Computer Engineering

Medical implants based on wireless communication will play crucial role in healthcare systems. Some applications need to know the exact position of each implant. RF positioning seems to be an effective approach for implant localization. The two most common positioning data typically used for RF positioning are received signal strength and time of flight of a radio signal between transmitter and receivers (medical implant and network of reference devices with known position). This leads to positioning methods: received signal strength (RSS) and time of arrival (ToA). Both methods are based on trilateration. Used positioning data are very important, but the positioning algorithm which estimates the implant position is important as well. In this paper, the proposal of novel algorithm for trilateration is presented. The proposed algorithm improves the quality of basic trilateration algorithms with the same quality of measured positioning data. It is called Enhanced Positioning Trilatera...

With the advancement of new technologies, the number of connected devices, the amount of data generated, and the need to build an intelligently connected network of things to improve and enrich the human ecosystem open new doors to modifications and adaptations of current cellular network infrastructures. While more focus is given to low power wide area (LPWA) applications and devices, a significant challenge is the definition of Internet of Things (IoT) use cases and the value generation of applications on already existing IoT devices. Smartphones and related devices are currently manufactured with a wide range of smart sensors such as accelerometers, video sensors, compasses, gyros, proximity sensors, fingerprint sensors, temperature sensors, and biometric sensors used for various purposes. Many of these sensors can be automatically expanded to monitor a user’s daily activities (e.g., fitness workouts), locations, movements, and real-time body temperatures. Mobile network operator...

This paper proposes an approach which safeguards confidentiality to avoid disclosures of features within a multiple IoT situation, that is, a setup of objects in networks that communicate with each other. Two ideas derived from the theory of databases, namely k-anonymity and t-certitude, form our basis. They are used to cluster the objects to provide a unitary view of them and their characteristics. In fact, the use of anonymity and t-closeness robustly ensures privacy for derived groups. Furthermore, description of the object grouping scheme that preserves privacy, which represents the core of our approach was studied. Eventually, we illustrated the corresponding security model and analyzed the associated properties. The study also provided important advantages for the protection of user privacy in all those situations where knowledge of object features may help an attacker to obtain information about user habits and behavior. This study prevents not only the disclosure of informat...