fi=vertaisarvioitu|en=peerReviewed|

As autonomous vehicles (AVs) enter pedestrian-centric environments, existing vehicle-mounted external human–machine interfaces (eHMIs) often fall short in shared spaces due to line-of-sight limitations, inconsistent signaling, and increased decision latency on pedestrians. To address these challenges, we introduce the Smart Pole Interaction Unit (SPIU), an infrastructure-based eHMI that decouples intent signaling from vehicles and provides context-aware, elevated visual cues. We evaluate SPIU using immersive VR-AWSIM simulations in four high-risk urban scenarios: four-way intersections, autonomous mixed traffic, blindspots, and nighttime crosswalks. The experiment was developed in Japan and replicated in Norway, where forty participants engaged in 32 trials each under both SPIU-present and SPIU-absent conditions. Behavioral (response time) and subjective (acceptance scale) data were collected. Results show that SPIU significantly improves pedestrian decision-making, with reductions ranging from 40% to over 80% depending on scenario and cultural context, particularly in complex or low-visibility scenarios. Cross-cultural analyses highlight SPIU’s adaptability across differing urban and social contexts. We release our open-source Smartpole-VR-AWSIM framework to support reproducibility and global advancement of infrastructure-based eHMI research through reproducible and immersive behavioral studies.

Predicting future heart rate (HR) not only helps in detecting abnormal heart rhythms but also provides timely support for downstream health monitoring services. Existing methods for HR prediction encounter challenges, especially concerning privacy protection and data heterogeneity. To address these challenges, this paper proposes a novel HR prediction framework, PCFedH, which leverages personalized federated learning and prototypical contrastive learning to achieve stable clustering results and more accurate predictions. PCFedH contains two core modules: a prototypical contrastive learning-based federated clustering module, which characterizes data heterogeneity and enhances HR representation to facilitate more effective clustering, and a two-phase soft clustered federated learning module, which enables personalized performance improvements for each local model based on stable clustering results. Experimental results on two real-world datasets demonstrate the superiority of our approach over state-of-the-art methods, achieving an average reduction of 3.1% in the mean squared error across both datasets. Additionally, we conduct comprehensive experiments to empirically validate the effectiveness of the key components in the proposed method. Among these, the personalization component is identified as the most crucial aspect of our design, indicating its substantial impact on overall performance.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

The gradual maturation of mobile augmented reality (AR) and localization technologies is enabling the development of immersive AR-enabled indoor localization and navigation systems. Existing indoor localization technologies (e.g., WiFi, infrared, Bluetooth) and navigation services do not provide intuitive 3D AR experiences and can be expensive to deploy. This paper introduces WebARNav, a cross-platform indoor localization system that provides user-friendly AR navigation services with low overhead and remarkable accuracy. First, we propose a lightweight location fusion framework for indoor navigation on the mobile web, which leverages accurate edge-supported vision localization to guide and correct lightweight pedestrian dead reckoning localization. Second, we improve the accuracy of localization using an attention-based feature extraction method and a dual-stream retrieval and co-visibility re-ranking technique for initial localization. Third, we significantly improve accuracy and speed up retrieval as users move by generating a topological map for traveling localization. We conducted extensive experiments on various indoor datasets to demonstrate localization accuracy and navigation experience. The study shows that WebARNav achieves a localization frequency of over 30 Hz and reduces the average trajectory error by 76% and 95% for single- and multi-floor office scenes, respectively, compared to the PDR-only method. The proposed traveling localization method also reduces the localization latency by 15.2%, 55.1%, and 98.6% in the baseline datasets, with an accuracy improvement of over 4%.

