Adaptive Smart Traffic Accidents Management System

Faisal Yousef Alzyoud, Abdallah Altahan Alnuaimi, Faiz Al Shrouf

Abstract


The proliferation of smart devices, IoT applications and wireless communication technologies contribute in countries development, society’s security, cost reduction, and customer services satisfactions; since they are used in different aspects of our life. Traffic congestion and accidents are increased recently and reached critical limits, so these contribute in initiating sever problems for researchers, governments and industry over the last few decades. Traffic accidents have many defects relating to increase number of death, infrastructure distribution, and health injuries; therefore, there is a crucial need to develop and modify an approach that utilizes the new technology to limit and prevent the traffic accidents.  Wireless sensors networks are developed to support smart solutions in smart cities like smart traffic, smart grid and others. In this research we developed a comprehensive approach to achieve the following three important goals in smart accident elimination. The first goal is to minimize the number of exchange information packets between sensors to save the battery life through developing and adapting clustering schema to minimize the number of exchanges information packets. The second goal is to calculate and determine the optimum route from accident location to the nearest rescue location by developing a dynamic routing schema   that is calculated by the control station depending on a cost heuristics function. The third goal is to predicate the accident causes and minimize the probability of accidents occur using a warning message schema and drawing some obstacles on some routing paths. Cupcarbon simulator and MATLAB software tool are developed to simulate different scenarios in order to proof the research goals.


Keywords


Clustering,Cupcarbon, IoT, WSN, 5G

Full Text:

PDF



International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923
Creative Commons License
Indexing:
Scopus logo IET Inspec logo DBLP logo EBSCO logo Ulrich's logo MAS logo