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MOFARO: A multi-objective optimization framework for dynamic task allocation in edge-fog-cloud mobile crowdsensing systems
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AI
DOI：10.1007/s10586-026-06473-3.png)
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Abstract
En 中文
Nowadays, Mobile Crowdsensing (MCS) is a crucial Internet of Things (IoT) paradigm leveraging mobile sensors for large-scale data collection. Dynamic task allocation remains a key challenge in real-world deployments. This study introduces Multi-Objective Fractal-based Artificial Rabbit Optimization (MOFARO), a multi-objective model within the Artificial Rabbit Optimization Algorithm for task allocation in edge-fog-cloud MCS environments. Tasks are modeled as Directed Acyclic Graphs (DAGs), incorporating Markov chain mobility predictions and experimental constraints to minimize execution time, energy consumption, cost, and Quality of Service (QoS) violations. Simulations on established workflows yield a Pareto front hypervolume of 0.85. MOFARO reduces execution time by 12–25%, energy consumption by 9–20%, costs by 11–27%, and improves QoS by 13–25% relative to baselines. It outperforms state-of-the-art methods by 1.8–3.9% in success rate, validating its superiority for scalable, robust MCS in dynamic settings.
Keywords:
Mobile Crowdsensing
Task Allocation
Multi-Objective Optimization
Fog Computing
Edge Computing
Task Offloading
Resource Management
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K