The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control.

The Internet of Medical Things (IoMT) permits continuous patient monitoring, remote diagnosis, and intelligent clinical decision support using networked medical devices. However, IoMT environments are very challenging, given their stringent latency requirements, device diversity, and dynamic clinical workflows, which hinder traditional static access control (AC) approaches from performing effectively. Traditional AC systems, such as role-based AC (RBAC) and fixed attribute-based AC (ABAC), fail to adapt to real-time contextual changes, evolving user behavior, and trust dynamics in medical Internet of Things (IoT) systems. In addition, AI-driven AC solutions often operate as closed box models, limiting transparency and regulatory compliance. This article proposes a trust aware and explainable AC framework specifically designed for IoMT systems, integrating a tree-based ensemble learning model for trust evaluation, explainable artificial intelligence (XAI), and a lightweight hybrid blockchain architecture suitable for real-time IoMT deployments. The framework dynamically evaluates access requests using contextual and behavioral attributes and generates human-interpretable explanations for each access decision using SHAP-based XAI, enabling transparency and compliance with healthcare regulations. To meet IoMT latency and scalability requirements, computationally intensive machine learning (ML) and explainability tasks are executed off-chain, while access decisions and audit logs are immutably recorded using a hybrid blockchain composed of Hyperledger Fabric and Hyperledger Besu. A prototype implementation in a realistic IoMT environment demonstrates that the proposed framework achieves an access decision accuracy of 93.4%, with an average end-to-end latency of 1.8 s, which is suitable for real-time medical workflows. The off-chain AI integration reduces blockchain execution costs by 81% compared to fully on-chain solutions, while preserving security, auditability, and system reliability. These results indicate that trust aware and explainable AC can significantly enhance the security, transparency, and operational robustness of next-generation IoMT systems.

Future Internet ecosystems increasingly rely on distributed and intelligent data infrastructures, yet evidence of their evolution is fragmented across scholarly publications, patents, open-source repositories, and technology news. This study proposes the Semantic–Temporal–Authority Evidence Fusion Model (STA-EFM), a multi-source information-processing framework for identifying, ranking, and forecasting emerging data-infrastructure trajectories relevant to Future Internet ecosystems. STA-EFM integrates arXiv, PatentsView/USPTO, and GitHub records, with GDELT providing a limited complementary technology-news signal, harmonizes heterogeneous metadata, and assigns records to ten predefined technology classes using Sentence-BERT prototype similarity. A manually reviewed sample of 500 records was used to validate semantic relevance and taxonomy assignment. Source-normalized semantic evidence was combined with temporal activity, growth, momentum, recency, and source-specific authority indicators to generate monthly STA scores. A semantic threshold of 0.60 achieved an accuracy of 0.8800 and an F1-score of 0.9088, while taxonomy assignment achieved 0.8830 accuracy. Source-aware filtering reduced 2,069,979 records to 3337 relevant records. Federated Data Architectures achieved the highest mean STA score 0.3970, followed by Digital Twin Infrastructure and Graph and Vector Databases. Across the tested STA-level weighting configurations, Spearman rank correlations with the baseline ranged from 0.8667 to 0.9879. The framework provides an interpretable and traceable mechanism for monitoring emerging Future Internet infrastructure technologies.

Since 1980, the arrival of the internet has made fabulous changes, and presently, the Internet of Things (IoT) is having the same track. IoT becomes more attractive due to its potential, but on the other hand, the IoT network is targeted to be demolished. IoT networks are always under the risk of denial of services (DoSs) attacks, which have shocking significance. Under this situation, the need for cybersecurity actions like intrusion detection systems (IDSs) are very much essential. The scope of this article is to propose an IDS for big data architecture. The IoT dataset (BoT‐IoT) was used with libraries of Apache Spark, and experimental work was evaluated on the F1 measure. The dataset was divided into few parts; the partial part of dataset was examined by random forest for binary classification resulted in 98.6% F1 measure. Main categorical phase was resulted in 98.4% and subcategory classification resulted in 84.1% F1 measure. For overall classification of dataset decision tree resulted in 96.4 for binary classification, 78.6 for major category and 74% for categorical classification.

The increasing demand for sustainable and energy-aware agricultural practices due to climate change, limited natural resources, and increasing food requirements has accelerated the adoption of Internet of Things (IoT) and machine learning (ML) technologies in smart farming. This study proposes a solar-powered IoT and ML-based framework for energy-aware crop suitability prediction in precision agriculture. The system employs low-power IoT sensors to continuously monitor important environmental and soil parameters, including temperature, humidity, soil moisture, soil temperature, heat index, nitrogen (N), phosphorus (P), and potassium (K) levels. To reduce dependence on conventional energy sources, the framework is powered by solar energy, making it suitable for deployment in remote and resource-constrained agricultural environments. The collected sensor data are transmitted to a cloud-based platform for storage and real-time monitoring. Different ML models, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and Neural Network (NN), are implemented for crop suitability classification while evaluating their predictive performance and computational overhead. A real-world dataset containing 11,644 initial sensor records was collected from agricultural regions of Sindh, Pakistan, covering four major crops: cotton, rice, sugarcane, and wheat. After preprocessing, 11,643 valid records were retained for model training and evaluation. Since the original dataset was imbalanced, particularly for the Sugarcane class, tabular augmentation was applied exclusively to the training set, while the test set remained unchanged to ensure an unbiased evaluation. Experimental results demonstrate that the proposed framework provides reliable crop suitability prediction and supports data-driven agricultural decision-making under resource-constrained field conditions. Among the evaluated models, RF achieved the highest held-out test performance, with an accuracy of 94.3331%, precision of 94.5985%, recall of 94.3331%, and F1-score of 94.3727%. By integrating solar-powered IoT infrastructure with ML-based analytics, class-imbalance handling, and computational-efficiency evaluation, the proposed framework offers a practical and scalable solution for energy-aware suitability assessment in resource-constrained farming environments.