The extensive growth of technology and the predominance of Internet of Things (IoT) devices in the field of biomedical, transportation, industry, environmental, and healthcare systems has led to a rapid expansion of non-invasive measurement methods and adoption of wearable sensors. These applications are used in every sphere of human life producing a huge amount of data. The effective management of this data initiates the need of machine learning algorithms and deep learning techniques for achieving insight, accurate decision making, and predictions. Additionally, the data from various activities in versatile fields act as a valuable resource for academic purposes and improvement of clinical operations. The recent pandemic has established the fact that prevailing health issues have tremendous consequences on the economic, political aspects of the society. Energy-efficient pervasive computing systems have evolved as a predominant solution in such circumstances, considered compatible with IoT and machine learning algorithms.

Sensor technologies involve devices that generates a digital output based on the detection of some physical phenomenon in terms of events or changes in a relevant environment. There exists various types of sensors catering to the needs of different applications, namely, image sensors, monitoring sensors, bio-chemical sensors, bio sensors, neuromorphic sensors, electronic, mechanical, proximity, health related and many more. The applicability of the sensors depends on the sensor resolution, which is also termed as measurement resolution. It refers to the smallest change to be detected in the quantity that is being measured. The numerical resolution of the digital output is usually the resolution of a sensor with a digital output. The precision of the measurement is impacted by the resolution, wherein the accuracy of a sensor could be much lower than its resolution. The sensors enable collection of data from versatile domains using IoT devices ensuring optimum resolution. This acts as the basis for predictive analytics using advanced machine learning models. The predictive results provide interesting insights, perspectives enabling accurate decision making in various fields of research and development.

This Special Issue solicits high quality original research works and review articles that leverage sensors to solve major issues in environmental and geolocation fields.

Potential topics include but are not limited to the following:

• Sensors in medical and healthcare fields
• Sensors in transportation
• Green IoT sensors with wireless networks
• Sensors in agriculture data
• IoT applications with cloud sensors
• Sensors in manufacturing and industry fields
• Big data analytics with sensor data
• Sensors in the electronic field along with artificial intelligence
• Blockchain technology in sensor-based Green IoT
• Sensors with machine learning
• Vision, perception, and sensing for robots and UAVs
• Sensors in navigation, and geolocation