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<p>The Internet of Things (IoT) points to billions of connected devices that share data through the Internet. However, the increasing volume of data generated by IoT devices makes remote cloud data centers inefficient for delay-sensitive applications. In this regard, fog computing, which brings computation closer to the data source, plays a significant role in addressing the above issue. However, resource constraints in fog computing demand an effective task-scheduling technique to handle the enormous volume of data. Many researchers have proposed a variety of heuristic and meta-heuristic approaches for effective scheduling; however, there is still scope for improvement. In this paper, we propose EMAPSO (energy makespan-aware PSO). The simultaneous minimization of makespan and energy is presented as a bi-objective optimization problem. The approach also considered the load-balancing factor while assigning a task to a VM in a fog/cloud environment. The proposed algorithm, EMAPSO, is compared to standard PSO, Modified PSO (MPSO), Bird swarm optimization (BSO), and the Bee Life Algorithm (BLA). The experimental results show that the proposed method outperforms the compared algorithms in terms of resource utilization, makespan, and energy consumption.</p>

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Energy and makespan optimised task mapping in fog enabled IoT application: a hybrid approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月14日

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  • Niva Tripathy,
  • Sampa Sahoo,
  • Norah Saleh Alghamdi,
  • Wattana Viriyasitavat,
  • Gaurav Dhiman

摘要

The Internet of Things (IoT) points to billions of connected devices that share data through the Internet. However, the increasing volume of data generated by IoT devices makes remote cloud data centers inefficient for delay-sensitive applications. In this regard, fog computing, which brings computation closer to the data source, plays a significant role in addressing the above issue. However, resource constraints in fog computing demand an effective task-scheduling technique to handle the enormous volume of data. Many researchers have proposed a variety of heuristic and meta-heuristic approaches for effective scheduling; however, there is still scope for improvement. In this paper, we propose EMAPSO (energy makespan-aware PSO). The simultaneous minimization of makespan and energy is presented as a bi-objective optimization problem. The approach also considered the load-balancing factor while assigning a task to a VM in a fog/cloud environment. The proposed algorithm, EMAPSO, is compared to standard PSO, Modified PSO (MPSO), Bird swarm optimization (BSO), and the Bee Life Algorithm (BLA). The experimental results show that the proposed method outperforms the compared algorithms in terms of resource utilization, makespan, and energy consumption.