Prediction and management in energy harvested wireless sensor nodes

Piorno, Joaquín Recas; Bergonzini, Carlo; Atienza, David; Rosing, Tajana

doi:10.1109/wirelessvitae.2009.5172412

Cited by 112 publications

(30 citation statements)

References 10 publications

(13 reference statements)

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“…Many different prediction models have been proposed in recent years. Most of them use energy observations in prior days to predict future energy availability: QL-SEP [6], EWMA [7], WCMA [8], ASEA [9], Pro-Energy [10,11], IPro-Energy [12], SEPCS [13], UD-WCMA [14], LINE-P [15], and Adaptive LINE-P [16]. This class of models requires maintaining locally collected data about the energy harvested during a number of prior days.…”

Section: Related Workmentioning

confidence: 99%

“…Different estimation techniques have been proposed in the last few years. Most of them use past energy patterns to predict future energy availability, thus requiring one to maintain several profiles of the energy harvested by the EH node for a number of prior days [6][7][8][9][10][11][12][13][14][15][16]. In this paper, we present a novel solar energy model, named the Solar Altitude Angle (SAA) model, that adopts a completely different approach.…”

Section: Introductionmentioning

confidence: 99%

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A Solar Altitude Angle Model for Efficient Solar Energy Predictions

Herrería-Alonso

Suárez-González

Rodríguez-Pérez

et al. 2020

Sensors

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Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Solar Altitude Angle Model for Efficient Solar Energy Predictions

Herrería-Alonso

Suárez-González

Rodríguez-Pérez

et al. 2020

Sensors

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“…Several energy prediction models have been proposed in the literature [Kansal et al 2007;Recas Piorno et al 2009;Cammarano et al 2012;Moser et al 2007;Noh and Kang 2011;Lu et al 2010]. We specifically focused on three of them: the Exponential Weighted Moving Average (EWMA) prediction model [Kansal et al 2007], the WeatherConditioned Moving Average (WCMA) algorithm [Recas Piorno et al 2009], and the PROfile energy (Pro-Energy) predictor [Cammarano et al 2012].…”

Section: Short-term Solar Energy Predictions En-masse Uses An Energymentioning

confidence: 99%

“…We specifically focused on three of them: the Exponential Weighted Moving Average (EWMA) prediction model [Kansal et al 2007], the WeatherConditioned Moving Average (WCMA) algorithm [Recas Piorno et al 2009], and the PROfile energy (Pro-Energy) predictor [Cammarano et al 2012]. WCMA and EWMA assume the energy generation on a typical day to be related to the energy generation at the same time on the previous days.…”

Section: Short-term Solar Energy Predictions En-masse Uses An Energymentioning

confidence: 99%

Sensor Mission Assignment in Rechargeable Wireless Sensor Networks

Porta

Petrioli

Phillips

et al. 2014

ACM Trans. Sen. Netw.

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Sensor mission assignment involves matching the sensing resources of a wireless sensor network (WSN) to appropriate tasks (missions), which may come to the network dynamically. Although solutions for WSNs with battery-operated nodes have been proposed for this problem, no attention has been given to networks whose nodes have energy-harvesting capabilities and are powered in part by uncontrollable environmental sources, which impose quite a different energy model. In this article we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, specifically tailored for energyharvesting mission-centric WSNs. To assess the performance of our proposed solution we have interfaced TelosB nodes with solar cells and performed extensive experiments to derive models and traces of solar energy acquisition. We use such real-life traces in our simulations. A comparative performance evaluation between EN-MASSE and other schemes previously proposed in the literature has shown that our solution significantly outperforms existing energy-harvesting-unaware mission assignment schemes. Moreover, using our analytical model as a benchmark, we also show that the profit earned by EN-MASSE is close to the optimum. Finally, we have implemented our proposed solution in TinyOS and experimentally validated its performance, showing the effectiveness of our approach.

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“…In traditional sensor networks, nodes may consider their residual energy for sleep time adaptation. In modern, energy-harvesting sensor networks [9], it is even possible to consider future energy prospectives: Energy intake predictions [16] may improve network throughput, reliability, and lifetime: However, sleep time adaptation is a possible extension only, and choosing the right adaptation scheme is a challenging task. Due to space constraints, we do not explore this complex field in this paper.…”

Section: Sleep Time Adaptationmentioning

confidence: 99%

Opportunistic, Receiver-Initiated Data-Collection Protocol

Unterschütz

Renner

Turau

2012

Lecture Notes in Computer Science

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Abstract. This paper presents and evaluates ORiNoCo, a novel datacollection and event-reporting protocol for sensor networks. ORiNoCo is built upon the asynchronous duty-cycle protocol RI-MAC and breaks with the tradition of exchanging extensive neighborhood information, a cornerstone of many competing collection protocols and one of their major source of communication overhead and energy expenditure. The merit of this venture is an opportunistic, energy-efficient, latency-reducing, and self-stabilizing protocol. ORiNoCo comes at virtually no extra costs in terms of memory demand and communication overhead compared to RI-MAC. We derive theoretical boundaries for the improvements in radio efficiency, latency, and energy-consumption. ORiNoCo is verified with these findings via simulation and compared with CTP. ORiNoCo achieves lower energy-consumption while reducing end-to-end delays.

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Prediction and management in energy harvested wireless sensor nodes

Cited by 112 publications

References 10 publications

A Solar Altitude Angle Model for Efficient Solar Energy Predictions

A Solar Altitude Angle Model for Efficient Solar Energy Predictions

Sensor Mission Assignment in Rechargeable Wireless Sensor Networks

Opportunistic, Receiver-Initiated Data-Collection Protocol

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