BigData 2017 - IEEE Communications Magazine Feature Topic on Emerging Trends, Issues and Challenges in Big Data and Its Implementation towards Future Smart Cities

The world is experiencing a period of extreme urbanization. Cities in the 21st century will account for nearly 90% of the global population growth, 80% of wealth-creation and 60% of total energy consumption. The world urbanization continues to grow, and the global population is expected to double by 2050. Smart Cities are emerging as a priority for research and development across the world. In general, Smart cities integrate multiple Internet of Things (IoT) and emerging communication technologies such as fifth generation (5G) solutions in a secure fashion to manage a city’s assets, such as transportation systems, hospitals, water supply networks, waste management. The goal of building a smart city is to improve the quality of life by using technology to improve the efficiency of services and meet residents’ needs.
Smart cities‘ economic growth and large-scale urbanization drive innovation and new technologies. Technology is driving the way city officials interact with the community and the city infrastructure. The rapid progress in smart cities research is posing enormous challenge in terms of large amounts and various types of data at an unprecedented granularity, speed, and complexity are increasingly produced by the sensors of IoT via emerging communication technologies. Meanwhile, the accumulation of huge amounts of data can be used to support smart city components to reach the required level of sustainability and improve living standards. Smart cities have become data-driven, thus effective computing and utilization of big data such as distributed and parallel computing, artificial intelligence and cloud/fog computing are key factors for success in future smart cities. The use of big data can certainly help create cities where infrastructure and resources are used in a more efficient manner.
Any smart city project willing to use big data will need to capture, store, process and analyze a large amount of data generated by several sources to transform the data into useful knowledge that is applicable to a decision-making process. For example, with the help of big data and its Implementation, citizens could rapidly find available parking slots in large urban areas; big data can contribute in the city’s efforts to reduce pollution through the deployment of street sensors. These sensors can measure traffic flows at different times as well as total emissions. The government can implement actions to divert traffic to less congested areas in a move to reduce carbon emissions in a particular area.
This Feature Topic (FT) is intended to encourage high-quality researchers in big data and its Implementation for future smart cities, and push the theoretical and practical research forward for a deeper understanding of future smart city constructions and operations.
- FOCUS FOR THE FT
In this FT, we would like to try to answer some (or all) of the following questions:
How to analyze the mass data that IOT devices produce by future smart cities? How to design the algorithm to process the mass data? How to utilize the machine learning and artificial intelligence techniques to improve the quality of life for future smart cities? How to utilize the “big data” to improve the QoS for future smart cities? How to guarantee the security and the privacy when mass data generated by IOT devices of future smart cities? How to diagnose the fault among the mass IOT devices of future smart cities? How to design the hardware to be suitable to process the mass data, among others?
Topics of interest include, but are not limited to:
Distributed and parallel algorithms for big data in smart cities
Big data analytics in data processing center for smart cities
Cloud/fog computing in data processing center for smart cities
The application of mobile cloud/fog computing for smart cities
Fault tolerance, reliability and survivability in smart cities
E-health and connected healthcare systems in smart cities
Cyber-physical and social computing and networks in smart cities
Environmental and urban monitoring in smart cities
QoS and QoE of systems, applications, and services for smart cities
Safety, security, privacy and trust in applications and services for smart cities
Other topics related to big data and its implementations for smart cities
- SUBMISSIONS
Articles should be tutorial in nature, with the intended audience being all members of the communications technology community. They should be written in a style comprehensible to readers outside the specialty of the article. Mathematical equations should not be used (in justified cases up to three simple equations are allowed). Articles should not exceed 4500 words (from introduction through conclusions). Figures and tables should be limited to a combined total of six. The number of references is recommended not to exceed 15. In some rare cases, more mathematical equations, figures, and tables may be allowed if well-justified. In general, however, mathematics should be avoided; instead, references to papers containing the relevant mathematics should be provided. Complete guidelines for preparation of the manuscripts are posted at http://www.comsoc.org/commag/paper-submission-guid.... Please send a PDF (preferred) or MSWORD formatted paper via Manuscript Central (http://mc.manuscriptcentral.com/commag-ieee). Register or log in, and go to Author Center. Follow the instructions there. Select "December 2017 / Emerging Trends, Issues and Challenges in Big Data and Its Implementation towards Future Smart Cities" as the Feature Topic category for your submission.
- IMPORTANT DATES
Manuscript Submission: 1 April 2017 [Extended to 20 April 2017]
Decision Notification: 1 August 2017
Final Manuscript Submission: 15 September 2017
Expected Publication Date: December 2017
- GUEST EDITORS
Guangjie Han, Hohai University, China hanguangjie-AT-ieee.org
Sammy Chan, City University of Hong Kong, Hong Kong, China eeschan-AT-cityu.edu.hk
Jaime Lloret, Universidad Politecnica de Valencia, Spain jlloret-AT-dcom.upv.es
Mohsen Guizani, University of Idaho, USA mguizani-AT-ieee.org
Liangtian Wan, Nanyang Technological University, Singapore wan.liangtian.2015-AT-ieee.org
Wael Guibene, Intel Labs, Ireland wael.guibene-AT-intel.com