Editing Emerging Research Directions (section)

== 7.4 Energy-Efficient Edge Architectures ==

The exponential growth of the Internet of Things (IoT) devices, coupled with the emergence of artificial intelligence (AI) and high-speed communication networks (5G/6G), has led to the proliferation of edge computing. In an edge computing paradigm, data processing is distributed away from centralized cloud data centers and relocated closer to the data source or end-users. This architectural shift offers benefits such as reduced network latency, efficient bandwidth usage, and real-time analytics. However, the distribution of processing to a multiplicity of geographically dispersed devices has profound implications for energy consumption. Although large-scale data centers have been the subject of extensive research concerning energy efficiency, smaller edge nodes—including micro data centers, IoT gateways, and embedded systems—also generate significant carbon emissions [1].


=== 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 [2].

[[File:arch.png|600px|thumb|center| 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 [3].

[[File:flow.png|600px|thumb|center| 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 ===

Research on hardware-focused strategies for reducing the carbon footprint at the edge has been extensive. Xu et al. examined system-on-chips (SoCs) designed specifically for energy efficiency, integrating ultra-low-power states and selective core activation [4]. Mendez and Ha evaluated heterogeneous multicore processors for embedded systems, highlighting the benefits of activating only the cores necessary to meet real-time performance requirements [5]. Similarly, the introduction of custom AI accelerators has been shown to yield significant power savings for neural network inference tasks [6].

Bae et al. emphasized that sustainable manufacturing practices and the use of recycled materials can reduce the overall lifecycle emissions of edge devices [7]. Kim et al. explored biologically inspired materials to enhance heat dissipation at the package level, while Liu and Zhang demonstrated that compact liquid-cooling solutions are viable even for micro data centers near the edge [8][9].


{| class="wikitable" style="width:100%; text-align:left;"
|+ Representative Hardware-Level Studies in Edge Computing
|-
! Study
! Key Focus
! Contributions
! Findings
|-
| [4] Xu et al.
| Ultra-low-power SoC design
| Introduced SoC with power gating and selective core activation
| Significant reduction in idle power consumption
|-
| [5] Mendez and Ha
| Heterogeneous multicore processors
| Evaluated activating only necessary cores for real-time tasks
| Improved balance of performance and energy usage
|-
| [6] Ramesh et al.
| Custom AI accelerators
| Developed specialized hardware for on-device inference
| Reported substantial energy savings in neural network operations
|-
| [7] Bae et al.
| Sustainable manufacturing
| Employed lifecycle assessments and recycled materials
| Achieved measurable decrease in manufacturing emissions
|-
| [8] Kim et al.
| Biologically inspired packaging
| Integrated biomimetic materials for enhanced heat dissipation
| Reduced cooling energy overhead and improved thermal performance
|-
| [9] Liu and Zhang
| Liquid cooling solutions
| Demonstrated compact liquid-cooling viability in micro data centers
| Quantitative improvement in cooling efficiency
|}

=== Software-Level Optimizations ===
Energy-aware software design is integral to achieving sustainability in edge computing. Wan et al. initiated the discussion on applying dynamic voltage and frequency scaling (DVFS) within edge-based real-time systems [10]. Martinez et al. refined DVFS strategies by incorporating reinforcement learning methods that adaptively tune voltage and frequency according to workload fluctuations, illustrating substantial improvements in power efficiency [11]. On the task scheduling front, Li et al. proposed multi-objective algorithms to distribute computing workloads among heterogeneous IoT gateways, balancing performance, latency, and energy considerations [12].

Partial offloading techniques have also gained traction, particularly in AI inference. Zhang et al. presented a partitioning mechanism whereby only computationally heavy layers of a neural network are offloaded to specialized infrastructure, while simpler layers run on the edge device [13]. Hassan et al. and Moreno et al. examined lightweight containerization at the edge, demonstrating that resource overhead can be minimized through optimized container runtimes such as Docker, containerd, and CRI-O [14][15].
{| class="wikitable" style="width:100%; text-align:left;"
|+ Representative System-Level and Policy-Focused Studies
|-
! Study
! Contribution
! Findings
! Implications
|-
| [16] Chiang et al.
| Proposed an edge–fog–cloud migration framework
| Demonstrated dynamic workload relocation based on resource availability
| Highlighted potential for reduced overall carbon footprint
|-
| [18] Qiu et al.
| Developed sleep-mode protocols for 5G base stations
| Showed drastic energy reduction during off-peak usage
| Enabled significant cost savings and lowered emissions
|-
| [17] Yang et al.
| Introduced carbon-intensity-aware scheduling
| Aligned workload placement with regional grid data
| Improved sustainability in multi-tier edge–fog–cloud environments
|-
| [22] White et al.
| Proposed standardized carbon footprint metrics
| Offered a uniform reporting structure for edge infrastructures
| Facilitated consistent policy and regulatory compliance
|-
| [24] Johnson et al.
| Analyzed economic incentives for green edge computing
| Demonstrated effectiveness of tax benefits and carbon credits
| Encouraged broader adoption of low-power deployments
|}


