Just Accepted
RACEME: Embedded Intelligence for Correlation-driven Predictive Energy-harvesting Management in LoRa Networks
Abstract
The prolonged lifetime of energy-harvesting (EH) LoRa networks requires that all EH LoRa sensors utilize available harvested energy in an energy-neutral manner to avoid power failures. This requirement is challenging to fulfill due to the unpredictability of ambient energy sources and the spatio-temporal heterogeneity of sensors’ harvesting abilities. We present RACEME, a novel predictive EH-management framework by exploiting the embedded intelligence capability of energy-harvesting LoRa devices. It empowers energy-neutral operation in EH LoRa networks by leveraging the spatio-temporal correlation between EH LoRa sensors to optimize the harvested energy utilization. RACEME is an integrated system that consists of embedded machine learning (ML) for predictive EH management on the sensors and online cluster-based data reduction and recovery on the server. To maximize the accuracy of EH predictions, RACEME allows each sensor to implement an ML pipeline locally. Coupled with the cluster-based data-reduction feedback from the server, RACEME enables the sensors with higher harvested-energy availability and communication quality to transmit critical data more frequently in a probabilistic manner without sacrificing data quality. Compared with the state of the art, the experimental results reveal that RACEME improves EH prediction accuracy, network lifetime, and transmission overhead by up to 1.7, 2, and 8 times, respectively.
References
[1]
Kofi Sarpong Adu-Manu, Nadir Adam, Cristiano Tapparello, Hoda Ayatollahi, and Wendi Heinzelman. 2018. Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review. ACM Trans. Sen. Netw. 14, 2, Article 10 (April 2018), 50 pages. https://doi.org/10.1145/3183338
[2]
Moez Ali. 2020. PyCaret: An open source, low-code machine learning library in Python. https://pycaret.org
[3]
Gilles Callebaut, Geoffrey Ottoy, and Liesbet van der Perre. 2019. Cross-Layer Framework and Optimization for Efficient Use of the Energy Budget of IoT Nodes. In 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 1–6. https://doi.org/10.1109/WCNC.2019.8885739
[4]
Alessandro Cammarano, Chiara Petrioli, and Dora Spenza. 2016. Online Energy Harvesting Prediction in Environmentally Powered Wireless Sensor Networks. IEEE Sensors Journal 16, 17 (2016), 6793–6804. https://doi.org/10.1109/JSEN.2016.2587220
[5]
Martina Capuzzo, Carmen Delgado, Ashish Kumar Sultania, Jeroen Famaey, and Andrea Zanella. 2021. Enabling Green IoT: Energy-Aware Communication Protocols for Battery-Less LoRaWAN Devices. In Proceedings of the 24th International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (Alicante, Spain) (MSWiM ’21). Association for Computing Machinery, New York, NY, USA, 95–98. https://doi.org/10.1145/3479239.3485676
[6]
LoRa Alliance Technical Committee. 2020. LoRaWAN® L2 1.0.4 Specification (TS001-1.0.4). LoRa Alliance: Fremont, CA, USA(2020).
[7]
Semtech Corporation. 2020. SX1276/77/78/79 - 137 MHz to 1020 MHz Low Power Long Range Transceiver Datasheet, Rev. 7. Semtech, May (2020).
[8]
Koustabh Dolui, Ashok Samraj Thangarajan, Thibo Claes, Sam Michiels, and Danny Hughes. 2022. Towards On-Board Learning for Harvested Energy Prediction. In Proceedings of the 6th International Workshop on Embedded and Mobile Deep Learning (Portland, Oregon) (EMDL ’22). Association for Computing Machinery, New York, NY, USA, 7–12. https://doi.org/10.1145/3539491.3539593
[9]
Rashad Eletreby, Diana Zhang, Swarun Kumar, and Osman Yağan. 2017. Empowering Low-Power Wide Area Networks in Urban Settings. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (Los Angeles, CA, USA) (SIGCOMM ’17). Association for Computing Machinery, New York, NY, USA, 309–321. https://doi.org/10.1145/3098822.3098845
[10]
Sezana Fahmida, Venkata P Modekurthy, Mahbubur Rahman, Abusayeed Saifullah, and Marco Brocanelli. 2020. Long-Lived LoRa: Prolonging the Lifetime of a LoRa Network. In 2020 IEEE 28th International Conference on Network Protocols (ICNP). IEEE, 1–12. https://doi.org/10.1109/ICNP49622.2020.9259375
[11]
National Centers for Environmental Information. 2017. USCRN/USRCRN Hourly Files. https://www.ncei.noaa.gov/pub/data/uscrn/products/hourly02/
[12]
National Centers for Environmental Information. 2017. USCRN/USRCRN Subhourly Files. https://www.ncei.noaa.gov/pub/data/uscrn/products/subhourly01/
[13]