The energy consumption of 5G base stations (BSs) is significantly higher than that of 4G BSs, creating challenges for operators due to increased costs and carbon emissions. Existing solutions address this issue by switching off BSs during specific periods or forming cooperation coalitions where some BSs deactivate while others serve users. However, these approaches often rely on fixed geographic configurations, making them unsuitable for urban areas with numerous BSs and mobile users. To tackle these challenges, we propose a hierarchical reinforcement learning (RL) framework for energy conservation in large-scale 5G networks. In the upper-layer, we propose a deep Q-network integrated with a graph convolutional network that dynamically groups BSs into coalitions from a macro perspective. This layer focuses on high-level coalition formation to optimize system-wide energy efficiency by considering the global state of the network. In the lower-layer, we combine attention mechanism with multi-agent RL and graph convolutional networks to design a scalable algorithm that maximizes local energy efficiency through optimizing the cooperation within each coalition. These two layers align global coalition dynamics with local intra-coalition cooperation to achieve system-wide energy optimization. Moreover, we accurately model large-scale urban 5G scenarios leveraging a high-fidelity network simulator, which enables our RL framework to learn from real-world feedback. Extensive experiments conducted with the simulator demonstrate that our proposed framework achieves remarkable energy savings of up to 75.6%, significantly outperforming baseline approaches. These findings highlight the effectiveness and superiority of our hierarchical RL optimization framework in addressing the energy consumption challenges faced by large-scale 5G networks.

Mobile Augmented Reality (MAR) applications pose unique challenges due to computation intensity, constrained device resources, and high interactive rendering requirements. The emergence of 5 G and edge computing offers opportunities to offload computation to the edge and cloud, indirectly enhancing the computing capability and usage duration of MAR devices. However, existing general task offloading and multipath transmission techniques do not address the challenges in offloading path selection with multiple edges, dynamic resource competition awareness, and spatial computation with strong task dependencies. This paper contributes FPSelector, a flexible path selector for MAR offloading. We present a two-tier MAR-specific offloading scheme with multiple edge nodes. In offloading decisions, we design a reinforcement learning model to generate the selection policy for each packet of an AR data stream. This model incorporates an action masking mechanism, a comprehensive reward function, and state features complemented by a resource prediction module, making FPSelector aware of dynamic heterogeneous environments. Moreover, we propose an online learning strategy to facilitate real-time selection. To validate its efficacy, we compare FPSelector's performance against leading schedulers under various scenarios, demonstrating a notable reduction of 9.9% and 9.6% in overall completion time for 4 K and 8 K video-based MAR applications compared to its closest competitor.

Foundation models (FMs) have achieved state-of-the-art performance across various domains, benefiting from their vast number of parameters and the extensive amount of publicly available training data. However, real-world deployments reveal challenges such as system heterogeneity, where not all devices can handle the complexity of FMs, and emerging privacy concerns that limit the availability of public data. To address these challenges, we propose HeLoRA, a novel approach combining low-rank adaptation (LoRA) with federated learning to enable heterogeneous federated fine-tuning. HeLoRA allows clients to fine-tune models with different complexities by adjusting the rank values of LoRA matrices, tailoring the process to each device’s capabilities. To tackle the challenge of aggregating models with different structures, HeLoRA introduces two variants, i.e., HeLoRA-Pad and HeLoRA-KD. HeLoRA-Pad employs context-based padding to standardize the LoRA matrices, aligning them with the global model through a rank-based adaptive aggregation strategy. In contrast, HeLoRA-KD leverages the idea of deep mutual learning for aggregation, allowing heterogeneous models to retain their original structures. Extensive experiments with various datasets and ablation studies demonstrate that HeLoRA outperforms existing baselines, promising to enhance the practical deployment of FMs in diverse real-world environments.

As privacy concerns continue to grow, federated learning (FL) has gained significant attention as a promising privacy-preserving technology, leading to considerable advancements in recent years. Unlike traditional machine learning, which requires central data collection, FL keeps data localized on user devices. However, conventional FL assumes that all clients operate with identical model structures initialized by the server. In real-world applications, system heterogeneity is common, with clients possessing varying computational capabilities. This disparity can hinder training for resource-limited clients and result in inefficient resource use for those with greater processing power. To address this challenge, model-heterogeneous FL has been introduced, enabling clients to train models of varying complexity based on their hardware resources. This paper reviews state-of-the-art approaches in model-heterogeneous FL, analyzing their strengths and weaknesses, while identifying open challenges and future research directions. To the best of our knowledge, this is the first survey to specifically focus on model-heterogeneous FL.

