1 research outputs found
Enhanced Traffic Congestion Management with Fog Computing: A Simulation-based Investigation using iFog-Simulator
Accurate latency computation is essential for the Internet of Things (IoT)
since the connected devices generate a vast amount of data that is processed on
cloud infrastructure. However, the cloud is not an optimal solution. To
overcome this issue, fog computing is used to enable processing at the edge
while still allowing communication with the cloud. Many applications rely on
fog computing, including traffic management. In this paper, an Intelligent
Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic
congestion in heavily populated smart cities. The proposed system is
implemented using fog computing and tested in a crowded city. Its performance
is evaluated based on multiple metrics, such as traffic efficiency, energy
savings, reduced latency, average traffic flow rate, and waiting time. The
obtained results are compared with similar techniques that tackle the same
issue. The results obtained indicate that the execution time of the simulation
is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The
paper addresses various issues, including CPU usage, heap memory usage,
throughput, and the total average delay, which are essential for evaluating the
performance of the ITCMS. Our system model is also compared with other models
to assess its performance. A comparison is made using two parameters, namely
throughput and the total average delay, between the ITCMS, IOV (Internet of
Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS).
Consequently, the results confirm that the proposed system outperforms the
others in terms of higher accuracy, lower latency, and improved traffic
efficiency