LoRaWAN has become a foundational technology in the Internet of Things (IoT) landscape due to its long-range communication and energy efficiency. However, its default Adaptive Data Rate (ADR) mechanism struggles to adapt to dynamic environments and dense network deployments, where co-channel interference and the coupling between transmission parameters limit its ability to ensure reliable and energy-efficient communication. To address these challenges, this paper proposes MARLTC, an ADR mechanism based on Multi-Agent Rein-forcement Learning (MARL) that jointly optimizes spreading factor and transmission power using realistic observable metrics, including recent transmission history, observed signal-to-noise ratios, and distribution of spreading factors in the network. The transmission configuration problem is modeled as a cooperative Markov Game and solved using the Centralized Training and Decentralized Execution (CTDE) paradigm, where pre-trained policies are deployed at end devices to infer suitable transmission parameters with reduced convergence time. The results using a realistic LoRaWAN simulator show that MARLTC achieves up to 67.9% faster convergence, 62.1% higher energy efficiency, and a 5.0% better packet delivery ratio compared to state-of-the-art approaches, highlighting its scalability and responsiveness in dense deployments. The practical feasibility of MARLTC is further validated using physical LoRaWAN hardware, proving that the resulting policies meet the memory and timing constraints of resource-constrained IoT devices.

Fog-cloud task scheduling faces the dual challenge of maintaining critical IoT workloads despite node failures while adhering to strict energy budgets. We present SIREN, a game-theoretic framework that treats fog nodes as strategic players, embedding reliability benefits and DVFS-aware energy costs directly into their payoffs. By searching the joint strategy space with a Memory-Driven Grey Wolf Optimizer (MDGWO), SIREN adapts placements, selective replication, and frequency settings to workload dynamics. Extensive evaluations on the Alibaba 2018 and Google 2011 cluster traces and on a latency-critical healthcare application demonstrate that SIREN converges to near-Nash schedules that minimize energy while maximizing reliability. Results confirm that SIREN delivers (i) 100% task success rates in critical healthcare scenarios, (ii) 2.08×–4.24× lower worst-case energy consumption than leading baselines, and (iii) a 3.9×–5.8× reduction in network usage, establishing a new benchmark for resilient, energy-efficient fog computing.

The rapid growth of cloud computing and the expansion of edge and IoT technologies are becoming essential for meeting the performance, scalability, and latency requirements of modern distributed service-based applications. While these applications facilitate the accommodation of real-world workloads, their placement across a computing continuum spanning the cloud, edge, and IoT remains challenging. Existing service scheduling works often rely on simulations or orchestration systems limited to a single cluster. While simulations fail to capture real-world constraints, single-cluster orchestration systems introduce significant overhead and cannot capture the heterogeneity, network latency, and cross-cluster dependencies across cloud, edge, and IoT layers. In this paper, we introduce Pulse, a fully distributed scheduling system designed to optimize the deployment of distributed service-based applications across multi-cluster environments. Pulse leverages a two-phase distributed multi-objective optimization approach: locally optimizing for monetary cost and fairness within clusters, and globally optimizing for monetary cost and latency across multiple clusters. Pulse is built atop a lightweight orchestration framework, which enables service coordination across cloud, edge, and IoT layers, ensuring adaptability to heterogeneous infrastructures and the latency among geo-distributed clusters. To validate our approach, we conduct a comprehensive real-world evaluation on the Grid’5000 infrastructure, demonstrating that Pulse outperforms state-of-the-art scheduling methods by improving total resource utilization by 34.5%, reducing monetary cost by 82.6%, and lowering the average end-to-end network latency among services by 75.0%. These results highlight Pulse’s effectiveness in managing large-scale, service-based applications in realistic, multi-cluster environments.

The relentless growth of mobile Internet of Things (IoT) devices has shifted computation toward a distributed computing continuum, spanning edge, fog, and cloud layers, where energy efficiency, low latency, and dynamic node mobility are critical yet often conflicting goals. Existing scheduling frameworks struggle to balance these demands under real-world conditions, especially as device movement and heterogeneous workloads increase system complexity. We present MOSAIC, a mobility-aware scheduling and resource management framework designed to optimize performance in dynamic IoT environments. Our approach introduces three key innovations. First, a refined five-tier architecture extends the traditional edge-fog-cloud hierarchy by adding proximity, local, and regional mobility layers, enabling computation to follow mobile users more effectively and reducing unnecessary network traffic. Second, MOSAIC integrates a preemption-aware dynamic scheduler with an Adaptive-$\lambda$reinforcement learning-based resource manager that adapts based on workload changes and mobility patterns, prioritizing energy-efficient edge execution while meeting strict deadlines. Third, the framework utilizes real-world mobility traces, including Levy-Walk, Random-Walk, and Geolife, to drive reconfiguration and improve decision accuracy. We evaluate MOSAIC through a large-scale deployment across three geographically distributed regions of the Grid'5000 testbed, using realistic workflows and mixed periodic/DAG task loads. Our results show that, compared to state-of-the-art schedulers, MOSAIC reduces energy consumption by 35.9%–×1.5, lowers latency by 42.8%–×4.9, and shortens makespan by 22.6%–×7.2, all while maintaining 100% deadline satisfaction across diverse mobility scenarios.

This paper presents DIST, a novel distributed reinforcement learning-based (DRL) framework for energyefficient and reliable task scheduling and resource allocation in fog computing, low-latency computing solutions driven by the rapid deployment of IoT devices, and time-sensitive applications. DIST is built based on a novel distributed Q-learning to enable fog nodes to learn an optimal strategy to balance energy consumption, task execution time, and system reliability. The main novelty includes a cooperative Dynamic Voltage and Frequency Scaling-enabled task scheduling policy that dynamically adjusts node energy level to ensure power consumption reduction without sacrificing deadline adherence or reliability. The results demonstrate that DIST reduces energy consumption by up to 52.26%, realizes 38% higher success rates, and reduces task wait times by up to 46.77%, compared with state-of-the-art algorithms.

Fog computing enhances cyber-physical systems (CPS) by processing data closer to the network edge. However, the performance of fog nodes is critical for maintaining system responsiveness and quality of service (QoS). This paper introduces MoTiCPS, a novel task scheduling and resource allocation method built on the Osprey Optimization Algorithm (OOA). MoTiCPS improves task reliability and balances resource use across edge devices, optimizing fog node performance under real-time constraints. Simulation results show that MoTiCPS increases task success rates by 32% and reduces energy consumption by 39%, significantly outperforming benchmark methods. These improvements highlight MoTiCPS's potential to enhance the efficiency and scalability of CPSs in various application domains.