This paper studies mobility strategies to control positions of mobile robots in a mobile sensor network in order to maximize lifetime of the network. With communication and mobility energy costs modeled, the problem is formulated as a nonlinear non-convex optimization problem, then reformulated into a convex optimization problem. The separable property of the system is then exploited by Lagrangian duality, and the solution is obtained by distributed saddle-point computations. Computer simulations showed that the proposed distributed algorithm can quickly converge to the optimal solution, and it also justifies the use of mobility for energy efficiency by showing its significant improvement to the network lifetime and relatively low cost in mobility. Furthermore, the proposed energy optimization framework can accommodate different mobile sensor network models.
This paper investigates mobility strategies of mobile robots to improve the lifetime of a mobile sensor network with energy harvesting capability. The network lifetime problem is formulated as a nonlinear non-convex optimization problem, which is solved distributively by a series of convex approximations and a novel saddle-point computation algorithm. The convergence of the proposed method is guaranteed. Computer simulations showed quick convergence to the optimal solution in most cases, and verified the use of mobility for energy efficiency by showing its significant improvement to the network lifetime and relatively low cost in mobility.
This paper studies optimal control in a sensor network system consisting of mobile robots to minimize the overall energy consumption of the whole network. With communication energy cost and mobility energy cost taken into consideration, the problem is formulated as an optimal control of a hybrid system, which is solved by switched Linear Quadratic Regulator (LQR). Though switched LQR obtains globally optimal solution, the computational complexity is too high to implement the algorithm when the control horizon expands. For this reason, we resort to a switched system version of Receding Horizon Control (RHC), which is stable and provides a solution close to the optimal one. Finally, in order to attenuate the complexity due to the large networked system, the centralized RHC is modified into a distributed algorithm, which converges to a solution that can approximate the optimizer quite well as verified by simulations.Index Terms-Mobile ad hoc network, energy optimization, optimal control, hybrid control systems, distributed algorithm.
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