Deep learning and metaheuristics application in internet of things: A literature review

Mohamed Akram khelili, Sihem slatnia, Okba kazar, Abdelhak merizig, Seyedali mirjalili

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Nowadays, every kind of devices with different sizes and shapes, from lamp to kitchen appliances and industrial machines, are connected and shares information digitally in large scale. Despite this tendency to use Internet in such gadgets, vast amounts of data are generated creating new challenges for researchers to analyze and control them. On the other side, Deep Learning (DL) is an appropriate tool for dealing with Internet of Things (IoT) needs, such as analyzing data, making predictions, classifying data. Acquiring the most accurate neural network inside a sensible run-time is a challenge. However, metaheuristics are the key to the success of the application of DL on IoT big data due to non-deterministic polynomial time (NP hard) problems in these areas. Many papers were published about metaheuristic in optimizing deep leaning models, but the literature lacks a study that precisely investigate the relationship between IoT, deep learning and metaheuristic. In this paper, a review of the metaheuristic's usages in the realm of IoT are presented.

Original languageEnglish
Article number104792
JournalMicroprocessors and Microsystems
Publication statusPublished - Apr 2023


  • Artificial Intelligence
  • Big data
  • Deep learning
  • Internet of things
  • Machine learning
  • Metaheuristic


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