A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning
- Title
- A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning
- Creator
- Singh J.; Dabas P.; Bhati S.; Kumar S.; Upreti K.; Shaik N.
- Description
- This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. 2023 IEEE.
- Source
- Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 432-438.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Cloud Data Centers; Deep Reinforcement Learning; Edge Computing; Energy Efficiency; Smart Surveillance
- Coverage
- Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Dabas P., Dr Akhilesh das Gupta Institute of Technology and Management, Dept. of Computer Science & Engineering, Delhi, India; Bhati S., Jecrc University, Department of Computer Applications, Jaipur, India; Kumar S., Raj Kumar Goel Institute of Technology, Department of Information Technology, Ghaziabad, India; Upreti K., Christ University (Deemed to Be University), Department of Computer Science, Ghaziabad, India; Shaik N., SRIT-Autonomous Anantapur, Department of Computer Science Engineering, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034233-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh J.; Dabas P.; Bhati S.; Kumar S.; Upreti K.; Shaik N., “A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19676.