Abstract
Frustrations, monetary losses, lost time, high fuel consumption and CO emissions are some of the problems caused by traffic jams in urban centers. In an attempt to solve this problem, this article proposes a traffic service to control congestion, named FOXS–Fast Offset Xpath Service. FOXS aims to reduce the problems generated by a traffic jam in a distributed way through roads classification and the suggestion of new routes to vehicles. Unlike the related works, FOXS is modeled using the Fog computing paradigm. Therefore, it is possible to take advantage of the inherent aspects of this paradigm, such as low latency, processing load balancing, scalability, geographical correlation and the reduction of bandwidth usage. In order to validate FOXS, our performance evaluation considers two realistic urban scenarios with different characteristics. When compared with related works, FOXS shows a reduction in stop time by up to 70%, the CO emissions by up to 29% and, the planning time index by up to 49%. When considering communication evaluation metrics, FOXS reaches a better result than other solutions on the packet collisions metric (up to 11.5%) and on the application delay metric (up to 30%).
Highlights
The unplanned development of urban centers often is associated with severe socio-economic problems
To verify the behavior of the solutions according to the route size, we present the metrics: Route size Histogram with its CDF and Planning time index (PTI) by route size presenting the route size distribution and the relation between the PTI and the route size range
Transmitted messages: the total number of messages transmitted; Collisions per packets sent: the percentage of collided packet per all packet sent; Network delay: the average time to spread messages to all vehicles; Application delay: the average time for the application to receive the new route when requested, with the service response time and retransmission time when necessary; New route accepted: the average of new route accepted per vehicle in simulation; Cloudlet routes computed: the average of routes computed per Cloudlet; Cloudlet computation heat-map: representing the amount of routing executed by regions
Summary
The unplanned development of urban centers often is associated with severe socio-economic problems. The Fog paradigm has lower computing capacity when compared with the Cloud, they can use the Cloud data centers whenever necessary This approach forms a multi-tier architecture (see Figure 1), which is hierarchically organized with varying types of capabilities and end-user proximity. FOXS uses the Cloudlets to monitor the traffic conditions and to calculate the vehicle route In this way, FOXS allows that the computational power resides closer to where it is most required, dividing the system load and increasing the overall scalability of the system and holding the capability to collect, process and store large volumes of data.
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