# CATSWoTS: Context Aware Trustworthy Social Web of Things System
## 2 Background Study
### Trust Management Systems
No proper mechanism for trust management was presented in this paper. [PERSON] et al. [22] proposed Context-Aware trust management. After information gathering, an entity was selected and the potential candidate nodes were selected. For the final selection, these selected set of nodes compete, weights were computed and the best-rated nodes were given by the trust manager. After this, a feedback report was sent by the client node to the trust manager which was maintained there for future use. To differentiate it from an adaptive process, a learning phase also called cognitive process was done. The process of updating record was done by the trust manager based on these reports. Through experimental validation, it was identified that several attacks like bad-mouthing, on-off attack were stopped. The research tackled the issue of attack models. However, a QoS service based evaluation was not considered in the research. [PERSON] et al. [3] introduced a trust protocol for Service Oriented based IoT system. An adaptive filtering technique was devised to regulate the constraints. Validation was done through NS-3 simulation tool. The criteria for indirect (recommendations) experience and direct (experience and feedback) were suggested in a user-centric environment. Three relations like social contact, Community of Interest (CoI) and friendship were considered. A profile was preserved by each user which was shared with other devices of the user. Opportunistic service attack, self-promoting attacks, bad-mouthing, and ballot-stuffing attacks were taken care of in the proposed system. Furthermore, a weighted parameter technique was used. A storage management technique that is important for small devices was also devised. A comparative analysis with EigenTrust and PeerTrust was done. By managing the list at the user end made it difficult to share the experience with those users who are not part of social relations. Therefore, it is difficult to establish more precise trust/reputation. In addition, context-awareness was not given attention. A Context Awareness architecture for the Internet of Things (CA4 IOT) architecture was proposed for task automation by selecting sensors. It reduced the difficulty for users who faced in selecting the appropriate sensor [23]. The proposed architecture consisted of four internal layers: Data, Semantics, and Context Dissemination Layer (DSCDL), Processing and Reasoning Layer (CPRL), Context and Semantic Discovery Layer (CSDL), and Sensor Data Acquisition Layer (SDAL); and two external layers: user layer and sensing layer. The different components and layers worked together for the automation of filtering, fusion and reasoning mechanisms. The results of experiments showed that the proposed architecture had improved the context-aware capabilities of MW and had enabled to establish sensing as a service platform. However, the system did not consider QoS criteria for the trust evaluation. A framework for context-aware service composition for IoT based smart cities was proposed [24]. The framework was an abstract service model based on wEASEL. The abstract model represented services and specification, signature and conversation represented user tasks. Furthermore, it supported dynamic reasoning. The services were selected against a user's task and then coordination was made to find various possible combinations to satisfy the user criteria. The framework was evaluated using OWLS-TC4 testbeds by the combination of composite and simple web services. It was identified that no QoS criteria was taken care of in this research. Apriori Learning and Bayesian Classifier based trust model was proposed in [25]. Two steps were defined for decision-making in which behavioral pattern was identified using a priori learning and Bayesian was used for the decision. In addition, the entity's experience was presented in the form of a vector for evaluating trust. Various attacks are tackled in the system. However, the research did not focus on context awareness and QoS based trustworthy evaluation.