In the metaverse era, point cloud video (PCV) streaming on mobile XR devices is pivotal. While most current methods focus on PCV compression from traditional 3-DoF video services, emerging AI techniques extract vital semantic information, producing content resembling the original. However, these are early-stage and computationally intensive. To enhance the inference efficacy of AI-based approaches, accommodate dynamic environments, and facilitate applicability to metaverse XR devices, we present ISCom, an interest-aware semantic communication scheme for lightweight PCV streaming. ISCom is featured with a region-of-interest (ROI) selection module, a lightweight encoder-decoder training module, and a learning-based scheduler to achieve real-time PCV decoding and rendering on resource-constrained devices. ISCom’s dual-stage ROI selection provides significantly reduces data volume according to real-time interest. The lightweight PCV encoder-decoder training is tailored to resource-constrained devices and adapts to the heterogeneous computing capabilities of devices. Furthermore, We provide a deep reinforcement learning (DRL)-based scheduler to select optimal encoder-decoder model for various devices adaptivelly, considering the dynamic network environments and device computing capabilities. Our extensive experiments demonstrate that ISCom outperforms baselines on mobile devices, achieving a minimum rendering frame rate improvement of 10 FPS and up to 22 FPS. Furthermore, our method significantly reduces memory usage by 41.7% compared to the state-of-the-art AITransfer method. These results highlight the effectiveness of ISCom in enabling lightweight PCV streaming and its potential to improve immersive experiences for emerging metaverse application.

Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest.

Recently, artificial intelligence paves the way for the development of smart services for people anytime and anywhere, which poses great challenges on accessing computing resources. Multi-access edge computing complements existing cloud computing infrastructure at the edge of the network, where mobile users can offload computationally intensive tasks of smart applications to edge servers that are in proximity to the users themselves. Existing offloading schemes mainly focus on selecting edge servers for each offloading task with the goal of optimizing the overall average latency. However, the solutions with the optimal overall average latency may be not the most suitable for all offloading tasks. There is still a possibility that offloading leads to an extreme case of ultra-high latency, which is not acceptable for latency-sensitive applications. To address this problem, we therefore introduce modern portfolio theory (MPT) to jointly consider the overall average latency and potential risks in optimal edge server selection. The task offloading problem is regarded as an investment portfolio with the objective of maximizing the ‘return’ while minimizing the risk. Combining MPT with deep reinforcement learning (DRL), we design two proximal policy optimization (PPO)-based task offloading algorithms to jointly optimize these two objectives. The algorithm computes a portfolio for each mobile user that enables the diversification of edge server selection, thereby minimizing the risk and the average latency. Extensive simulation results based on three real-world trace datasets show that our algorithms significantly outperform the state-of-the-art solutions and can reduce the overall average latency and the risk by 59% and 85% at most, respectively.

Mobile video streaming enables flexible delivery of videos to mobile devices, supporting emerging video formats. The transition from conventional 2D videos to immersive formats, such as virtual reality and holographic videos, significantly increases the demand for computation and network resources. Existing streaming techniques are predominantly developed for specific video types, neglecting fair adaptive transmission and optimal resource utilization in services involving multiple video types. This paper investigates hybrid video streaming, encompassing 2D, 360-degree, and volumetric videos. To accommodate resource-intensive hybrid video streaming on mobile devices, we proposeHiVAT, an adaptive transcoding-based system that ensures Quality of Experience (QoE) for each stream type. We contribute 1) a transcoding-based framework to address the challenges of high bandwidth and decoding overhead on mobile devices; 2) a universal QoE model involving traditional factors, viewport smoothness, degree of immersion, etc., for transcoded video streams; 3) a multi-agent adaptive bitrate controller that collaboratively determines hybrid video quality levels to achieve high and fair QoE across multiple streams; and 4) a learning-based task scheduler to optimize computation resource usage, thereby improving the overall serviceability of the system. We evaluateHiVATagainst state-of-the-art methods, witnessing an average QoE improvement of 5.9% and 9.9% on linear and logarithmic metrics, respectively.

