| Recent development in sensor platforms enables sensors to harvest various forms of renewable energy from the environment, such as solar, wind, thermal, and vibration. Although this provides sensors extended lifetime, time varying and dissimilar recharging rates of sensor nodes pose new challenges. To ensure no sensor ever runs out of energy, routing paths and data collection rates must adapt to the recharging capabilities of the sensors. Prior works on routing and data collection in sensor networks have focused on static resources. Thus, they can not be applied here. The goal of this dissertation is to design techniques and protocols that enable everlasting operation for rechargeable sensor networks.;In this dissertation, we first propose an adaptive data collection framework that optimizes the network utility by computing a proportionally fair rate assignment under the presence of renewable energy resources. The framework consists of three algorithms. The first algorithm, called QuickFix, allows the sensor nodes to quickly adapt their sampling rates and routing paths according to their short term average replenishment rates within an epoch when the underlying routing structure is a given DAG. To handle variations in recharging rates within an epoch, a localized energy management algorithm, called SnapIt, is adopted. The SnapIt algorithm dynamically adjusts the sampling rates computed by the QuickFix algorithm so that sensor's battery can be maintained at a target level, which ensures the perpetual operation of the network. To further handle the cases in which the underlying DAG routing structure is unknown, we propose a heuristic algorithm that can construct an approximately load-balanced DAG and dynamically configure the DAG to achieve high network utility.;Since maxmin fairness is also widely used in the literature and requires a different solution approach, we further explore the problem of computing a maxmin fair rate assignment and the associated routing paths in rechargeable sensor networks. We first show that constructing a data collection tree with the highest optimal maxmin fair rate assignment is NP-hard. Then, we propose a fast distributed algorithm that can jointly compute a near optimal maxmin rate assignment and the associated routing paths when the detailed recharging profile is known. We conduct large scale simulations with real solar radiation measurements from the National Renewable Energy Laboratory. The results show that our algorithm is close to the optimum.;Besides data collection, data dissemination is also an important feature of sensor networks as software may require an update to address unforeseen challenges imposed by the environment or the introduction of new user requirements. However, data dissemination can be a difficult task in perpetual sensor networks because the battery levels of sensor nodes could be low after operating for a long period of time and sensor's recharging capability is extremely dynamic. Since none of the existing data dissemination protocols consider sensors' recharging capability, they can either fail to deliver the data reliably or incur a high latency. To address this problem, we propose a new data dissemination protocol that is aware of the latency incurred by both recharging and interference. Using extensive simulations, we show that the proposed protocol can achieve lower latency when the recharging rates of the sensors are low. |
