Big data processing in fog: smart parking case study

Sang Nguyen, Zoran Salcic, Xuyun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)

Abstract

The introduction of Internet of Things (IoT) and its positive effects within city life context will be first seen in the applications that most obviously affect people's lives, such as improving traffic efficiency, reducing time spent in vehicles while travelling around the city and generally mitigating traffic congestion. Vehicle parking and its management represents one of the major issues that directly affects people's time and can have significant financial effects, thus making it directly interesting to both service providers and users. Parking can be more efficient by making it smarter. This can be achieved by extensive use of IoT-based sensing in carparks, then processing and further contextualising the huge amount of generated data for two types of goals: (1) long-term goals of efficient carparks management and (2) short-term goal of helping the drivers by reducing time for finding a suitable carpark. In this paper we propose an IoT-based platform for monitoring carpark occupancy around a city and then doing data analytics near its sources, at the fog level, without streaming and storing all sensing data in the cloud. The data analytics system in our platform uses Hadoop MapReduce and is run on a cluster of commodity computers at each fog computing node. We explore the efficiency and scalability of the approach by performing data analytics tasks related to smart parking on the parking datasets of various sizes collected from a real sensor-based system and by extrapolating it by significant increase in its size.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications
Subtitle of host publicationISPA/IUCC/BDCloud/SocialCom/SustainCom 2018
EditorsJinjun Chen, Laurence T. Yang
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages127-134
Number of pages8
ISBN (Electronic)9781728111414
ISBN (Print)9781728111414
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018 - Melbourne, Australia
Duration: 11 Dec 201813 Dec 2018

Conference

Conference16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018
CountryAustralia
CityMelbourne
Period11/12/1813/12/18

Keywords

  • Big data analytics
  • Hadoop MapReduce
  • Internet of Things (IoT)
  • Scalability
  • Smart parking

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