Visible to the public Biblio

Filters: Author is Karmakar, G.  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
I
Islam, M. M., Karmakar, G., Kamruzzaman, J., Murshed, M..  2019.  Measuring Trustworthiness of IoT Image Sensor Data Using Other Sensors’ Complementary Multimodal Data. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :775–780.
Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data.