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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Bangare, Manoj L.
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Economics, Econometrics & Finance Electrical & Electronics Engineering

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Development of internet of vehicles and recurrent neural network enabled intelligent transportation system for smart cities Surve, Jyoti; Bangare, Manoj L.; Bangare, Sunil L.; Pol, Urmila R.; Mali, Manisha; Meenakshi, Meenakshi; Alsalmani, Abdullah; Morsi, Sami A.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp291-300

Abstract

The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.
Task scheduling algorithm using grey wolf optimization technique in cloud computing environment Khaleelahmed, Shaik; Selvaraj, Sivakumar; Mohite, Rajendra B.; Bangare, Manoj L.; Bangare, Pushpa M.; Kulkarni, Shriram S.; Ajibade, Samuel-Soma M.; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7695

Abstract

Scheduling refers to the process of allocating cloud resources to several users according to a schedule that has been established in advance. It is not possible to get acceptable performance in settings that are distributed without proper planning for simultaneous processes. When developing productive schedules in the cloud, it is necessary for work scheduling to take a variety of constraints and goals into consideration.When dealing with activities that have performance optimization limits, resource allocation is a very important aspect to consider. When it comes to cloud computing, the only way to achieve great performance, high profits, high scalability, efficient provisioning, and cost savings is with an exceptional task scheduling system. This article presents a grey wolf optimization (GWO) based framework for efficient task scheduling in cloud computing environment. The proposed algorithm is compared with particle swarm optimization (PSO) and flower pollination algorithm (FPA) and GWO is performing task scheduling in less execution time and cost in comparison with PSO and FPA techniques. Execution time taken by GWO to finish 200 task in 120.2 ms. It is less than the time taken by PSO and FPA algorithm to finish same number of tasks.
Co-Authors Ajibade, Samuel-Soma M. Alsalmani, Abdullah Bangare, Pushpa M. Bangare, Sunil L. Khaleelahmed, Shaik Kulkarni, Shriram S. Mali, Manisha Meenakshi, Meenakshi Mohite, Rajendra B. Morsi, Sami A. Pol, Urmila R. Raghuvanshi, Abhishek Selvaraj, Sivakumar Surve, Jyoti