Nthai, RKobo, Hlabishi IMaswikaneng, SNdlovu, L2026-09-292026-09-292026-08http://hdl.handle.net/10204/14960The rapid proliferation of the Internet of Things (IoT) has made wireless sensor networks (WSNs) a cornerstone technology for modern applications, from smart cities to industrial automation. However, the inherent constraints of WSNs, such as limited energy, bandwidth, and computational power, make them highly susceptible to network congestion. This issue leads to packet loss, increased latency, reduced throughput, and significant degradation in quality of service (QoS). Although traditional congestion control mechanisms offer some relief, they often lack the adaptability required for the complex and dynamic nature of modern WSNs. This research examines the critical role of emerging technologies in mitigating congestion in WSNs, focusing on the integration of software-defined networking (SDN) and machine learning (ML). This study provides a review of this integration, often termed ML-SDWSN, analysing how centralised, programmable control of SDN can be enhanced by predictive and adaptive capabilities of ML to create more robust congestion control frameworks. It presents a comparative analysis of traditional methods against these novel SDN/MLbased strategies, highlighting performance improvements in throughput, energy efficiency, and network optimisation. Furthermore, this study identifies key limitations and open challenges, particularly the computational overhead of implementing ML algorithms in SDWSN environments for real-time control. By synthesising the current state of the art and outlining future research directions, this review underscores the significant potential of lightweight, hybrid artificial intelligence (AI)-driven solutions to alleviate congestion, thereby enhancing network capacity, ensuring QoS, and improving the overall efficiency and longevity of these networks.FulltextenInternet of ThingsCongestionSoftware-Defined NetworksMachine LearningWireless Sensor NetworksConference Presentationn/a