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

Kakkar, Ajay K.

doi:10.1002/dac.4366

Cited by 29 publications

(18 citation statements)

References 204 publications

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“…These challenging issues include transmission overhead, node localization, node failure, network efficiency, scalability, and reliability. ML is applied to improve the network lifetime by forecasting the available amount of energy at a particular time slot [80]. ML approaches can be categorized into three main categories, supervised learning, unsupervised learning, and reinforcement learning approaches.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.

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Section: Machine Learning-based Methodsmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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“…These methods include techniques like energy‐balancing, energy conservation, and energy‐harvesting techniques as energy sharing as latest methods. Sharma and Kakkar 137 have presented the recent review of the energy harvesting techniques. They have proposed deployment of solar panels for the energy harvesting and node recharging.…”

Section: Open Issues and Challengesmentioning

confidence: 99%

Survey on energy efficient dynamic sink optimum routing for wireless sensor network and communication technologies

Gupta

Singh

2022

Int J Communication

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Wireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid-based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering-based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid-based dynamic sink routing scenario. The significance of clustering-based protocols for improving lifetime of the network is evaluated as case study. The clustering-based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks.

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“…Predictors: Numerous energy predictors for solar energy have been proposed. These algorithms can be based on statistical models, physical models, and machine learning or hybrid methods [10]. Examples of energy prediction algorithms include Exponentially Weighted Moving‐Average (EWMA) [1], Improved Pro‐Energy [18], Q‐learning based solar energy prediction (QL‐SEP) [2], or Artificial Neural Network [19].…”

Section: Related Workmentioning

confidence: 99%

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

Stricker

Forno

Thiele

2022

IET Computers & Digital Tech

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Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non‐deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting‐based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non‐robust system's performance by up to 13.8 times in a real‐world setting.

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

Cited by 29 publications

References 204 publications

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Survey on energy efficient dynamic sink optimum routing for wireless sensor network and communication technologies

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

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