Sensor Network Optimization using Multi-Agent Negotiation (SNOMAN)

Missile defense takes place in an unpredictable, real-time environment and thus requires an adaptive approach to optimization that dynamically allocates sensors and their supporting resources in response to changing goals and constraints. Here, we propose a system for Sensor Network Optimization using Multi-Agent Negotiation (SNOMAN) to meet the challenge of this real-time resource allocation problem. This approach applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. The negotiation mechanism and bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. Negotiation works continuously, providing dynamic adaptation to changes in the mission environment. Negotiation also minimizes communication resource requirements, ensuring that the system scales well to more complex sensor networks.