Optimal data collection in wireless sensor networks with correlated energy harvesting

We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate

Modeling the residual energy and lifetime of energy harvesting sensor nodes

and aVailability of Energy, an analytical framework providing closed form expressions for residual energy and lifetime prediction of wireless sensor nodes. SAVE models a wide umbrella of input factors, including channel characteristics, different energy sources and harvesting policies, link layer parameters (e.g., error control and duty cycling) and various data traffic generation models. Our framework uses stochastic semi-Markov models to derive the residual energy distribution for each harvesting node accounting for practically observed temporal variations. We validate the analytical expressions derived by SAVE by means of simulations, and show that SAVE predictions provide a remarkably close match to the simulation results. I