Abstract
Mobile edge computing (MEC) is a technology that transfers resource to the edge of network, which spares more attention to giving users easier access to network and computation resources. Due to the large amount of data computation needed for devices in Internet of Things, MEC technology is applied to improve computing efficiency. Though MEC can be applied to the Internet of Things, it needs further consideration on how to efficiently and reasonably allocate computing resources, and how to minimize the computing time of all users. This paper proposes a computing resources allocation scheme based on hybrid quantum-behaved particle swarm optimization. Simulation experiments with the network environment based on the Internet of Things is carried out. The results show that this algorithm can accelerate the whole computing process and reduce the number of iterations.
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Acknowledgment
The work presented in this paper was partially supported by the 2015 National Natural Science Foundation of China (Grant number 61401381).
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Dai, S., Liwang, M., Liu, Y., Gao, Z., Huang, L., Du, X. (2018). Hybrid Quantum-Behaved Particle Swarm Optimization for Mobile-Edge Computation Offloading in Internet of Things. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_26
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DOI: https://doi.org/10.1007/978-981-10-8890-2_26
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