Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the...

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Vydáno v:Journal of cloud computing : advances, systems and applications Ročník 11; číslo 1; s. 1 - 17
Hlavní autoři: Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Rahiman, Amir Rizaan Abdul
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 04.06.2022
Springer Nature B.V
SpringerOpen
Témata:
Attribute reduction
Augmented reality
Computation offloading
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Decomposition
Electronic devices
Energy consumption
Energy storage
Genetic algorithms
Information Systems Applications (incl.Internet)
Internet of Things
Java
Mobile edge computing
Optimization
Particle swarm optimization
Reduction
Response time (computers)
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Task and resource scheduling
Task scheduling
Computation offloading
Task and resource scheduling
Attribute reduction
Mobile edge computing
ISSN:2192-113X, 2192-113X
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Shrnutí:The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.
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ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-022-00288-4