Technological developments, such as mobile augmented reality (MAR) and Internet of Things (IoT) devices, have expanded available data and interaction modalities for mobile applications. This development enables intuitive data presentation and provides real-time insights into the user’s context. Due to the proliferation of available IoT data sources, user interfaces (UIs) have become complex and diversified, while mobile devices have limited screen spaces. This state increases the necessity of design principles that help to secure sufficient user experience (UX). We found that studies of design principles for IoT-enabled MAR applications are limited. Therefore, we conducted a systematic literature review to identify existing design principles applicable to IoT-enabled MAR applications. From the state-of-the-art research, we compiled and categorized 26 existing design principles into seven categories. We analyzed the UIs of three IoT-enabled MAR applications with the identified design principles and user feedback gathered from each application’s evaluation to understand what design principles can be considered in designing these applications. Among the 26 principles, we find eight principles that are commonly identified as possible improvements for the applications based on their purposes. We demonstrate the practical use of the identified principles by redesigning the UIs, and we propose five new design principles derived from the application analysis. As a result, we summarized a total of 31 design principles, including the five new ones. We expect that our findings will give insight into the UX/UI design of IoT-enabled MAR applications for researchers, educators, and practitioners interested in UX/UI development.

Recently, there has been growing interest in the research community in exploring the potential of blockchain technology for various Internet of Things (IoT)-based domains, such as smart cities. While several open-source blockchain platforms like Hyperledger Fabric are readily available, integrating them into large-scale IoT infrastructures remains challenging due to factors such as performance, cost, and scalability. Simulation studies provide a viable option for carefully assessing the applicability of blockchains in dynamic, large-scale, and heterogeneous IoT scenarios. In theory, integrating IoT simulators with actual blockchain platforms is possible. However, the challenge lies in addressing the disparity between the distinctive environments of IoT simulators and blockchain platforms. This paper introduces a novel middleware architecture, SimBlockLink, that overcomes these challenges in enabling interaction between blockchain platforms and IoT simulators. SimBlockLink bridges the distinct operational facets of both, supporting thorough evaluations of real blockchain-based solutions in simulated IoT scenarios. We present a study where SimBlockLink connects IoTsim-Osmosis, an IoT simulation tool, with an actual Hyperledger Fabric blockchain platform. To demonstrate scalability, we conduct a large-scale city firefighting case study involving 30,000 IoT devices, validating SimBlockLink's ability to sustain high-volume transaction loads. In addition, we provide a security and privacy analysis of the middleware, outlining threat models and built-in mechanisms such as transport layer security (TLS) channels, concurrency control, and fail-safe logic to ensure robust and dependable operation. Our results demonstrate that SimBlockLink enables effective interaction between blockchain deployments and simulated IoT models, ensuring smooth interoperability and efficient evaluation of key blockchain performance metrics, including throughput, latency, and transaction success/failure rates.

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it; all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research.

Real-time anomaly detection in energy consumption is crucial for identifying technical inefficiencies and user behavior issues that lead to energy waste. Traditional methods rely on utilizing large historical consumption patterns, but data limitations in certain domains hinder the application of these systems. This study addresses this challenge by leveraging transfer learning with long short-term memory networks, using school building energy datasets as a source domain to improve performance in data-scarce target domains. The evaluated models were trained to forecast 8-h energy consumption and detect anomalies. Results show that transfer learning models trained with 40 % of the dataset generalize better, reducing sensitivity to minor fluctuations and lowering false alarm rates compared to baseline models which trained on full training dataset. Those models tend to overfit to small variations, which led to increased false positives. These findings highlight the transfer learning effectiveness in improving anomaly detection reliability, ensuring models focus on consistent and persistent changes in consumption patterns.

Interactivity is a metric that measures the level of control or manipulation users may exert over a system, software, or service. It is considered a key dimension in cloud-gaming services, measuring how effectively users can control and respond to game events in real-time. Although widely acknowledged by standards such as ITU-T Rec. G.1051, G.1072, its relationship to other quality dimensions such as video, audio, and overall Quality of Experience (QoE), remains not thoroughly examined. In this paper, we present a novel Bayesian Network-based framework to model and analyze interactivity alongside other quality factors under varied network conditions. Our method enables probabilistic inference and sensitivity analysis across perceptual variables, offering explainable insights into their mutual influence. Using data from two (\textit{N1}=30, and \textit{N2}=31 subjects) subjective studies — one for Virtual Reality Cloud Gaming (VRCG) and another for Mobile Cloud Gaming (MCG) — we show interactivity is most sensitive to round trip time (RTT) but resilient to jitter (RJ) effect. Further, an assessment of the seven interactivity-only variables shows that their distributions change uniformly under varying network conditions, suggesting that interactivity may be captured by a single metric. Finally, sensitivity analyses indicate that QoE is a more representative metric than interactivity for quality assessment in cloud gaming over heterogeneous access networks.

Cloud gaming (CG) services are a key enabler of more accessible and affordable gaming experiences for users worldwide. However, to deliver these services with sufficient Quality of Experience (QoE) over heterogeneous access networks, stakeholders, including network and cloud service providers, and game developers, must first understand their specific performance requirements. For that, this study investigates QoE of PC-based CG under controlled and repeatable network conditions, emulating a range of impairments including round-trip time (RTT), packet loss (PL), random jitter (RJ), bursty jitter (BJ), and bitrate. Two representative games, Terraria and Counter-Strike 2, were tested across 15 network conditions, streamed using the Moonlight/Sunshine platform. Thirty participants rated their QoE, with orchestration and data collection managed by the ALTRUIST tool. Results show that low bitrate (0.5Mb/s), high RTT (402ms), bursty jitter (702ms), and high RJ (std=3) significantly degrade QoE, especially in QoS-sensitive games. Additionally, we evaluate mobile-CG trained QoE models on PC-CG data and observe significant prediction errors, highlighting the importance of accounting for differences in streaming platform and context when estimating QoE. These findings provide valuable insights for stakeholders optimizing PC-CG over HANs.

In the future, extended reality (XR) applications will be hosted on cloud and edge infrastructures and streamed over heterogeneous access networks such as Wi-Fi and 6G. These infrastructures promise ubiquity but are prone to stochastic conditions, such as network congestion and wireless signal fading and attenuation, that can be detrimental to the quality of experience (QoE) regarding XR applications. This paper presentsextensive and novel results assessing the impact of networkconditions (N = 20) on users’ QoE via realistic subjective tests(N = 28) involving factors such as round-trip time (RTT), jitter(RJ), and packet loss (PL) in a social XR application context. Our results indicate that social XR applications require stringent quality of service to support users’ QoE. We demonstrate that RTT values up to 77 ms do not significantly impact users’ QoE. Furthermore, combined (PL and RTT) values lead to significant QoE degradation when RTT values exceed 77 ms and PL exceeds 2%. We also demonstrate that even minimal jitter values, with 1 standard deviation beyond 52 ms RTT values,can lead to significant QoE degradations. Furthermore, jitter values exceeding 3 standard deviations for 27ms RTT value and beyond should be avoided. Finally, based on network traffic data between Sweden and various AWS data centers in Europe, we show that social XR applications can be hosted at a few datacenter locations with minimal QoE impact for wired network connections. However, due to high jitter values, both 4G and 5G networks are not conducive to users’ QoE.