A Hybrid Consensus Method for Energy-Efficient and Secure IoT Data Sharing in Fog Computing, Integrating Delegated Proof of Stake and Whale Optimization Techniques

Journal of ISMAC, Journal Year: 2025, Volume and Issue: 6(4), P. 308 - 326

Published: Jan. 1, 2025

The rapid development of the Internet Things (IoT) and its widespread applications in fog computing environments have underscored urgent need for secure, scalable, energy-efficient data exchange mechanisms. This study introduces a hybrid consensus architecture designed to address these challenges by combining Delegated Proof Stake (DPoS) Whale Optimization Techniques (WOT). primary objective this model is optimize resource allocation, enhance security, minimize energy consumption while ensuring scalable efficient sharing within fog-based IoT networks. proposed methodology utilizes DPoS limit node validation select group trusted delegates, reducing computational overhead improving scalability streamlining process. Meanwhile, WOT enhances decision-making mimicking bubble-net feeding behavior humpback whales, allowing dynamic optimization allocation. integration two techniques significantly boosts system performance. Empirical results demonstrate that achieves 95% increase security 94% improvement efficiency compared conventional methods. Additionally, optimizes processing times, increases throughput, minimizes latency, facilitating real-time, low-latency communication essential applications. combination balances utilization effectively addresses trade-offs between efficiency, scalability. Consequently, DPoS-WOT emerges as robust practical solution efficient, environments.

Language: Английский

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Hybrid Prairie Dog and Dwarf Mongoose optimization algorithm-based application placement and resource scheduling technique for fog computing environment

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 7, 2025

The fog computing paradigm is better for creating delay-sensitive applications in Internet of Things (IoT). As the devices are resource constrained, deployment diversified IoT requires effective ways determining available resources. Therefore, implementing an efficient management strategy optimal choice satisfying application Quality Service (QoS) requirements to preserve system performance. Developing with many QoS criteria a non-deterministic polynomial time (NP) complete problem. study applies Hybrid Prairie Dog and Dwarf Mongoose Optimisation Algorithm-based Resource Scheduling (HPDDMOARS) Technique effectively position meet criteria. This HPDDMOARS technique formulated as weighted multi-objective placement mechanism which targets optimizing three main parameters that considered energy, cost makespan into account. It employed Optimization Algorithm (PDOA) exploring possibility helps mapping services scenario. also derived significance (DMOA) exploiting local factors helped at least one objective index. hybridized benefits PDOA DMOA mutually balancing phases exploration exploitation such potential between tasks computational resources can be achieved environment. experimental validation proposed different number confirmed minimized energy consumptions 22.18%, reduced 24.98%, lowered 18.64% than baseline metaheuristic approaches.

Language: Английский

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Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things

Future Internet, Journal Year: 2025, Volume and Issue: 17(1), P. 37 - 37

Published: Jan. 15, 2025

The Internet of Things (IoT) represents a rapidly growing field, where billions intelligent devices are interconnected through the Internet, enabling seamless sharing data and resources. These smart typically employed to sense various environmental characteristics, including temperature, motion objects, occupancy, transfer their values nearest access points for further analysis. exponential growth in sensor availability deployment, powered by recent advances fabrication, has greatly increased complexity IoT network architecture. As market these sensors grows, so does problem ensuring that networks meet high requirements availability, dependability, flexibility, scalability. Unlike traditional networks, systems must be able handle massive amounts generated frequently-used resource-constrained devices, while efficient dependable communication. This puts constraints on design IoT, mainly terms required reliability, To this end, work considers deploying technology distributed edge computing enable applications over dense with announced requirements. proposed depends at two levels: multiple fog computing. structure increases scalability, model energy nodes introduced. An energy-offloading method is considered manage energy, efficiently. developed was evaluated using testbed. Heterogeneous evaluation scenarios metrics were considered. achieved higher efficiency 19%, resource utilization 54%, latency 86%, reduced congestion 92% compared networks.

Language: Английский

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Energy-efficient task offloading and efficient resource allocation for edge computing: a quantum inspired particle swarm optimization approach

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

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Comparative Analysis of Rule-based Heuristic Algorithms for Microservice Chain Placement in Fog Computing

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104299 - 104299

Published: Feb. 1, 2025

Language: Английский

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