Towards a Fog Enabled Efficient Car Parking Architecture

Abstract

The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one's car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloudbased applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking. INDEX TERMS Fog computing, smart car parking, fog-based smart car parking, image processing.

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

“…Huang et al [28] proposed a mathematical model to generate automated test cases based on path coverage for a dynamic fog environment. Avaisi et al [29] proposed an efficient car parking system that uses fog computing concepts to reduce network latency and delay. The solution is tested using iFogSim and compared with a similar solution in a cloud-based environment.…”

BlockEdge: Blockchain-Edge Framework for Industrial IoT Networks

“…Citation information: DOI 10.1109/ACCESS.2020.3017891, IEEE Access In Table 2, we mentioned all the relevant simulation parameters taken during the performance evaluation of the proposed framework. With the thorough study of various relevant research papers, we have analyzed that the configuration of each device is application specific and vary according to the requirements of a particular IoT application [68], [69], [70], [71]. The latency parameters between the devices have been set for the simulation using Traceroute [70].…”

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

“…For optimized task scheduling, Jayasena and Thisarasinghe [ 49 ] compared the whale optimization algorithm with several heuristic and meta-heuristic algorithms in a smart healthcare application model using the iFogSim simulator tool. Fog computing-based architecture for efficient car parking is proposed in [ 50 ] and the result of simulations performed in iFogSim show that network consumption and latency in the proposed architecture is less than in the cloud-based architecture. Mahmud et al [ 51 ] have labeled iFogSim as the most effective tool among the available simulators for simulating applications in fog computing architectures.…”

“…Each fog node contributes some of its local resources to interconnect with neighboring fog nodes to fulfill their processing and storage requirements [ 53 ]. In our proposed framework, we do not consider the latency factor in communication between the fog nodes, considering it an advantage of fog computing interoperability features [ 50 ]. To differentiate among different patients, specific indexes, for example, HS11 defining patient 1 of hospital 1, are used as shown in Figure 3 .…”