A-WEAR · A network for dynamic WEarable Applications with pRivacy constraints

A network for dynamic WEarable Applications with pRivacy constraints

Rapid technology advancement, economic growth, industrialization, and evolution in the information technology have paved the way for developing a new niche of small body-worn personal devices gathered together under the title of wearable technologies. The growing interest from end-users has resulted in introducing the first generation of mass consumer wearables. Evidently, the trailblazing ones were not designed with strict restriction (energy efficiency, privacy, security, communication, etc.) in mind, and, thus, research on this topic has remained fragmented. Advanced and sophisticated batteries, lightweight security enablers, and communication technologies could be already procurable on the wearable devices. Additional solutions for efficient utilization of the processing power, especially for recent computationally and power-hungry applications, are still a white spot on the wearable technology roadmap. A-WEAR EU project has enhanced the understanding of how the superimposition of those technologies would improve wearable devices' performance metrics, including energy efficiency, and has explored novel and emerging research solutions such as edge computing's paradigm and collaborative positioning. In addition to the scientific breakthroughs (detailed below), A-WEAR project has also educated 15 Early Stage Researchers, with most of them having already graduated with a PhD degree and the rest expected to graduate during this Fall or early next year. Dissemination activities targeted not only the scientific community, but also the general public, including the end-users in wearable domain, through multi-dimensional social media activities such as A-WEAR Youtube and Twitter channels and blog entries at the project webpage. The project's main two target groups in wearables applications were the social applications, such as eHealth and social networking, and the industrial applications, such as automation halls, industrial robotics, and the automotive industry. Such applications were chosen because they had different requirements in terms of computational costs, communications latency, precision, security, and privacy of both communication and localization and related tradeoffs. As of 15.8.2023, 8 out of the 15 A-WEAR ESRs (i.e., 53%) have already defended their double PhD degree thesis and published their PhD theses in open access. Two more ESRs have already submitted their PhD manuscript for review, and three more are expected submit their thesis manuscript by November 2023; the last one is expected to have his thesis submitted to review by early 2024. The A-WEAR scientific objectives have been as follows: O1. To provide the society with general knowledge of dynamic wearable networks in terms of localization, connectivity, privacy, and security; O2. To identify vulnerabilities and offer innovative solutions in crowdsourced-, cloud-, edge-, and fog-based wearable architectures in the telecommunications area; O3. To design and develop privacy-enhanced and location-aware wearable technologies; O4. To improve the communications between wearable devices, their smartphone gateways and the infrastructure networks; O5. To develop new open-source software for wearables in social/eHealth/industrial applications. In terms of scientific breakthroughs, we would like to pinpoint the following strong results within A-WEAR network: proposing novel lossy compression methods for performance-restricted devices, analyzing deep learning-based new localization and optimization methods, highly improving the performance metrics of indoor collaborative and standalone wireless positioning methods, deeply investigating the tradeoffs between privacy and utility and between performance and energy consumption in a variety of wearable scenarios and with various technologies(UWB, BLE, WiFi, LoRa), analyzing the mobility-awareness in directional deafness within mmWave communications and modeling system-level dynamics of direct Extended Reality sessions over mmWave links with wearable helmets and glasses, developing machine-learning-based algorithms and techniques for detecting hand tremors and bradykinesia in Parkinson's patients and for analyzing electroencephalogram data, which is used to study brain activity using wearable devices and implementing a first A-WEAR bracelet embedding machine learning algorithms for e-health applications, investigating the integration of artificial intelligence to facilitate seamless communication between terrestrial (e.g., wearables) and non-terrestrial networks (e.g., UAV-based and satellite-based), exploring cryptographic protocols to enhance the communication privacy and security using wearable devices and constrained devices, optimizing the placement of social digital twins in edge internet-of-things and wearable networks, and proposing novel unsupervised learning approaches for device-to-device-assisted multicast scheduling in wearable networks and beyond.

Data: CORDIS, © European Union