The vision of the Internet of Things (IoT) is based on the idea of offering connectivity to every physical object (e.g., thermometers, banknotes, smart TVs, bicycles, etc.). This connectivity ensures that immediate information about these objects and their surroundings can be obtained and therefore decisions can be taken based on real-time information. This allows increased productivity and efficiency. One of the most important implementations of the IoT is the smart (or digital) cities where the information collected from the connected devices is used in, for instance, configuring energy systems, enhancing the traffic, controlling pollution or ensuring security. However, there is no guarantee that all objects will provide information because, for example, some may be out of service or have lost connectivity bearing in mind that many objects in an IoT network are characterized by their limited resources (e.g., battery life, computing, and connection capacity). Moreover, the decision in an IoT network is mostly based on the information provided by a subset of the objects rather than all of them. In addition, the obtained information can be contradictory for many reasons, such as a defect in the object or malicious interference either in the object itself or during the communication process. Therefore, it is necessary to provide a measure that reflects to what extent the decision in an IoT network is trustful. In this paper, an approach based on statistical science is proposed to measure the trustworthiness of information collected from heat sensors. An architecture and algorithm, based on the confidence interval measurement to reduce the time taken to verify and check the trustworthiness of network sensors or any other type of IoT device.
Alkhodre, A. B. (2018). Statistical-based trustful access control framework for smart campuses. International Journal of Advanced Computer Science and Applications, 9(9), 111–117. https://doi.org/10.14569/ijacsa.2018.090915