Francesco Fraternali, Bharathan Balaji, Dhiman Sengupta, Dezhi Hong, and Rajesh K. Gupta. 2020. Ember: Energy Management of Batteryless Event Detection Sensors with Deep Reinforcement Learning. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems (Virtual Event, Japan) (SenSys ’20). Association for Computing Machinery, New York, NY, USA, 503–516. https://doi.org/10.1145/3384419.3430734
[14]
Akshay Gadre, Revathy Narayanan, Anh Luong, Anthony Rowe, Bob Iannucci, and Swarun Kumar. 2020. Frequency Configuration for Low-Power Wide-Area Networks in a Heartbeat. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA, 339–352. https://www.usenix.org/conference/nsdi20/presentation/gadre
[15]
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A Survey on Concept Drift Adaptation. ACM Comput. Surv. 46, 4, Article 44 (March 2014), 37 pages. https://doi.org/10.1145/2523813
[16]
Amalinda Gamage, Jansen Christian Liando, Chaojie Gu, Rui Tan, and Mo Li. 2020. LMAC: Efficient Carrier-Sense Multiple Access for LoRa. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (London, United Kingdom) (MobiCom ’20). Association for Computing Machinery, New York, NY, USA, Article 43, 13 pages. https://doi.org/10.1145/3372224.3419200
[17]
Weifeng Gao, Wan Du, Zhiwei Zhao, Geyong Min, and Mukesh Singhal. 2019. Towards Energy-Fairness in LoRa Networks. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 788–798. https://doi.org/10.1109/ICDCS.2019.00083
[18]
Weifeng Gao, Zhiwei Zhao, and Geyong Min. 2020. AdapLoRa: Resource Adaptation for Maximizing Network Lifetime in LoRa Networks. In 2020 IEEE 28th International Conference on Network Protocols (ICNP). IEEE, 1–11. https://doi.org/10.1109/ICNP49622.2020.9259398
[19]
Kai Geissdoerfer, Raja Jurdak, Brano Kusy, and Marco Zimmerling. 2019. Getting More out of Energy-Harvesting Systems: Energy Management under Time-Varying Utility with PreAct. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks (Montreal, Quebec, Canada) (IPSN ’19). Association for Computing Machinery, New York, NY, USA, 109–120. https://doi.org/10.1145/3302506.3310393
[20]
Kai Geissdoerfer and Marco Zimmerling. 2022. Learning to Communicate Effectively Between Battery-free Devices. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). USENIX Association, Renton, WA, 419–435. https://www.usenix.org/conference/nsdi22/presentation/geissdoerfer
[21]
Sukanya Jewsakul and Edith C.H. Ngai. 2023. EmbientLoRa: Embedded Intelligence for Predictive Energy Harvesting and Management in LoRa Networks. In Proceedings of the 2023 International Conference on Embedded Wireless Systems and Networks (Rende, Italy) (EWSN ’23). Association for Computing Machinery, New York, NY, USA, 231–236.
[22]
Sukanya Jewsakul and Edith C. H. Ngai. 2023. ENORA: Empowering Energy-Neutral Operation in LoRa Networks via Embedded Intelligence. IEEE Network 37, 4 (2023), 127–134. https://doi.org/10.1109/MNET.010.2200662
[23]
Aman Kansal, Jason Hsu, Sadaf Zahedi, and Mani B. Srivastava. 2007. Power Management in Energy Harvesting Sensor Networks. ACM Trans. Embed. Comput. Syst. 6, 4 (Sept. 2007), 32–es. https://doi.org/10.1145/1274858.1274870
[24]
National Renewable Energy Laboratory. 2000. NREL National Solar Radiation Database. https://registry.opendata.aws/nrel-pds-nsrdb
[25]
Jansen C. Liando, Amalinda Gamage, Agustinus W. Tengourtius, and Mo Li. 2019. Known and Unknown Facts of LoRa: Experiences from a Large-Scale Measurement Study. ACM Trans. Sen. Netw. 15, 2, Article 16 (Feb. 2019), 35 pages. https://doi.org/10.1145/3293534
[26]
Amjad Yousef Majid, Patrick Schilder, and Koen Langendoen. 2020. Continuous Sensing on Intermittent Power. In 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 181–192. https://doi.org/10.1109/IPSN48710.2020.00-36
[27]
Paul J. Marcelis, Nikolaos Kouvelas, Vijay S. Rao, and R. Venkatesha Prasad. 2022. DaRe: Data Recovery Through Application Layer Coding for LoRaWAN. IEEE Transactions on Mobile Computing 21, 3 (2022), 895–910. https://doi.org/10.1109/TMC.2020.3016654
[28]
Alessandro Montanari, Manuja Sharma, Dainius Jenkus, Mohammed Alloulah, Lorena Qendro, and Fahim Kawsar. 2020. ePerceptive: Energy Reactive Embedded Intelligence for Batteryless Sensors. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems (Virtual Event, Japan) (SenSys ’20). Association for Computing Machinery, New York, NY, USA, 382–394. https://doi.org/10.1145/3384419.3430782
[29]
Di Mu, Yitian Chen, Junyang Shi, and Mo Sha. 2020. Runtime Control of LoRa Spreading Factor for Campus Shuttle Monitoring. In 2020 IEEE 28th International Conference on Network Protocols (ICNP). IEEE, 1–11. https://doi.org/10.1109/ICNP49622.2020.9259383
[30]
Jon Nordby, Mark Cooke, and Adam Horvath. 2019. emlearn: Machine Learning inference engine for Microcontrollers and Embedded Devices. https://doi.org/10.5281/zenodo.2589394
