Data Analytics in IoT Ecosystems

Dear Colleagues,

The Internet of Things (IoT) network consists of low processing smart devices connected together for sharing and processing information. This information can be shared, processed, and analyzed to improve the performance of these devices. A smart city is a concept where all smart devices share their information and resources using a centralized ecosystem of devices. With the increased developments and improvements in processing the capabilities of smartphones, cloud computing, and edge computing, the processing, storage, and analytics capability of smart cities have improved tremendously.

The growing number of Internet of Things (IoT) devices deployed across all parts of the smart ecosystem have the potential to change our understanding of how smart cities work. Due to the recent developments witnessed in big data analytics, machine learning, artificial intelligence (AI), and data visualization are providing city municipalities and administrators with tools to organize and analyze these data flows. At the same time, smart cities are developing various platforms for data integration and sharing of information that is accessible to all stakeholders and potential smart solution providers. For this reason, the benefits offered by big data are an important element of many smart city strategies.

The number of devices in a smart city keeps changing. Therefore, the vendor-specific approach for the development of smart devices often leads to compatibility issues. In order to address these challenges and to ensure smooth connectivity of these devices, vendor-agnostic approaches can be adopted through open-source software and software-defined networks (SDN), which can use isolation approaches for improved management and coordination of smart devices.

There is immense potential for the better use of data across all city services, but city managers also need to address some major challenges. Some of these are common problems that have long beset large-scale data analytics projects, such as ensuring data quality and understanding the priority objectives for any application. However, the lack of data skills may be the biggest barrier of all to effectively using big data for smart city management.

The aim of this Special Issue is to provide a foundation to researchers for sharing their experiences in defining the issues, challenges, and solutions for addressing IoT and its related challenges. We are actively seeking contributions in the best interest of the smart city and IoT research and development community.

Potential topics include but are not limited to:

• IoT datasets;
• Edge computation for IoT systems;
• Efficient algorithms, applications, architecture, and protocols for smart city data;
• Big data analytics for urban IoTs;
• Deep learning techniques for IoT datasets;
• Intelligent data processing techniques for smart cities;
• Machine learning techniques for smart cities;
• Network resource management techniques for efficient IoT management;
• Risk and disaster management prediction using IoT behavior;
• Data frameworks for smart cities;
• Smart city and urban infrastructure development;
• Standardization efforts for IoT and smart city;
• Smart mobility and information forensics in smart cities;
• Smart IoT/mobile-based solutions for disabled people.

Dr. Mohammad M. Banat
Prof. Dr. Mikael Gidlund
Prof. Dr. Rebeca P. Díaz Redondo
Dr. Aaqif Afzaal Abbasi
Dr. Lorena Parra
Guest Editors

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• Datasets
• Analytics
• Artificial intelligence
• Edge computation
• Cloud computing
• Sensors
• Smart cities
• Mobility
• Deep learning