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Recently, the convergence of the paradigm of the Internet of Things (IoT) with social networking has been widely recognized as an emerging interdisciplinary area, lately, referred to as the Social Internet of Things (SIoT). SIoT enables the object-to-object interaction and provides the platform for these objects to autonomously socialize with the other objects in the network in a bid to overcome the key challenges of IoT, i.e., data discovery and composition, network navigability, trust management, etc. Trust plays a significant role while establishing these inter-object social relationships and it is also essential to observe the trustworthiness of an object before relying on information provided by them. A number of trust evaluation models for SIoT have been proposed in the literature, nevertheless, most of these models suffer in validating and testing their respective models due to lack of appropriate datasets. To address this issue, this paper proposes a scalable plug and play trust platform referred to as SCalable and Robust Trust platform for SIoT (SCaRT-SIoT) to provide the dataset to test and analyze various SIoT-based trust models of the research community.

Inadequate sleep has very bad impacts on human daily life activities. Sleep disorders are one of the main reasons for the inadequate sleep problem. Diagnosis of these disorders is a very challenging task because they occur at night when the patients are asleep and require a full night monitoring to diagnose. Conventional sleep monitoring techniques are not feasible due to various reasons such as they require the person to wear multiple invasive sensors, the cost is high and also need special environment for monitoring. In this work, we propose a non-invasive and cost-effective solution based on passive RFID technology for sleep monitoring. We attach passive RFID tags to the shirt of the person and collect low-level data which can capture the information about the vital signs of the person. The proposed solution can detect the on-bed movements which provide useful information about the sleep of the person. We propose an adaptive threshold-based technique which can effectively identify the stable breathing and apnea regions from the collected breathing signal. The stable region is used for estimating the breathing rate. Our solution can monitor vital signs such as respiration rate during the overnight sleep and can detect sleep apnea. We implement the proposed solution using COTS RFID tags in the real world scenarios and the results show that our system achieves 100% accuracy for apnea detection and above 95% for breath rate estimation.