Application areas such as real-time visual analytics over high-bandwidth 6G networks, low-power camera networks in remote or low-light environments and surveillance drones, usually operate under insufficient lighting conditions. The captured images are often of low quality, poor resolution and poor visual clarity, leading to reduced visibility, color distortion, and amplified noise. Existing methods of Low-light image enhancement suffer from low accuracy with compromised reliability, trust and fairness. Inspired by the zero-reference learning paradigm of Zero-DCE++, this work aims to investigate the impact of data pre-processing and augmentation strategies for improving the performance of real-time, mission-critical security systems where low-light surveillance images are used for critical decision making. The proposed method uses FFDNet for denoising, exposure fusion for illumination improvement and data augmentation for bias mitigation and performance optimization through diverse training samples. The method is curated for edge deployment on constrained IoT hardware, with low latency and energy efficient usage in 6G-IoT visual systems. The proposed model is aimed at performance improvement on trust driven visual improvements, reduced distributional bias, and deployment fairness across diverse lighting conditions and scenarios. Comparative analysis demonstrates that with the help of zero-reference deep curve estimation, the proposed,DA-Zero-DCE++, pipeline achieves improved performance as compared to state-of-the-art low-light image enhancement methods. Our best configuration, which combines exposure fusion-based augmentation and mild denoising using FFDNet, achieves an average PSNR of 15.34dB, SSIM of 0.4869, and MAE of 40.87 on theSICE datasetat 1200 × 900 resolution. For high-level vision applications such as real-time visual analytics over highbandwidth 6G networks, low-power camera networks in remote or low-light environments, the performance is further validated on DarkFace dataset where high average precision at intersection over union of 0.5 is achieved.

The rapid evolution of Artificial Intelligence of Things (AIoT) is accelerating the development of smart societies, where interconnected consumer electronics such as smartphones, IoT devices, smart meters, and surveillance systems play a crucial role in optimizing operational efficiency and service delivery. However, this hyper-connected digital ecosystem is increasingly vulnerable to sophisticated Android malware attacks that exploit system weaknesses, disrupt services, and compromise data privacy and integrity. These malware variants leverage advanced evasion techniques, including permission abuse, dynamic runtime manipulation, and memory-based obfuscation, rendering traditional detection methods ineffective. The key challenges in securing AIoT-driven smart societies include managing high-dimensional feature spaces, detecting dynamically evolving malware behaviours, and ensuring real-time classification performance. To address these issues, this paper proposed an AI-powered Android Malware Detection (AIMD) framework designed for AIoT-enabled smart society environments. The framework extracts multi-level features (permissions, intents, API calls, and obfuscated memory patterns) from Android APK files and employs graph embedding techniques (DeepWalk and Node2Vec) for dimensionality reduction. Feature selection is optimized using the Red Deer Algorithm (RDA), a metaheuristic approach, while classification is performed through an ensemble of machine learning models (Support Vector Machine, Decision Tree, Random Forest, Extra Trees) enhanced by bagging, boosting, stacking, and soft voting techniques. Experimental evaluations on CICInvesAndMal2019 and CICMalMem2022 datasets demonstrate the effectiveness of the proposed system, achieving malware detection accuracies of 98.78% and 99.99%, respectively. By integrating AI-driven malware detection into AIoT infrastructures, this research advances cybersecurity resilience, safeguarding smart societies against emerging threats in an increasingly connected world.

ABSTRACT Objective To design a secure Federated Learning (FL) framework for Internet of Medical Things (IoMT) that protects sensitive patient data from both classical and quantum attacks. Methods Proposed the QSFedMA‐IoMT protocol integrating quantum and classical security techniques. Utilized entanglement‐based E91 protocol for generating a highly secure root key to establish trust. Applied BB84 protocol for efficient generation of per‐round session keys during FL updates. Incorporated classical cryptographic scheme AES‐GCM for secure communication. Employed privacy‐enhancing techniques such as norm‐clipping and Gaussian noise to mitigate information leakage during model training. Results Our work demonstrates robust resistance against both classical and quantum adversaries, while enhancing data privacy through secure key distribution and differential privacy mechanisms. It ensures the integrity of model updates within the federated learning process and achieves an effective balance between strong security guarantees and computational efficiency, making it well‐suited for IoMT environments. Conclusion The QSFedMA‐IoMT protocol delivers a robust and practical hybrid framework for securing federated learning in healthcare systems. By integrating E91 and BB84 protocols, it strengthens key management and trust establishment. The combination of quantum security with classical privacy‐preserving techniques ensures resilience, scalability, and efficiency. Overall, this work provides a promising direction for secure and privacy‐aware federated learning in next‐generation IoMT applications.

