Distributed estimation in energy harvesting wireless sensor networks

In this chapter, distributed estimation is examined for energy-harvesting wireless sensor networks (WSNs), where the energy available at the sensors is converted entirely from ambient sources. In this application, each sensor takes a local measurement of the common parameter of interest and forwards it to the fusion center, where the final estimate is performed. Due to the randomness of the energy arrival, the transmission energy and status of the energy-harvesting sensors are unknown and, thus, the final maximum likelihood estimate at the fusion center can be computed using the expectation-maximization (EM) algorithm. Furthermore, by taking into consideration the spatial heterogeneity of the energy arrival, the sensor deployment problem is also examined for the purpose of reconstructing the entire random field. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.