Abstract :
Distributed inference (e.g., detection, estimation, learning, etc.) is one of the primary applications of wireless sensor networks. This paper presents an overview of recent results by the author and co-workers in this area. The focus is on results in distributed learning and sensor scheduling, but some issues relating to energy efficiency are also discussed briefly
Keywords :
inference mechanisms; learning (artificial intelligence); scheduling; telecommunication computing; wireless sensor networks; distributed inference; distributed learning; energy efficiency; sensor scheduling; wireless sensor networks; Acoustic sensors; Approximation algorithms; Collaborative work; Energy efficiency; Inference algorithms; Intelligent networks; Iterative algorithms; Sensor fusion; Sensor phenomena and characterization; Wireless sensor networks;
