Proximity Based Authentication in IoT

IoT consists of essentially thousands of tiny sensor nodes interconnected to the internet, each one of which executes the programmed functions under memory and power limita- tions. The sensor nodes are distributed mainly for gathering data in various situations. IoT envisions the future technologies such as e-health, smart city, auto-mobiles automa- tion, construction sites automation, and smart home. Secure communication of data under memory and energy constraints is major challenge in IoT. Authentication is the first and important phase of secure communication. This study presents a protocol to authenticate resource constraint devices in physical proximity by solely using the shared wireless communication interfaces. This model of authentication only relies on the abundance of ambient radio signals to authenticate in less than a second. To evaluate the designed protocol, SkyMotes are emulated in a network environment simulated by Contiki/COOJA. Results presented during this study proves that this approach is immune against passive and active attacks. An adversary located as near as two meters can be identified in less than a second with minimal expense of energy. Since, only radio device is used as required hardware for the authentication, this technique is scalable and interoperable to heterogeneous nature of IoT.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.