Emerging Research Directions

7.1 Task and Resource Scheduling

https://ieeexplore.ieee.org/document/9519636 Q. Luo, S. Hu, C. Li, G. Li and W. Shi, "Resource Scheduling in Edge Computing: A Survey," in IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2131-2165, Fourthquarter 2021, doi: 10.1109/COMST.2021.3106401. keywords: {Edge computing;Processor scheduling;Task analysis;Resource management;Cloud computing;Job shop scheduling;Internet of Things;Internet of things;edge computing;resource allocation;computation offloading;resource provisioning},

https://www.sciencedirect.com/science/article/abs/pii/S014036641930831X Congfeng Jiang, Tiantian Fan, Honghao Gao, Weisong Shi, Liangkai Liu, Christophe Cérin, Jian Wan, Energy aware edge computing: A survey, Computer Communications, Volume 151, 2020, Pages 556-580, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2020.01.004. (https://www.sciencedirect.com/science/article/pii/S014036641930831X) Abstract: Edge computing is an emerging paradigm for the increasing computing and networking demands from end devices to smart things. Edge computing allows the computation to be offloaded from the cloud data centers to the network edge and edge nodes for lower latency, security and privacy preservation. Although energy efficiency in cloud data centers has been broadly investigated, energy efficiency in edge computing is largely left uninvestigated due to the complicated interactions between edge devices, edge servers, and cloud data centers. In order to achieve energy efficiency in edge computing, a systematic review on energy efficiency of edge devices, edge servers, and cloud data centers is required. In this paper, we survey the state-of-the-art research work on energy-aware edge computing, and identify related research challenges and directions, including architecture, operating system, middleware, applications services, and computation offloading. Keywords: Edge computing; Energy efficiency; Computing offloading; Benchmarking; Computation partitioning

https://onlinelibrary.wiley.com/doi/10.1002/spe.3340 https://www.sciencedirect.com/science/article/abs/pii/S0167739X18319903 Wazir Zada Khan, Ejaz Ahmed, Saqib Hakak, Ibrar Yaqoob, Arif Ahmed, Edge computing: A survey, Future Generation Computer Systems, Volume 97, 2019, Pages 219-235, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.02.050. (https://www.sciencedirect.com/science/article/pii/S0167739X18319903) Abstract: In recent years, the Edge computing paradigm has gained considerable popularity in academic and industrial circles. It serves as a key enabler for many future technologies like 5G, Internet of Things (IoT), augmented reality and vehicle-to-vehicle communications by connecting cloud computing facilities and services to the end users. The Edge computing paradigm provides low latency, mobility, and location awareness support to delay-sensitive applications. Significant research has been carried out in the area of Edge computing, which is reviewed in terms of latest developments such as Mobile Edge Computing, Cloudlet, and Fog computing, resulting in providing researchers with more insight into the existing solutions and future applications. This article is meant to serve as a comprehensive survey of recent advancements in Edge computing highlighting the core applications. It also discusses the importance of Edge computing in real life scenarios where response time constitutes the fundamental requirement for many applications. The article concludes with identifying the requirements and discuss open research challenges in Edge computing. Keywords: Mobile edge computing; Edge computing; Cloudlets; Fog computing; Micro clouds; Cloud computing

https://www.sciencedirect.com/science/article/abs/pii/S1383762121001570 Akhirul Islam, Arindam Debnath, Manojit Ghose, Suchetana Chakraborty, A Survey on Task Offloading in Multi-access Edge Computing, Journal of Systems Architecture, Volume 118, 2021, 102225, ISSN 1383-7621, https://doi.org/10.1016/j.sysarc.2021.102225. (https://www.sciencedirect.com/science/article/pii/S1383762121001570) Abstract: With the advent of new technologies in both hardware and software, we are in the need of a new type of application that requires huge computation power and minimal delay. Applications such as face recognition, augmented reality, virtual reality, automated vehicles, industrial IoT, etc. belong to this category. Cloud computing technology is one of the candidates to satisfy the computation requirement of resource-intensive applications running in UEs (User Equipment) as it has ample computational capacity, but the latency requirement for these applications cannot be satisfied by the cloud due to the propagation delay between UEs and the cloud. To solve the latency issues for the delay-sensitive applications a new network paradigm has emerged recently known as Multi-Access Edge Computing (MEC) (also known as mobile edge computing) in which computation can be done at the network edge of UE devices. To execute the resource-intensive tasks of UEs in the MEC servers hosted in the network edge, a UE device has to offload some of the tasks to MEC servers. Few survey papers talk about task offloading in MEC, but most of them do not have in-depth analysis and classification exclusive to MEC task offloading. In this paper, we are providing a comprehensive survey on the task offloading scheme for MEC proposed by many researchers. We will also discuss issues, challenges, and future research direction in the area of task offloading to MEC servers. Keywords: Multi-access edge computing; Task offloading; Mobile edge computing; Survey

