The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

The tens of billions of devices expected to be connected to the Internet will include so many sensors that the demand for sensor-based services is rising. The task of effectively utilizing the enormous numbers of sensors deployed is daunting. The need for automatic sensor identification has expanded the need for research on sensor similarity searches. The Internet of Things (IoT) features massive non-textual dynamic data, which is raising the critical challenge of efficiently and effectively searching for and selecting the sensors most related to a need. Unfortunately, single-attribute similarity searches are highly inaccurate when searching among similar attribute values. In this paper, we propose a group-fitting correlation calculation algorithm (GFC) that can identify the most similar clusters of sensors. The GFC method considers multiple attributes (e.g., humidity, temperature) to calculate sensor similarity; thus, it performs more accurate searches than do existing solutions.

The booming developments in embedded sensor technique, wireless communication technology, and information processing theory contribute to the emergence of Internet of Things (IoT), which aims at perceiving and connecting the physical world. In recent years, a growing number of Internet-connected sensors have published their real-time state about the real-world objects on the Internet, which makes the content-based sensor search a promising service in the Internet of Things (IoT). However, classical search engines focus on searching for static or slowly varying data, rather than object-attached sensors. Besides, the existing sensor search systems fail to support the search mode based on a given measurement range. Furthermore, accessing all available sensors to find sought targets would result in tremendous communication overhead. Thus an accurate matching estimation mechanism is proposed to support the search mode based on a given search range and improve the efficiency and applicability of existing sensor search systems. A time-dependent periodical prediction method is presented to periodically estimate the sensor output, which combines with the during the period feedback prediction method that can fully exploit the verification information for enhancing the prediction precision of sensor reading to efficiently serve the needs of sensor search service. Simulation results demonstrate that our prediction methods can achieve high accuracy and our matching estimation mechanism can dramatically reduce the communication overhead of sensor search system.