The convergence of Internet of things (IoT) and 5G holds immense potential for transforming industries by enabling real-time, massive-scale connectivity and automation. However, the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay. These generated enormous complex, high-dimensional, high-volume, and high-speed data also brings challenges on its storage, transmission, processing, and energy cost, due to the limited computing capabilities, battery capacity, memory, and energy utilization of current IoT networks. In this paper, a seamless architecture by combining mobile and cloud computing is proposed. It can agilely bargain with 5G-IoT devices, sensor nodes, and mobile computing in a distributed manner, enabling minimized energy cost, high interoperability, and high scalability as well as overcoming the memory constraints. An artificial intelligence (AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities. The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability, compression ratio, and energy saving. This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.

Side-channel analysis (SCA) has emerged as a research hotspot in the field of cryptanalysis. Among various approaches, unsupervised deep learning-based methods demonstrate powerful information extraction capabilities without requiring labeled data. However, existing unsupervised methods, particularly those represented by differential deep learning analysis (DDLA) and its improved variants, while overcoming the dependency on labeled data inherent in template analysis, still suffer from high time complexity and training costs when handling key byte difference comparisons. To address this issue, this paper introduces invariant information clustering (IIC) into SCA for the first time, and thus proposes a novel unsupervised learning-based SCA method, named IIC-SCA. By leveraging mutual information maximization techniques for automatic feature extraction of power leakage data, our approach achieves key recovery through a single training session, eliminating the prohibitive computational overhead of traditional methods that require separate training for all possible key bytes. Experimental results on the ASCAD dataset demonstrate successful key extraction using only 50000 training traces and 2000 attack traces. Furthermore, compared with DDLA, the proposed method reduces training time by approximately 93.40​% and memory consumption by about 6.15%, significantly decreasing the temporal and resource costs of unsupervised SCA. This breakthrough provides new insights for developing low-cost, high-efficiency cryptographic attack methodologies.

Single-phase non-isolated microinverters used in photovoltaic (PV) systems commonly encounter two persistent challenges: High-frequency leakage current and fluctuating power delivery. This paper presents a novel single-phase, non-isolated multi-input microinverter topology with a common-ground structure that effectively eliminates ground leakage current without requiring additional active components. The proposed microinverter architecture integrates a dual-boost configuration and uses only four active switches. This is especially advantageous in terms of the component count, which is beneficial to enhance reliability, reduce cost, and simplify the overall system design. With one, two, or four PV inputs, it can operate without interruption under unbalanced voltage or partial shading and even if some inputs drop to zero. A tailored modulation scheme minimizes conduction losses while maintaining a stable direct-current (DC)-link voltage, and a decoupling capacitor efficiently absorbs the single-phase pulsating power, thus overcoming one major limitation in existing microinverter designs. By validating with a 1-kW GaN-based prototype, both the simulated and experimental results demonstrate its high efficiency, robustness, and practical suitability for cost-effective PV applications, with a peak efficiency value of 94.8%.