The Internet of Things (IoT) has revolutionized the connectivity of diverse sensing devices, generating an enormous volume of data. However, applying machine learning algorithms to sensing devices presents substantial challenges due to resource constraints and privacy concerns. Federated learning (FL) emerges as a promising solution allowing for training models in a distributed manner while preserving data privacy on client devices. We contribute SAFI , a semi-asynchronous FL approach based on clustering to achieve a novel in-cluster synchronous and out-cluster asynchronous FL training mode. Specifically, we propose a three-tier architecture to enable IoT data processing on edge devices and design a clustering selection module to effectively group heterogeneous edge devices based on their processing capacities. The performance of SAFI has been extensively evaluated through experiments conducted on a real-world testbed. As the heterogeneity of edge devices increases, SAFI surpasses the baselines in terms of the convergence time, achieving a speedup of approximately × 3 when the heterogeneity ratio is 7:1. Moreover, SAFI demonstrates favorable performance in non-independent and identically distributed settings and requires lower communication cost compared to FedAsync. Notably, SAFI is the first Java-implemented FL approach and holds significant promise to serve as an efficient FL algorithm in IoT environments.

The prediction of health metrics for drivers has become increasingly crucial due to the potential impact of drivers' health conditions on traffic accidents. Heart attack is one of the primary causes of health-related traffic tragedies. However, drivers' heart rate (HR) is considered highly-private data, which should not be collected by a centralized server for training the prediction model. To this end, we contribute FedHeart, a novel privacy-preserving federated learning (FL) system for HR prediction. We observe distinct HR changes when drivers are in steady-state and changing-state conditions, and thus we utilize FL to train two separate models for these states. To enhance the prediction accuracy, we incorporate contrastive learning to extract HR features. Through experiments on two real-world datasets, we validate the efficiency of the proposed system in accurately predicting HR during driving scenarios.

Health monitoring scenarios involve multiple users with conflicting privacy concerns. We envision extended reality facilitating smooth interactions among users while resolving potential conflicts arising from users' conflicting goals, ensuring that sensitive information is not unintentionally revealed. We contribute to an adaptive approach to resolving decision conflicts and content sharing and a scenario-centric access control model with a strategy mediator.

Unmanned Aerial Vehicles (UAVs) equipped with air quality sensors offer a powerful solution for increasing the spatial and temporal resolution of air quality data, searching and detecting emission sources, and monitoring emissions from fixed and mobile sources. Despite the numerous advantages of using UAVs, their use, however, presents several challenges that limit their broader adoption. For example, UAVs require efficient algorithms and components to minimize power consumption, the overall payload used on UAVs needs to be small to ensure optimal portability which poses limitations on the sensors that can be integrated with UAVs, and there is a need for specialized algorithms, e.g., for identifying and locating air pollution sources. Currently, most solutions for UAV-based air quality monitoring focus on specific challenges or demonstrating the potential of using UAVs, and there is a lack of comprehensive overview of the research field and its open challenges. In this paper, we contribute a systematic review of UAV-based air quality monitoring, highlighting and analyzing technical solutions and challenges, and identifying open challenges with the aim of providing a research roadmap for the path forward.

Screen lock is a critical security feature for smartphones to prevent unauthorized access. Although various screen unlocking technologies, including fingerprint and facial recognition, have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3D-printed head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth enables users to unlock their smartphones by leveraging the EMG data of the smartphone users collected from Myo armbands. When training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband, which makes EmgAuth free of calibration. We conduct extensive experiments including 53 participants and the evaluation results verify that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different screen sizes and for different scenarios. EmgAuth shows great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links. However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors. Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services. To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services. Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure. We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare. Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods. To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