7.4 Energy-Efficient Edge Architectures

The exponential growth of Internet of Things (IoT) devices, combined with the rise of artificial intelligence (AI) and advanced communication networks like 5G and 6G, is driving the expansion of edge computing. In this model, data processing shifts from centralized cloud data centers to nodes closer to the data source or end-users. This architectural shift reduces network latency, optimizes bandwidth usage, and enables real-time analytics. However, distributing processing across many geographically dispersed devices also has significant implications for energy consumption. While large data centers have been extensively studied for energy efficiency, smaller edge nodes, such as micro data centers, IoT gateways, and embedded systems, also contribute notably to carbon emissions.

High-Level Edge-Fog-Cloud Architecture

Modern IoT and AI systems often rely on an edge-fog-cloud architecture. Data collected by IoT sensors typically undergoes initial processing at edge nodes or micro data centers. This local processing minimizes the data volume that must be sent to the cloud, thereby reducing network congestion and latency. Intermediate fog nodes can then aggregate data from multiple edge devices for further analysis or buffering, while centralized cloud data centers handle large-scale storage and intensive computational tasks.

Simplified edge–fog–cloud architecture. IoT devices collect data at the edge, which is processed locally by edge nodes (micro data centers). Fog nodes handle intermediate processing, while cloud data centers provide large-scale analytics and storage.

Lifecycle of an Edge Device

Evaluating the carbon footprint of edge devices requires considering their entire lifecycle. The manufacturing phase often involves substantial energy consumption and the use of raw materials. During deployment, the energy efficiency of operation including effective cooling, is critical. Ongoing maintenance and updates can extend a device's lifespan, while end-of-life disposal or recycling presents further environmental challenges. Each stage of the lifecycle offers opportunities for reducing carbon emissions through measures such as modular upgrades, the use of recycled materials, and environmentally responsible disposal practices.

Lifecycle stages of an edge device. Each phase, from material extraction and manufacturing to final disposal, impacts the overall carbon footprint. Interventions such as using recycled materials, adopting modular components, and extending product lifespans can substantially reduce environmental impact.

Hardware-Level Approaches

Efforts to reduce the carbon footprint at the edge have explored various hardware-focused strategies. These include the design of system on-chips (SoCs) with ultra-low power states and selective core activation, and the use of heterogeneous multicore processors that activate only the necessary cores to meet performance demands. The development of custom AI accelerators has also shown promise in significantly lowering power consumption during neural network inference. In addition, sustainable manufacturing practices and the incorporation of recycled materials, along with innovative packaging solutions inspired by biological systems and compact liquid-cooling methods, contribute to reducing overall energy use.

Representative Hardware-Level Studies in Edge Computing

Study	Key Focus	Contributions	Findings
Template:Cite	Ultra-low-power SoC design	Introduced SoC with power gating and selective core activation	Demonstrated a significant reduction in idle power consumption
Template:Cite	Heterogeneous multicore processors	Proposed activating only necessary cores for real-time tasks	Showed improved balance of performance and energy usage
Template:Cite	Custom AI accelerators	Developed specialized hardware for on-device inference	Reported substantial energy savings in neural network operations
Template:Cite	Sustainable manufacturing	Employed life-cycle assessments and recycled materials	Achieved a measurable decrease in overall manufacturing emissions
Template:Cite	Biologically inspired packaging	Integrated biomimetic materials for enhanced heat dissipation	Reduced cooling energy overhead and improved thermal performance

Software-Level Optimizations

Energy-aware software design plays a critical role in enhancing sustainability at the edge. Techniques such as dynamic voltage and frequency scaling (DVFS) adjust system performance based on workload fluctuations, thereby improving power efficiency. Advances in DVFS now include adaptive approaches that incorporate machine learning to fine-tune voltage and frequency settings dynamically. Additionally, task scheduling algorithms that distribute computing workloads among heterogeneous IoT gateways balance performance, latency, and energy consumption. Partial offloading techniques have also emerged, where computationally intensive parts of a task are offloaded to specialized infrastructure while simpler processes are handled locally. Lightweight containerization further minimizes resource overhead during deployment.

