The distributed nature of Internet of Things (IoT) and its rapid increase on a large scale raises many security and privacy issues. Access control is one of the major challenges currently addressed through centralized approaches that may rely on a third party and they are constrained by availability and scalability, which may result in a performance bottleneck. Therefore, this paper proposes a novel solution to manage the delivery of lightweight and decentralized secure access control of an IoT system based on a multi-agent system and a blockchain. The main objective of the proposed solution is to build Blockchain Managers (BCMs) for securing IoT access control, as well as allowing for secure communication between local IoT devices. Moreover, the solution also enables secure communication between IoT devices, fog nodes and cloud computing.

The growth of the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to various cybersecurity attacks and threats, which lead to negative consequences. Cyberattacks can damage not just the IoMT devices in use but also human life. Currently, several security solutions have been proposed to enhance the security of the IoMT, employing machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. Furthermore, blockchain technology enables a decentralized approach to the healthcare system, eliminating some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects malicious data measured by medical sensors to find fraudulent data. As a result, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. Additionally, a blockchain network is used in the fog layer to ensure that the data is not altered, enhancing the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% based on the F1-score accuracy metric.

Software-defined networking (SDN) has emerged as a flexible and programmable network architecture that takes advantage of the benefits of global visibility and centralized control over a network. One of the main properties of the SDN architecture is the ability to offer a northbound interface (NBI), which enables network applications to access the SDN controller resources. However, the NBI can be compromised by a malicious application due to the lack of standardization and security aspects in the most current NBI designs. Therefore, in this paper, we propose a novel comprehensive security solution for securing the application–controller interface, named BCNBI. We propose a controller-independent lightweight blockchain architecture and exploit the security features of blockchain while limiting the blockchain’s computational overhead. BCNBI automatically verifies application and SDN controller credentials through token-based authentication. The proposed solution enforces fine-grained access control for each application’s API request and classifies the permission set into strict and normal policies, in order to add an extra level of security. In addition, the trustworthiness of applications is evaluated in order to prevent malicious activities. We implemented our blockchain-based solution to analyze its security, based on the confidentiality–integrity–availability model criteria, and evaluated the introduced overhead in terms of processing time and packet overhead. The experimental results demonstrate that the BCNBI can effectively secure the NBI, based on the fundamental security goals, while introducing insignificant overhead.

The growth in the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to diverse cybersecurity attacks and threats that lead to negative consequences. Cyberattacks can harm not just the IoMT devices being used but also human life. Currently, several security solutions are proposed to enhance the security of the IoMT, which uses machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. In addition, blockchain technology enables a decentralized approach to the healthcare system and eliminates some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects anomalies in data measured by medical sensors to find fraudulent data. Therefore, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. In addition, a blockchain network is used in the fog layer to ensure that the data is not altered, boosting the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% on the F1-score accuracy metric.

Quantum technology has the potential to revolutionize sensors and the Internet of Things (IoT). Efficient and secure data transfer among sensors and IoT devices is a major challenge. To address this, the manuscript presents coherent manipulation of the scattering cross section and its sensitivity based on subluminal propagation and rotary photon drag, using an atomic medium with two levels coupled with a cavity. The group index of the proposed atomic medium is measured in the range of $$-0.1\le n_g\le 150$$- 0.1 ≤ n g ≤ 150 and group velocity in the range of $$2\times 10^6$$2 × 10 6 $$m/s\le v_g\le \pm 2\times 10^9$$m / s ≤ v g ≤ ± 2 × 10 9 m/s. The maximum delay time is reported to $$t_g=0.3$$t g = 0.3 $$\mu s$$μ s and rotary photon drag to $$\theta _d=\pm 5$$θ d = ± 5 micro radian. This effect of photon drag is used in sensing, energy harvesting, and optical switching in IoT. The scattering cross-section of $$0.1\times 10^{21}$$0.1 × 10 21 $$m^2$$m 2 is reported. The scattering cross-section impacts the efficiency and reliability of quantum-based communication for IoT. The maximum cross-sectional sensitivity is measured to $$S_\sigma =0.4\times 10^{-19}m^2/n_r$$S σ = 0.4 × 10 - 19 m 2 / n r . The cross-sectional sensitivity potentially impacts secure communication and highly precise detection in sensors and IoT.

Rapid growth in electronic devices and smart appliances has significantly increased household energy consumption, peak load demand, and electricity costs. Enhancing energy efficiency in smart homes is, therefore, a critical challenge for both sustainability and affordability. This paper proposes a novel Hybrid Genetic Algorithm and Improved Dandelion Optimizer (HGAIDO) framework that intelligently schedules and manages household appliances integrated with photovoltaic (PV) systems. Unlike conventional metaheuristics, HGAIDO leverages the global search capability of Genetic Algorithms and the exploitation strength of the Improved Dandelion Optimizer, enhanced with gamma distribution, to achieve superior convergence and optimization performance. Extensive MATLAB simulations demonstrate that the proposed HGAIDO reduces energy consumption by 16.3% (171.5→143.5 kW) and lowers electricity costs by 26.2% (153.8→113.5 Rs) compared to unscheduled usage. Moreover, the Peak-to-Average Ratio is reduced from 4.3382 to 0.94461, highlighting improved load balancing and reduced grid dependency. HGAIDO outperforms standalone GA and DO algorithms in terms of convergence speed, execution time, and complexity, while ensuring high user comfort and adaptability. The findings confirm that integrating HGAIDO with IoT-enabled smart homes and PV systems not only minimizes energy consumption and costs but also contributes to sustainable living. This innovative hybrid optimization framework offers a scalable, cost-effective, and environmentally conscious solution for the next generation of smart home energy management.