Plus-profile energy harvested prediction and adaptive energy management for solar-powered wireless sensor networks
Authors: 
Yuanxiang Wang, Zhen Xu, Lei YangAuthors Info & Claims
Yuanxiang Wang, Zhen Xu, Lei YangAuthors Info & ClaimsPages 7585 - 7603
Abstract
Wireless sensor networks (WSNs) are mostly used for monitoring the environment; however, they are usually powered by non-rechargeable batteries with limited energy. Solar energy harvesting is an attractive solution to the limit by charging the sensor nodes; however, the harvested solar energy is easily affected by weather conditions. Based on the characteristics of uncertainty and intermittency of solar energy, this paper proposes a plus-profile solar energy prediction algorithm. This algorithm makes the prediction of future available solar energy by finding the data in the dataset that is most similar to the data of the day and combining it with recent weather trend. According to the predicted result, the paper further proposes an adaptive energy management scheme to suit the harvested energy. In the scheme, sensor nodes can adaptively adjust task scheduling to achieve energy neutrality. The simulation results show that compared with other algorithms, the prediction accuracy of the proposed prediction algorithm is improved by 17.7 and 22.4%, respectively, and the proposed energy management scheme reduced energy loss by 6.2 and 46.8%, respectively.
References
[1]
Lanzolla A and Spadavecchia M Wireless sensor networks for environmental monitoring Sensors 2021 21 4 1172
[2]
Rokonuzzaman M, Mishu MK, Amin N, et al. Self-sustained autonomous wireless sensor network with integrated solar photovoltaic system for internet of smart home-building (IoSHB) applications Micromachines 2021 12 6 653
[3]
Evangelakos EA, Kandris D, Rountos D, et al. Energy sustainability in wireless sensor networks: an analytical survey J Low Power Electron Appl 2022 12 4 65
[4]
Junesco D, Supriyanto E, Hasan A, et al. (2021) QoS analysis of WSN (Wireless Sensor Network) using node MCU and accelerometer sensors on bridge monitoring systems. In: IOP Conference Series: Materials Science and Engineering, vol 1108, no 1. IOP Publishing, p 012025.
[5]
Haseeb K, Ud Din I, Almogren A, et al. An energy efficient and secure IoT-based WSN framework: an application to smart agriculture Sensors 2020 20 7 2081
[6]
Adu-Manu KS, Adam N, Tapparello C, et al. Energy-harvesting wireless sensor networks (EH-WSNs) a review ACM Trans Sens Netw (TOSN) 2018 14 2 1-50
[7]
Sharma H, Haque A, and Jaffery ZA Solar energy harvesting wireless sensor network nodes: a survey J Renew Sustain Energy 2018 10 2 023704
[8]
Kansal A, Hsu J, Zahedi S, et al. Power management in energy harvesting sensor networks ACM Trans Embed Comput Syst (TECS) 2007 6 4 32-es
[9]
Piorno JR, Bergonzini C, Atienza D, et al. (2009) Prediction and management in energy harvested wireless sensor nodes. In: 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology. IEEE, pp 6–10
[10]
Aoudia FA, Gautier M, Berder O (2016) Fuzzy power management for energy harvesting Wireless Sensor Nodes. In: 2016 IEEE International Conference on Communications (ICC). IEEE, pp 1–6
[11]
Hsu RC, Liu CT, and Wang HL A reinforcement learning-based ToD provisioning dynamic power management for sustainable operation of energy harvesting wireless sensor node IEEE Trans Emerg Top Comput 2014 2 2 181-191
[12]
Noh DK and Kang K Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance J Comput Syst Sci 2011 77 5 917-932
[13]
Dehwah AH, Elmetennani S, and Claudel C UD-WCMA: An energy estimation and forecast scheme for solar powered wireless sensor networks J Netw Comput Appl 2017 90 17-25
[14]
Kosunalp S A new energy prediction algorithm for energy-harvesting wireless sensor networks with Q-learning IEEE Access 2016 4 5755-5763
[15]
Cheng H, Xie Z, Wu L, Yu Z, and Li R Data prediction model in wireless sensor networks based on bidirectional LSTM EURASIP J Wirel Commun Netw 2019 2019 1-12
[16]
