Clustering Sensors in Wireless Ad Hoc Networks Operating in a Threat Environment

Dipesh J. Patel, Rajan Batta, Rakesh Nagi
2005 Operations Research  
Sensors in a data fusion environment over hostile territory are geographically dispersed and change location with time. In order to collect and process data from these sensors an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the
more » ... pected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed. Fusion of data from multiple sensors (e.g., radars on the ground and sonars under water) facilitates a better judgment of the scenario since we get multiple inputs to verify the authenticity of the information -see, e.g., Hall and Llinas (2001). The scenario that we are referring to is a dynamic battlefield in which enemy movement is occurring and targets are being tracked. Data from multiple sensors are being fused so as to yield better estimates for target tracks. In such a circumstance both the sensors and the fusion bed (e.g., an Airborne Warning and Control System (AWACS)) are mobile. Such a network has to rely on wireless transfer of information in an ad hoc network. The focus of our work is to provide a robust architecture for performing fusion in a hostile environment, where communication links are prone to enemy attack. We assume that sensor movement patterns are known. Our goal is to find the best locations for clusterheads (fusion nodes), for each time period over the planning horizon. We tradeoff the need to provide maximal expected coverage in a hostile environment versus the cost of frequent relocation of clusterheads. We utilize a dynamic version of a maximal expected covering location model (MEXCLP) due to Daskin (1983) to optimize the reallocation of clusterhead assignments between sensors over a given time horizon. It is important to emphasize that ours is a strategic model which will help plan a robust communication scheme over a mission duration. It does not deal with detailed communication issues, e.g., assignment of specific channels to a particular communication link. Instead it focuses on the development of a plan to strategically relocate clusterhead locations so as to maximize the overall efficiency of the communication system -which includes maximizing the expected coverage of sensors and minimizing the overhead cost of changing clusterhead locations. The model and solution methodology developed in this paper is being used in the development of a testbed for tracking, fusion and networking in a military setting (Frontline 2004). Literature Review An ad hoc network is a self-organizing multi-hop wireless network, which relies neither on fixed infrastructure nor on predetermined connectivity. All the entities in an ad hoc network can be mobile. The communication between network components is carried over a wireless medium and the network topology changes depending on the node mobility. The main advantage of such networks is that they can be rapidly deployed and therefore applications of these are in situations which either lack fixed infrastructure
doi:10.1287/opre.1040.0171 fatcat:gijk67o5sjgybfqlyo2znz7r5q