Internet of things (IoT) allows people and things to be connected anytime, anywhere with anything. With the advent of better wireless technology and an increase in smartphone usage, a new mode of data collection (a.k.a. mobile IoT) has emerged.
Nowadays, smartphones are equipped with different types of sensors. As a result, people can easily participate in the sensing of their surroundings. Mobile IoT has several practical applications: air quality monitoring, traffic monitoring, reporting from disaster situations, and environment monitoring. These applications are usually open to the public and receive sensor data from multiple participants, which influences the reduction of data sparsity at lower costs in comparison with traditional sensor networks. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. Hence, it is crucial to predict those missing or imprecise data values from the correlated data streams. In this talk, I will present my research that aims at developing real-time analysis models, best suitable for mobile environments for enhancing data accuracy and integrity. Moreover, I will discuss different real-life applications of IoT.