Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.

Faculty attitude is an important aspect in determining their readiness for online education. This study seeks to understand the attitude of teachers in higher education regarding online teaching and learning. There were 759 participants from different colleges and universities in India (92 professors, 73 associate professors, and 594 assistant professors). This study was completed during the lockdown owing to Covid-19 outbreak. After reviewing relevant literature, data was initially gathered using Google forms based on the "Attitude Scale towards Online Teaching and Learning for Higher Education Teachers". Scale reliability was verified with the Cronbach Alpha and split-half reliability. Interrelationships between the constructs of attitude were examined using PLS-SEM. The study also revealed the existence of parallel and serial mediations between the constructs. It was established that knowledge could lead to responsiveness only in the presence of appreciation and proficiency. Hence, appreciation and proficiency are important constructs for teachers' responsiveness towards online education.

In today’s era, thinking of Vehicular Ad-hoc Network (VANET) as a midrib for the leaf of academic, social, corporate, and economic activities will not be erroneous. To avoid any panic situations like road accidents, heavy traffic jams, etc., the timely availability of correct information is compulsory. The presence of malicious nodes within the network will ruin the dream of establishing a safe, secure, and accident-free vehicular network. This objective can be fulfilled only when malicious nodes within the network are identified correctly and respective actions are taken at the right time. Therefore, there is a great requirement for efficient and intelligent misbehavior detection techniques to deal with such situations. Vehicular networks are very prone to numerous attacks, such as Sybil attacks, unauthorized access, etc. due to their dynamic nature. The main goal of this study is to discuss and bundle various available misbehavior detection schemes and respective solutions to cope with harmful attackers in the network. We have categorized different misbehavior detections on the criteria of architecture, approach, node-centric, and data-centric. The subcategorization is also given within the paper. One section of this paper focuses on the role of machine learning techniques in misbehavior detection as an emerging foot strap for further enhancement. A comparative analysis of various misbehavior detection schemes is also conducted based on performance measures like accuracy, False Positive Rate, Recall, Precision and F-measurement. Finally, the paper concluded by discussing open issues and various research challenges associated with misbehavior detection in the Vehicular Ad-hoc Network.

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