Wireless Sensor Networks (WSNs) are fundamental to the expansion of the Internet of Things (IoT), yet severe node energy constraints remain the primary bottleneck for remote environmental monitoring where power infrastructure is unavailable. Because these devices rely on finite battery capacities, optimizing energy usage is critical for maximizing network longevity and architectural sustainability. Sourcing literature across the Scopus, IEEE Xplore, and Elsevier digital databases, this study executes a systematic review evaluating a final cohort of n=86 contemporary energy management frameworks published between 2020 and 2026. The analysis synthesizes advanced multi-tier optimization techniques, specifically focusing on hierarchical clustering methodologies, metaheuristic routing protocols, and advanced scheduling algorithms. Beyond traditional approaches, the technical findings investigate the cross-layer impacts of duty cycle scheduling, transmission power control, and sleep protocols on maintaining rigid network coverage and connectivity. Ultimately, this review identifies significant research gaps regarding topological fault tolerance and localized load imbalances near base stations. The findings highlight how the strategic integration of cohesive, cross-layer hybrid optimization strategies can mitigate active energy dissipation, providing actionable technical recommendations for future IoT-based WSN architectures.
David Ochola, Okuthe P. Kogeda· Digital· 0 citations
The increasing complexity and volume of mobile network traffic present significant challenges to maintain consistent Quality of Service (QoS) across diverse applications. Accurate traffic classification enables application-aware resource allocation by distinguishing applications with different bandwidth, latency, and reliability requirements. Traditional classification techniques, including port-based identification and Deep Packet Inspection (DPI) have become inadequate and less effective due to widespread encryption, port masquerading, and growing privacy concerns. This paper presents a supervised learning-based approach for application-level network traffic classification specifically as a foundation for QoS optimization in future 5G networks. Since publicly available labeled 5G traffic datasets remain limited, this study uses the MIRAGE-2019 mobile traffic dataset as a proxy dataset to evaluate the proposed classification framework. A Random Forest classifier was implemented using flow-level statistical features extracted from the mobile application traffic. The framework further incorporates a rule-based QoS policy mapping informed by RFC 4594 DiffServ service class guidelines to assign application-specific priority levels, bandwidth requirements, latency sensitivity, and jitter tolerance. Experimental evaluation achieved an overall classification Accuracy of 71.83%, a Macro F1-score of 0.6701, and a Weighted F1-score of 0.7227 across twenty mobile applications. Although the experiments were conducted using a pre-5G mobile traffic dataset, the results demonstrate that supervised machine learning can effectively classify encrypted mobile application traffic and provide a practical foundation for application-aware QoS policy enforcement in future 5G and next-generation mobile networks.
Mohammed Aqeel Ismail, Okuthe P. Kogeda· Computers· 0 citations
Efficient coexistence of eMBB and URLLC services remains a critical challenge in AI-native Radio Access Networks (RANs). This paper proposes a two-timescale Hierarchical Reward Weighting (HRW) framework based on multiobjective reinforcement learning for context-aware O-RAN slicing under a Constrained Markov Decision Process (CMDP) formulation. The proposed architecture separates long-term policy adaptation from fast-timescale radio scheduling, mitigating the non-stationarity inherent in multiobjective RAN optimization. At the slow layer, a non-realtime RIC rApp exploits a long-term network context and a differentiable Softmax mapping to adapt slice reward preferences. These policies are propagated through the $O$ -RAN control hierarchy to guide downstream scheduling decisions. At the fast layer, decentralized scheduling agents embedded within the Open Distributed Unit (O-DU) MAC layer execute sub-millisecond Physical Resource Block (PRB) allocation and packet preemption, avoiding near-RT RIC transport latency constraints. Evaluated under a multiuser MIMO-OFDMA environment, the proposed framework improves resource utilization by up to 60.8% over static partitioning while maintaining bounded URLLC tail-latency behavior and strict Service Level Agreement (SLA) compliance. The results demonstrate the feasibility of AI-native hierarchical O-RAN control and align with the ITU-T visions for autonomous 6G RAN intelligence.
Charles Ssengonzi, Okuthe P. Kogeda, T. Olwal· 2026 ITU Kaleidoscope - AI a...· 0 citations