The rapid proliferation of Internet of Things (IoT) devices across domains such as smart transportation, healthcare, and smart city infrastructure has intensified the demand for low-latency, energy-efficient, and scalable computing paradigms. While cloud computing has traditionally served as the backbone for IoT data processing, its inherent limitations have catalyzed the emergence of edge/fog computing, forming a distributed edge-cloud continuum. This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: (1) the transition from centralized to distributed and collaborative intelligence, (2) cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and (3) the nascent integration of foundation models into edge environments. We ground our discussion in three complementary case studies: smart transportation as a representative vertical domain, cross-domain heterogeneous IoT application scheduling as a horizontal perspective, and industrial transferred arc plasma monitoring as an emerging industrial IoT scenario, and conclude with forward-looking research directions, including quantum-enhanced edge optimization, edge-native continual learning, digital twin-driven resource orchestration, and neuromorphic computing for ultra-low-power edge AI.
Zhiyu Wang, Nilotpal Kapri, L. Bittencourt et al.· Frontiers in The Internet of...· 0 citations
Prefix Key-Value (KV) reuse avoids repeated prefill in Large Language Model (LLM) inference, but local misses require recomputation or replica fetches. Their relative cost varies with hardware, prefix depth, KV goodput, and replica location, making hit-rate-based placement suboptimal. To address this issue, we propose an epoch-level planner, PrefixPlace, which assigns prefix-complete targets under memory budgets and profiled demand, compute, and transfer costs. The objective decomposes into local-copy value plus first-replica coverage, and source-dependent costs yield a monotone facility-location objective; each worker update is an additive rooted-tree problem solved exactly in O(nk) time for n chunks and capacity k, giving a fixed-order 1/2-approximation that coordinate refinement and order-diverse starts improve without weakening. T4, L4, and A100 measurements reveal distinct regimes. Across 432 instances with exact optima, PrefixPlace averages 99.84% of optimum and never falls below 98.02%. In Retrieval-Augmented Generation (RAG) replays, it improves materialization-cost saving by 40.3% over vLLM Automatic Prefix Caching (vLLM-APC) and 6.3% over the best offline baseline. On WikiQA, gains are 40.4% and 5.3%. Finally, PrefixPlace solves a 50,000-node, 16-worker placement in 12.3 s on one processor, enabling timely replanning.
Results show that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure, which shows that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure.