The rapid growth of Internet-of-Things (IoT) applications has generated interest from many industries and a need for graduates with relevant knowledge. An IoT system is comprised of spatially distributed interactions between humans and various interconnected IoT components. These interactions are contextualized within their ambient environment, thus impeding educators from recreating authentic tasks for hands-on IoT learning. We propose LearnIoTVR, an end-to-end virtual reality (VR) learning environment which helps students to acquire IoT knowledge through immersive design, programming, and exploration of real-world environments empowered by IoT (e.g., a smart house). The students start the learning process by installing virtual IoT components we created in different locations inside the VR environment so that the learning will be situated in the same context where the IoT is applied. With our custom-designed 3D block-based language, students can program IoT behaviors directly within VR and get immediate feedback on their programming outcome. In the user study, we evaluated the learning outcomes among students using LearnIoTVR with a pre- and post-test to understand to what extent does engagement in LearnIoTVR lead to gains in learning programming skills and IoT competencies. Additionally, we examined what aspects of LearnIoTVR support usability and learning of programming skills compared to a traditional desktop-based learning environment. The results from these studies were promising. We also acquired insightful user feedback which provides inspiration for further expansions of this system.

Augmented Reality (AR), which blends physical and virtual worlds, presents the possibility of enhancing traditional toy design. By leveraging bidirectional virtual-physical interactions between humans and the designed artifact, such AR-enhanced toys can provide more playful and interactive experiences for traditional toys. However, designers are constrained by the complexity and technical difficulties of the current AR content creation processes. We propose MechARspace, an immersive authoring system that supports users to create toy-AR interactions through direct manipulation and visual programming. Based on the elicitation study, we propose a bidirectional interaction model which maps both ways: from the toy inputs to reactions of AR content, and also from the AR content to the toy reactions. This model guides the design of our system which includes a plug-and-play hardware toolkit and an in-situ authoring interface. We present multiple use cases enabled by MechARspace to validate this interaction model. Finally, we evaluate our system with a two-session user study where users first recreated a set of predefined toy-AR interactions and then implemented their own AR-enhanced toy designs.

Ambient Backscatter Communication (AmBC) is a promising technology for green IoT, which overcomes the energy, cost and spectrum resource shortage challenges. In AmBC, the weak backscatter link strength caused by the double-fading is a big challenge for IoT device symbol detection. This paper focuses on receiver design with the M PSK RF source symbol. Most previous works have not considered the issue of channel estimation that is very important for wireless communications. We focus on precisely estimating the channels at each of the AmBC receiving antennas, in particular on resolving the quadrant ambiguities in the channel estimates. The proposed channel estimator makes use of both the pilot and the data symbols of the RF source to eliminate quadrant ambiguity and enhance the estimation accuracy. We propose a novel distance detector (DD) that makes its decision in each IoT symbol interval based on which of the calibrated channel estimates the current channel estimate is closer to in Euclidean distance. What is more, we propose two modified distance detection methods to enhance the bit error rate (BER) performance. Finally, extensive numerical results show that the proposed distance detection method achieves performance comparable to that of the optimal detector with perfect channel state information (CSI).