Smart City Surveillance in Fog Computing

The Internet and Internet of Things (IoT) make the Smart City concept an achievable and attractive proposition. Efficient information abstraction and quick decision making, the most essential parts of situational awareness (SAW), are still complex due to the overwhelming amount of dynamic data and the tight constraints on processing time. In many urban surveillance tasks, powerful Cloud technology cannot satisfy the tight latency tolerance as the servers are allocated far from the sensing platform; in other words there is no guaranteed connection in the emergency situations. Therefore, data processing, information fusion and decision making are required to be executed on-site (i.e., near the data collection locations). Fog Computing, a recently proposed extension of Cloud Computing, enables on-site computing without migrating jobs to a remote Cloud. In this chapter, we firstly introduce the motivations and definition of smart cities as well as the existing challenges. Then the concepts and advantages of Fog Computing are discussed. Additionally, we investigate the feasibility of Fog Computing for real-time urban surveillance using speeding traffic detection as a case study. Adopting a drone to monitor the moving vehicles, a Fog Computing prototype is developed. The results validate the effectiveness of our Fog Computing based approach for on-site, online, uninterrupted urban surveillance tasks.

[1]  Suman Srinivasan,et al.  Airborne traffic surveillance systems: video surveillance of highway traffic , 2004, VSSN '04.

[2]  Haibin Ling,et al.  Robust visual tracking using ℓ1 minimization , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[4]  Rajesh Kannan Megalingam,et al.  Smart Traffic Controller Using Wireless Sensor Network for Dynamic Traffic Routing and over Speed Detection , 2011, 2011 IEEE Global Humanitarian Technology Conference.

[5]  Sergio A. Velastin,et al.  A Review of Computer Vision Techniques for the Analysis of Urban Traffic , 2011, IEEE Transactions on Intelligent Transportation Systems.

[6]  Haibin Ling,et al.  Real time robust L1 tracker using accelerated proximal gradient approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Michael Batty,et al.  Smart Cities, Big Data , 2012 .

[8]  R. Kitchin,et al.  The real-time city? Big data and smart urbanism , 2013, GeoJournal.

[9]  Jiang Zhu,et al.  Fog Computing: A Platform for Internet of Things and Analytics , 2014, Big Data and Internet of Things.

[10]  Ivan Stojmenovic,et al.  The Fog computing paradigm: Scenarios and security issues , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[11]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[12]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[13]  Genshe Chen,et al.  Summary of methods in Wide-Area Motion Imagery (WAMI) , 2014, Defense + Security Symposium.

[14]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[15]  Daniel Arribas-Bel,et al.  Accidental, open and everywhere: Emerging data sources for the understanding of cities , 2014 .

[16]  Genshe Chen,et al.  A container-based elastic cloud architecture for real-time full-motion video (FMV) target tracking , 2014, 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[17]  Qun Li,et al.  Fog Computing: Platform and Applications , 2015, 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb).

[18]  Vladimir Stantchev,et al.  Smart Items, Fog and Cloud Computing as Enablers of Servitization in Healthcare , 2015 .

[19]  Qun Li,et al.  A Survey of Fog Computing: Concepts, Applications and Issues , 2015, Mobidata@MobiHoc.

[20]  Genshe Chen,et al.  Pseudo-real-time Wide Area Motion Imagery (WAMI) processing for dynamic feature detection , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[21]  Hui Chen,et al.  A literature survey on smart cities , 2015, Science China Information Sciences.

[22]  Roger Zimmermann,et al.  Dynamic Urban Surveillance Video Stream Processing Using Fog Computing , 2016, 2016 IEEE Second International Conference on Multimedia Big Data (BigMM).

[23]  Genshe Chen,et al.  Real-time WAMI streaming target tracking in fog , 2016, SPIE Defense + Security.