Representative System-Level and Policy-Focused Studies

Study	Contribution	Findings	Implications
Template:Cite	Proposed an edge-fog-cloud migration framework	Demonstrated dynamic workload relocation based on resource availability	Highlighted potential for reduced overall carbon footprint
Template:Cite	Developed sleep-mode protocols for 5G base stations	Showed drastic energy reduction during off-peak usage	Enabled significant cost savings and lowered emissions
Template:Cite	Introduced carbon-intensity-aware scheduling	Aligned workload placement with regional grid data	Improved sustainability in multi-tier edge-fog-cloud environments
Template:Cite	Proposed standardized carbon footprint metrics	Offered a uniform reporting structure for edge infrastructures	Facilitated consistent policy and regulatory compliance
Template:Cite	Analyzed economic incentives for green edge computing	Demonstrated effectiveness of tax benefits and carbon credits	Encouraged broader adoption of low-power SoCs and practices

System-Level Coordination and Policy Frameworks

Reducing the carbon footprint in edge computing requires coordinated efforts across hardware, network, and management layers. Integrated edge-fog-cloud architectures facilitate workload migration across nodes based on resource availability and carbon intensity variations. Adaptive networking protocols, such as those that enable sleep modes during off-peak hours or consolidate workloads among neighboring gateways, further enhance energy efficiency. AI-based methods now play an increasingly important role by predicting resource utilization and carbon intensity to enable proactive power management.

Policy and regulatory frameworks also contribute significantly by promoting standardized carbon footprint metrics and setting minimum energy efficiency levels for edge devices. Incentives like carbon credits and tax benefits support the transition to greener technologies, while eco-design principles—such as modular battery packs and real-time energy monitoring—help extend hardware lifespans and reduce electronic waste.

Key Strategies for Reducing Carbon Emissions

Effective strategies for mitigating carbon emissions in edge computing span multiple system layers. On the hardware front, deploying ultra-low-power SoCs, optimizing chip layouts, and adopting innovative packaging materials can substantially reduce power consumption. Complementary software approaches include power-aware scheduling, partial offloading of computational tasks, and containerized orchestration with minimal resource overhead. AI-driven coordination helps predict workload spikes, carbon intensity variations, and thermal thresholds, allowing systems to scale resources proactively.

Integrated measures for carbon footprint reduction also involve incorporating localized renewable energy sources—such as solar or wind power—at edge sites. Although practical deployment may face challenges in certain regions, these initiatives, together with supportive government policies and industry standards, are pivotal in driving sustainable practices across the sector.

Integrated Measures for Carbon Footprint Reduction in Edge Computing

Dimension	Techniques / References	Contributions	Findings
Hardware	Low-power SoCs Template:Cite, AI accelerators Template:Cite	Minimized idle power and specialized hardware for inference	Achieved notable reductions in power usage for diverse workloads
Software	DVFS with RL Template:Cite, partial offloading Template:Cite	Dynamically adjusted CPU frequency and partitioned compute tasks	Demonstrated adaptive energy savings under varying load conditions
System Orchestration	Edge-fog-cloud migration Template:Cite, container optimization Template:Cite	Relocated tasks across network layers and used lightweight virtualization	Showed improved resource utilization and reduced operational overhead
Policy/Regulation	Carbon credits Template:Cite, standardized metrics Template:Cite	Encouraged or mandated greener practices through financial/reporting mechanisms	Facilitated consistent adoption of sustainability measures across stakeholders

Open Challenges

Despite significant progress, challenges remain. The heterogeneity of edge devices complicates the implementation of uniform energy-saving strategies. The inconsistent availability of energy monitoring and carbon-intensity data across different regions hinders real-time optimizations. Additionally, critical applications such as autonomous vehicles or healthcare systems face trade-offs between reliability and energy efficiency, where any disruption or delay is unacceptable. Diverse policy frameworks across regions further complicate regulatory compliance for global operators. Finally, security and privacy concerns related to AI-driven power management and data collection must be addressed to prevent potential vulnerabilities.

Future Directions

Emerging approaches such as federated learning for energy management offer promising avenues for distributed collaboration without compromising data privacy. Cross-layer co-design—integrating hardware, operating system functions, and application-level optimizations—could yield greater efficiency improvements than isolated strategies. The development of dynamic carbon-aware energy markets, where edge nodes schedule tasks based on real-time energy prices and carbon intensity, presents an innovative framework for sustainable resource allocation. Standardized metrics and benchmarking tools, akin to data center Power Usage Effectiveness (PUE), are also needed to enable consistent comparisons and drive further advancements in edge computing sustainability.