Secure and usable user authentication on mobile headsets is a challenging problem. The miniature-sized touchpad on such devices becomes a hurdle to user interactions that impact usability. However, the most common authentication methods, i.e., the standard QWERTY virtual keyboard or mid-air inputs to enter passwords are highly vulnerable to shoulder surfing attacks. In this paper, we present PassWalk, a keyboard-less authentication system leveraging multi-modal inputs on mobile headsets. PassWalk demonstrates the feasibility of user authentication driven by the user's gaze and lateral shifts (i.e., footsteps) simultaneously. The keyboard-less authentication interface in PassWalk enables users to accomplish highly mobile inputs of graphical passwords, containing digital overlays and physical objects. We conduct an evaluation with 22 recruited participants (15 legitimate users and 7 attackers). Our results show that PassWalk provides high security (only 1.1% observation attacks were successful) with a mean authentication time of 8.028s, which outperforms the commercial method of using the QWERTY virtual keyboard (21.5% successful attacks) and a research prototype LookUnLock (5.5% successful attacks). Additionally, PassWalk entails a significantly smaller workload on the user than the current commercial methods.

Augmented Reality (AR) provides immersive user experiences by overlaying digital information on physical environments. Context-awareness is crucial for delivering relevant augmentations that best suit users' requirements and their en-vironments. In this article, we combine context-aware reasoning with emerging AR applications to provide the most relevant infor-mation according to user and environment contexts. To support the best possible quality of experience, 5G edge computing enables the distribution of computation-intensive AR tasks to edge servers through 5G networks. We develop ConAR, a context-aware head-mounted display AR system that is deployed on the edge and cloud leveraging both environmental sensors and user profile context for navigation. ConAR is composed of a HoloLens application and a paired mobile client, which contains a context model for air quality forecasting, and rendering recommendations on holograms through a HoloLens 2 device. We evaluate our system performance by deploying our proposed air quality prediction algorithm on the edge and cloud while communicating to them using 5G and LTE connections. We measure network quality metrics and find the deployment on the edge with 5G connections significantly outperforms alternative solutions. Our results demonstrate that the 5G edge computing is suitable for supporting latency-sensitive analysis tasks for context-aware AR.

Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.

Mobile Augmented Reality (MAR) mixes physical environments with user-interactive virtual annotations. Immersive MAR experiences are supported by computation-intensive tasks, which are typically offloaded to cloud or edge servers. Such offloading introduces additional network traffic and influences the motion-to-photon latency (a determinant of user-perceived quality of experience). Therefore, proper multimedia protocols are crucial to minimise transmission latency and ensure sufficient throughput to support MAR performance. Relatedly, 5G is a potential MAR supporting technology and is widely believed to be faster and more efficient than its predecessors. However, the suitability and performance of existing multimedia protocols for MAR in the 5G edge context have not been explored. In this work, we present a detailed evaluation of several popular multimedia protocols (HLS, MPEG-DASH, RTP, RTMP, RTMFP, and RTSP) and transport protocols (QUIC, UDP, and TCP) with a MAR system on a real-world 5G edge testbed. The evaluation results indicate that RTMP has the lowest median client-to-server packet latency on 5G and LTE for all image resolutions. In terms of individual image resolutions, from 144p to 480p over 5G and LTE, RTMP has the lowest median packet latency of $14.03\pm 1.05 {\mathrm ms}$. Whereas for jitter, HLS has the smallest median jitter across all image resolutions over LTE and 5G with medians of 2.62 ms and 1.41 ms, respectively. Our experimental results indicate that RTMP and HLS are the most suitable protocols for MAR.

Smartwatches and other wearables are characterized by small-scale touchscreens that complicate the interaction with content. In this paper, we present Force9, the first optimized miniature keyboard leveraging force-sensitive touchscreens on wrist-worn computers. Force9 enables character selection in an ambiguous layout by analyzing the trade-off between interaction space and the easiness of force-assisted interaction. We argue that dividing the screen's pressure range into three contiguous force levels is sufficient to differentiate characters for fast and accurate text input. Our pilot study captures and calibrates the ability of users to perform force-assisted touches on miniature-sized keys on touchscreen devices. We then optimize the keyboard layout considering the goodness of character pairs (with regards to the selected English corpus) under the force-based configuration and the users? familiarity with the QWERTY layout. We finally evaluate the performance of the trimetric optimized Force9 layout, and achieve an average of 10.18 WPM by the end of the final session. Compared to the other state-of-the-art approaches, Force9 allows for single-gesture character selection without addendum sensors.

