A Bee Colony-Based Optimized Searching Mechanism in the Internet of Things

Muhammad Sher Ramzan (), Anees Asghar, Ata Ullah, Fawaz Alsolami and Iftikhar Ahmad
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Muhammad Sher Ramzan: Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Anees Asghar: Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Ata Ullah: Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Fawaz Alsolami: Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Iftikhar Ahmad: Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Future Internet, 2024, vol. 16, issue 1, 1-16

Abstract: The Internet of Things (IoT) consists of complex and dynamically aggregated elements or smart entities that need decentralized supervision for data exchanging throughout different networks. The artificial bee colony (ABC) is utilized in optimization problems for the big data in IoT, cloud and central repositories. The main limitation during the searching mechanism is that every single food site is compared with every other food site to find the best solution in the neighboring regions. In this way, an extensive number of redundant comparisons are required, which results in a slower convergence rate, greater time consumption and increased delays. This paper presents a solution to optimize search operations with an enhanced ABC (E-ABC) approach. The proposed algorithm compares the best food sites with neighboring sites to exclude poor sources. It achieves an efficient mechanism, where the number of redundant comparisons is decreased during the searching mechanism of the employed bee phase and the onlooker bee phase. The proposed algorithm is implemented in a replication scenario to validate its performance in terms of the mean objective function values for different functions, as well as the probability of availability and the response time. The results prove the superiority of the E-ABC in contrast to its counterparts.

Keywords: data replication; bee colony optimization; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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