Jul 2026· Technium Sustainability· Vol 11, pp. 148-163· 0 citations
TL;DR
The Hierarchical RAN Energy Optimization (HREO) framework is proposed, a three-tier, AI-driven, multi-vendor architecture that converts energy efficiency from a static configuration into a predictive, closed-loop control problem and is localised to Saudi conditions through a temperature-dependent power model and a high-irradiance solar profile.
Abstract
The rapid densification of 4G and 5G radio access networks (RAN) across the Kingdom of Saudi Arabia is driving a steep rise in energy consumption and operating expenditure, placing telecom operations in direct tension with the decarbonisation commitments of the Saudi Green Initiative and the Kingdom's 2060 net-zero target. The RAN is the dominant energy consumer in a mobile network, and conventional static energy-saving features are poorly matched to the extreme diurnal traffic and temperature swings of the Arabian Peninsula, where ambient heat both raises baseline power draw and inflates cooling load. This paper proposes the Hierarchical RAN Energy Optimization (HREO) framework, a three-tier, AI-driven, multi-vendor architecture that converts energy efficiency from a static configuration into a predictive, closed-loop control problem. A reactive cell tier exposes the advanced sleep modes (SM1–SM4) of heterogeneous Tier-1 vendor equipment; a predictive AI tier, hosted as a Near-RT RIC xApp, pairs a per-cell LSTM traffic forecaster with a reinforcement-learning sleep-policy agent whose reward explicitly trades energy saving against a hard quality-of-service penalty and a switching-cost term; and a network orchestration tier distributes policy across the multi-vendor estate through the O-RAN O1 interface and prioritises sites powered by solar-battery micro-grids. The framework is localised to Saudi conditions through a temperature-dependent power model and a high-irradiance solar profile, and all targets are expressed against published vendor power ratings and 3GPP baselines rather than proprietary operator data. The result is a deployable, vendor-neutral pathway that aligns national RAN operations with the Saudi Green Initiative while protecting user experience.
Water quality management in large-scale institutional infrastructures faces significant challenges due to the high latency of manual sampling and the energy–connectivity trade-offs in traditional IoT deployments. This paper proposes HydroSense AI, a robust three-tier IoT framework designed for real-time multi-parameter water quality monitoring and predictive analytics. The system integrates a heterogeneous sensing layer (pH, TDS, turbidity, and temperature) with a hybrid communication architecture, utilising Long Range (LoRa) technology for low-power transmission over long ranges (manufacturer-rated for line-of-sight distances of up to 16 km, and validated up to 2 km within a dense campus environment in this study), bridged via an ESP32-based gateway to the cloud. To address the critical issue of energy autonomy in remote sensing nodes, we implement a hardware-synchronised duty-cycling mechanism using a DS3231 Real-Time Clock (RTC), enabling precise deep-sleep scheduling and significantly extending battery operational life. Beyond data acquisition, the framework incorporates AI-driven trend-forecasting and anomaly-detection models to provide early warnings of water degradation through a Telegram-integrated alert system. Experimental validation over an extended deployment period demonstrates high measurement stability, with the forecasting model achieving a one-step (10-min) normalised RMSE of 0.0063 (equivalent to 0.033 pH units) for pH and 0.0298 (17.0 ppm) for TDS on a held-out test partition; a benchmark against persistence and ARIMA baselines is also provided. A complete measured energy decomposition of the deployed node is reported: hardware-synchronised duty cycling reduces the quiescent current to 18.2 μA, and with a 12 s acquisition window at 112 mA on a 10-min cycle, the mean current is 2.26 mA, corresponding to an estimated 46 days of unattended operation on a 2500 mAh cell. Critically, the acquisition window accounts for 99.2% of the per-cycle energy budget and the sleep interval for only 0.8%, so quiescent current—the figure of merit most often reported as evidence of low-power design—is shown not to be the binding constraint for sensor-dominated nodes of this class. The results indicate that the proposed hybrid architecture offers a 99.8% packet delivery ratio for sustainable water management.