Mobile Augmented Reality (MAR) provides a unique experience where the physical world is augmented with virtual annotations. MAR involves computation-heavy algorithms that could potentially be offloaded to edge servers on 5G networks, which significantly enhances MAR with reduced communication latency and more stable network connections, therefore leading to seamless MAR user experiences. In this demo, we show a running MAR system deployed on a 5G edge test bed and present latency results.

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM’s performance deterioration with the increase of sequence length. A sliding window enables predicting the long¬term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.

Screen lock is a critical security feature for smart-phones to prevent unauthorized access. Although various screen unlocking technologies including fingerprint and facial recognition have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3D-printed head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth leverages the Myo armband to collect the EMG data of smartphone users and enables users to unlock their smartphones when picking up and watching their smartphones. In particular, when training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband. We conduct experiments including 40 participants and the evaluation results show that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different sizes and at different locations, and is applicable for users with different postures. EmgAuth bears great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.

Touchscreen interfaces are shrinking and even dis-appearing on mobile headsets. The existing approaches for text acquisition on mobile headsets, for instance, speech commands and hand gestures, are cumbersome and coarse. In this paper, we show the feasibility of interaction on a miniature area as small as 12 * 13 mm2that offers an input alternative on small form-factor devices such as smartwatches, smart rings, or the spectacles frames of mobile headsets. To this end, we propose and implement two interaction approaches, namely FRS and DupleFR, for acquiring textual contents on mobile headsets. Both approaches leverage force-assisted interaction on a miniature-size interface. They enable the user to acquire textual content with various granularities such as characters, words, sentences, paragraphs, and the entire text. After 8 sessions, 22 participants with FRS and DupleFR achieve the peak performance of respectively 11.455 and 10.611 seconds per textual acquisition with accuracy rates of 91.41% and 94.95%. Although FRS and DupleFR as indirect manipulations are disadvantageous, they are at least 37.06% faster than the commercial standards designated to direct manipulation on touchscreens.

Wearable devices collect user information about their activities and provide insights to improve their daily lifestyles. Smart health applications have achieved great success by training Machine Learning (ML) models on a large quantity of user data from wearables. However, user privacy and scalability are becoming critical challenges for training ML models in a centralized way. Federated learning (FL) is a novel ML paradigm with the goal of training high quality models while distributing training data over a large number of devices. In this demo, we present FL4W, a FL system with wearable devices enabling training a human activity recognition classifier. We also perform preliminary analytics to investigate the model performance with increasing computation of clients.

Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance.

Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance.

Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to re-think its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model.

The quality of service of a mobile application is critical to ensure user satisfaction. Techniques have been proposed to accomplish adaptation of quality of service dynamically. However, there is still a limited understanding about how to provide a utility model for code execution. One key challenge is modeling the level of quality in the code execution that can be provisioned by the cloud. Since the allocation of cloud resources has a cost, it is important to optimize cloud usage. We propose a software-defined networking approach that allows modeling and controlling code acceleration of a mobile application deployed across multiple type of devices. By segregating the computational requirements of the mobile application into groups, we were able to define the acceleration needed by each group of devices. As the computational requirements of a device can change across time, a mobile device can be re-assigned to another group based on demand. Our SDN approach implements a model that allows the system to predict workload based on acceleration groups. Evaluating our system in a real testbed showed that it is possible to predict workload and allocate optimal resources to handle that workload with 87.5% accuracy.

