A.5. I/O and Data Communications
Somayyeh Jafarali Jassbi; Sajjad Daliri
Abstract
The rapid growth of the Internet‑of‑Things (IoT) imposes significant challenges on task offloading in fog environments, including service latency, resource constraints, and trust management. Fog computing mitigates these limitations by moving computation and storage closer to end devices. This paper ...
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The rapid growth of the Internet‑of‑Things (IoT) imposes significant challenges on task offloading in fog environments, including service latency, resource constraints, and trust management. Fog computing mitigates these limitations by moving computation and storage closer to end devices. This paper presents BCOFF (Blockchain‑based Computation Offloading Framework for Fog), a secure and efficient framework that jointly optimizes resource allocation and enables verifiable task offloading. In BCOFF, resource allocation is performed using the Grey Wolf Optimization (GWO) algorithm, while blockchain provides a tamper-resistant execution record. Specifically, the blockchain serves three purposes: (i) recording offloading decisions and cryptographic hashes of task results to support post‑execution auditability, (ii) validating the integrity of returned results by matching them with the on‑chain hash reference, and (iii) coordinating consensus among fog nodes through a lightweight Validator‑Selection Proof‑of‑Stake (VNPoS) mechanism. VNPoS is a simplified adaptation of the Nominated Proof‑of‑Stake (NPoS) model that selects validators using stake‑based nomination with variance‑aware stake normalization. By avoiding computationally intensive cryptographic puzzles, VNPoS significantly reduces consensus overhead and is therefore suitable for resource‑constrained fog environments. Experimental evaluation using the iFogSim simulator with workloads of 800–1500 tasks shows that BCOFF reduces execution time by 15–27%, lowers host‑selection latency by 22–25%, and decreases energy consumption by 5–9% compared with existing approaches. These results demonstrate that integrating GWO‑based scheduling with the VNPoS blockchain mechanism provides a more efficient and verifiable fog-offloading framework.
A.5. I/O and Data Communications
Farzane Shirazi; Nazbanoo Farzaneh
Abstract
Efficient allocation of parking spaces in urban environments remains a significant challenge due to diverse user preferences such as cost, proximity, and convenience. This paper proposes a novel intelligent parking assignment framework based on the Cheetah Optimization Algorithm (COA), a bio-inspired ...
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Efficient allocation of parking spaces in urban environments remains a significant challenge due to diverse user preferences such as cost, proximity, and convenience. This paper proposes a novel intelligent parking assignment framework based on the Cheetah Optimization Algorithm (COA), a bio-inspired metaheuristic mimicking the adaptive hunting behavior of cheetahs. The method integrates user-specific criteria in a multi-stage process, first collecting system and driver data, then applying COA to optimize parking space allocation. Compared to deep reinforcement learning and other metaheuristics like Genetic Algorithm and Whale Optimization Algorithm, COA demonstrates faster convergence, and improved solution quality. The results confirm that COA is an effective and robust approach for real-time, personalized smart parking management in dynamic urban settings.
