The findings reveal that the proposed framework outperforms the methods compared by achieving 22.5% lower latency and 23% lower energy consumption.
Abstract
The rapid development and expansion of the Internet of Things (IoT) ecosystem require managing increasing computational demands with the aid of advanced hierarchical architectures. The integration of a complete four-tier hierarchy—including Mobile, Edge, Fog, and Cloud layers—and the priority requirements are being overlooked in the literature despite the effort of existing studies. The goal of this study is to fill these gaps by proposing a novel, context-aware task-offloading framework designed for multi-dimensional ecosystems involving multi-server and multi-application environments. A targeted biomimetic approach is utilized at the core of this research. The decentralized foraging behavior of biological swarms is translated into a concrete engineering solution. This solution is designed specifically for computational offloading and resource management. To achieve this, a “Learning-guided Meta-heuristic” hybrid model is developed. Within this framework, bio-inspired Ant Colony Optimization (ACO) is directly integrated with an Upper Confidence Bound (UCB)-inspired exploration mechanism. Natural, pheromone-based imitation is solely relied upon by traditional biomimetic algorithms. In contrast, higher-order cognitive learning is fully incorporated by this hybrid synergy. Consequently, underlying system dynamics are adaptively learned. Local minima traps are also successfully avoided. This avoidance is achieved by dynamically selecting the optimal layer for each individual task. Both energy consumption and latency are optimized simultaneously. Meanwhile, strict operational feasibility is ensured through a dynamic penalty-based mechanism. Battery and deadline constraints are explicitly handled by this mechanism. Extensive simulations demonstrate the superiority of the proposed UCB-ACO model over state-of-the-art meta-heuristics, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), ACO, Artificial Bee Colony Optimization (ABO), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the proposed framework outperforms the methods compared by achieving 22.5% lower latency and 23% lower energy consumption. This study effectively maps the current literature and then introduces a pioneering solution for next-generation resource management in distributed computing.
The Multi-Node Allocation (MNA) problem in Internet of Things (IoT) networks arises when application requirements exceed the capacity of a single node, requiring job distribution across multiple devices. This problem is challenging in large-scale heterogeneous environments once it involves optimizing resource utilization, bandwidth consumption, and latency within a rapidly expanding search space. This paper proposes two scalable approaches: a greedy heuristic called Demand Index Multi-Node Allocation (DI-MNA) and a hybrid evolutionary algorithm (NSGA-Hyb) that combines DI-MNA with NSGA-III. Both methods use bounded combinatorial exploration and a normalized demand index to guide the search efficiently. The approaches are evaluated on IoT networks ranging from 10 to 1000 nodes under different workload conditions and compared with an optimal Branch and Bound (B&B) algorithm for small instances. Results show that DI-MNA achieves near-optimal solutions in small networks while maintaining low computational cost as network size grows. In large-scale scenarios, DI-MNA consistently matches or outperforms the evolutionary methods and sustains runtime speedups of up to 51× over NSGA-Hyb and more than 7.8×106 over B&B. These findings demonstrate that DI-MNA provides an effective balance between solution quality, scalability, and computational efficiency for resource allocation in large-scale IoT networks.
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
Energy remains the most critical and limiting resource in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) systems, directly constraining network lifetime, scalability, and real-world deployability. Although multi-hop routing is widely adopted to reduce transmission energy and balance traffic load, recent solutions increasingly rely on metaheuristic optimization and machine learning techniques whose computational, control, and learning overhead is rarely accounted for. This leads to a fundamental energy–intelligence trade-off that challenges the sustainability of intelligent routing in resource-constrained environments. This paper presents a critical, energy-centric review of multi-hop routing approaches for IoT and WSNs proposed between 2018 and 2025. Heuristic, metaheuristic, dynamic and Heterogeneous routing, reinforcement learning, deep reinforcement learning, and explainable AI-based protocols are systematically analyzed with an emphasis on net energy efficiency, scalability, feasibility on constrained devices, and model realism, rather than reported performance gains alone. The analysis reveals that energy is predominantly treated as a secondary optimization objective rather than as a governing system constraint. To address this limitation, we propose a hybrid and explainable routing framework governed by energy awareness, in which intelligence activation is explicitly conditioned on its net energy benefit. This perspective provides a principled foundation for sustainable and trustworthy intelligent routing in next-generation IoT and WSN systems.
Moez Elarfaoui, Hamdi Ouechtati, Nadia Ben Azzouna· International Conference on...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has placed unprecedented pressure on the network edge, where applications such as augmented reality, real-time analytics, and autonomous navigation demand low latency and tight energy budgets that traditional cloud-centric architectures cannot meet. Multi-access Edge Computing (MEC) addresses this gap by relocating computation closer to end users, but the core question of where and how each task should be executed remains open: rulebased and single-objective offloading strategies fail to simultaneously balance service latency, energy efficiency, and user experience under dynamic, large-scale conditions. In this paper we propose TARLOT (Two-Agent Reinforcement Learning Offloading Tasks), a cooperative framework for threetier IoT–MEC–Cloud environments. TARLOT decouples the offloading decision from the resourceallocation problem and assigns each to a dedicated Q-learning agent, so that the two subproblems are specialised independently while still being optimised jointly. The framework is evaluated on PureEdgeSim under heterogeneous IoT workloads, device densities ranging from 200 to 2,400, and diverse application profiles, and is compared against five widely-used baselines (Random, Round-Robin, Trade-Off, Pure-Edge, and Pure-Cloud). At 2,400 devices, TARLOT delivers an average service time of 1.1 s (against 4.3 s for Pure-Cloud), a Quality of Experience of 0.77 (against 0.22 for Pure-Cloud), a task-failure rate below 2 % (against nearly 14 % for Pure-Cloud), and a per-device energy consumption of only 3.6 W (against 11.2 W for Pure-Cloud) — roughly a 68 % reduction. Balanced CPU utilisation across the local, edge, and cloud tiers further confirms that TARLOT prevents resource bottlenecks, establishing it as a practical solution for next-generation large-scale IoT deployments.
Oussama Lagnfdi, Marouane Myyara, A. Darif· International journal of Com...· 0 citations
The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources, confirming the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.
H. Merouani, S. Bendib, H. Moumen et al.· Revista Internacional de Mét...· 0 citations