Jeya Sutha Mariadhason, Emerson Raja Joseph, Purushothaman Srinivasan et al.· Italian National Conference...· 0 citations
On-demand data sensing and Wireless Power Transfer (WPT) enable sustainable operation in large-scale Industrial Internet of Things (IIoT) networks. Existing scheduling frameworks are inherently reactive, initiating charging only after an IoT node’s residual energy falls below a predefined threshold, which increases the risk of energy outages and service disruption. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework for self-sustainable IIoT networks under stochastic operating conditions. The proposed framework integrates three key components. First, a DT layer continuously mirrors IoT node states and predicts future energy availability over a finite prediction horizon. Second, a predictive charging strategy proactively schedules WPT before energy depletion occurs. Third, physical-layer security and DT-based anomaly detection protect the network against eavesdropping, false-data injection, and energy depletion attacks. Fourth, the scheduling framework is reformulated using chance constraints to explicitly account for uncertainty in wireless channels, energy consumption, and DT prediction errors while providing probabilistic reliability guarantees. The resulting sensing and WPT scheduling problems are formulated as multi-slot integer optimization problems and solved using branch-and-bound, with a low-complexity greedy heuristic for latency-sensitive deployments. Simulation results show that the proposed framework reduces energy outage events to below 1%, maintains reliable operation under increasing energy uncertainty with only a modest sensing-utility reduction, and incurs negligible computational overhead compared with the deterministic formulation.
An Artificial Intelligence (AI) centric network management framework that combines 5G radio access and core capabilities, Software-Defined Networking (SDN) for centralized and programmable control and Multi-access Edge Computing (MEC) for localized and low latency processing is introduced.
Praveen Kumar, S. Hashmi, Preeti Singhwal et al.· International journal of com...· 0 citations
High penetrations of distributed energy resources require energy regulation that combines cloud-level global optimization with edge-level fast response. This paper proposes EC-HDT, a device-edge-cloud hierarchical digital twin in which a lightweight graph-attention-temporal-convolution estimator reconstructs local states under asynchronous, noisy, and missing measurements, while a cloud predictor and model predictive controller perform rolling economic optimization. A five-factor decision weight based on communication latency, information freshness, estimation confidence, operational risk, and edge computational load continuously allocates control authority between edge and cloud, and a quadratic-programming safety layer enforces physical constraints. On the IEEE 33-bus system, EC-HDT achieves a nodal-voltage MAE of 0.0076 p.u., mean/P95 end-to-end latencies of 56.4/89.4 ms, and a 99.2% control success rate; the daily operating cost is 3.51% lower than that of the fixed-fusion scheme. The results indicate that state-aware edge-cloud coordination can improve the latency-economy-safety trade-off in distribution-system regulation.
Saudi Arabia’s expanding programme of international sporting, cultural, religious, and technology events is creating a new class of telecommunications operational challenge. Annual events such as Hajj and Riyadh Season already generate highly concentrated digital demand, while upcoming national milestones—including the AFC Asian Cup 2027, Expo 2030 Riyadh, and the FIFA World Cup 2034—will require uninterrupted connectivity across stadiums, exhibition districts, transportation systems, fan zones, airports, hotels, government platforms, and emergency-service networks.
The telecommunications challenge associated with these events extends beyond radio-access congestion. Modern mobile services increasingly depend on distributed telecom-cloud environments comprising virtualized network functions, cloud-native network functions, Kubernetes clusters, edge-computing nodes, service-mesh components, storage platforms, transport connectivity, orchestration systems, and multi-vendor infrastructure. A failure in any of these layers can affect large numbers of subscribers even when sufficient radio capacity remains available. Telecom operators must therefore move from infrastructure-centred monitoring toward predictive, service-aware, and autonomously recoverable cloud operations.
