MOTO-MASSA: multi-objective task offloading based on modified sparrow search algorithm for fog-assisted IoT applications

With the ongoing advancements and extensive utilization of internet of things (IoT) technologies, Fog computing architecture has become a hot research topic in recent years. This architecture supports numerous Cloud functionalities while addressing shortcomings using fog nodes (FNs) located close to...

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Vydáno v:	Wireless networks Ročník 31; číslo 2; s. 1747 - 1762
Hlavní autoři:	Khedr, Ahmed M., Alfawaz, Oruba, Alseid, Marya, El-Moursy, Ali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.02.2025 Springer Nature B.V
Témata:	Algorithms Ant colony optimization Cloud computing Communications Engineering Computation offloading Computer architecture Computer Communication Networks Convergence Edge computing Electrical Engineering Engineering Internet of Things IT in Business Multiple objective analysis Network latency Networks Nodes Optimization Original Paper Parameter modification Parameter sensitivity Particle swarm optimization Response time Search algorithms Swarm intelligence Wireless Sensor Network (WSN) Task Offloading Sparrow Search Algorithm (SSA) Multi-Objective Optimization Fog Computing
ISSN:	1022-0038, 1572-8196
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Abstract	With the ongoing advancements and extensive utilization of internet of things (IoT) technologies, Fog computing architecture has become a hot research topic in recent years. This architecture supports numerous Cloud functionalities while addressing shortcomings using fog nodes (FNs) located close to users. FNs focus on providing processing and storage resources to resource-constrained IoT devices that cannot enable IoT applications with intense computational demands. Also, the proximity of FNs to IoT nodes satisfies the demands for latency-sensitive IoT applications. However, due to the high demand for IoT task offloading along with the resource limitations associated with IoT, it is crucial to develop an effective task-offloading solution that takes into account a number of quality parameters. Motivated by this, a Multi-Objective Task Offloading method is proposed based on the modified sparrow search algorithm (MOTO-MSSA) for offloading the tasks to FNs. MOTO-MSSA is portrayed as a multi-objective optimization method for reducing cost and response time. Extensive simulations demonstrate the superiority of MOTO-MSSA over existing techniques in three different situations with varying number of FNs, service availability, and data arrival rates. The proposed MOTO-MSSA demonstrates a significantly faster convergence speed, being approximately 2, 3.2, 3.4, 3.5, and 3.7 times faster than sparrow search algorithm (SSA), ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony optimization (ABC), and round robin (RR), respectively. In scenario 1, it reduces the average response time (ART) by 5%, 12%, 16%, 11%, and 30% compared to SSA, ACO, PSO, ABC, and RR, respectively. Additionally, MOTO-MSSA reduces costs by approximately 2%, 9%, and 11% compared to SSA, ACO, and PSO. The results reveal that MOTO-MSSA boosts convergence speed and exceeds existing techniques in terms of cost and response time with minimum overhead.
AbstractList	With the ongoing advancements and extensive utilization of internet of things (IoT) technologies, Fog computing architecture has become a hot research topic in recent years. This architecture supports numerous Cloud functionalities while addressing shortcomings using fog nodes (FNs) located close to users. FNs focus on providing processing and storage resources to resource-constrained IoT devices that cannot enable IoT applications with intense computational demands. Also, the proximity of FNs to IoT nodes satisfies the demands for latency-sensitive IoT applications. However, due to the high demand for IoT task offloading along with the resource limitations associated with IoT, it is crucial to develop an effective task-offloading solution that takes into account a number of quality parameters. Motivated by this, a Multi-Objective Task Offloading method is proposed based on the modified sparrow search algorithm (MOTO-MSSA) for offloading the tasks to FNs. MOTO-MSSA is portrayed as a multi-objective optimization method for reducing cost and response time. Extensive simulations demonstrate the superiority of MOTO-MSSA over existing techniques in three different situations with varying number of FNs, service availability, and data arrival rates. The proposed MOTO-MSSA demonstrates a significantly faster convergence speed, being approximately 2, 3.2, 3.4, 3.5, and 3.7 times faster than sparrow search algorithm (SSA), ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony optimization (ABC), and round robin (RR), respectively. In scenario 1, it reduces the average response time (ART) by 5%, 12%, 16%, 11%, and 30% compared to SSA, ACO, PSO, ABC, and RR, respectively. Additionally, MOTO-MSSA reduces costs by approximately 2%, 9%, and 11% compared to SSA, ACO, and PSO. The results reveal that MOTO-MSSA boosts convergence speed and exceeds existing techniques in terms of cost and response time with minimum overhead.
Author	Alfawaz, Oruba Alseid, Marya Khedr, Ahmed M. El-Moursy, Ali
Author_xml	– sequence: 1 givenname: Ahmed M. orcidid: 0000-0001-7957-7862 surname: Khedr fullname: Khedr, Ahmed M. email: akhedr@sharjah.ac.ae organization: Department of Computer Science, University of Sharjah – sequence: 2 givenname: Oruba surname: Alfawaz fullname: Alfawaz, Oruba organization: Research Institute of Sciences and Engineering, University of Sharjah – sequence: 3 givenname: Marya surname: Alseid fullname: Alseid, Marya organization: Department of Computer Engineering, University of Sharjah – sequence: 4 givenname: Ali surname: El-Moursy fullname: El-Moursy, Ali organization: Department of Computer Engineering, University of Sharjah
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SubjectTerms	Algorithms Ant colony optimization Cloud computing Communications Engineering Computation offloading Computer architecture Computer Communication Networks Convergence Edge computing Electrical Engineering Engineering Internet of Things IT in Business Multiple objective analysis Network latency Networks Nodes Optimization Original Paper Parameter modification Parameter sensitivity Particle swarm optimization Response time Search algorithms Swarm intelligence
Title	MOTO-MASSA: multi-objective task offloading based on modified sparrow search algorithm for fog-assisted IoT applications
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