Editing Emerging Research Directions (section)

=== 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
|}