The Internet of Things (IoT) produces a large amount of data that needs to be processed and analyzed before use. Mobile edge computing (MEC) shifts computing services closer to the end-user or the source of data, such as an IoT mobile device. This enables IoT data to be gathered and processed at the edge where the device is located. This is quicker than sending the data back to a data center or cloud to identify patterns that initiate actions more quickly such as anomaly detection for predictive maintenance. The ability of IoT devices to utilize computing power is becoming increasingly valuable to rapidly analyze data in real-time. IoT is valuable in nearly every sector, spanning from manufacturing and logistics to retail and resource management. IoT captures data from a network of connected “things” that includes drones, delivery trucks, medical devices, security cameras, and construction equipment.

While IoT sensors and devices collect many valuable insights, they also generate massive high-speed data streams that are difficult to process, analyze, store, and secure. IoT data is also highly perishable, and without the right algorithms and systems, organizations can miss opportunities to act on time-sensitive insights with high potential value. MEC is a network architecture concept that enables an IT service environment at the edge of a cellular network. In MEC, applications can perform better, and network congestion can be reduced by performing data processing tasks closer to the cellular customer. Due to the tremendous interest from industry and academia for the design and development of innovative techniques and tools to address large, complex information processing issues, researchers have adopted high-performance mobile edge computing for big data information system problems to deliver efficient solutions within a reasonable time. However, the rapid development of technologies such as the IoT and mobile devices has created computational and storage problems. The massive quantities of data generated from these technologies require online data processing techniques with high computational and storage capabilities. MEC is an effective solution to handle these issues. MEC can solve many challenges of real-time big data information systems. However, there are still many problems that need to be tackled before IoT online information systems can apply the MEC paradigm in full. These issues include real-time data processing and mining, data heterogeneity, resource constraints of mobile devices for online processing, scalability, and data privacy.

This Special Issue will collate the most recent research outcomes and developments that focus on practical and theoretical contributions of Intelligent MEC for processing IoT sensor data stream online. Moreover, the authors are expected to investigate state-of-art architectures, research issues, achievements, and applications in the field of real-time processing and analyzing of IoT sensor data in intelligent MEC.

Potential topics include but are not limited to the following:

• Real-time data stream mining of IoT data stream in MEC
• Online anomaly detection for IoT data stream in MEC
• IoT data stream process, analysis, and storage based on resource-constrained mobile edge devices
• Efficient resource allocation strategies of mobile edge devices for IoT data management
• Blockchain-based IoT data communication and management in MEC
• Online processing and management of IoT sensor images and videos in MEC
• Intelligent data fusion algorithms for heterogeneous data management in MEC
• Security and privacy protection of IoT data communication in MEC
• MEC-based deep learning and machine learning algorithms for online IoT data analysis
• Design and development of dedicated services and applications for online information processing in MEC
• Design of protocols, systems, architectures, and models for online IoT data management in MEC