Optimal Power Management with Guaranteed Minimum Energy Utilization for Solar Energy Harvesting Systems

Ahmed, Rehan; Buchli, Bernhard; Draskovic, Stefan; Sigrist, Lukas; Kumar, Pratyush; Thiele, Lothar

doi:10.1145/3317679

Cited by 27 publications

(23 citation statements)

References 21 publications

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“…The problem of computing the dynamic power management profile efficiently was explored in [14]. When ample historical data about the harvestable energy is available, a power management scheme proposed in [13] gives probabilistic guarantees that a minimal energy utilization will always be realized. Examples of uninterrupted environmental monitoring nodes operating over multiple years are given in [18], [19] for a high-mountain environment, in [20] for a remote field, and in [21] for a deep forest scenario.…”

Section: Related Workmentioning

confidence: 99%

“…In a realistic setting, a perfect estimate of future harvested energy is not available. Therefore, we employ the well known concept of model predictive control with a finite horizon, as used in [13], [29]. In contrast to the optimal clairvoyant solution shown before, this heuristic solution can cope with deviations between the predicted and actual harvested, or scheduled and actually used energy.…”

Section: Finite Horizon Controlmentioning

confidence: 99%

“…In this section we use simulations to illustrate the difference between the optimal clairvoyant solution u * (t), the finite horizon solution u F (t), and a baseline simple heuristic approach that does not make use of energy predictions. In addition to the scenario described below, we also investigated a hypothetical system deployed in a remote outdoor area with the goal of operating for multiple years, as in many previous results such as [13], [27]. The corresponding results are described in the technical report.…”

Section: Trace-based Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Power Management for Energy Harvesting Systems with A Backup Power Source

Draskovic

Thiele

2021

2021 10th Mediterranean Conference on Embedded Computing (MECO)

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Energy harvesting has been extensively used to allow for long-term and unattended operation of nodes in large-scale distributed systems. However, the smaller the relative rechargeable energy storage of the harvesting system, the higher is the sensitivity of its operation to short-term non-deterministic changes of the harvested power or the current power demand. A reliable and predictable node operation can be achieved with an additional backup battery. First commercial products that allow for an efficient energy exchange between energy harvester, backup battery and energy consumer are available. But, there are no energy management algorithms that optimize the longterm node utility while maximizing the lifetime of the node in terms of its backup battery. We formally model the problem, provide an optimal solution for a class of node utility functions, and implement the strategy on a resource-constrained hardware. Extensive simulations as well as experiments on indoor solar data traces show the accuracy of the abstract model, the run-time overhead and efficiency of our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Finite Horizon Controlmentioning

confidence: 99%

Section: Trace-based Experimentsmentioning

confidence: 99%

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Optimal Power Management for Energy Harvesting Systems with A Backup Power Source

Draskovic

Thiele

2021

2021 10th Mediterranean Conference on Embedded Computing (MECO)

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“…Simulation results showed that both resource allocation schemes lead to maximum utilization of harvested energy with minimum variability and upgrade the network performance. Ahmed et al presented a stochastic power management approach that leads to stochastic models of harvested solar irradiance on the basis of historical data. The work was focused on maximizing the minimum energy consumption in all time intervals and prevented battery depletion state.…”

Section: Node Level Power Managementmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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“…For selecting the optimal policy at run time, the sensor node deploys a duty-cycle adaptation mechanism that updates . The adaptation and energy estimation procedures used for this are beyond the scope of this work, we refer the reader to existing run-time adaptation methods [1,7,21,28]. To adapt the communication policy accordingly, the optimal selection policy with the activation interval closest to the new duty-cycle is loaded from the policy pool and forwarded to the run-time packet selection.…”

Section: Energy Harvesting Awarenessmentioning

confidence: 99%

Harvesting-Aware Optimal Communication Scheme for Infrastructure-Less Sensing

Sigrist

Ahmed

Gómez

et al. 2020

ACM Trans. Internet Things

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Sensing systems for long-term monitoring constitute an important part of the emerging Internet of Things. In this domain, energy harvesting and infrastructure-less communication enable truly autonomous and maintenance-free operation of sensor nodes gathering long-term environmental data. Due to the infrastructureless nature of the communication receivers are not always available. The variable energy provided by the environment and the receiver's mobility lead to non-deterministic node availability. In this work, we study infrastructure-less data transmission schemes to optimize communication when both senders and receivers exhibit intermittent behavior. We rely on the notion of data utility, describing the importance of sensed data to the receiver, to determine an optimal communication scheme. Deriving the communication policy that maximizes the utility of the received data is shown to be a convex optimization problem. The resulting scheme is implemented and validated on a batteryless Bluetooth Low Energy sensor node that communicates to commodity smartphones. Our evaluation demonstrates that the model accurately captures the application scenario with a maximum Root-Mean-Square Error (RMSE) of less than 0.016 in data reception probability. The communication scheme's adaptiveness to variable harvesting conditions is experimentally demonstrated under varying harvesting conditions and is shown to significantly increase the data utility. CCS Concepts: • Information systems → Mobile information processing systems; • Computer systems organization → Sensor networks; Sensors and actuators; • Hardware → Sensor applications and deployments.

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Optimal Power Management with Guaranteed Minimum Energy Utilization for Solar Energy Harvesting Systems

Cited by 27 publications

References 21 publications

Optimal Power Management for Energy Harvesting Systems with A Backup Power Source

Optimal Power Management for Energy Harvesting Systems with A Backup Power Source

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Harvesting-Aware Optimal Communication Scheme for Infrastructure-Less Sensing

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