Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that we transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. We have various techniques to resolve these issues, such as interference, noise, fading in the communications, etc. In this paper we have surveyed the different parameters of a security design model to highlight the vulnerabilities. Further we have discussed the various attacks on different layers of the TCP/IP model along with their mitigation techniques. We also elaborated on the applications of WSNs in healthcare, military information integration, oil and gas. Finally, we have proposed a solution to enhance the security of WSNs by adopting the alpha method and handshake mechanism with encryption and decryption.

Wireless sensor networks (WSNs) have been applied in networking devices, and a new problem has emerged called source-location privacy (SLP) in critical security systems. In wireless sensor networks, hiding the location of the source node from the hackers is known as SLP. The WSNs have limited battery capacity and low computational ability. Many state-of-the-art protocols have been proposed to address the SLP problems and other problems such as limited battery capacity and low computational power. One of the popular protocols is random path routing (RPR), and in random path routing, the system keeps sending the message randomly along all the possible paths from a source node to a sink node irrespective of the path’s distance. The problem arises when the system keeps sending a message via the longest route, resulting because of high battery usage and computational costs. This research paper presents a novel networking model referred to as calculated random path routing (CRPR). CRPR first calculates the top three shortest paths, and then randomly sends a token to any of the top three shortest calculated paths, ensuring the optimal tradeoff between computational cost and SLP. The proposed methodology includes the formal modeling of the CRPR in Colored Petri Nets. We have validated and verified the CRPR, and the results depict the optimal tradeoff.

Based on social exchange theory, social psychology theories, and despotic leadership theory, this study explored the impact of petty tyranny on employee turnover intentions. Specifically, the authors examined the mediating effect of toxic workplace environments through emotional exhaustion on this relationship among academicians. The authors surveyed 421 employees using a five-point Likert scale across six universities in Lahore, Pakistan and employed a time-lag research design. Partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses, including performance comparisons of various algorithms, were used to test the relationships among the variables. The analysis results of the study suggested that petty tyranny does not significantly and directly contribute to employee turnover intentions; however, this relationship is positively and significantly mediated by toxic workplace environments and emotional exhaustion. The results indicated that toxic workplace environments and emotional exhaustion also have a direct effect on employee turnover intentions. A serial full mediation was found between petty tyranny and turnover intentions, mediated through a toxic workplace environment and emotional exhaustion. Similarly, results from the performance comparison of various algorithms reveal trade-offs between precision, recall, and processing time, with ZeroR and Stacking REP Tree emerging as the most effective in terms of overall model accuracy. This study contributes to the literature by examining petty tyranny, workplace environment, and emotional exhaustion, highlighting the need to address tyrannical behavior to improve employee retention in academic organizations. Our study offers valuable practical implications, emphasizing addressing these issues to reduce turnover in academic organizations. Our study also provides recommendations for future research directions.

The rapid adoption of cloud-Internet-of-Things (CIoT) systems in biomedical human-cyber-physical systems (HCPS) has raised significant concerns regarding data security, privacy, and scalability. To address these challenges specifically within healthcare environments, we propose a novel IBS-ECDHE framework that integrates Identity-Based Signatures (IBS) and Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) key exchange to provide robust and lightweight security for biomedical HCPS. Our framework leverages blockchain technology to decentralize identity management and access control, ensuring secure authentication and maintaining the integrity of sensitive biomedical data exchanged between IoT-enabled medical devices. By incorporating smart contracts, we automate key management and enforce stringent privacy and data integrity guarantees critical to biomedical applications. The proposed system was implemented on a Windows 10 PC and evaluated using various performance metrics, including authentication time, message size, transaction latency, and computational overhead. Experimental results demonstrate that IBS-ECDHE reduces authentication time by up to 76 % compared to traditional PKI systems, decreases message size by 40 %, and achieves lower blockchain transaction latency. The system also ensures scalability and energy efficiency, with parallel processing reducing latency by 37 %. The innovation of this approach lies in the combination of IBS with ECDHE for mutual authentication and the use of blockchain for decentralized identity management and secure real-time biomedical data exchange. This solution offers substantial improvements in security, privacy, and performance, making it highly suitable for next-generation biomedical HCPS.

The best interconnected profitable projects, tourism has come a long way since the technology allows tourists to watch & make their journeys without the necessity to go a tourism company because Blockchain has potential to transform this Industry. With the advancement of Internet marketing, visitors are able to access and view the information in the real time on their gadgets such as Laptops, Computers, Tablets, & Smartphones and as a result they can select their own contents. One of the strong growing technologies and the most cutting edge technologies is tourism blockchain. The Blockchain offers many advantages to the tourism business as it can help to the increase profitability, improvement of the customer satisfaction & to improve the performance. In this paper, I present the current situation in Pakistan regarding the utilization the technology of blockchain in tourism business. The agenda of this paper includes discovery of why blockchain technology couldn’t be used in tourism business, and what tasks should be managed.

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.