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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Okuthe P. Kogeda

Tshwane University of Technology

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Review Open access Aug 2026

Energy Optimization Strategies in IoT-Based Wireless Sensor Networks: A Systematic Review

A systematic review evaluating a final cohort of n=86 contemporary energy management frameworks published between 2020 and 2026 highlights 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 · 0 citations
Open access Jul 2026

Machine Learning-Based Mobile Traffic Classification for QoS-Oriented Network Management

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 · 0 citations
Jul 2026

Hierarchical Multi-Objective Learning for Context-Aware 5G Ran Slice Resource Allocation

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 · 0 citations

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