=== System-Level Coordination and Policy Frameworks ===
A holistic perspective that spans hardware, network, and orchestration layers has been pivotal in advancing carbon footprint reduction. Chiang et al. and Yang and Li introduced integrated edge–fog–cloud architectures, showing how workload migration across geographically distributed nodes can leverage variations in carbon intensity [16][17]. Qiu et al. and Nguyen et al. developed adaptive networking protocols to reduce base-station energy consumption, such as utilizing sleep modes during off-peak periods or coordinating workload consolidation across neighboring edge gateways [18][19]. These system-wide efforts are increasingly driven by AI-based methods, where machine learning algorithms predict resource utilization or carbon intensity to trigger proactive power management [20][21].

Policy and regulation also play a crucial role. White et al. underscored the need for standardized carbon footprint metrics in edge infrastructures [22], while Gao et al. examined regional regulations enforcing minimum energy efficiency levels for gateways and micro data centers [23]. Johnson et al. explored how carbon credits or tax benefits can incentivize low-power chipset adoption, and Schaefer et al. investigated the impact of green certifications on consumer purchasing behaviors [24][25]. Devic et al. integrated eco-design principles, such as modular battery packs and real-time energy monitoring, to extend hardware life and reduce e-waste [26].



=== Key Strategies for Reducing Carbon Emissions ===
Recent publications demonstrate that strategies to mitigate carbon emissions in edge computing frequently span multiple system layers. Hardware-centric measures include deploying ultra-low-power SoCs, optimizing chip layouts, and adopting novel packaging materials to improve heat dissipation. Complementary software-based techniques revolve around power-aware scheduling, partial offloading, and containerized orchestration with minimal resource overhead. AI-driven coordination has also gained traction in predicting workload spikes, carbon intensity variations, and thermal thresholds, thus enabling proactive resource scaling.

Integrating localized renewable energy sources such as solar or wind power at edge sites can enhance sustainability, although practical deployment remains challenging in certain regions. Government policies and industry standards further encourage the adoption of green practices, including energy efficiency mandates and carbon credits. Eco-design principles, which consider recyclability and modular maintenance, help to reduce e-waste and extend device lifespans.

{| class="wikitable" style="width:100%; text-align:left;"
|+ Integrated Measures for Carbon Footprint Reduction in Edge Computing
|-
! Dimension
! Techniques / References
! Contributions
! Findings
|-
| Hardware
| Low-power SoCs ([4] Xu et al.) and AI accelerators ([6] Ramesh et al.)
| Minimized idle power and specialized hardware for inference
| Notable reductions in power usage across diverse workloads
|-
| Software
| DVFS with reinforcement learning ([11] Martinez et al.) and partial offloading ([13] Zhang et al.)
| Dynamically adjusted CPU frequency and partitioned compute tasks
| Demonstrated adaptive energy savings under varying load conditions
|-
| System Orchestration
| Edge–fog–cloud migration ([16] Chiang et al.) and container optimization ([14] Hassan et al.)
| Relocated tasks across network layers using lightweight virtualization
| Improved resource utilization and reduced operational overhead
|-
| Policy/Regulation
| Carbon credits ([24] Johnson et al.) and standardized metrics ([22] White et al.)
| Encouraged greener practices through financial and reporting mechanisms
| Facilitated consistent adoption of sustainability measures across stakeholders
|}

=== Open Challenges ===
Despite clear progress, several open challenges persist. One concern is the wide heterogeneity of edge devices, complicating unified energy-saving approaches. Energy monitoring and carbon-intensity data are not consistently available worldwide, impeding real-time or dynamic optimizations [17]. Trade-offs between reliability and energy efficiency are particularly evident in mission-critical scenarios such as autonomous vehicles or healthcare, where service disruptions or latency spikes may be unacceptable [27]. Current policy frameworks differ across regions, creating fragmented regulations and disjointed compliance requirements for global operators [23]. Furthermore, security and privacy concerns arise when implementing AI-driven power management and data collection, as such systems may become attack vectors or inadvertently compromise sensitive user information [21].

=== Future Directions ===
Federated learning for energy management represents a promising avenue, allowing distributed edge nodes to collaborate on model training without consolidating sensitive data [21]. Cross-layer co-design, integrating hardware, operating system functionality, and application-level optimizations, could offer more substantial efficiency gains than focusing on single layers. The development of dynamic carbon-aware energy markets, where edge nodes can schedule tasks based on real-time prices and carbon intensity, also presents a compelling framework for sustainable resource allocation [17]. Standardized metrics and benchmarking tools for energy usage and emissions, analogous to data center metrics like Power Usage Effectiveness (PUE), would further facilitate solution comparisons across device types and vendors, while life-cycle assessments (LCAs) need to be embedded into procurement processes for edge hardware [28].


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