Research Topics

ESR1: Energy-efficient edge computing based gateways for wearable networks

Objectives: Reduce the energy of individual communications system blocks in wearable networks by up to 90-95% by applying approximate computing; Highly optimize the edge-based wearable network gateways; Improve edge/fog computing capabilities, security features and energy-efficiency; Implement a proof-of-concept on a programmable/ reconfigurable communications platform such as NI URSPs

Expected Results: Proof-of-concept for edge computing with high energy efficiency. Double PhD degree from TUT and URC.

Planned secondment(s): 1. URC, 12 cumulative months starting M21 to work on edge network parameter optimization and to attend lectures. 2. T6E, 2
months, starting M39 complementary skill training on fundraising, project raising, social innovation, and project management

Double PhD degree from: TUT and URC Fellow

ESR2: Large-scale crowdsourcing-based wearables data gathering and processing

Objectives: Create novel robust approaches for location databases storage, compression and transfer of wearables-based crowdsensed data; Detect outliers and model statistically spurious interferences in crowdsourcing-based wearables data; Study the vulnerabilities of crowdsourced wearables data for public safety and methods to increase the safety; Increase crowdsensing efficiency in terms of data storage and transfer data rates by 40%

Expected Results: Robust approaches for location-related databases storage, compression and transfer of large-scale wearables-based data; urban planning enhancements through adequate crowdsourcing mechanisms

Planned secondment(s): 1. UJI, 6 months starting M25 to collect and analyse statistically crowdsource data with UJI SW and to attend lectures at UJI. 2. IDOM, 3 months starting M37; work on urban planning enhancements through crowdsourcing

Joint PhD degree from: TUT and UJI Fellow

ESR3: Privacy-aware approaches for wireless IoT localization on wearable devices

Objectives: Identify the privacy and security related challenges in the IoT positioning on wearable devices, from both the network-side and the user point of view; Define the Key Performance Indicators of privacy, robustness, and security of a localization algorithm on a wearable device; Develop passive localization methods for future wireless networks and cellular IoT standards, such as 802.11az, LP-WPANs, etc.; Test the methods in an industrial setting focused on digitalized society

Expected Results: Measurement and simulation-based performance analysis and derivation of privacy and security measurable parameters in localization; new privacy-aware positioning methods and algorithms; new robust privacy-aware IoWT positioning methods; comparative performance analysis in dynamic wearables localization vulnerabilities from network and user’s point of view; implementation of the algorithms onto a digital platform on digital data management

Planned secondment(s): 1. UPB, 9 months, starting M21, work on IoT cm-level localization and collection of ECTS for PhD, 2. DLI, 3 months starting M37 for a case study of implementing the IoT privacy-aware localisation into a digital platform

Double PhD degree from: TUT and UPB Felow

ESR4: mmWave & 5G in wearables

Objectives: Deeply and cross-disciplinarily understand the networking constraints and trust challenges of emerging wearables in mmWave bands; Ensure that wearable-centric information is produced and consumed appropriately by a multitude of devices and users of future 5G networks; Study mmWave interference in commuters equipped with AR/VR glasses Develop a proof-of-concept demonstrator for mmWave wearable communications and networking

Expected Results: proof-of-concept demonstrator, platform-level solutions and new types of user applications and services in future mmWave-based wearables

Planned secondment(s): 1. BUT, 12 cumulative months starting M21, work on AR/VR-based wearables and collection of ECTS for PhD, 2. Ericsson, 3 months, starting M39 to test the proof-of-concept in real-life industrial scenario

Joint PhD degree from: TUT and BUT Fellow

ESR5: Cloud Platform for context-adaptive positioning and localization on wearable devices

Objectives: Identify and analyse the target GNSS-denied scenarios for localization using wearable devices.; Extract scenario features for its classification; Study the deep learning techniques for positioning context identification; Define the protocols for storing data, needed to positioning, to enhance positioning interoperability and for developing the indoor positioning systems in the cloud platform

Expected Results: i) An open platform that enhances the interoperability between devices and localization algorithms with high degree of diversity (technologies and methodologies), considering the privacy and security restrictions; ii) A common framework to ensure the usage of standards for localization (e.g. IndoorGML and ISO 18305) and a comprehensive evaluation of proposed/implemented methods within the platform; iii) Advanced methods to allow the platform decide/suggest which indoor positioning methods and technologies are appropriate for a particular indoor environment by means of machine learning methods

Planned secondment(s): 1.TUT: 6 cumulative months, starting M25, work on deep learning techniques in wireless positioning and attending TUT postgraduate relevant courses; 2. GrupoS2, 3 months, starting M31 for work on security protocols

Joint PhD degree from: UJI and TUT Fellow

ESR6: Collaborative techniques for infrastructure-less Indoor Positioning Systems

