Nowadays, blockchain as emerging computer technology is gradually applied to the Internet of Things (IoT) industry. However, high traffic load and network congestion have become the main obstacles to the integration of IoT and blockchain, thanks to the vast amount of IoT devices as well as the considerable information redundancy of blockchain. In IoT-blockchain, the number of IoT devices and the blockchain network scale are supposed to establish an appropriate balance. Therefore, building a precise traffic model between the blockchain and IoT devices is crucial for designing and optimizing an IoT-Blockchain network. This paper proposes an innovative traffic model of the IoT-blockchain system, which describes traffic of blockchain nodes under the different sizes of IoT input and network scale. We implement a lightweight Bitcoin-like blockchain based on PoW and connect with several IoT simulators to conduct various network traffic experiments. Our mathematical model and experimental results demonstrate the relationship between self-similarity, network traffic, blockchain scales, and the number of IoT inputs.

Many recent studies have explored user-defined interactions for touch and gesture-based systems through end-user elicitation. While these studies have facilitated the user-end of the human-computer dialogue, the subsequent design of gesture representations to communicate gestures to the user vary in style and consistency. Our study explores how users interpret, enact, and refine gesture representations adapting techniques from recent elicitation studies. To inform our study design, we analyzed gesture representations from 30 elicitation papers and developed a taxonomy of design elements. We then conducted a partnered elicitation study with 30 participants producing 657 gesture representations accompanied by think-aloud data. We discuss design patterns and themes that emerged from our analysis, and supplement these findings with an in-depth look at users' mental models when perceiving and enacting gesture representations. Finally, based on the results, we provide recommendations for practitioners in need of "visual language" guidelines to communicate possible user actions.

360-degree video is increasingly used to create immersive user experiences; however, it is typically limited to a single user and not interactive. Recent studies have explored the potential of 360 video to support multi-user collaboration in remote settings. These studies identified several challenges with respect to 360 live streams, such as the lack of gaze awareness, out-of-sync views, and missed gestures. To address these challenges, we created 360Anywhere, a framework for 360 video-based multi-user collaboration that, in addition to allowing collaborators to view and annotate a 360 live stream, also supports projection of annotations in the 360 stream back into the real-world environment in real-time. This enables a range of collaborative augmented reality applications not supported with existing tools. We present the 360Anywhere framework and tools that allow users to generate applications tailored to specific collaboration and augmentation needs with support for remote collaboration. In a series of exploratory design sessions with users, we assess 360Anywhere's power and flexibility for three mobile ad-hoc scenarios. Using 360Anywhere, participants were able to set up and use fairly complex remote collaboration systems involving projective augmented reality in less than 10 minutes.

Augmented Reality (AR) developers face a proliferation of new platforms, devices, and frameworks. This often leads to applications being limited to a single platform and makes it hard to support collaborative AR scenarios involving multiple different devices. This paper presents XD-AR, a cross-device AR application development framework designed to unify input and output across hand-held, head-worn, and projective AR displays. XD-AR's design was informed by challenging scenarios for AR applications, a technical review of existing AR platforms, and a survey of 30 AR designers, developers, and users. Based on the results, we developed a taxonomy of AR system components and identified key challenges and opportunities in making them work together. We discuss how our taxonomy can guide the design of future AR platforms and applications and how cross-device interaction challenges could be addressed. We illustrate this when using XD-AR to implement two challenging AR applications from the literature in a device-agnostic way.