Cross-Layer Resource Allocation in Energy Harvesting Sensor Networks

Recent advances in the field of wireless communications and data acquisition have enabled us with the unique capability to remotely sense our environment. Data acquisition networks can be used to sense natural as well as human-created phenomena. Several experimental networks are already in place for studying earthquakes, fire, and glacial movements, which are all critical to human existence. As these applications require deployment in remote and hard-to-reach areas, it is to ensure that such networks can operate unattended for long durations. The lack of easy access to a continuous power source in most scenarios and the limited lifetime of batteries have hindered the deployment of such networks. To that end, new and exciting developments in the areas of renewable sources of energy suggest that this is feasible. These renewable sources of energy could be attached to new nodes and would typically provide energy replenishment at a slow rate (compared to the rate at which energy is consumed by a continuous stream of packet transmissions) that could be variable and dependent on the surroundings. For example, self-powered sensors have been developed that rely on harvesting strain and vibration energies from their working environment, as well as other types of energy sources including solar cells. Further, sensor networks could be comprised of highly heterogeneous nodes in which some nodes may have more efficient sources of energy replenishment than others (including some nodes that may have no renewable sources of energy), thus making it imperative that energy efficient mechanisms be put in place to manage these networks. The design and control of networks with the added dimension of renewable energy makes the problem of managing these networks substantially different from their non-replenishment counterparts. For example, in the case of networks