Data centric trust evaluation and prediction framework for IOT

Abstract

Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas.

A Framework for Exploiting Internet of Things for Context-Aware Trust-Based Personalized Services

“…In the second layer, we have the context-aware trust-based personalization-related components. Among these components are the trust (evaluation) manager, which is responsible for computing trustworthiness of an entity of the platform [16]. We also have a contextual pro le model, recommendation manager, feedback manager, and contextual pro le manager.…”

“…Each of these components will be discussed in the next sections. e framework consists of various processing modules to be incorporated into our trust-based platform for intelligent service delivery in the IoT environment [16].…”

“…Whenever a user submits a feedback about the service he/she has just consumed, the feedback manager sends the feedback information to the trust component (TC), using the provided API. Details of mechanisms for trust score computation can be found in [16].…”

“…However, Jayasinghe et al have provided computational models for trust computation in [16], interested readers can check the reference for details. However, the trust score for a given context of consumption is computed using the implicit or explicit rating information obtained from the relevance feedback process, and this score is associated with the context, service, and preference of the service for a given user in every consumption session.…”

“…However, collecting data, such as context data, from IoT devices poses a new challenge in terms of security and reliability. us, trust management is emerging as a powerful tool that can be used to address reliability of not only context data itself but also that of context providers, context information consumers, users, and services [5,[15][16][17][18].…”