5G mobile Networks for Future Smart City


Given the vast amount of collected data saved by the IoT or residing in information storage, the public has grown increasingly concerned, demanding stricter security and privacy, with the expectation that the data provided will be appropriately protected by the city and the government. The currently available fifth generation (5G) network offers three features: vast broadband, ultra-low latency, and massive connectivity. These features enable any type of device – even single sensor equipment- to connect online and contribute data to the smart city system. To improve resource efficiency, all of the devices in a smart city system (e.g., transportation, medical care, electricity, disaster prevention, etc.) could use artificial intelligence to conduct big data analyses to better understand user traffic, logistics, and resource usages. Smart city systems could use high-performance technology such as cloud computing, fog computing, and high-consumption sensors to handle massive amounts of data in order to satisfy the demands of the public. On the other hand, the large number of sensors and Internet devices required to build a smart city system will consume a lot of energy. The development of Intelligent Green Communication Networks could reduce the energy consumption of the 5G network and increase the sustainability of the power equipment. Intelligent Green Communication Networks could significantly reduce the number of smart city sensors and 5G network small cells, further supporting a high efficiency and low power consumption environment for a smart city.

Papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format (see Author’s kit section below).

Workshop Papers will be published in a dedicated section of the SGIoT Conference Proceedings. The materials presented in the position papers should not be published or under submission elsewhere. All submissions will be reviewed by the Program Committee and external experts to reach a decision on acceptance.

Submit paper


Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal. Combining the concepts of the 5G network and Intelligent Green Communication Networks will allow for a wider range of more effective solutions for smart city systems. Topics appropriate for this special issue include:

  • AI and machine learning for smart cities
  • Quality of service in 5G for smart cities
  • Next generation mobile network intelligent processing technologies
  • Software Defined Networks in 5G for smart cities
  • Data transmission technology and applications for smart cities
  • Low-power encryption and decryption in 5G
  • Intelligent green communication network designs and implementations for smart cities
  • Security and privacy technologies for smart cities
  • NB-IoT designs and implementations for smart cities
  • Emerging Internet of Things applications for smart cities


(All deadlines refer to the UTC Time Zone)

Manuscript Submission:  August 17, 2020

Acceptance Notification:  September 21, 2020

Final Manuscript: November 2, 2020

Instructions and Templates

Papers must be formatted using the Springer LNICST Authors’ Kit.

Instructions and templates are available from Springer’s LNICST homepage:

Please make sure that your paper adheres to the format as specified in the instructions and templates.

When uploading the camera-ready copy of your paper, please be sure to upload both:

  • a PDF copy of your paper formatted according to the above templates, and
  • an archive file (e.g. zip, tar.gz) containing the both a PDF copy of your paper and LaTeX or Word source material prepared according to the above guidelines.

Workshop Chairs:

  • Mu-Yen Chen, Ph.D
    • Department of Information Management, National Taichung University of Science and Technology, Taiwan
  • Pedro Peris López, Ph.D
    • Department of Computer Science, Universidad Carlos III de Madrid, Spain
  • Jose de Jesus Rubio, Ph.D
    • Instituto Politécnico Nacional, Mexico D.F., Mexico
  • Hsin-Te Wu, Ph.D
    • Department of Computer Science & Information Engineering, National Ilan University, Taiwan