Shu T, Chen J, Bhargava VK, et al. An energy-efficient dual prediction scheme using LMS filter and LSTM in wireless sensor networks for environment monitoring IEEE Internet Things J 2019 6 4 6736-6747
[17]
Deb M and Roy S Enhanced-pro: a new enhanced solar energy harvested prediction model for wireless sensor networks Wirel Pers Commun 2021 117 1103-1121
[18]
Hassan M, Bermak A (2012) Solar harvested energy prediction algorithm for wireless sensors. In: 2012 4th Asia Symposium on Quality Electronic Design (ASQED). IEEE, pp 178–181
[19]
Zou T, Lin S, Feng Q, et al. Energy-efficient control with harvesting predictions for solar-powered wireless sensor networks Sensors 2016 16 1 53
[20]
Zhou H, Liu Q, Yan K, et al. Deep learning enhanced solar energy forecasting with AI-driven IoT Wirel Commun Mob Comput 2021 2021 1-11
[21]
Barrera JM, Reina A, Maté A, et al. Solar energy prediction model based on artificial neural networks and open data Sustainability 2020 12 17 6915
[22]
Malik P, Gehlot A, Singh R, et al. A review on ANN based model for solar radiation and wind speed prediction with real-time data Arch Computat Methods Eng 2022 29 5 3183-3201
[23]
Aoudia FA, Gautier M, and Berder O RLMan: an energy manager based on reinforcement learning for energy harvesting wireless sensor networks IEEE Trans Green Commun Netw 2018 2 2 408-417
[24]
Vigorito CM, Ganesan D, Barto AG (2007) Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In: 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. IEEE, pp 21–30
[25]
Ge Y, Nan Y (2020) Adaptive energy management by reinforcement learning in cluster-based solar powered wsns. In: 2020 7th International Conference on Information Science and Control Engineering (ICISCE). IEEE, pp 2303–2307
[26]
Hsu RC, Lin TH, and Su PC Dynamic energy management for perpetual operation of energy harvesting wireless sensor node using fuzzy Q-learning Energies 2022 15 9 3117
[27]
Rioual Y, Le Moullec Y, Laurent J, et al (2018) Reward function evaluation in a reinforcement learning approach for energy management. In: 2018 16th Biennial Baltic Electronics Conference (BEC). IEEE, pp 1–4
[28]
[30]
Ali MI, Al-Hashimi BM, Recas J, et al (2010) Evaluation and design exploration of solar harvested-energy prediction algorithm. In: 2010 Design, Automation and Test in Europe Conference and Exhibition (DATE 2010). IEEE, pp 142–147
[31]
Cammarano A, Petrioli C, Spenza D (2012) Pro-Energy: a novel energy prediction model for solar and wind energy-harvesting wireless sensor networks. In: 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012). IEEE, pp 75–83
[32]
Pendem S and Suresh K Energy harvesting using adaptive duty-cycling algorithm-wireless sensor networks Energy 2017 13 3 100-109
Recommendations
Balancing energy consumption with mobile agents in wireless sensor networks
For Wireless Sensor Networks (WSNs), an unbalanced energy consumption will decrease the lifetime of network. In this paper, we leverage mobile agent technology to investigate the problem of how to balance the energy consumption during data collection in ...
Enabling Battery-Less Wireless Sensor Operation Using Solar Energy Harvesting at Locations with Limited Solar Radiation
SENSORCOMM '10: Proceedings of the 2010 Fourth International Conference on Sensor Technologies and ApplicationsEnvironmental monitoring applications demand wireless sensor networks to operate over a long period of time. Although energy consumption of these systems has been tremendously reduced, lifetime of sensor nodes is still limited by the capacity and ...
Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network
In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 08 November 2023
Accepted: 19 October 2023
Author Tags
Qualifiers
- Research-article
Funding Sources
- Hubei Provincial Natural Science Foundation of China
- Wuhan Polytechnic University reform subsidy project
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 06 Oct 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in