Profit-Optimized Computation Offloading With Autoencoder-Assisted Evolution in Large-Scale Mobile-Edge Computing

Haitao Yuan, Qinglong Hu, Jing Bi, Jinhu Lu, Jia Zhang, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Cloud-edge hybrid systems are known to support delay-sensitive applications of contemporary industrial Internet of Things (IoT). While edge nodes (ENs) provide IoT users with real-time computing/network services in a pay-as-you-go manner, their resources incur cost. Thus, their profit maximization remains a core objective. With the rapid development of 5G network technologies, an enormous number of mobile devices (MDs) have been connected to ENs. As a result, how to maximize the profit of ENs has become increasingly more challenging since it involves massive heterogeneous decision variables about task allocation among MDs, ENs, and a cloud data center (CDC), as well as associations of MDs to proper ENs dynamically. To tackle such a challenge, this work adopts a divide-and-conquer strategy that models applications as multiple subtasks, each of which can be independently completed in MDs, ENs, and a CDC. A joint optimization problem is formulated on task offloading, task partitioning, and associations of users to ENs to maximize the profit of ENs. To solve this high-dimensional mixed-integer nonlinear program, a novel deep-learning algorithm is developed and named as a Genetic Simulated-annealing-based Particle-swarm-optimizer with Stacked Autoencoders (GSPSA). Real-life data-based experimental results demonstrate that GSPSA offers higher profit of ENs while strictly meeting latency needs of user tasks than state-of-the-art algorithms.

Original languageEnglish (US)
Pages (from-to)11896-11909
Number of pages14
JournalIEEE Internet of Things Journal
Issue number13
StatePublished - Jul 1 2023

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications


  • Autoencoders
  • computation offloading
  • high-dimensional optimization algorithms
  • mobile-edge computing (MEC)
  • particle swarm optimization


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