1,674 research outputs found
Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review
Vehicular congestion is directly impacting the efficiency of the transport
sector. A wireless sensor network for vehicular clients is used in Internet of
Vehicles based solutions for traffic management applications. It was found that
vehicular congestion detection by using Internet of Vehicles based connected
vehicles technology are practically feasible for congestion handling. It was
found that by using Fog Computing based principles in the vehicular wireless
sensor network, communication in the system can be improved to support larger
number of nodes without impacting performance. In this paper, connected
vehicles technology based vehicular congestion identification techniques are
studied. Computing paradigms that can be used for the vehicular network are
studied to develop a practically feasible vehicular congestion detection system
that performs accurately for a large coverage area and multiple scenarios. The
designed system is expected to detect congestion to meet traffic management
goals that are of primary importance in intelligent transportation systems
Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters
Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using communitydetection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computingbased service placement scheme
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