[31]
Hong Kong Observatory. 2023. Regional weather in Hong Kong – the latest 1–minute global solar radiation and direct solar radiation and diffuse radiation. https://data.gov.hk/en-data/dataset/hk-hko-rss-latest-one-minute-solar-radiation-info
[32]
Hong Kong Observatory. 2023. Regional weather in Hong Kong – the latest 1–minute mean air temperature. https://data.gov.hk/en-data/dataset/hk-hko-rss-latest-one-minute-mean-air-temp
[33]
Nurani Saoda, Wenpeng Wang, Md Fazlay Rabbi Masum Billah, and Bradford Campbell. 2022. An Energy Supervisor Architecture for Energy-Harvesting Applications. In 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 323–336. https://doi.org/10.1109/IPSN54338.2022.00033
[34]
Raquel Sebastião and José Maria Fernandes. 2017. Supporting the Page-Hinkley Test with Empirical Mode Decomposition for Change Detection. In Foundations of Intelligent Systems, Marzena Kryszkiewicz, Annalisa Appice, Dominik Ślezak, Henryk Rybinski, Andrzej Skowron, and Zbigniew W. Raś (Eds.). Springer International Publishing, Cham, 492–498.
[35]
Shaswot Shresthamali, Masaaki Kondo, and Hiroshi Nakamura. 2017. Adaptive Power Management in Solar Energy Harvesting Sensor Node Using Reinforcement Learning. ACM Trans. Embed. Comput. Syst. 16, 5s, Article 181(Sept. 2017), 21 pages. https://doi.org/10.1145/3126495
[36]
Adarsh Pal Singh and Sachin Chaudhari. 2020. Embedded machine learning-based data reduction in application-specific constrained IoT networks. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (Brno, Czech Republic) (SAC ’20). Association for Computing Machinery, New York, NY, USA, 747–753. https://doi.org/10.1145/3341105.3373967
[37]
Naomi Stricker and Lothar Thiele. 2022. Accurate Onboard Predictions for Indoor Energy Harvesting using Random Forests. In 2022 11th Mediterranean Conference on Embedded Computing (MECO). IEEE, 1–6. https://doi.org/10.1109/MECO55406.2022.9797188
[38]
Thiemo Voigt, Martin Bor, Utz Roedig, and Juan Alonso. 2017. Mitigating Inter-network Interference in LoRa Networks. In Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks (Uppsala, Sweden) (EWSN ’17). Junction Publishing, USA, 323–328.
[39]
LoRa Alliance Technical Committee Regional Parameters Workgroup. 2021. RP002-1.0.3 LoRaWAN® Regional Parameters. LoRa Alliance: Fremont, CA, USA(2021).
[40]
Nuzhat Yamin and Ganapati Bhat. 2021. Online Solar Energy Prediction for Energy-Harvesting Internet of Things Devices. In 2021 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED). IEEE, 1–6. https://doi.org/10.1109/ISLPED52811.2021.9502504
[41]
Nuzhat Yamin and Ganapati Bhat. 2022. Near-Optimal Energy Management for Energy Harvesting IoT Devices Using Imitation Learning. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 41, 11(2022), 4551–4562. https://doi.org/10.1109/TCAD.2022.3198909
Index Terms
- RACEME: Embedded Intelligence for Correlation-driven Predictive Energy-harvesting Management in LoRa Networks
Recommendations
FioRa: Energy Neutrality-aware Multicast Firmware Distributions in Energy-harvesting LoRa Networks
BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationEfficient firmware distributions in energy-harvesting (EH) LoRa networks require that EH LoRa sensors simultaneously receive data fragments from a server without facing power failures. This requirement is difficult to satisfy due to the impact of EH ...
Joint Energy Management for Distributed Energy Harvesting Systems
SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor SystemsEmploying energy harvesting to power the Internet of Things supports their long-term, self-sustainable, and maintenance-free operation. These energy harvesting systems have an energy management subsystem to orchestrate the flow of energy and optimize ...
Adaptive Duty Cycle Control for Optimal Stochastic Energy Harvesting
Energy harvesting in wireless sensors is expected to improve the environmental footprint of sensors by reducing the polluting need of using and replacing batteries through autarkic operation. Recent advances in energy harvesting technology lead towards ...
Comments
Information & Contributors
Information
Published In
EISSN:1550-4867
Table of ContentsPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Journal Family
Publication History
Online AM: 24 January 2025
Accepted: 28 December 2024
Revised: 15 August 2024
Received: 02 November 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 157Total Downloads
- Downloads (Last 12 months)157
- Downloads (Last 6 weeks)157
Reflects downloads up to 03 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in