This study proposes the ResilienceCloud-KSA Framework, an AI-native telecom-cloud resilience model designed for high-demand Saudi mega-events. The framework introduces two original components. The first, EventCloud-Predict, is a multimodal service-risk forecasting engine that combines infrastructure telemetry, application-performance indicators, historical incidents, event schedules, crowd-movement forecasts, service dependencies, change records, and environmental information to estimate service-degradation risks from fifteen minutes to several hours in advance. The second, AutoRecover-KSA, is a five-layer operational architecture that coordinates anomaly detection, service-impact assessment, AI-assisted root-cause analysis, policy-governed remediation, and post-recovery learning across multi-vendor telecom-cloud platforms.
Unlike conventional capacity-planning models, the proposed framework concentrates on service continuity and cloud reliability. It addresses failures such as container exhaustion, Kubernetes-node degradation, storage latency, network-function instability, database saturation, service-mesh errors, unsuccessful software changes, virtual-machine failures, edge-cloud disconnection, certificate expiration, and cross-domain dependency breakdowns. ResilienceCloud-KSA continuously evaluates how these technical conditions may affect customer-facing services, including mobile broadband, digital ticketing, public-safety communications, transportation applications, payment platforms, media services, and event-management systems.
The research applies a mixed-method conceptual methodology combining a structured review of telecom-cloud assurance, artificial intelligence for IT operations, cloud-native orchestration, predictive maintenance, digital twins, and autonomous networks. The framework is further informed by Saudi mega-event operational requirements, international event-connectivity experience, telecom-cloud reference architectures, and established standards from 3GPP, ETSI, TM Forum, the Cloud Native Computing Foundation, and the O-RAN Alliance. An analytical scenario-based evaluation is used to assess the framework under representative Expo 2030 and FIFA World Cup 2034 operating conditions.
The findings suggest four principal outcomes. First, service degradation during mega-events cannot be managed effectively through isolated fault alarms because individual services depend on interconnected radio, transport, core, cloud, edge, application, and data layers. A service-topology model is therefore required to convert infrastructure alarms into customer-impact predictions. Second, predictive risk scoring can improve operational readiness by identifying probable resource exhaustion, network-function instability, and cascading dependency failures before customer experience is affected. Third, policy-controlled autonomous recovery—including workload scaling, container restart, traffic rerouting, service relocation, resource reallocation, rollback, and failover—can reduce recovery time when combined with human approval for high-impact actions. Fourth, a continuously updated digital operational twin can support pre-event testing, real-time decision-making, and post-event learning across geographically distributed venues.
ResilienceCloud-KSA includes an explicit human-governance mechanism called the Event Operations Authority Gate. Low-risk, reversible actions may be executed automatically within approved policies, while actions affecting public-safety services, broadcast systems, core-network functions, regulatory controls, or large subscriber populations require authorized human approval. The framework also maintains an explainable audit trail recording the detected condition, predicted impact, recommended response, approval decision, executed action, and measured recovery outcome.
The study concludes that successful telecommunications delivery for Saudi mega-events will depend not only on installing additional network capacity, but also on ensuring that the underlying telecom cloud can anticipate failures, preserve service continuity, and recover safely at machine speed. The proposed framework provides Saudi operators with a structured, multi-vendor, AI-native approach to predictive service assurance and autonomous cloud resilience. It supports phased implementation through recurring national events, Expo 2030, and FIFA World Cup 2034 while aligning with Saudi Vision 2030 priorities concerning digital leadership, operational excellence, artificial-intelligence adoption, and resilient national infrastructure.
A constrained, risk-sensitive multi-agent reinforcement learning framework is presented for joint task offloading and EH scheduling in battery-less 6G industrial networks, yielding a Pareto-non-dominated, statistically validated policy.
G. Marzoog, Ammar Kazm, Mustafa K. Ati· Sulaimani Journal for Engine...· 0 citations
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