Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

Abstract

This article presents an algorithm for adaptive sensor activity scheduling (A-SAS) in distributed sensor networks to enable detection and dynamic footprint tracking of spatial-temporal events. The sensor network is modeled as a Markov random field on a graph, where concepts of Statistical Mechanics are employed to stochastically activate the sensor nodes. Using an Ising-like formulation, the sleep and wake modes of a sensor node are modeled as spins with ferromagnetic neighborhood interactions; and clique potentials are defined to characterize the node behavior. Individual sensor nodes are designed to make local probabilistic decisions based on the most recently sensed parameters and the expected behavior of their neighbors. These local decisions evolve to globally meaningful ensemble behaviors of the sensor network to adaptively organize for event detection and tracking. The proposed algorithm naturally leads to a distributed implementation without the need for a centralized control. The A-SAS algorithm has been validated for resource-aware target tracking on a simulated sensor field of 600 nodes.

Statistical Mechanics-Inspired Modeling of Heterogeneous Packet Transmission in Communication Networks

“…The behavior and topological organization of communication networks [6] and sensor networks [7] have similar characteristics as many natural and human-engineered systems, such as those found in the disciplines of sociology [8], biology [9], and finance [10]. Phase transition is a characteristic phenomenon of complex systems, consisting of interacting and interdependent dynamics, where a nonsmooth change in the output behavior may take place with a relatively small variation of the system parameter(s).…”

Adaptive Scheduling in Wireless Sensor Networks Based on Potts Model

“…The idea of using Potts model in wireless sensor networks was expressed in an algorithm called A-SAS [2]. This algorithm is a distributed algorithm for wireless sensor networks to be able to detect and track rare and random events.…”

“…Many researches were done due to wireless sensor Networks's challenge and energy saving of these Networks, particularly in application of target tracking and various algorithms has been designed. One of these algorithms is an algorithm of adaptive sensor activity scheduling (A-SAS) algorithm [2] in distributed sensor networks, where the sensor network is modeled as a Markov random field on a graph. The sleep and wake modes of a sensor node are modeled as spins with ferromagnetic neighborhood interactions in the Statistical Mechanics setting.…”

Distributed network control for mobile multi-modal wireless sensor networks

“…The transmission power of a single node is 0.3 W and the reception power is 0.2 W. We recall that a sensor broadcasts its observed subpattern only if the stimulus created by the target is above a certain threshold. To save more energy, we consider a sleep-and-awake schedule for the sensor nodes, A-SAS, proposed by [26]. For a 1500 second-simulation, around 50% of the nodes are awake at any moment.…”