Searching strategy for multi-target discovery in wireless networks

In this paper, we address a fundamental problem concerning the optimal searching strategy in terms of searching cost for the multi-target discovery problem in wireless networks. In order to find the nearest k targets from a total of m members with the least cost , how many searching attempts should we use, and how large should each searching area be? After providing the applications that motivate our research, we model the problem and derive a general formula for the expected cost as a function of the parameters of the number of searching attempts n and the searching area for each attempt, A_i. Based on this formula, we propose several algorithms to determine the optimal parameters to achieve the minimal cost, either pre-calculated or performed online. Using the optimal parameters derived from analysis, we experiment with these algorithms on general wireless network scenarios. The results show that our algorithms perform consistently close to optimal, and they exhibit much better performance than other heuristic schemes. The desired performance is achieved by adapting the searching radius to estimates of network parameters such as the total number of nodes and the total number of targets.