A Layered Framework for Energy-Efficient Edge Computing in Sustainable IoT Systems

Abstract

The proliferation of Internet of Things (IoT) installations has resulted in an increased number of sensing, communication, storage, and computation operations performed by devices with limited resources. A typical cloud-centric approach results in unnecessary communication overhead, latency, and additional energy expenditure to transport large volumes of raw sensor data to remote datacenters. The proposed design uses adaptive sampling, data aggregation, compression, a workload-aware offloading mechanism, dynamic voltage/frequency scaling where available, and container consolidation and renewable-energy awareness, and carbon-aware workload placement, all of which are aligned with the direction of current Multi-access Edge Computing (MEC) standardization, and are now considering energy management, power information, and application-specific energy policies as first-class concerns.

Introduction

The text presents LEES-IoT (Layered Energy-Efficient and Sustainable IoT), a proposed framework for reducing the energy consumption and environmental impact of IoT systems while maintaining quality of service (QoS).

• Problem: IoT devices consume significant energy, especially through sensing and wireless data transmission. This is particularly challenging for battery-powered and remotely deployed devices.

• Role of Edge Computing: Edge computing reduces energy and latency by processing data closer to where it is generated, thereby reducing long-distance communication and cloud traffic.

• Research Goal: The framework aims to jointly optimize sensing, communication, data processing, edge resources, and cloud coordination to minimize energy and environmental costs without sacrificing application performance.

• Six Layers of LEES-IoT:

  1. Sensing & Device: Adaptive sampling, sleep scheduling, and duty cycling.

  2. Communication: Data aggregation, packet reduction, and low-power networking.

  3. Edge Data Reduction: Filtering, compression, and feature extraction before transmission.

  4. Edge Computing: Offloading, workload placement, DVFS, resource consolidation, and idle-node sleep.

  5. Orchestration & Sustainability: Energy/carbon monitoring and multi-objective resource optimization.

  6. Cloud/Service: Efficient storage, batch processing, model training, and workload migration.

  • A cross-layer management plane monitors energy, QoS, security, and system conditions.

• Methodology: The framework uses mathematical models for:

  • Adaptive sensing frequency based on activity and battery pressure.

  • Communication energy consumption.

  • Deciding whether local processing is more energy-efficient than transmitting data.

  • Task completion time and total energy consumption.

• Energy-Aware Algorithm: The proposed orchestration algorithm continuously collects device, network, edge/cloud, QoS, and renewable-energy information. It then predicts workload, energy, latency, traffic, and carbon costs and selects the best task placement using a multi-objective optimization function.

Conclusion

This paper proposed the LEES-IoT framework for a layered approach to energy-efficient edge computing[6]. The paper\'s argument is that sustainability cannot be achieved by optimized IoT devices, networks, or edge servers in isolation, but rather through cross-layer information exchange about sensing, communications, data reduction, computation, orchestration, and cloud coordination in a management plane. The framework [9]combines adaptive sensing, data reduction, energy-aware offloading, resource consolidation, renewable-aware scheduling, and carbon-aware placement. A mathematical formulation and evaluation methodology were provided to establish reproducibility.The framework is deliberately [7]presented as a research architecture and evaluation plan. Its effectiveness must be demonstrated through controlled experiments or simulations rather than assumed. Future implementations should measure actual power consumption on representative IoT and edge hardware, evaluate multiple workloads and network conditions, and report energy, latency, traffic, reliability, and carbon metrics together. Such validation can[10] identify which combinations of mechanisms provide sustainability gains without jeopardizing the responsiveness and dependability of IoT services.

References

[1] A. Kumar et al., “Exploring Sustainable Practices for Energy Conservation in IoT Systems—An Extensive Overview,” 2nd International Conference on Networking, Embedded and Wireless Systems (ICNEWS), 2024, DOI: 10.1109/ICNEWS60873.2024.10731068. [2] ETSI, “Multi-access Edge Computing (MEC),” ETSI MEC technical group and related specifications, accessed September 2026. [3] ETSI, “GS MEC 002 V4.2.1: Multi-access Edge Computing (MEC); Use Cases and Requirements,” May 2026, section on MEC Energy Management. [4] “Navigating the Edge-Cloud Continuum: A State-of-Practice Survey,” IEEE Access, vol. 14, pp. 40622–40647, Mar. 2026, DOI: 10.1109/ACCESS.2026.3673012. [5] “Paradigm Shift Toward Distributed Learning in IoT Intelligence: A Comprehensive Survey of Opportunities and Challenges,” IEEE Internet of Things Journal, vol. 13, no. 10, pp. 20128–20154, 2026, DOI: 10.1109/JIOT.2026.3657733. [6] “Management and Orchestration of Edge Computing for IoT: A Comprehensive Survey,” IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14307–14331, Aug. 2023, DOI: 10.1109/JIOT.2023.3245611. [7] ETSI, “Enabling Multi-access Edge Computing in Internet-of-Things: How to deploy ETSI MEC and oneM2M,” ETSI White Paper, June 2023. [8] ETSI, “GS MEC 003: Multi-access Edge Computing (MEC); Framework and Reference Architecture,” current ETSI MEC specification family. [9] ETSI, “GS MEC 033: Multi-access Edge Computing (MEC); IoT API,” ETSI MEC specification family. [10] IEEE/ACM research literature on energy-efficient IoT, edge offloading, resource orchestration, and sustainable computing should be added to the final bibliography after the target journal/conference style and database requirements are confirmed.

Copyright

Copyright © 2026 Farooque Alam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.