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Item type:Publication, Access status: without files , Perpetual data collection with energy-harvesting sensor networks(2014-08-08); ; ; 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.Publicationtype: Journal Article33 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Online energy assessment with supercapacitors and energy harvesters(2014-03); ; Combining energy harvesting with energy-aware load adaptation and scheduling enables perpetually operating sensor networks. The practical realization of this goal yet requires methods for reliable and precise holistic online energy assessment. While the building blocks - assessing residual energy, predicting energy intake, and tracing energy consumption - have been studied in detail, the analysis of their interaction on a real platform has been neglected. This paper answers the question, whether these techniques can be easily joined to give a precise and correct picture of a sensor node's energetic state and behavior. For this purpose, we model the energy flow of a prototype energy-harvesting and supercapacitor-powered sensor node. We show that in a real deployment simple supercapacitor models suffice for energy assessment, while capacity calibration is mandatory yet practicable. We evaluate the joint performance of state-of-the-art energy assessment based on an outdoor field test. We verify the system model and show the feasibility of holistic online energy assessment, which tolerates small configuration errors, achievable with a combination of generic configuration and online calibration. We analyze the feasibility of forecasting a node's future energy reserve and find that the presented method produces accurate results for uniformly distributed consumption profiles.Publicationtype: Journal Article21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Aggregating sensor data from overlapping multi-hop network neighborhoods: Push or pull?(2008-10-08); Network neighborhoods are a key communication abstraction in sensor networks, allowing sensor nodes to collect and aggregate sensor data from nearby other nodes. In many applications, multi-hop neighborhoods of several nodes overlap, such that nodes participate in many neighborhoods, having to contribute their data items to all containing neighborhoods. We consider two orthogonal approaches to efficiently support this data aggregation problem. A push-based approach, where each node floods its data item in a multi-hop neighborhood, and a pull-based approach, where each node collects data from nodes in a multi-hop network neighborhood using a spanning tree. Our goal is to identify situations where one approach outperforms the other. For this, we implement these protocols in TOSSIM, study overhead and yield as a function of the fraction of nodes in the network that perform data aggregation over a multi-hop neighborhood, and report our findings.Publicationtype: Conference Paper34