Environmental Energy Management in Sensor Networks

Energy is a major constraint in the design of embedded sensor networks. The energy supplied by batteries with acceptable form factor may not be sufficient for several applications. Extracting energy from the environment is a feasible alternative in many practical scenarios. We present methods to make the most of the available energy in a distributed system and to operate reliably from highly variable energy sources. We also demonstrate the first version of our hardware which is compatible with several existing sensor nodes for enabling energy harvesting

Environmental Energy Harvesting in Wireless Sensor Networks

The possible applications of wireless sensor networks that make use of today’s latest technology in the areas both of communications and embedded systems are growing as fast as the technology itself, as new ways of incorporating sensors in the environmen

Performance aware tasking for environmentally powered sensor networks

The use of environmental energy is now emerging as a feasible energy source for embedded and wireless computing systems such as sensor networks where manual recharging or replacement of batteries is not practical. However, energy supply from environmental sources is highly variable with time. Further, for a distributed system, the energy available at its various locations will be different. These variations strongly influence the way in which environmental energy is used. We present a harvesting theory for determining performance in such systems. First we present a model for characterizing environmental sources. Second, we state and prove two harvesting theorems that help determine the sustainable performance level from a particular source. This theory leads to practical techniques for scheduling processes in energy harvesting systems. Third, we present our implementation of a real embedded system that runs on solar energy and uses our harvesting techniques. The system adjusts its performance level in response to available resources. Fourth, we propose a localized algorithm for increasing the performance of a distributed system by adapting the process scheduling to the spatio-temporal characteristics of the environmental energy in the distributed system. While our theoretical intuition is based on certain abstractions, all the scheduling methods we present are motivated solely from the experimental behavior and resource constraints of practical sensor networking systems