The combination of Device-to-Device (D2D) Communication in 5G Cellular Networks and vehicular networks will not only increase the performance of vehicular networks, but also increase the revenues for network operators and services providers. This paper proposes a 5G D2D routing method oriented to vehicular networks, which can increase the connectivity and scalability of vehicular networks while alleviating the traffic load of 5G base stations. Vehicular nodes are clustered according to their communication range, the speed and direction of the movements. Data are transferred among vehicles in the same and neighbor clusters in the D2D mode, only certain data packets are needed to be transferred through the 5G base stations. In this way, the traffic load of 5G based stations can be reduced greatly, whereas the data transmission delay of the vehicular networks can also be guaranteed. Simulation results show that our cluster-based routing mechanism is stable and inexpensive, and is suitable for the vehicular networks.

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

The infrastructure built in the City of Oulu provides rich information about the city environment and objects moving in it. We utilize this infrastructure in building an IoT system for data-intensive smart city services; by collecting data from real city environment and developing analysis methods for these data. We are building Smart City Traffic Pilot on top of the infrastructure to provide the functionality to collect the data and perform the analysis. Based on this experience, we present in this article requirements for data-intensive smart city services. Moreover, we describe four implemented use cases for utilizing rich data sources available in the smart city: situational picture, driving coach, real time reasoning, and mobile code. A lively collaboration between a large number of different actors is essential in realizing these use cases. Finally, we discuss how the use cases fulfill the requirements and the lessons we have learnt.

The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

This paper presents our research in developing a model for the dynamic generation of indoor maps with crowdsourcing. With approximation of the user traces, we generate a point cloud and develop the topology of the space from time based segmentation of the traces. Moreover, we add semantic information for navigation and localization enabled maps. We discuss motivation, research objectives, and detailed research methods in this paper.

Summary The development of Internet of Things (IoT) applications can be facilitated by encoding the meaning of the data in the messages sent by IoT nodes, but the constrained resources of these nodes challenge the common Semantic Web solutions for doing this. In this article, we examine enabling technologies for adding semantics to the IoT. Especially, we analyze data formats, which enable IoT applications consume semantic IoT data in a straightforward and general fashion, and evaluate resource usage of different alternatives with a sensor system. Our experiment illustrates encoding and decoding of different data formats and shows how big a difference a data format can make in energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.

This paper proposes context modelling and reasoning to enable intelligent services in a ubiquitous campus. An ontology-based modelling includes upper level context modelling and domain-specific modelling for the campus area. Ontological and rule-based inferencing, which facilitate ubiquitous functionality for daily life, are implemented by utilizing the context model developed. A student assistant scenario is presented, demonstrating the usefulness of ontological context modelling and reasoning for highly distributed environments, such as a university campus.

With the advancement of mobile devices' capabilities, it is possible to implement knowledge-based systems on the mobile devices. This development introduces a challenge of transferring knowledge between mobile devices and knowledge-based systems on the server side. This paper presents a novel representation, Entity Notation, to tackle this challenge. It can represent ontology knowledge in a straightforward fashion and allows incremental transfer of ontology. This unique feature makes Entity Notation an ideal solution for transferring knowledge in highly dynamic ubiquitous environments. Moreover, Entity Notation has a short format suitable for communication when resources are constrained. We address the design issues of the representation, demonstrate its usability by a small ontology, and evaluate it based on a set of ubiquitous ontologies.

In this paper, we present our work towards achieving context-awareness in mobile devices by combining Semantic Web technology with sensory data. Our investigation shows that some context data pertaining to the user, such as location, time, and physical surroundings, is vital for the realization of intelligent maps. Hence, embedding context-awareness into intelligent maps may prompt the usability of mobile map applications. Aiming at this goal, we suggest Semantic Web technology-based solution. We present a data representation, Entity Notation, to connect sensors to Resource Description Framework (RDF), the basis of Semantic Web data, in the data interchange level. At the same time, our data representation is lightweight enough that any resource-constrained sensors can support and process it. Ontology and ontology-based inference engine are developed to reason on the sensory data. Finally, intelligent maps could utilize its inference output to achieve context-awareness. We demonstrate our methods with a simulator and discuss the future work.