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Renewable solar energy harvesting systems have received considerable attention as a possible substitute for conventional chemical batteries in sensor networks. However, it is difficult to optimize the use of solar energy based only on empirical power acquisition patterns in sensor networks. We apply acquisition patterns from actual solar energy harvesting systems and build a framework to maximize the utilization of solar energy in general sensor networks. To achieve this goal, we develop a cross-layer optimization-based scheduling scheme called binding optimization of duty cycling and networking through energy tracking (BUCKET), which is formulated in four-stages: 1) prediction of energy harvesting and arriving traffic; 2) internode optimization at the transport and network layers; 3) intranode optimization at the medium access control layer; and 4) flow control of generated communication task sets using a token-bucket algorithm. Monitoring of the structural health of bridges is shown to be a potential application of an energy-harvesting sensor network. The example network deploys five sensor types: 1) temperature; 2) strain gauge; 3) accelerometer; 4) pressure; and 5) humidity. In the simulations, the BUCKET algorithm displays performance enhancements of ~12-15% over those of conventional methods in terms of the average service rate. [ABSTRACT FROM PUBLISHER]

Owing to the increasingly large volume and complexity of captured videos, renewable energy systems based on solar energy are of particular interest in the design of energy harvesting (EH) wireless visual sensor networks (WVSNs). Since additional energy consumption following image capture occurs owing to image processing, mote operation, data transmission, and reception, the capture rate significantly affects the lifetime of a node. To this end, we explore a novel energy-efficient framework for EH-WVSN design by developing an optimal algorithm named capture rate and pervasive network control for multicamera networks where the quality of service is maximized by obtaining optimal values for the capture rate, allocated energy, and transmit power, based on field of view-based networking in the presence of event and power acquisition patterns. Through simulations, we demonstrate the feasibility of EH-WVSNs in terms of energy consumption, energy allocation, and capture rate in a realistic scenario (parking surveillance). [ABSTRACT FROM PUBLISHER]