In an era of rapid technological advancement, applications of the Internet of Things (IoT) have become an indispensable part of our modern day-to-day lives. These applications can be observed in a myriad of different domains: green cities, transportation, electric power management, healthcare, agriculture, and many more. At their core, the applications are designed pattern recognition algorithms and deep learning networks with smart wireless sensors and can be seen through signal detection and processing, image/video and language understanding, and multimedia communication.

Found in smart sensor networks, smart sensors are now playing a role of increasing importance in the Internet of Things. Used to detect the parameters of a chosen environment, smart sensors are usually composed of wireless or non-wireless sensors, microprocessors, and varying communication technologies. Microprocessors perform two primary roles: to transform the data collected from the sensors into the required processing data type and to perform basic statistical analysis on the collected data. Communication technologies are then applied to transmit information to other sensors. Such smart sensors are able to accurately and automatically collect data on the surrounding environment while simultaneously reducing erroneous noise. Networks with smart sensors are widely applied in different fields such as automated detection systems, smart grids, refrigeration and heating exchange systems, and other contemporary IoT applications. Although there is a myriad of different applications of smart sensor networks, the basic functions of the smart sensor network remain the same for every application. When it comes to smart sensor networks, high expectations are held regarding performance safety, efficiency, reliability, and resilience for users. However, before improving the performances of target models, the most critical issue would be to evaluate network functionalities accurately and efficiently. Strategies to design novel networks or enhance existing networks can then be implemented or developed in accordance with the evaluation results.

This Special Issue welcomes the latest contributions from industry or academia focused on challenges for improving the various performances of and evaluation techniques for different smart sensor networks. We invite researchers to contribute original research articles as well as review articles that will promote continuing efforts to realize the improvements and evaluation techniques for smart sensor networks.

Potential topics include but are not limited to the following:

• Network reliability for smart sensor networks
• Network resilience for smart sensor networks
• Security, robustness, and reliability of smart sensor networks
• Efficiency for smart sensor networks
• Foundations and theories for smart sensor networks
• Smart computing for smart sensor networks
• Strategies for unreliable smart sensor networks
• Embedded structure for smart sensor networks
• Machine learning analytics for smart sensor networks
• Big data management for smart sensor networks
• Data mining for smart sensor networks
• Applications of smart sensor networks