In a facility location problem (FLP) we are given a set of facilities and a set of clients, each of which is to be served by one facility. The goal is to decide which subset of facilities to open, such that the clients will be served at a minimal cost. In this paper we investigate the FLP in a setting where the cost depends on data known only to the clients. This setting typifies modern distributed systems: peer-to-peer file sharing networks, Grid systems, and wireless sensor networks. All of them need to perform network organization, data placement, collective power management, and other tasks of this kind. We propose a local and efficient algorithm that solves FLP in these settings. The algorithm presented here is extremely scalable, entirely decentralized, requires no routing capa-

Abstract. In this paper we address a well-known facility location problem (FLP) in a sensor network environment. The problem deals with finding the optimal way to provide service to a (possibly) very large number of clients. We show that a variation of the problem can be solved using a local algorithm. Local algorithms are extremely useful in a sensor network scenario. This is because they allow the communication range of the sensor to be restricted to the minimum, they can operate in routerless networks, and they allow complex problems to be solved on the basis of very little information, gathered from nearby sensors. The local facility location algorithm we describe is entirely asynchronous, seamlessly supports failures and changes in the data during calculation, poses modest memory and computational requirements, and can provide an anytime solution which is guaranteed to converge to the exact same one that would be computed by a centralized algorithm given the entire data.