skip to main content
10.1145/3649153.3649864acmconferencesArticle/Chapter ViewAbstractPublication PagescfConference Proceedingsconference-collections
research-article
Open access

Energy-Aware IoT Deployment Planning

Published: 02 July 2024 Publication History

Abstract

Increasingly, the Internet of Things (IoT) is evolving toward an architecture consisting of sensing and actuation devices communicating with edge computers and storage systems. These "edge deployments" localize communication, computation, and storage for security, increased efficiencies (e.g. lower latency response), and reliability. In settings where electrical power infrastructure is lacking, however, these deployments typically rely on renewable energy and battery storage for power.
In this paper, we investigate power-optimizing scheduling for IoT communication in edge deployments that compose battery-powered sensors. We focus on radio communication since it is often the largest component of the sensor battery budget in edge deployments. We model radio communication between sensors and co-located base stations using their distance, bandwidth availability, and duty cycle for data transmission. We present three heuristic approaches that allocate radio bandwidth to minimize sensor power consumption. We consider both shared and decentralized battery infrastructure. We empirically analyze these approaches in terms of performance and computational efficiency and present a methodology for using these techniques to inform configuration and management of energy-efficient edge deployments.

References

[1]
2023. Mixed Integer Distributed Ant Colony Optimization. http://www.midaco-solver.com/ Accessed Sep 2023.
[2]
Cosmin Avasalcai, Christos Tsigkanos, and Schahram Dustdar. 2019. Decentralized Resource Auctioning for Latency-Sensitive Edge Computing. In 2019 IEEE International Conference on Edge Computing (EDGE). 72--76.
[3]
Cosmin Avasalcai, Christos Tsigkanos, and Schahram Dustdar. 2021. Adaptive Management of Volatile Edge Systems at Runtime With Satisfiability. ACM Trans. Internet Technol. 22, 1 (2021).
[4]
Nikolaj BjØrner, Anh-Dung Phan, and Lars Fleckenstein. 2015. VZ - An Optimizing SMT Solver. In Proceedings of the 21st International Conference on Tools and Algorithms for the Construction and Analysis of Systems - Volume 9035. 194--199.
[5]
Lianjie Cao, Anu Merican, Diman Zad Tootaghaj, Faraz Ahmed, Puneet Sharma, and Vinay Saxena. 2021. ECaaS: A Management Framework of Edge Container as a Service for Business Workload. In Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking (Online, United Kingdom) (EdgeSys '21). Association for Computing Machinery, New York, NY, USA, 73--78.
[6]
IBM ILOG Cplex. 2009. V12. 1: User's Manual for CPLEX. International Business Machines Corporation 46, 53 (2009), 157.
[7]
Leonardo de Moura and Nikolaj Bjørner. 2008. Z3: An Efficient SMT Solver. In Tools and Algorithms for the Construction and Analysis of Systems. 337--340.
[8]
Rong Du, Ming Xiao, and Carlo Fischione. 2019. Optimal Node Deployment and Energy Provision for Wirelessly Powered Sensor Networks. IEEE Journal on Selected Areas in Communications 37, 2 (2019), 407--423. https://doi.org/10.1109/JSAC.2018.2872380
[9]
Michelle Effros, Andrea Goldsmith, and Muriel Médard. 2010. The rise of instant wireless networks. Scientific American 302, 4 (2010), 72--77.
[10]
A. Rosales Elias, N. Golubovic, C. Krintz, and R. Wolski. 2017. Where's the Bear? - Automating Wildlife Image Processing Using IoT and Edge Cloud Systems. In Internet-of-Things Design and Implementation (IoTDI), 2017 IEEE/ACM Second International Conference on. IEEE, 247--258.
[11]
Julien Gascon-Samson, Mohammad Rafiuzzaman, and Karthik Pattabiraman. 2017. ThingsJS: Towards a Flexible and Self-Adaptable Middleware for Dynamic and Heterogeneous IoT Environments. In Proceedings of the 4th Workshop on Middleware and Applications for the Internet of Things. Association for Computing Machinery, New York, NY, USA, 11--16. https://doi.org/10.1145/3152141.3152391
[12]
Pradipta Ghosh, Jonathan Bunton, Dimitrios Pylorof, Marcos A. M. Vieira, Kevin Chan, Ramesh Govindan, Gaurav S. Sukhatme, Paulo Tabuada, and Gunjan Verma. 2023. Synthesis of Large-Scale Instant IoT Networks. IEEE Transactions on Mobile Computing 22, 3 (2023), 1810--1824.
[13]
Marco Giordani, Takayuki Shimizu, Andrea Zanella, Takamasa Higuchi, Onur Altintas, and Michele Zorzi. 2019. Path loss models for V2V mmWave communication: Performance evaluation and open challenges. In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 1--5.
[14]
N. Golubovic, C. Krintz, R. Wolski, B. Sethuramasamyraja, and B. Liu. 2018. A Scalable System for Executing and Scoring K-Means Clustering Techniques and Its Impact on Applications in Agriculture. International Journal of Big Data Intelligence (July 2018).
[15]
N. Golubovic, R. Wolski, C. Krintz, and M. Mock. 2019. Improving the Accuracy of Outdoor Temperature Prediction by IoT Devices. In IEEE Conference on IoT.
