Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm

Nematollahi, Masoud; Ghaffari, Ali; Mirzaei, Abbas

doi:10.1007/s10586-023-04041-7

Cited by 10 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Nematollahi, Ghaffari & Mirzaei (2023) designed a novel architecture for offloading jobs and allocating resources in the IoT. The architecture consisted of sensors, controllers, and fog computing (FC) servers, with the second layer employing the subtask pool approach for job offloading and utilizing the Moth-Flame Optimization (MFO) algorithm combined with opposition-based learning (OBL) for resource allocation, referred to as OBLMFO.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

show abstract

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Nematollahi, et al [28] have proposed a novel architecture for offloading jobs and allocating resources for the IoT by incorporating Fog Computing (FC). They aim to address the limitations of low processing power and the need for efficient www.ijacsa.thesai.org data processing and management in IoT applications.…”

Section: Kim and Komentioning

confidence: 99%

A QoS-Aware Resource Allocation Method for Internet of Things using Ant Colony Optimization Algorithm and Tabu Search

YIN,

2023

IJACSA

View full text Add to dashboard Cite

In today's computing era, the Internet of Things (IoT) stands out for its implementation of automation, highquality ecosystems, creative and efficient services, and higher productivity. IoT has found applications in various fields, such as education, healthcare, agriculture, military, and industry, where diverse resource requirements present a major challenge. To address this issue, we propose a novel QoS-aware resource allocation method for IoT systems. Our approach combines the Ant Colony Optimization (ACO) and Tabu Search (TS) algorithms to manage resources effectively, minimize energy consumption, reduce communication delays, and enhance overall system performance. Experimental results demonstrate the efficiency and effectiveness of our approach, with significant improvements in QoS metrics compared to traditional methods. By merging ACO and TS algorithms, our research contributes to the advancement of IoT capabilities, energy conservation, and business optimization.

show abstract

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

Rouhifar,

Hedayati,

Aghazarian

2024

Cluster Comput

View full text Add to dashboard Cite

Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm

Cited by 10 publications

References 54 publications

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

A QoS-Aware Resource Allocation Method for Internet of Things using Ant Colony Optimization Algorithm and Tabu Search

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

Contact Info

Product

Resources

About