Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO₂ emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the $T_{min}$ (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with $R^2$ = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions.

The fifth generation (5G) mobile network delivers high peak data rates with ultra-low latency and massive network capacity. Wireless sensor network (WSN) in Internet of Thing (IoT) architecture is of prominent use in 5G-enabled applications. The electronic healthcare (e-health) system has gained a lot of research attention since it allows e-health users to store and share data in a convenient way. By the support of 5G technology, healthcare data produced by sensor nodes are transited in the e-health system with high efficiency and reliability. It helps in reducing the treatment cost, providing efficient services, better analysis reports, and faster access to treatment. However, security and privacy issues become big concerns when the number of sensors and mobile devices is increasing. Moreover, existing single-server architecture requires to store a massive number of identities and passwords, which causes a significant database cost. In this paper, we propose a three-factor fast authentication scheme with time bound and user anonymity for multi-server e-health systems in 5G-based wireless sensor networks. In our work, the three-factor authentication scheme integrating biometrics, password, and smart card ensures a high-security sensor-enabled environment for communicating parties. User anonymity is preserved during communication process. Besides, time bound authentication can be applied to various healthcare scenarios to enhance security. The proposed protocol includes fast authentication, which can provide a fast communication for participating parties. Our protocol is also designed with multi-server architecture to simplify network load and significantly save database cost. Furthermore, security proof and performance analysis results show that our proposed protocol can resist various attacks and bear a rational communication cost.

6G technology is now attracting many scientific researchers due to its prominent features including high mobility, high data rate, high operating frequency, and ultra-low end-to-end delay. Compared to the 5G, 6G has certain advantages that can transform smart healthcare to a new advanced intelligent healthcare system, where multiple issues and concerns of the services (e.g., slow ambulance response) are effectively resolved. In such a communication system, patients and service providers communicate with each other via an open internet channel. Since the healthcare data is important and very sensitive, security and privacy in the healthcare network become prominent. In this paper, we introduce a 6G-aided intelligent healthcare environment. Our work also proposes a solution called Centerless User-Controlled Single Sign-On (CL-UCSSO) for achieving a convenient and cost-saving communication in a multi-server system constructed. A three-factor mechanism (combining smart card, password and biometrics) and time bound property are integrated to design the protocol with fast authentication that allows patients and providers to efficiently establish secure communications. Security proof of the proposed protocol is provided using well-known verification tools including RoR model, AVISPA simulation and BAN logic. Results of performance comparisons on various aspects show that our work provides more functionalities and incurs less cost compared with the related works.

Healthcare is now an important part of daily life because of rising consciousness of health management. Medical professionals can know users’ health condition if they are able to access information immediately. Telemedicine systems, which provides long distance medical communication and services, is a multi-functional remote medical service that can help patients in bed in long-distance communication environments. As telemedicine systems work in public networks, privacy preservation issue of sensitive and private transmitted information is important. One of the means of proving a user’s identity are user-controlled single sign-on (UCSSO) authentication scheme, which can establish a secure communication channel using authenticated session keys between the users and servers of telemedicine systems, without threats of eavesdropping, impersonation, etc., and allow patients access to multiple telemedicine services with a pair of identity and password. In this paper, we proposed a smartcard-based user-controlled single sign-on (SC-UCSSO) for telemedicine systems that not only remains above merits but achieves privacy preservation and enhances security and performance compared to previous schemes that were proved with BAN logic and automated validation of internet security protocols and applications (AVISPA).

Electronic healthcare (e-health) has gained more and more research attention in recent years, due to its flexibility and convenience. E-health is efficiently enabled by telecare medicine information system (TMIS). TMIS provides seamless transfer and timely sharing of medical information for specific healthcare services. Since communications in TMIS are carried out through unreliable channels, data security and user privacy concerns become prominent. With traditional single-server architecture, users must store massive credentials, which causes inefficient communication and significant overhead. Moreover, user credentials in previously proposed schemes are stored at server side, suffering potential risks. Our work proposes a three-factor user-controlled single sign-on (UCSSO) with fast authentication and privacy protection for TMIS. The contributions of this paper are as follows. Our work integrates three factors including password, smart card and biometrics in authentication procedure, for providing a high-security and privacy-preserved communication. We introduce single sign-on solution that allows users to log in to multiple servers using a single password. User-controlled mechanism is proposed to address insider attacks and the risk that registration center may be compromised. The proposed scheme is designed with fast authentication mechanism that helps to efficiently establishes new session key. Our work is proved secure using BAN logic, ROR model, and AVISPA toolset. The results of performance comparison show that our scheme provides more security properties and bears the least overhead, compared with competitive schemes.

In the era of Industry 4.0 and digital transformation, smart grid (SG) plays an important role in the construction of various smart places. Logs produced by electrical appliances recording power consumption within the grid contain private information and may be collected as evidence for the services of dispute resolution or digital forensics. Therefore, security of the logs is of paramount importance. Our work designs a blockchain-enabled log management scheme with nonrepudiation for SGs. In this article, we employ ciphertext-policy attribute-based encryption to establish a fine-grained access control of the logs, which addresses data confidentiality concern. In addition, we introduce a hybrid blockchain system generating novel signature chains, for achieving an efficient private log protection system with a lot of properties including data loss prevention, tamper resistance, immutability and nonrepudiation. Employing various cost-saving solutions, our scheme achieves an efficient communication with more functionalities and security features, compared with the predecessor works.