Objectives: Test and experiment with various fingerprinting technologies (WIFI, magnetic field) and peer-to-peer communication protocols, with respect to range, battery power, data security, privacy, etc; Develop a baseline collaborative indoor positioning system using the selected technologies; Experiment the developed collaborative indoor positioning system, and to compare them to existing non-collaborative algorithms along different dimensions (e.g., accuracy, battery drainage); Ensure anonymous collaboration and to avoid location leakage

Expected Results: A collaborative positioning system that exploits a set of measurements through peer-to-peer communication with nearby users instead of using the prone-to-error single measure approach; A proof-of-concept for infrastructure-less indoor positioning with validation in an industrial environment

Planned secondment(s): 1) TUT, 6 cumulative months, starting M32, to compare collaborative with non-collaborative techniques and collect ECTS needed for PhD. 2)WPS: 3 months, starting M38, to implement a proof of concept for collaborative infrastructure-less positioning with mesh networks

Joint PhD degree from: UJI and TUT Fellow

ESR7: Urban Mobility Observatory: balancing usefulness and privacy

Objectives: Investigate human attitude towards mobility data sharing, across various dimensions (motivations, willingness, purpose, incentive, etc.); Learn more about methodologies for pedestrian data sharing while walking in the city (ants analogy); Test machine learning possibilities, in real-time and post-processing, of pedestrian data

Expected Results: Dataset collected from pedestrian volunteers, to be used for future research; Models and methodologies for how pedestrians can share data with selected others; Methodologies and recommendations for machine learning and big data processing of pedestrian shared data

Planned secondment(s): 1.BUT: 12 cumulative months, starting M21, work on machine learning for the urban mobility observatory and local training at BUT . 2. CPD, 1 months starting M33 to work on public safety aspects, 3. S2G, 2 months starting M41 for training on cybersecurity

Joint PhD degree from: UJI and BUT Fellow

ESR8: Centimeter-level accuracy for IoT localization of wearable devices

Objectives: Explore indoor positioning technologies that promise high performance localization and orientation, such as hybrid magnetic-WiFi-BLE solutions and UWB positioning; Create a set of open-source software tools for accurate indoor positioning; Explore the trade-offs between instrumentation and training, energy consumption and performance, privacy and convenience.

Expected Results: Novel high-accuracy hybrid positioning algorithms; open-source SW for cm-level accuracy of wearables localization and tracking; industrial testbed of high-accuracy localization at NXP premises

Planned secondment(s): 1. TUT, 9 months, starting M21, work on hybridization techniques in wireless localization and course attendance at TUT, 2. NXP, 3 months, starting M39, work on the test-bed in industrial settings

Double PhD degree from: UPB and TUT Fellow

ESR9: Industrial wearables for work safety

Objectives: Reduce the work place injuries in industrial environments by at least 85%, through the use of accurate person tracking through wearable devices, such as bracelets and wearables weaved in overalls and head-helmets; Detect unsafe postures and high-risk motions through wireless-based motion detectors; Optimize the interoperability of IoT wearables sensors used in real-time complex event processing; Test the developed algorithms in an industrial underground environment, such as mines and tunnels while in the industrial secondment

Expected Results: novel wearables-based algorithms for increased work safety in industrial environments; open-source SW for accurate person tracking through wearables; faster algorithms for interoperable real-time sensor processing

Planned secondment(s): 1. BUT, 12 cumulative months starting M21, work on person tracking algorithm development and collecting ECTS needed for double PhD, 2. Beia, 3 months, starting M40, work on testing the developed algorithms in an industrial environment

Double PhD degree from: UPB and BUT Fellow

ESR10: Wearables for eHealth

Objectives: Build a novel eHealth architecture based on wireless communication modules and links and wearable materials; Create advanced algorithms used for decision making and data extracting in the eHealth architecture; Prototype and test the proposed architecture using Arduino boards and Raspberry Pi-s.

Expected Results: open-source SW and applications for eHealth wearables; novel eHealth architecture based on wireless communication modules and links and wearable materials; advanced algorithms used for decision making and data extracting in the eHealth architecture

Planned secondment(s): 1. URC, 12 cumulative months, starting M21, work on decision making algorithms for SIoT and eHealth and attending local postgraduate courses, 2. CIT, 3 months, starting M37 to test the eHealth architecture on a robotic platform for AAL

Double PhD degree from: UPB and URC Fellow

ESR11: Low-latency machine learning and data mining for wearable devices

Objectives: Develop new accurate and fast algorithms that can effectively learn from data to execute various difficult tasks and to identify similarities, outliers or correlation within wide range of input signals harnessed from wearables; Investigate both structured and unstructured data through deep neural networks; Apply artificial intelligence processing in order to open new perspectives and to significantly increase the diversity of applications of wearable devices; Develop a machine learning platform for applications such as healthcare, industry and services

Expected Results: Robust algorithms deployable on massively parallel hardware for real-time application of signal analysis; algorithms based on the deep neural networks; methodologies with fast response to the data changes; time effective training computed by cloud; communication with the cloud; application in wearable devices.

