Handover Management for Delay-sensitive IoT Services on Wireless Software-defined Network Platforms

Mohseni, Hamed; Eslamnour, Behdis

doi:10.1109/iicita.2019.8808840

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Zhang et al [24] proposed a seamless handover authentication protocol rush for 5 g heterogeneous networks to ensure the security and efficiency of frequent handover in 5 g wireless roaming environment. Mohseni et al [25] proposed a handoff scheme based on hierarchical SDN, which reduces the number of handoffs in the mobile process. Bi et al [26] proposed a SDN-based mobility management (SDN-MM) solution to decouple mobility management and packet forwarding.…”

Section: Seamless Handovermentioning

confidence: 99%

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Zhang

Luo

2021

Knowl Inf Syst

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With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost.

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Section: Seamless Handovermentioning

confidence: 99%