| Summary: | With the increasing requirements for computing in modern society, Multi-access Edge Computing (MEC) has received widespread attention for meeting low-latency. In MEC network, mobile devices can offload computing-intensive tasks to edge servers for computing. Wireless Power Transmission (WPT) provides initial energy for mobile devices, and the tasks of mobile devices consume energy when they are locally calculated or completely offloaded. The combination of the two technologies forms the Wireless Powered Mobile Edge Computing (WP-MEC) network. In this article, considering the impact of WPT transmission time <inline-formula> <tex-math notation="LaTeX">$\tau _{0}$ </tex-math></inline-formula>, we study the offloading and scheduling of tasks for multiple mobile devices in the WP-MEC network, which is an NP-hard problem. We formulate this scheduling problem to minimize the time delay under the constraint of WPT transmission energy. We regard our problem studied in this paper as a multidimensional knapsack problem (MKP). The difference is that the knapsack capacity in MKP is limited, while in our problem, the knapsack that one item can choose is limited. Therefore, we improve the Artificial Fish Swarm Algorithm (AFSA) and propose Computation Scheduling Based on the Artificial Fish Swarm Algorithm (CS-AFSA) to find the optimal scheduling. We encode a scheduling scheme as an artificial fish and regard the delay corresponding to the scheduling as the optimization object. The optimal artificial fish can be gradually approached and determined through the swarm, follow and prey behavior of artificial fish. The optimal artificial fish is the optimal scheduling scheme. More importantly, based on the original behavior of AFSA, we also improve the scheme that does not meet the WPT energy constraint, including the modification of infeasible artificial fish and insufficient artificial fish. Besides, we also consider how to find the best WPT transmission time <inline-formula> <tex-math notation="LaTeX">$\tau _{0}$ </tex-math></inline-formula>. Finally, we perform data simulation on the proposed algorithm.
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