The mass and widespread adoption of connected mobile devices has brought about enormous social changes with significant economic, cultural and technological impacts on a society that is becoming increasingly connected. Therefore, energy efficiency in future mobile generations is becoming increasingly significant for cellular network operators. To realize future green networks based on energy efficient architecture that meets the holistic requirements in terms of capacity, this study presents an overview of the principal driving factors triggered by the next mobile generation, 5G, to fulfil the needed requirements. A detailed discussion of the various advantages and disadvantages of each of these techniques is performed to contribute to understanding how the green radio architecture for future mobile networks can be designed and what challenges may oppose this realization.

Internet of things (IoT)‐based monitoring in aquaculture presents significant challenges in remote, off‐grid environments where conventional power infrastructure is unavailable. Reliable, continuous and environmentally sustainable energy sources are essential for ensuring autonomous sensor operation and data collection integrity. This study proposes a hybrid energy harvesting system that integrates photovoltaic (PV) panels, lithium‐ion batteries and Microbial Fuel Cells (MFCs) as a novel bioelectrochemical energy source for distributed aquaculture monitoring networks. The MFC subsystem (0.01 L flat‐plate configuration, operating at 4.7 k optimal resistance with optimal generated power up to 6.657 nW) generates bioelectrochemical power for parallel battery supplementation. Experimental evaluation under realistic IoT load conditions demonstrates that the bioelectrochemical‐augmented hybrid system successfully maintains stable autonomous operation while reducing lithium battery discharge burden by 6.1% throughout complete discharge cycles directly extending battery operational lifespan. This reduction decreases electronic waste, minimises carbon footprint through extended battery lifecycle and reduces pollution and environmental burden. This work demonstrates that bioelectrochemical energy integration is technically viable for aquaculture monitoring and similar off‐grid IoT applications, establishing MFCs as a sustainable alternative energy source for greener and more resilient remote sensing infrastructure.

The deployment of Deep Learning (DL) for anomaly detection in Industrial IoT (IIoT) is critically hampered by the non-stationary nature of industrial data streams and the lack of forensic-grade explainability. This systematic review synthesizes 48 peer-reviewed studies (2021–2025) to quantify the performance collapse of static models under concept drift and to establish operational criteria distinguishing post hoc feature attribution (Type A XAI) from forensic root-cause diagnosis (Type B XAI). Our analysis reveals three critical findings: (1) static DL models suffer a 15–22% F1-score degradation across wastewater, manufacturing, and energy sectors when deployed in non-stationary environments, rendering them operationally non-viable without continuous adaptation; (2) the current literature remains saturated with Type A explainability (80% of corpus through 2023), creating a Forensic Gap where operators receive statistical correlations but lack actionable maintenance directives; and (3) emerging 2024–2025 research marks a paradigm shift toward Type B methodologies, yet no unified framework bridges real-time detection with deep causal reasoning. To address these gaps, we contribute the following: (1) a validated operational taxonomy (Cohen’s $\kappa =0.84$) with reproducible five-criterion rubric enabling forensic XAI classification; (2) the first quantitative synthesis of drift penalties in industrial deployments; and (3) a three-tier Edge-Cloud Forensic XAI architecture that achieves 70% communication payload reduction via compressed latent vectors while integrating tnGAN-based data imputation (handling 20–30% missing data) and physics-guided causal reasoning engines. Our framework decouples millisecond-level edge detection from 1–3 s cloud-based forensic diagnosis, ensuring both operational responsiveness and actionable industrial insight. We conclude that the future of safety-critical IIoT demands “Forensic-by-Design” architectures leveraging machine unlearning for drift adaptation and LLM-based natural language interfaces for operator-facing explanations, positioning Industry 5.0 to bridge the gap between algorithmic detection and human-centered decision support.