| Title |
Big data analytics with oppositional moth flame optimization based vehicular routing protocol for future smart cities |
| ID_Doc |
41790 |
| Authors |
Aljehane, NO; Mansour, RF |
| Title |
Big data analytics with oppositional moth flame optimization based vehicular routing protocol for future smart cities |
| Year |
2022 |
| Published |
Expert Systems, 39, 5 |
| DOI |
10.1111/exsy.12718 |
| Abstract |
Presently, smart city is designed to enhance the quality of life in city, fulfil the safety of the people, safe travelling, etc. Besides, big data has attracted significant attention among researchers in different fields as a large amount of data is being produced with diverse day-to-day applications. Besides, Vehicular adhoc network (VANET) is a kind of mobile adhoc network (MANET) that considers the vehicles as the nodes in a network. Since the VANET generates large amount of data, big data analytics can be used to gain meaningful understanding for improving the traffic management process such as planning, engineering, and operations. This paper designs a Big Data Analytics with Oppositional Moth Flame Optimization based Vehicular Routing Protocol for Future Smart Cities. The presented model maps the features of VANET with the attributes of the big data. In addition, oppositional moth flame optimization based vehicular routing (OMFOVR) technique is developed for VANET over the Hadoop Map Reduce standalone distributed framework. For validating the effectual performance of the proposed OMFOVR technique, a series of experiments were performed and the results are compared with the conventional NetBeans IDE platform. The experimental values showcased the betterment of the OMFOVR technique on the selection of routes over the compared methods. |
| Author Keywords |
big data analytics; Hadoop; optimization algorithm; routing; smart city; vehicular networks |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000655239800001 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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