The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well-organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information-centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in-network caching and named-based routing capabilities. Since vehicles are mobile devices, it is very crucial to select a caching node, which is secure and reliable. The security of data is quite important in the vehicular named data networking (VNDN) environment due to its vital importance in saving the life of drivers and pedestrians. To overcome the issue of security and reduce network load in addition to detect a malicious activity, we define a blockchain-based distributive trust model to achieve security, trust, and privacy of the communicating entities in VNDN, named secure vehicle communication caching (SVC-caching) mechanism for the placement of on-demand data. The proposed trust management mechanism is decentralized in nature, which is used to select a trustworthy node for cluster-based V2V communications in the VNDN environment. The SVC-caching strategy is simulated in the NS-2 simulator. The results are evaluated based on one-hop count, delivery ratio, cache hit ratio, and malicious node detection. The results demonstrate that the proposed technique improves the performance based on the selected parameters.

The development of informative workforce that is skilled in a specific profession is considered as the most recommended and desirable feature of any advanced state. Technical Education & Vocational Trainings provide golden opportunity of growth regarding the output of individuals and prosperity of employers. Subsequently it is the dire need of developing countries to invest in public vocational education and training sector (VET) for the progression of skillful societies. Process of manual predictions and analysis on the basis of students’ data to make decisions that will improve the overall teaching and learning is very difficult and tiring. Data mining is exceptionally helpful when we are talking about education data analysis and prediction. Data mining techniques are being used successfully in different areas especially in student educational and learning analytics called as Educational Data Mining (EDM). In this work, TEVTA students’ data is shaped as a ready-to-mine data set and then various data mining techniques are applied to derive interesting patterns that can potentially derive important decisions for improvement of learning process, enhancement of teaching method and overall development of whole system of technical education and vocational trainings. Besides presenting interesting analytics of TEVTA data, we develop classification problems to predict status of students after completing TEVTA courses. This classification can also help in evaluating success of TEVTA programs. This work can help in analyzing and predicting the aspects affecting students’ as well as institutes’ performance from different dimensions.