14 Fog Computing Model for Evolving Smart Transportation Applications

M. Muzakkir Hussain Mohammad Saad Alam and M.M. Sufyan Beg

14.1 Introduction

Due to the increased number of connected things in smart and industrial applications – more specifically, intelligent transportation systems (ITS), the growing volume and velocity of Internet of Things (IoT) data exchange – there is a great urgency for rigorous communication resources to address the bottlenecks in terms of data processing, data latency, and traffic overhead [1]. Fog computing emerges as an substitute for traditional cloud computing to support geographically distributed, latency sensitive, and QoS‐aware IoT applications while reducing the burden of data centers in traditional cloud computing [2]. In particular, fog computing due to its peculiarities (e.g., low latency, location awareness, and capacity of processing large number of nodes with wireless access) to support heterogeneity and real‐time applications is a potentially attractive solution to the delay and resource‐constrained large‐scale industrial applications [3].

However, with the benefits of fog computing, the research challenges arise while realizing fog computing for such applications [4]. For instance, how should we handle different protocols and data format from highly dissimilar data sources in fog layer? How do we determine which data should be processed in cloud or be processed in fog layer (task association, resource allocation/provisioning, ...

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