Internet of things is a current exploding area of research now in which the number of devices is expected to reach 20 billion in 2050. Devices are becoming intelligent and are being used for many applications which support day to day living of humans. Since the information gathered are being used by human users in most of the cases, trust of the information provider is to be questioned. Lack of trust obstructs the acceptance of these devices and technologies to be implemented. As the technology advances, the IoT network architecture must establish trust between the devices to increase its adaptability among the potential users. In this paper, we propose a Fog level trust establishment technique has been proposed for IoT devices using Node feedback mechanism. Since the trust factors are decided in the fog level, the nodes will know the trust of the other node which they need to communicate only and thereby avoiding bad-mouthing attacks. Considering the node capability in IoT environment, we adopt a mechanism for the calculating the trust of the node which keeps the overhead of the trust computing device to be low. This makes our implementation capable of expanding to handle large scale IoT networks. Information Entropy theory has been adopted to gather the feedback about a node in the fog level which breaks the barriers of subjective trust calculation in the traditional trust management schemes. Results shows an improvement in the efficiency compared to the existing methods.

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This is applied greatly in business enterprises and government sectors for delivering the services to their customers, clients and citizens. But, the interaction is successful only based on the trust that each device has on another. Thus trust is very much essential for a social network. As Internet of Things have access over sensitive information, it urges to many threats that lead data management to risk. This issue is addressed by trust management that help to take decision about trustworthiness of requestor and provider before communication and sharing. Several trust-based systems are existing for different domain using Dynamic weight method, Fuzzy classification, Bayes inference and very few Regression analysis for IoT. The proposed algorithm is based on Logistic Regression, which provide strong statistical background to trust prediction. To make our stand strong on regression support to trust, we have compared the performance with equivalent sound Bayes analysis using Beta distribution. The performance is studied in simulated IoT setup with Quality of Service (QoS) and Social parameters for the nodes. The proposed model performs better in terms of various metrics. An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services. The most salient features of IoT system is to design it with scalability, extendibility, compatibility and resiliency against attack. The existing works finds a way to integrate direct and indirect trust to converge quickly and estimate the bias due to attacks in addition to the above features.