These in recent days, the proliferation of the internet of things (IoT) within the emergence of five-generation (5G) networks has received a huge attention in both industrial and academic domains. A 5G network is a cornerstone of realizing the full potential of the IoT, which interconnects billions of devices wirelessly. However, wireless communication in IoT devices reveals tremendous security risks in different dimensions and precisely in the distribution of the user certificates. The existing X.509 PKI, or the proposed decentralized PKI based on blockchain solutions have lacked practicality, and continue to have security flaws, or have not yet gained widespread acceptance owing to complexity and deployment issues. We present a lightweight certificate in size (LightCert4IoTs) that is not issued by Certification Authorities (CAs) due to the cost and complexity of the assignment of a signed certificate. In LightCert4IoTs first, an end-user (i.e., mobile and IoT devices) issues a self-signed certificate and lets Local Registration Authorities (LRAs)/EDGE nodes to verify and validate the binding identity-self signed certificate of the users through the Ethereum blockchain where The Ethereum network is used as the global notary for the IoT light certificates by saving them in the blockchain immutable ledger. The LightCert4IoTs leverages the advantages of blockchain technology and smart contracts to address the existing challenges of PKI certificates in IoT devices, which neatly achieve certificate issuance, update, and revocation more securely and efficiently. Finally, the LightCert4IoTs experimental results show that LightCert, as compared to relevant solutions/baselines, achieves reasonable overheads and is suitable for use in low- constrained IoT devices where the memory and processor power are optimized.

In this paper, we develop an innovative series representation for the sum of Rician non-zero boresight pointing error random variates based on the k - μ distribution, which is suitable for multiple-input multiple-output (MIMO) transmission for the first time. Then, using this new representation, we introduce a novel closed-form probability density function (PDF) approximation for the sum of Gamma-Gamma random variates with generalized pointing errors and atmospheric attenuation of MIMO free-space optical (FSO) communications. Statistical Kolmogorov-Smirnov tests confirm the accuracy of this approximation over a wide range of channel conditions. The significance of this approximation is emphasized by deriving closed-form expressions for the ergodic capacity, outage probability, and average bit error rate (BER) using Meijer's G-function. This paper provides a comprehensive analysis of the performance of MIMO FSO systems utilizing the equal gain combining (EGC) diversity technique under various conditions, such as different numbers of transmitter and receiver, turbulence intensities, the effects of non-zero boresight pointing errors, and path attenuation. The results show that using MIMO technology with more transmitters and receivers significantly improves the performance of FSO communication compared to other diversity techniques, including single input single output (SISO), and multiple input single output (MISO) systems. Detailed evaluations of the ergodic capacity, outage probability, and average BER performance at high signal-to-noise ratios provide additional insights. Monte-Carlo simulation results demonstrate the accuracy of the proposed approach.

The security and reliability of free space optical (FSO) communications and the trade-off between the two are the most critical characteristics to emphasize in FSO systems, especially as optical wireless communications continue to evolve. In this work, we investigate the impact of opportunistic transmit aperture selection (TAS) on the security-reliability trade-off (SRT) of the multiuser multiple-input multiple-output (MU-MIMO) free space optical system. We consider a general scenario in which the FSO system consists of a transmitter side with $K$ users and a single receiver with $N$ apertures communicating over generalized Malaga-M turbulence channels. We derive the TAS combined signal-to-noise ratio (SNR) distributions for the FSO MIMO channel between the transmitting users and the receiver. A statistical analysis of the TAS scheme is performed, where the $k$ th best user with the highest SNR is selected. Based on this analysis, the closed-form expressions for the outage probability, average bit error rate, intercept probability, and SRT are derived. Then, the diversity advantages provided by the channel variations considering the atmospheric turbulence and generalized nonzero boresight pointing errors are further discussed. The results show that the considered TAS significantly improves the security, reliability, and SRT performance and has the potential to solve existing FSO communication problems. Monte Carlo simulations are used to verify the correctness of the numerical results.

Recently, the growth of the focus on internet of things (IoT) is rocketing in parallel with the evolution of the mobile fifth generation (5G) networks. To fully utilize IoT, which wirelessly connects billions of devices, a 5G network is essential. However, the provision of user’s certificates exposes the network to serious security threats. The LightCert4IoT leverages the advantages of EDGE nodes with blockchain technology and smart contracts to address the existing challenges of PKI (Public Key Infrastructure) certificates in IoT devices, which neatly achieves certificate issuance, update, and revocation more securely and efficiently. An end-user issues a self-signed certificate and lets Local Registration Authorities (LRAs)/EDGE nodes to verify and validate the binding identity-self signed certificate of the users through a blockchain such as Ethereum. This work analyzes the performance of LightCert4IoT in the IoT devices by utilizing the Cooja-Contiki simulator. The results show reduced energy consumption and memory size when compared to the conventional X509 certificate. As a conclusion, the LightCert4IoT meets the requirements of major IoT devices constraints. The paper also addresses the security threats with widespread use of IoT devices and analyses the adversary on security level from the authentication based on hardware token and blockchain and deploying firewall to protect servers against malicious attacks like DDOS. Scalability, resource limitations, privacy concerns are analyzed as IoT devices handle sensitive information, and the transparent and immutable nature of blockchain can raise data protection. Analysis of Network partitioning challenge and communication overhead.

The Internet of Things (IoT) is expanding rapidly, but the security of IoT devices remains a noteworthy concern due to resource limitations and existing security conventions. This research investigates and proposes the use of a Light certificate with the Constrained Application Protocol (CoAP) instead of the X509 certificate based on traditional PKI/CA. We start by analyzing the impediments of current CoAP security over DTLS with the certificate mode based on CA root in the constrained IoT device and suggest the implementation of LightCert4IoT for CoAP over DTLS. The paper also describes a new modified handshake protocol in DTLS applied for IoT devices and Application server certificate authentication verification by relying on a blockchain without the complication of the signed certificate and certificate chain. This approach streamlines the DTLS handshake process and reduces cryptographic overhead, making it particularly suitable for resource-constrained environments. Our proposed solution leverages blockchain to reinforce IoT gadget security through immutable device characters, secure device registration, and data integrity. The LightCert4IoT is smaller in size and requires less power consumption. Continuous research and advancement are pivotal to balancing security and effectiveness. This paper examines security challenges and demonstrates the effectiveness of giving potential solutions, guaranteeing the security of IoT networks by applying LightCert4IoT and using the CoAP over DTLS with a new security mode based on blockchain.

Cardiovascular disease (CVD) is a leading cause of global mortality, necessitating predictive models that are both accurate and interpretable. This study introduces the Hybrid Polynomial Ensemble Model (HPEM), a novel ensemble learning framework that combines Random Forest (RF), Gradient Boosting (GB), and Support Vector Machines (SVM), with Extreme Gradient Boosting (XGBoost) as a meta-learner. The model integrates advanced feature engineering, including second-degree polynomial expansion and mutual information-based selection, to enhance learning from heterogeneous patient data. HPEM was evaluated on three publicly available datasets, achieving an accuracy of 91%, 88%, and 89% respectively. It also attained F1-scores of 0.91, 0.88, and 0.89, and Area Under the Curve (AUC) values up to 0.96, outperforming baseline models across all metrics.To ensure transparency, explainability tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied, identifying key clinical predictors including cholesterol levels, exercise-induced angina, and age. The model also achieved Cohen’s Kappa scores above 0.79 and Matthews Correlation Coefficient (MCC) values above 0.80, demonstrating reliability and robustness. These results underscore the potential of HPEM as a clinically relevant tool for early detection and risk stratification of cardiovascular disease.