Efficient and low power utilizing clock synchronization is a challenging task for a wireless-sensor network (WSN). Therefore, it is crucial to design a light weight clock synchronization protocols for these networks. An adaptive clock offset prediction model for WSN is proposed in this paper that exchanges fewer synchronization messages to improve the accuracy and efficiency. Timing information required is collected by setting a small WSN set up to investigate the soil condition to control the irrigation in agriculture. The networks investigate soils moisture, temperature, humidity and pressure content along with the sensors clock offset. First, the prediction model perceives the existing sensor clock offset to observe the clock characteristics and delay. Then, a Gaussian function is applied for adjusting the parameters weight of the observed value in the prediction model. The system results demonstrate that the proposed Adaptive Non-Linear Gaussian Regression Synchronization (ANGRS) model utilizes 20% less energy as consumed by Time Sync Protocol (TPSN) for Sensor-Network and Reference Broadcast Synchronization (RBS) Protocol. It also reduces the synchronization error with respect to Root-Mean Square Error (RMSE) by 24.85% as compared to Linear Prediction Synchronization (LPS) with RMSE 28.72% in terms of accuracy.

In recent days, the IoT along with wireless sensor networks (WSNs), have been widely deployed for various healthcare applications. Nowadays, healthcare industries use electronic sensors to reduce human errors while analysing illness more accurately and effectively. This paper proposes an IoT-based health monitoring system to investigate body weight, temperature, blood pressure, respiration and heart rate, room temperature, humidity, and ambient light along with the synchronised clock model. The system is divided into two phases. In the first phase, the system compares the observed parameters. It generates advisory to parents or guardians through SMS or e-mails. This cost-effective and easy-to-deploy system provides timely intimation to the associated medical practitioner about the patient’s health and reduces the effort of the medical practitioner. The data collected using the proposed system were accurate. In the second phase, the proposed system was also synchronised using a linear quadratic regression clock synchronisation technique to maintain a high synchronisation between sensors and an alarm system. The observation made in this paper is that the synchronised technology improved the performance of the proposed health monitoring system by reducing the root mean square error to 0.379% and the R-square error by 0.71%.

Nature inspired algorithms are gaining popularity for optimizing complex problems. These algorithms have been classified into 2 general categories, namely Evolutionary and Swarm Intelligence, which have further been divided into a couple of algorithms. This paper presents a comparative study between Bat Algorithm, Genetic algorithm, Artificial Bee Colony Algorithm and Ant Colony Optimization Algorithm. These algorithms are compared on the basis of various factors such as Efficiency, Accuracy, Performance, Reliability and Computation Time. At the end, a table has been created which enables the reader to easily differentiate between them and realise which algorithm outperforms the others.

This paper introduces a novel integrated hybrid malware attack detection algorithm, focusing on enhancing cybersecurity within blockchain systems by addressing the prevalent challenges of Byzantine fault tolerance, Reentrancy, and DDOS attacks. The significance of this research lies in its contribution to safeguarding blockchain technology, a cornerstone for secure, decentralized digital transactions, against sophisticated malware threats. Current cybersecurity solutions frequently fall short of offering a complete defense mechanism, making it difficult to effectively combat a variety of dynamic malware attacks at the same time. Thus, the main objective of this research is to provide a hybrid framework that combines DDOS attack prevention, reentrancy attack detection, and Byzantine fault tolerance detection into a single, cohesive architecture. The proposed hybrid framework encompasses a detailed algorithmic approach integrating SHA-256 and DSA to analyze the aforementioned three malware attacks. A hybrid model combining these algorithms, implemented in one block, has been developed to mitigate malicious activity. These measures aim to improve computational complexity and expedite execution within the network of nodes. To test the efficacy of the proposed framework, the approach is tested on the NSL-KDD dataset to analyze the malicious activities. The performance analysis of the proposed frameworks presents a recall and F1 score of 73 and .68 respectively. Furthermore, for efficient mitigation, the time and space complexity analysis is performed on proposed algorithms for attack analysis, which resulted in a combination of constant and linear time complexity operations. The findings reveal that the proposed algorithm successfully identifies and mitigates the targeted malware attacks and maintains optimal performance in terms of time and space complexity. Specifically, the algorithm showcases linear and constant time complexities across different attack vectors, ensuring swift and scalable defense capabilities. This research’s contribution to the cybersecurity field is significant, offering a robust, scalable solution that enhances the resilience of blockchain networks against a broad spectrum of malware attacks.