A Systematic Review of Delay-Energy Trade-off in Mobile Edge Computing: Task Dependency, Heterogeneity, and Real-World Dataset-Driven Approaches (۲۰۱۹–۲۰۲۵)

The proliferation of latency-critical and energy-constrained applications in ۵G/۶G-enabled Internet of Things (IoT) has made the delay-energy trade-off the central challenge in Mobile Edge Computing (MEC). This paper presents a comprehensive systematic literature review (SLR) of ۳۰ high-impact studies published between ۲۰۱۹ and ۲۰۲۵ that explicitly address multi-objective optimization of end-to-end delay and energy consumption under realistic constraints including task dependency, device/server heterogeneity, deadline requirements, and trust-aware allocation. We propose a novel taxonomy based on system modeling, optimization paradigm, and evaluation methodology. Special attention is devoted to real-world and large-scale dataset-driven works (especially the EUA dataset), dependency-aware DAG modeling, and heterogeneity incorporation. Critical analysis reveals that state-of-the-art approaches achieve ۱۲–۳۸ % improvement in the delay-energy product compared to single-objective baselines, yet significant gaps remain in scalability, security integration, and real testbed validation. This review concludes with a detailed research roadmap for sustainable and trustworthy MEC systems.

کلیدواژه ها:

Mobile Edge Computing ، Delay-energy trade-off ، multi-objective optimization ، Task dependency