Abstract
The recent developments in technology are growing at a faster pace in numerous areas. The world’s first driverless public transport system in vehicles is on the schedule to be implemented in Dubai before EXPO 2020. The majority of research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information. So far various algorithms have been developed and implanted and different case studies have been conducted till date in the area of VANET. Various algorithms are analyzed such as TDMA, STDMA, and self-adaptive sensing model. The main purpose of the project is to enhance the self-adaptive sensing model. The main areas of concern in self-adaptive sensing model are the security issues. Performance requirements such as packet delivery ratio and delay are not given prime importance, thus making data collection ability of vehicular nodes in real application environment inferior. The security feature was added to self-adaptive sensing model and the performance analysis was carried out. A comparative study of the algorithms was carried and the results were analyzed and evaluated. The comparative analysis on throughput, delay, packet delivery ratio, and routing overhead shows the enhanced capability of secure data collection algorithm.
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Sheela K, G. (2021). Improved Security Models in Mobile Wireless Vehicle Networks. In: Hussain, C.M., Di Sia, P. (eds) Handbook of Smart Materials, Technologies, and Devices. Springer, Cham. https://doi.org/10.1007/978-3-030-58675-1_28-1
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DOI: https://doi.org/10.1007/978-3-030-58675-1_28-1
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