Trust Aware Data Aggregation mechanism for malicious node identification in WSN based IoT Environment

Abstract

As a promising paradigm, the increase in the productivility of Internet of Things (IoT) has contributed largely to the design of modern technology. WSN is an integral part of IoT and founds its application almost in every area of human life such as healthcare, agriculture. Moreover, data collected through these sensors is vulnerable for few application such as health domain , defence domain etc. Hence data collection and analysis is a major challenge. Data Aggregation is considered to be influential and effective mechanism for avoiding the issue of data redundancy and efficient designing of IoT. Despite of such successful implementation and plethora of work in data aggregation, security remains the top priority. Hence, in this research work we design and develop TADA (Trust Aware Data Aggregation) mechanism to provide the efficient and secure environment for data aggregation. In this mechanism, in order to achieve the trade-off between privacy and accuracy, noise are added to data, accuracy parameter and malicious node identification parameter is introduced; further considering these two general constraint is designed and optimization is carried out for malicious node identification and. Furthermore, TADA is evaluated considering the three important parameter i.e. malicious packet identification rate, throughput and packet misclassification rate