Self-organization of sensor networks for detection of pervasive faults

Resource aware operation of sensor networks requires adaptive re-organization to dynamically adapt to the operational environment. A complex dynamical system of interacting components (e.g., computer network and social network) is represented as a graph, component states as spins, and interactions as ferromagnetic couplings. Using an Isinglike model, the sensor network is shown to adaptively self-organize based on partial observation, and real-time monitoring and detection is enabled by adaptive redistribution of limited resources. The algorithm is validated on a test-bed that simulates the operations of a sensor network for detection of percolating faults (e.g. computer viruses, infectious disease, chemical weapons, and pollution) in an interacting multi-component complex system.