SWARM INTELLIGENCE IN INTERNET OF THINGS ENVIRONMENTS: A SCOPING REVIEW
DOI:
https://doi.org/10.35631/JISTM.1143010Keywords:
Internet of Things (IoT), IoT Optimization, Resource Allocation, Smart Cities, Swarm Intelligence (SI)Abstract
The rapid growth of the Internet of Things (IoT) has created significant challenges related to energy consumption, routing efficiency, resource allocation, communication reliability, and cybersecurity. Swarm Intelligence (SI) techniques have increasingly been adopted due to their adaptive and distributed optimization capabilities to address these issues. This study aimed to identify the SI techniques applied in IoT environments and to examine their major application domains and optimisation objectives. This scoping review is based on the six-stage framework proposed by Arksey and O'Malley. Scopus and Web of Science (WOS) are two databases used to find relevant studies based on the topic and published between 2020 and 2026. 19 journal articles were selected and analyzed using thematic and comparative approaches after applying the screening and eligibility criteria. The findings found that SI techniques have been widely adopted in a variety of IoT applications, including in cybersecurity, WSNs, fog computing, IIoT, smart cities, and intelligent transportation systems. Frequently applied SI techniques included PSO, ACO, ABC, SSA, CSA, ACS, FMGWO, and several hybrid optimisation frameworks. These approaches were found to improve energy efficiency, routing performance, resource allocation, QoS, communication reliability, intrusion detection, and network lifetime. The review also identified an increasing trend towards hybrid SI approaches that integrate swarm-based optimisation with deep learning, federated learning, and evolutionary optimization techniques. To conclude, this review demonstrates the significant contribution of swarm intelligence to IoT environment. Although there are some challenges that remain unresolved. In the future, research should focus on addressing these limitations to improve the adaptability and effectiveness of optimization strategies in IoT systems.
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