[16]
Najmul Hassan, Saira Andleeb Gillani, Ejaz Ahmed, Ibrar Yaqoob, and Muhammad Ali Imran. 2018. The Role of Edge Computing in Internet of Things. IEEE Communications Magazine 56 (2018), 110--115.
[17]
H. Jawad, R. Nordin, S. Gharghan, A. Jawad, and M. Ismail. 2017. Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors 17, 1781 (2017).
[18]
C. Krintz, R. Wolski, N. Golubovic, B. Lampel, V. Kulkarni, B. Sethuramasamyraja, B. Roberts, and B. Liu. 2016. SmartFarm: Improving Agriculture Sustainability Using Modern Information Technology. In KDD Workshop on Data Science for Food, Energy, and Water.
[19]
Yue Li, Mohammad Ghasemiahmadi, and Lin Cai. 2016. Uplink cooperative transmission for machine-type communication traffic in cellular system. In 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall). IEEE, 1--5.
[20]
Chang Liu, Jin Wang, Liang Zhou, and Amin Rezaeipanah. 2022. Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm. Neural Processing Letters 54, 3 (2022), 1823--1854.
[21]
Jude Okwuibe, Juuso Haavisto, Ivana Kovacevic, Erkki Harjula, Ijaz Ahmad, Johirul Islam, and Mika Ylianttila. 2021. SDN-Enabled Resource Orchestration for Industrial IoT in Collaborative Edge-Cloud Networks. IEEE Access 9 (2021).
[22]
G. Pottie and W. Kaiser. 2000. Wireless Integrated Network Sensors. Commun. ACM 43, 5 (2000).
[23]
A Prasanth and S Jayachitra. 2020. A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer-to-Peer Networking and Applications 13 (2020), 1905--1920.
[24]
T. Rault, A. Bouabdallah, and Y. Challal. 2014. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks 67 (2014).
[25]
Sabino Francesco Roselli, Kristofer Bengtsson, and Knut Åkesson. 2019. SMT Solvers for Flexible Job-Shop Scheduling Problems: A Computational Analysis. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).
[26]
M. Schlueter, J. Egea, and J. Banga. 2009. Extended Ant Colony Optimization for non-convex Mixed Integer Nonlinear Programming. Computers and Operations Research 36, 7 (2009).
[27]
Binbin Shi, Wei Wei, Yihuai Wang, and Wanneng Shu. 2016. A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks. Sensors 16, 10 (2016).
[28]
Hui Song, Rustem Dautov, Nicolas Ferry, Arnor Solberg, and Franck Fleurey. 2020. Model-Based Fleet Deployment of Edge Computing Applications. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS '20). Association for Computing Machinery, New York, NY, USA, 132--142.
[29]
UCSB SmartFarm 2024. UCSB SmartFarm. https://sites.cs.ucsb.edu/~ckrintz/projects/. [Online; accessed 17-April-2023].
[30]
Can Wang, Sheng Zhang, Yu Chen, Zhuzhong Qian, Jie Wu, and Mingjun Xiao. 2020. Joint configuration adaptation and bandwidth allocation for edge-based real-time video analytics. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 257--266.
[31]
Jun-Bo Wang, Jinyuexue Zhang, Changfeng Ding, Hua Zhang, Min Lin, and Jiangzhou Wang. 2020. Joint optimization of transmission bandwidth allocation and data compression for mobile-edge computing systems. IEEE Communications Letters 24, 10 (2020), 2245--2249.
[32]
Wenting Wei, Ruying Yang, Huaxi Gu, Weike Zhao, Chen Chen, and Shaohua Wan.2021. Multi-objective optimization for resource allocation in vehicular cloud computing networks. IEEE Transactions on Intelligent Transportation Systems 23, 12 (2021), 25536--25545.
[33]
Yu Yu, Jie Tang, Jiayi Huang, Xiuyin Zhang, Daniel Ka Chun So, and Kai-Kit Wong. 2021. Multi-objective optimization for UAV-assisted wireless powered IoT networks based on extended DDPG algorithm. IEEE Transactions on Communications 69, 9 (2021), 6361--6374.
[34]
Z3 Prover [n. d.]. Z3 Prover. https://github.com/Z3Prover/z3. [Online; accessed 12-September-2023].
[35]
Qunsong Zeng, Yuqing Du, Kaibin Huang, and Kin K Leung. 2020. Energy-efficient radio resource allocation for federated edge learning. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 1--6.
[36]
Sheng Zhang, Can Wang, Yibo Jin, Jie Wu, Zhuzhong Qian, Mingjun Xiao, and Sanglu Lu. 2021. Adaptive configuration selection and bandwidth allocation for edge-based video analytics. IEEE/ACM Transactions on Networking 30, 1 (2021), 285--298.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CF '24: Proceedings of the 21st ACM International Conference on Computing Frontiers
May 2024
345 pages
ISBN:9798400705977
DOI:10.1145/3649153
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 July 2024

Check for updates

Author Tags

  1. IoT deployment planning
  2. communication scheduling
  3. numerical optimization

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • NSF
  • DILUTE

Conference

CF '24
Sponsor:

Acceptance Rates

CF '24 Paper Acceptance Rate 33 of 105 submissions, 31%;
Overall Acceptance Rate 273 of 785 submissions, 35%

Upcoming Conference

CF '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 125
    Total Downloads
  • Downloads (Last 12 months)125
  • Downloads (Last 6 weeks)24
Reflects downloads up to 11 Feb 2025

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media