Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques.

In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

Wavelet Mutation with Aquila Optimization-Based Routing Protocol for Energy-Aware Wireless Communication.

Wireless sensor networks (WSNs) have been developed recently to support several applications, including environmental monitoring, traffic control, smart battlefield, home automation, etc. WSNs include numerous sensors that can be dispersed around a specific node to achieve the computing process. In WSNs, routing becomes a very significant task that should be managed prudently. The main purpose of a routing algorithm is to send data between sensor nodes (SNs) and base stations (BS) to accomplish communication. A good routing protocol should be adaptive and scalable to the variations in network topologies. Therefore, a scalable protocol has to execute well when the workload increases or the network grows larger. Many complexities in routing involve security, energy consumption, scalability, connectivity, node deployment, and coverage. This article introduces a wavelet mutation with Aquila optimization-based routing (WMAO-EAR) protocol for wireless communication. The presented WMAO-EAR technique aims to accomplish an energy-aware routing process in WSNs. To do this, the WMAO-EAR technique initially derives the WMAO algorithm for the integration of wavelet mutation with the Aquila optimization (AO) algorithm. A fitness function is derived using distinct constraints, such as delay, energy, distance, and security. By setting a mutation probability P, every individual next to the exploitation and exploration phase process has the probability of mutation using the wavelet mutation process. For demonstrating the enhanced performance of the WMAO-EAR technique, a comprehensive simulation analysis is made. The experimental outcomes establish the betterment of the WMAO-EAR method over other recent approaches.

Developing a blockchain-based digitally secured model for the educational sector in Saudi Arabia toward digital transformation.

New universities and educational organizations are increasing in Saudi Arabia with the increase in the need for high-quality education. This increased the need for a fast transformation to digitise the educational system in Saudi Arabia, which is one of the important pillars of the Saudi Vision 2030. The students who study in these organizations suffer the verification of academic records and other educational documents. Students who want to study at universities abroad also face the challenge of academic records and certificates verification. A secure, fast, and transparent model is required in the education sector in order to verify academic certificates issued by various educational organizations. Blockchain technology can be used with high data security to empower the educational sector of Saudi Arabia in the digital transformation and to help the educational organizations in verifying academic documents. In order to avoid any document fraud and forgery, along with the ease of verification of academic records and educational documents for the students. This research focuses on developing a model which will be helpful in achieving digital transformation in academic document verification by blockchain technology.

Assistive technology acceptance for visually impaired individuals: a case study of students in Saudi Arabia.

Assistive technology (AT) helps students who suffer from visual impairments to achieve their study goals; however, AT's adoption in Saudi universities is not yet explored. This paper adopts and then extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to incorporate factors influencing the AT's acceptance based on a designed survey. The survey data was analyzed using Structural Equational Modelling (SEM) with the Partial Least Squares (PLS) technique. The results showed that the factors influencing technology acceptance in this context differed from those previously found to influence acceptance in other contexts. The differences were further studied using post-interview, which shows that the differences are related to limited awareness of visual disability and AT and psychological sensitivity of disabled users in Saudi culture. Moreover, this study provides a list of recommendations for overcoming barriers that limit the acceptance of assistive techniques by Saudi students with visual disabilities. This work's results provide recommendations for the Saudi government and administrators concerning access to assistive technology in universities and facilitate access to other technologies and other contexts.