Edge computing framework for enabling situation awareness in IoT based smart city

Abstract The Internet of Things (IoT) offers a lot of benefits for building smart cities. Such cities will be able to utilize a huge number of heterogeneous IoT devices that can generate a sheer volume of data. So, considering this heterogeneity, one of the major challenges in smart cities is how to process this data and identify different situations for decision-makers on the basis of this data. The traditional cloud computing approach can provide enormous computing and storage facilities to support data processing. However, it requires all the data to be moved to the cloud from the edge devices of the user endpoint, thus introducing a high latency. In this paper, we used the edge computing approach to minimize such latency. Besides, as major portion of data is generated from the user endpoint, processing this data in the edge can significantly improve the performance. Our experiment shows that processing raw IoT data at the edge devices is effective in terms of latency and provides situational awareness for the decision makers of smart city in a seamless fashion.

[1]  B. Liang,et al.  Mobile Edge Computing , 2020, Encyclopedia of Wireless Networks.

[2]  Brian Suda.  SOAP Web Services , 2003 .

[3]  Schahram Dustdar,et al.  Efficient and Scalable IoT Service Delivery on Cloud , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[4]  Aiman Majid Nassar,et al.  The Internet of Things - A Survey , 2018, مؤتمرات الآداب والعلوم الانسانية والطبيعية.

[5]  Pablo Calcina Ccori,et al.  Device discovery strategies for the IoT , 2016, 2016 IEEE International Symposium on Consumer Electronics (ISCE).

[6]  Enrique Saurez,et al.  Incremental deployment and migration of geo-distributed situation awareness applications in the fog , 2016, DEBS.

[7]  Geoffrey Fox,et al.  Architecture and measured characteristics of a cloud based internet of things , 2012, 2012 International Conference on Collaboration Technologies and Systems (CTS).

[8]  M. Anwar Hossain,et al.  Adaptive and context-aware service composition for IoT-based smart cities , 2017, Future Gener. Comput. Syst..

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

[10]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[11]  Rajkumar Buyya,et al.  Indie Fog: An Efficient Fog-Computing Infrastructure for the Internet of Things , 2017, Computer.

[12]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[13]  Marcos Salganico,et al.  Tolerating concept and sampling shift in lazy learning using prediction error context switching , 1997 .

[14]  Mark H. Hansen,et al.  Urban sensing: out of the woods , 2008, CACM.

[15]  Jan Markendahl,et al.  WHO IS INVESTING IN MACHINE-TO-MACHINE COMMUNICATIONS? , 2013 .

[16]  Qun Li,et al.  Snoogle: A Search Engine for the Physical World , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[17]  Khaled A. Harras,et al.  Femto Clouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[18]  Bo Cheng,et al.  Situation-Aware IoT Service Coordination Using the Event-Driven SOA Paradigm , 2016, IEEE Transactions on Network and Service Management.

[19]  Beniamino Di Martino,et al.  Cloud Computing: Security, Privacy and Practice , 2015, Future Gener. Comput. Syst..

[20]  相原 龍,et al.  Alternating Direction Method of Multipliersを用いた声質変換のためのパラレル辞書学習 (音声) -- (第17回音声言語シンポジウム) , 2015 .

[21]  Hans Schaffers,et al.  Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation , 2011, Future Internet Assembly.

[22]  Bhavani M. Thuraisingham,et al.  Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space , 2010, ECML/PKDD.

[23]  William W. Cohen,et al.  Single-pass online learning: performance, voting schemes and online feature selection , 2006, KDD '06.

[24]  Daniel W. Engels,et al.  A secure IoT architecture for Smart Cities , 2016, 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[25]  Barry W. Boehm,et al.  Anchoring the Software Process , 1996, IEEE Softw..

[26]  Prem Prakash Jayaraman,et al.  Sensor discovery and configuration framework for the Internet of Things paradigm , 2013, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[27]  Ralf Tönjes,et al.  CityPulse: Large Scale Data Analytics Framework for Smart Cities , 2016, IEEE Access.

[28]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[30]  Kim-Kwang Raymond Choo,et al.  Cloud incident handling and forensic‐by‐design: cloud storage as a case study , 2017, Concurr. Comput. Pract. Exp..

[31]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[32]  Mica R. Endsley,et al.  Design and Evaluation for Situation Awareness Enhancement , 1988 .

[33]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[34]  Mauri Ferrandin,et al.  Internet of Things Data Storage Infrastructure in the Cloud Using NoSQL Databases , 2017, IEEE Latin America Transactions.

[35]  Wei Liu,et al.  Representation and recognition of situations in sensor networks , 2010, IEEE Communications Magazine.

[36]  Grigorios Tsoumakas,et al.  On the Utility of Incremental Feature Selection for the Classification of Textual Data Streams , 2005, Panhellenic Conference on Informatics.

[37]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[38]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[39]  Marcos Salganicoff,et al.  Density-Adaptive Learning and Forgetting , 1993, ICML.