Open Access

ARTICLE

MWaOA: A Bio-Inspired Metaheuristic Algorithm for Resource Allocation in Internet of Things

Rekha Phadke¹, Abdul Lateef Haroon Phulara Shaik², Dayanidhi Mohapatra³, Doaa Sami Khafaga^4,*, Eman Abdullah Aldakheel⁴, N. Sathyanarayana⁵

1 Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Nitte (Deemed University) Yelahanka, Bangalore, 560064, India
2 Department of Electronics and Communication Engineering, Ballari Institute of Technology and Management, Ballari, 583104, India
3 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, 522302, India
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Electronics and Communication Engineering, Vemana Institute of Technology, Bengaluru, 560034, India

* Corresponding Author: Doaa Sami Khafaga. Email:

Computers, Materials & Continua 2026, 86(2), 1-26. https://doi.org/10.32604/cmc.2025.067564

Received 07 May 2025; Accepted 18 July 2025; Issue published 09 December 2025

Abstract

Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability. During resource allocation, the MWaOA prevents early convergence, which aids in achieving a better balance between the exploration and exploitation phases during optimization. Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34% and minimizes the response time by 6% to 33% across different service arrival rates. Compared to traditional optimization algorithms, MWaOA reduces energy consumption by 5% to 30% and minimizes the response time by 4% to 28% across different simulation epochs. The proposed MWaOA provides adaptive and robust resource allocation, thereby minimizing transmission cost while considering network constraints and real-time performance parameters.

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Keywords

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