Internet of Vehicles (IoV) is a new type of network that enables communication among vehicles, infrastructure, and users, providing better traffic systems. When exchanging information, the IoV systems face challenges such as data privacy, access control, and trust management issues. The security mechanisms are utilized to address privacy and trust issues. However, existing solutions still face adaptability, scalability, and efficiency issues when handling the dynamic requirements of IoV ecosystems. This study addressed security issues by designing the Quantum-Inspired Metaheuristic Framework (QIMF), which integrates a quantum optimization algorithm and adaptive privacy agreements to enhance security between IoV entities. During the analysis, the trust score is evaluated for all entities, which helps to minimize unauthorized access and manage trustworthiness using the quantum-inspired approach while exchanging information. The private agreement is assessed via the computed trust score, which improves the policy’s construction. According to the policy, secure access control is achieved by integrating the quantum superposition and tunnelling features to ensure scalable and secure transactions. The discussed system is implemented using the Python tool, and the CICIoV2024 dataset is used to evaluate the discussed system’s efficiency. The QIMF achieves 99.2% trust accuracy with a minimum false acceptance and rejection rate. The integrated approach ensures scalable and secure solutions for improving vehicular networks.

In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our models outperform existing methods in terms of accuracy and reliability across all three datasets. The Pipelined Model, in particular, is notable for its ability to combine predictions from multiple models, leading to higher accuracy and impressive scalability. This scalability allows for their application in diverse fields like technology, finance, and healthcare. Furthermore, these models can be adapted for real-time and streaming data analysis, making them valuable for applications such as fraud detection, stock market prediction, and IoT sensor data analysis. Enhancements to the models also make them suitable for big data applications, ensuring their relevance for large datasets and distributed computing environments. It is important to acknowledge some limitations of our models, including potential data biases, specific assumptions, increased complexity, and challenges related to interpretability when using them in practical scenarios. Nevertheless, these innovations advance predictive modeling, and our comprehensive evaluation underscores their potential to provide increased accuracy and reliability across a wide range of applications. The results indicate that the proposed models outperform existing models in terms of accuracy and robustness for all three datasets. The source code can be found at https://huggingface.co/DebajyotyBanik/Ensemble-Pipelined-Regression/tree/main

Information-centric networking (ICN) allows data to be cached at each node in the network. It is vital in vehicular networks (VNs) to improve caching performance and reduce content delay in high-traffic scenarios. In cooperative VNs, the requested content can be cached in the base station or nearby nodes without fetching the requested content from the server. The existing content popularity approaches face challenges in predicting popular content due to a time-varying environment, resulting in popularity being changed frequently. It is hard to predict such content in highly dynamic vehicular traffic. Therefore, the current approaches are less practical in a realistic scenario. This paper proposes an intelligent caching method for massive traffic in VNs to address these issues based on deep transfer learning (DTL). The primary purpose of this study is to reduce the system cost and content delay by increasing the cache hit rate based on popular data in dynamic traffic. The proposed solution uses a collaborative cache with social interaction among clusters to share the most popular content (MPC). Furthermore, it designs a time-varying mechanism to predict content popularity in a highly dynamic environment and share the widespread knowledge with other target nodes based on DTL. In addition, a content update method is developed to address the content replacement in a cooperative cache environment. Based on thorough analysis and evaluation, similar and dissimilar contents on base stations are classified among source and target clusters. The extensive simulation and experimentation confirm that the developed work achieved better than baseline studies.

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high‐level systematic and taxonomical survey, we have organized the energy optimization strategies for EH‐WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross‐layer optimization, schemes based on cross‐layer optimization, sleep/wake‐up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility‐based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH‐WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.