References

1. Shi, Weisong (2020). "Edge computing: Vision and challenges". IEEE Internet of Things Journal, 7(5): 4238–4260.
2. Satyanarayanan, Mahadev (2019). "The Emergence of Edge Computing". Computer, 52(8): 30–39.
3. Abdollahi, Ali (2019). "Environmental implications of micro data centers: A case study". Sustainability, 11(10): 2728.
4. Xu, Lili (2019). "Energy-efficient SoC design for IoT edge devices". IEEE Transactions on Circuits and Systems I: Regular Papers, 66(8): 2952–2963.
5. Mendez, Carlos (2020). "Heterogeneous multicore edge processors for power-aware embedded systems". ACM Transactions on Design Automation of Electronic Systems, 25(6): Article 40.
6. Ramesh, K. (2022). "Low-power AI accelerators for on-device computer vision". IEEE Transactions on Circuits and Systems for Video Technology, 32(2): 360–372.
7. Bae, Seunghyun (2021). "Sustainable manufacturing of edge devices: A holistic analysis". Journal of Manufacturing Systems, 60: 426–439.
8. Kim, Hyunsu (2023). "Biologically inspired materials for enhanced cooling in edge devices". Nature Electronics, 6(2): 153–164.
9. Liu, Qian (2019). "Liquid cooling in micro data centers at the edge: A quantitative study". IEEE Transactions on Industrial Informatics, 15(10): 5551–5561.
10. Wan, Jiafu (2018). "Dynamic voltage and frequency scaling for real-time systems in edge computing". Journal of Parallel and Distributed Computing, 119: 29–40.
11. Martinez, Daniel (2021). "Reinforcement learning-based DVFS control for energy-efficient edge nodes". IEEE Transactions on Sustainable Computing, 6(3): 403–414.
12. Li, Guanyu (2019). "Multi-objective task scheduling for heterogeneous IoT gateways in edge computing". Future Generation Computer Systems, 100: 223–238.
13. Zhang, Fan (2022). "Partial offloading of convolutional neural networks in edge computing environments". IEEE Transactions on Industrial Electronics, 69(9): 8957–8967.
14. Hassan, Ahmed (2021). "Performance and energy analysis of containerization in edge micro data centers". IEEE Access, 9: 79028–79039.
15. Moreno, Juan (2023). "Kubernetes-based energy-aware orchestration for edge computing". ACM Transactions on Internet Technology, 23(1): Article 5.
16. Chiang, Mung (2018). "Fog and IoT: An overview of research opportunities". IEEE Internet of Things Journal, 5(4): 2451–2461.
17. Yang, Yifan (2023). "Carbon-intensity-aware workload placement in hybrid edge-fog-cloud environments". IEEE Transactions on Cloud Computing, 11(2): 548–561.
18. Qiu, Xiaobo (2020). "Adaptive sleep-mode protocols for 5G base stations in edge scenarios". IEEE Transactions on Green Communications and Networking, 4(3): 670–683.
19. Nguyen, Tuan (2021). "Collaborative workload consolidation for green edge computing". IEEE Transactions on Sustainable Computing, 6(4): 998–1009.
20. Tang, Wei (2019). "AI-driven power management in IoT edge devices using deep Q-networks". Sensors, 19(18): 4043.
21. He, Sheng (2022). "Federated learning for energy efficiency in edge networks: A novel predictive approach". Computer Networks, 202: 108614.
22. White, Robert (2020). "Standardizing carbon footprint metrics in edge computing infrastructures". IEEE Communications Standards Magazine, 4(3): 12–19.
23. Gao, Yuan (2023). "Regional regulations and compliance for edge energy efficiency: Framework and insights". IEEE Transactions on Sustainable Computing, 8(2): 546–560.
24. Johnson, Tyler (2021). "Incentivizing low-power edge deployments through carbon credits and tax reductions". IEEE Transactions on Industrial Informatics, 17(9): 6402–6413.
25. Schaefer, Vanessa (2022). "Green labels for IoT devices: Consumer awareness and adoption". Electronic Markets, 32(3): 1041–1056.
26. Devic, Aleksandar (2024). "Eco-design in edge hardware: Modular battery packs and real-time energy monitoring". IEEE Transactions on Components, Packaging and Manufacturing Technology, 14(3): 312–324.
27. Sakr, Mahmoud (2020). "A game-theoretic approach to minimizing energy consumption via offloading in edge-cloud environments". IEEE Transactions on Mobile Computing, 19(11): 2624–2638.
28. Du, Shengnan (2019). "Life cycle analysis of IoT devices: Energy, emissions, and disposal". Journal of Cleaner Production, 231: 341–350.