Aug 2026· Future Technology· Vol 5, pp. 45-56· 0 citations
TL;DR
DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management, is presented.
Abstract
The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
M. Harandi, Afshin Yaghoobi· Scientific Reports· 0 citations
The rapid proliferation of IoT devices and ecosystems creates significant challenges in managing increasing data traffic and service requests while maintaining system performance [1]– [3]. In oneM2M-based IoT systems, overloaded Common Service Entities (CSEs) can become bottlenecks, leading to resource saturation, higher latency, and request loss [4]. To address these challenges, this paper proposes IoTScal-2CoM-ALO, an adaptive load orchestration framework that introduces a two-level collaboration model (2CoM) enabling distributed CSEs to cooperate within and across domains. The framework incorporates an Adaptive Load Orchestration (ALO) mechanism that continuously monitors key performance indicators, including CPU utilization, memory consumption, round-trip time (RTT), and packet loss, to detect overload conditions and dynamically redirect traffic to suitable neighboring CSEs. The proposed approach is evaluated in a simulated distributed oneM2M environment under heterogeneous traffic conditions. Experimental results demonstrate significant performance improvements compared with non-collaborative and static collaboration approaches, achieving up to 73% reduction in memory consumption, RTT peak reductions of up to 4750 ms, and success rate improvements of approximately 4.8%. These results highlight the effectiveness of IoTScal-2CoM-ALO in improving resource utilization and maintaining service continuity in scalable IoT systems.
S. Abourriche, A. Zyane, A. Ghammaz· International Conference on...· 0 citations
The proliferation of Internet of Things (IoT) devices has generated unprecedented volumes of data, rendering traditional cloud-centric processing paradigms inadequate due to latency constraints, bandwidth limitations, and privacy concerns. This paper presents a comprehensive mathematical framework for distributed intelligence at the edge, enabling decentralized learning across heterogeneous IoT networks. We propose a novel Federated Edge Learning (FEL) architecture that integrates software-defined networking principles with gossip-based communication protocols to facilitate collaborative model training while preserving data locality. The framework addresses critical challenges including device heterogeneity, non-independent and identically distributed (non-i.i.d.) data distributions, resource constraints, and communication efficiency. We formalize the decentralized learning problem, derive convergence bounds under heterogeneous conditions, and introduce a multi-worker selection mechanism optimized through swarm learning principles. Experimental validation using real-world IoT datasets demonstrates that our approach achieves 30-50% reduction in training latency and 35-55% decrease in energy consumption compared to conventional federated averaging methods, while maintaining competitive accuracy of 92.86% on classification tasks . The proposed framework offers a scalable, privacy-preserving solution for deploying artificial intelligence at the network edge.
N.Durga, A. Mary Posonia, Selvakumar et al.· International journal of com...· 0 citations
The rapid growth of Internet of Things (IoT) deployments has intensified the need for efficient, decentralized computation management at the network edge. This paper presents a lightweight, neighbor-aware one-hop task offloading framework designed for resource-constrained IoT networks. The proposed adaptive scheme combines Exponential Weighted Moving Average (EWMA) load estimation with a queue-depth gate to prevent unnecessary offloading under transient load spikes, and an assignment-pressure mechanism to distribute tasks more evenly across neighboring nodes. We evaluate the framework using a custom-developed discrete-event simulator on a 90-node ringplus-chord topology with heterogeneous hotspot and light nodes, comparing against three baselines: local-only execution, random offloading, and least-loaded neighbor selection. Results show that a load-aware but pressure-unaware least-loaded strategy surprisingly produces the highest load variance (377.25), worse than random offloading (114.84), due to severe task funneling toward persistently fast nodes. The proposed scheme eliminates task drops entirely, achieves an average latency of 148.7 ms, and reduces task-count variance to 44.33 - an $8.5 \times$ improvement over the least-loaded baseline and 4.6× over local-only execution - while requiring only 28.21% of tasks to be offloaded. These results demonstrate that assignment-pressure tracking is essential for fair load distribution in energy-limited IoT deployments.
Faizan Haider, Alexandre dos Santos Roque, E. P. de Freitas· International Conference on...· 0 citations
As urban populations grow, smart cities increasingly depend on real-time environmental monitoring to enable sustainable development and efficient urban management. Conventional IoT systems often suffer from limited communication range, high power consumption, and unreliable data transmission. This paper presents a novel, scalable IoT architecture for smartcity monitoring that fully leverages ESP32 microcontrollers with integrated edge computing. The proposed design combines ESP-NOW, Wi-Fi, and MQTT protocols within a mesh-enabled framework, reducing latency and energy usage while enhancing resilience and coverage. Data is preprocessed at the edge before centralized aggregation on a Raspberry Pi backend, minimizing network overhead. Experimental results demonstrate that the architecture reliably communicates data, maintains precision, and exhibits strong resilience against node failures, including automatic gateway node replacement when disruptions occur. The design ensures robust fault tolerance and efficient operation, making it a practical and cost-effective solution for nextgeneration urban monitoring infrastructures.
Jimin Qian, Po-Ling Huang, Hsin-Tzu Lai et al.· International Conference on...· 0 citations
Massive Internet of Things (IoT) deployments increasingly share spectrum with incumbent, licensed, and unlicensed systems under uncertain traffic, fading, mobility, and intermittent coordination. Existing mechanisms, including fixed power limits, listen-before-talk procedures, spectrum access databases, and learning-based resource allocation, address important aspects of coexistence, but they do not provide a common control plane to translate a network-wide interference risk budget into lightweight guidance for many autonomous devices. This article introduces Distributed Spectrum Compliance and Orchestration (DISCO), a hierarchical architecture that separates local spectrum learning from edge-level compliance regulation and slower cloud or non-terrestrial-network context adaptation. DISCO is not presented as a new reinforcement-learning optimizer or as a replacement for statutory spectrum rules. Its contribution is a deployable compliance plane that monitors violation statistics, broadcasts a compact governance signal, and adjusts policy aggressiveness without centralizing every transmission decision. A 30-seed UAV coexistence case study illustrates the efficiency--risk trade-off: the reported mean throughput is 81.0~Mbps, 73\% above fixed-power control, while the mean violation rate is 0.053 compared with 0.126 for uncoordinated learning. Because the 95\% confidence interval, [0.030, 0.076], crosses the nominal target of 0.06, the evidence supports statistical regulation near the target, not guaranteed regulatory compliance. Deployment, complexity, adoption boundaries, and open validation requirements are discussed explicitly.