into the coverage control problem. To demonstrate the effectiveness of the proposed approaches, we have designed and developed a Small Robot Testbed in a laboratory setting, which offers an integrated environment that enables multiple nodes with onboard computation, sensing and wireless communication capabilities to form a cooperative system.