Efficient and Secure Communications in Smart Cities

Dear Colleagues,

Due to the popularity of smart devices, there has been a surging demand for personalized, low-latency artificial intelligence (AI) applications, such as intelligent driving, face recognition, intelligent surveillance, etc. The world is evolving in a smarter way, and it is moving from smart systems to smart cities. The development of smart city applications has a significant impact on citizens' lives, which can provide us with convenience in various areas. For example, people can use intelligent traffic management systems to monitor traffic flow and optimize traffic signaling to reduce traffic congestion. Additionally, people can utilize security surveillance systems to monitor high-crime areas or use sensors for early warning of events such as floods, landslides, hurricanes, or droughts.

However, all of the above-mentioned smart city service applications are realized through training machine learning models for use in the real-time analysis of city data. Model training requires millions of connected, intelligent sensing devices to directly upload their collected data, such as a user's location information or a car's driving trajectory, all to the cloud server via the Internet. However, sensing devices are commonly equipped with a limited communication network bandwidth. Thus, significant communication overhead and even a model training bottleneck with long communication latency can be easily generated under this condition, seriously affecting the service experience for demanding real-time applications. In addition, these data used for model training are usually multi-source heterogeneous data with different data structures or characteristics. In addition, a user’s private information is also involved in these data, easily creating the potential for a disastrous security threat to people if all of the city information is exposed or exploited by malicious attackers during the uploading process. Therefore, how to improve communication efficiency while providing privacy guarantees to make better decisions in real-time with such a large amount of streaming data is a major challenge for smart cities.

This Special Issue aims to investigate the state-of-the-art and future perspectives for efficient and secure communication in smart cities. We encourage high-quality original research, reviews, critical perspectives, and opinion articles. Potential topics include but are not limited to the following:

• urban computing;
• tourism data management and analysis;
• spatial-temporal data fusion and mining;
• smart traffic;
• multi-party security computing;
• privacy preserving;
• machine learning for smart city;
• distributed/federated learning for smart city;
• wireless/mobile network communication;
• communication optimization.

Dr. Hao Wang
Dr. Huan Wang
Guest Editors

Manuscript Submission Information

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• urban computing
• tourism data management and analysis
• spatial-temporal data fusion and mining
• smart traffic
• multi-party security computing
• privacy preserving
• machine learning for smart city
• distributed/federated learning for smart city
• wireless/mobile network communication
• communication optimization