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An energy‐aware method for task allocation in the Internet of things using a hybrid optimization algorithm
Author(s) -
Ren Xiaojun,
Zhang Zhijun,
Chen Shaochun,
Abnoosian Karlo
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5967
Subject(s) - computer science , particle swarm optimization , task (project management) , simulated annealing , internet of things , optimization problem , matlab , algorithm , mathematical optimization , engineering , systems engineering , embedded system , operating system , mathematics
Summary Internet of Things (IoT) is utilized as an emerging sample for defining the future of technology in which physical items like sensors, radio‐frequency identification tags, mobile phones, actuators, and so on, can have interaction together and have cooperation with their neighbors for obtaining joint objectives. The performance of the deployed tasks and applications on the network is considered as one of the critical goals in this model, which is achieved by the task allocation mechanism. Task allocation in the IoT is so complicated due to the intricate connection among machines. The task allocation problem is considered as an NP‐hard problem, so a new task allocation algorithm in the IoT environment is proposed using the combination of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithm. Also, the issues of the PSO algorithm, such as getting stuck in local optimization and not achieving an optimal response, forced us to present a method based on the combination of SA and the PSO algorithms. The results of simulation in MATLAB environment illustrated that the suggested method performs better compared to the PSO and SA‐based methods.