Planned secondment(s): 1. 12 cumulative months at TUT starting M21, work on outlier detection and gathering ECTS from local post-graduate relevant lectures; 2. 2 months at SWO starting M39 to integrate features of the developed platform with SWO real-time location platform for retail and sports apps

Joint PhD degree from: BUT and TUT Fellow

ESR12: Reliable and low-latency communication technologies for industrial wearable applications

Objectives: Identification of most critical performance metrics of emerging industrial wearable applications (e.g. augmented reality); Research and advanced theoretical / simulation-based analysis of novel wireless communication technologies fulfilling the observed KPIs; Design of universal communication architecture suitable for emerging industrial wearable applications; Development of analytical model of the selected wireless technology to analyse its performance in various industrial scenarios (indoor vs. outdoor, low vs. high-densified deployment, etc.); Development of proof-of-concept demonstrator implementing the “winning” wireless technology in the selected industrial wearable application.

Expected Results: Methodology for evaluation and comparison of KPIs of novel wireless technologies; analytical model of selected wireless technology fulfilling the requirements on reliability and latency; universal communication architecture suitable for emerging industrial wearable applications; PoC demonstrator of industrial wearable application; communication latency improvements up to 10% compared to state-of-the-art solutions

Planned secondment(s): 1. TUT, 12 cumulative months, starting M22 to work on wireless communication protocols for wearables and to attend TUT lectures, 2. NET, 3 months, starting M42, to test the developed proof-of-concept in an industrial environment

Joint PhD degree from: BUT and TUT Fellow

ESR13: Privacy-enhancing technologies and privacy-enhancing cryptography for wearables

Objectives: Design and evaluate novel cryptographic technologies for the protection of privacy and digital identity of electronic users, in particular those providing attribute-based authentication in electronic systems; Ensure the user authenticity in dynamic wireless wearable architectures; Find solutions to solve the inefficient revocation of invalid users, the missing identification of malicious users and low performance on constrained devices, such as wearables; Test and benchmark the developed algorithms on existing wearable hardware devices, such as personal tags, smart watch, smart cards

Expected Results: novel cryptographic protocols for attribute-based authentication; new privacy protection mechanisms of users in electronic systems; benchmarks and formally proven secure algorithms on wearable devices

Planned secondment(s): 1. 12 cumulative months at UJI, starting M21, work on state-of-the-art review and novel cryptographic techniques in IoWT and attending UJI lectures; 2. 2 months at NET starting M39, building a HW set-up for algorithm testing

Joint PhD degree from: BUT and UJI Fellow

ESR14: Social-aware discovery and data exchange among IoT devices over Edge Computing platforms

Objectives: Create novel interaction paradigms also taking into account the social and opportunistic relationships and interactions between entities involved in the Large-scale Smart Environments (LSE); Evaluate the role of social networks of objects in enabling the uprising paradigm of IoWT; Offer suitable architecture for properly exploiting Cloud and Fog computing paradigms in a joint way to support effective data exchange among IoT and IoWT devices moving across LSEs; Investigate the virtualization of IoT devices in fog/edge infrastructures

Expected Results: Smart techniques for IoT objects discovery leveraging the Social IoT paradigm; Definition of cognitive and social objects to support applications in smart environments with IoWT devices; Fog/Edge- based platforms supporting the migration of virtual images of IoT devices by also using the container technology; Orchestration mechanisms for resources in the envisaged platforms; Definition and validation of algorithms for social based crowdsourcing

Planned secondment(s): 1. 12 cumulative months at TUT, starting M13, work on communications protocols in IoWT and collecting ECTS from local TUT courses, 2. ERI, 2 months, starting M38, testing developed solutions with ERI infrastructure

Double PhD degree from: URC and TUT

ESR15: New Architecture, communication and networking protocols for supporting 5G-IoT wearable devices connectivity

Objectives: Explore the enhancements offered by a set of innovative 5G technologies in practical IoWT contexts; Study the multi-connectivity heterogeneous radio access technologies and mobility in IoWT; Investigate WiFi-Direct and 3GPP LTE with Proximity Services , nicknamed LTE-Direct, approaches for D2D communications

Expected Results: innovative algorithms for wearable devices, proximity services, and machine type communications; a complete set of models, frameworks, schemes, and algorithms useful for a complete characterization of the wearable services and applications in the converged 5G-IoT ecosystem; analytical and simulation-based validation methods

Planned secondment(s): 1. 12 cumulative months at UJI, starting M13, work on state-of-the-art and multi-connectivity novel solutions in IoWT, 2. IDOM, 3 months, starting M39, training on 5G and IoT standardization

Double PhD degree from: URC and UJI