Optimal Routing in Nanosensor Networks by a game theory approach

In this paper, we investigate a moving-Optimum Routing problem with nanosensor networks. Each nanosensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is “smart” enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the Optimum Routing problem more difficult. In this paper, a “Game Theory” approach for finding an optimum routing in a “Wireless Nanosensor Network” is discussed. WSN is one of the most interesting research fields in the communication networks, and is the center of attention in recent years. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. In our model, a pricing and payment technique is presented to obtain an optimum routing in a WSN by considering reliability, energy and traffic load. The proposed algorithm is able to find a path which improves network lifetime, load distribution and path reliability. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the Optimum Routing algorithm proposed in this paper provides a satisfactory result.