Abstract. The accuracy of link-quality estimators (LQE) is missioncritical in many application scenarios in wireless sensor networks (WSN), since the link-quality metric is used for routing decisions or neighborhood formation. Link-quality estimation must offer validity for different timescales. Existing LQEs describe and approximate the current quality in a single value only. This method leads to a limited accuracy and expressiveness about the presumed future behavior of a link. The LQE developed in this paper incorporates four quality metrics that give a holistic assessment of the link and its dynamic behavior; therefore, this research is an important step to achieving a higher prediction accuracy including knowledge about the short-and long-term behavior.

A sustainable, uniform, and utility-maximizing operation of energy-harvesting sensor networks requires methods for aligning consumption with harvest. This article presents a lightweight algorithm for online load adaptation of energy-harvesting sensor nodes using supercapacitors as energy buffers. The algorithm capitalizes on the elementary relationship between state of charge and voltage that is characteristic for supercapacitors. It is particularly designed to handle the nonlinear system model, and it is lightweight enough to run on low-power sensor node hardware. We define two energy policies, evaluate their performance using real-world solar-harvesting traces, and analyze the influence of the supercapacitor’s capacity and imprecisions in harvest forecasts. To show the practical merit of our algorithm, we devise a load adaptation scheme for multihop data collection sensor networks and run a 4-week field test. The results show that (i) choosing a duty cycle a priori is infeasible, (ii) our algorithm increases the achievable work load of a node when using forecasts, (iii) uniform and steady operation is achieved, and (iv) depletion can be prevented in most cases.

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