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| Funder | European Commission |
|---|---|
| Recipient Organization | Cognitechna Sro |
| Country | Czech Republic |
| Start Date | Jun 01, 2023 |
| End Date | May 31, 2026 |
| Duration | 1,095 days |
| Number of Grantees | 41 |
| Roles | Participant; Associated Partner; Coordinator; Third Party |
| Data Source | European Commission |
| Grant ID | 101112286 |
In LoLiPoP IoT innovative Long Life Power Platforms will be developed to enable retrofitting of wireless sensor network (WSN) modules in IoT applications.
This includes the development of algorithms to perform FUNCTIONALITIES like asset tracking and condition monitoring (for predictive maintenance). They can be used in APPLICATIONS such as industry 4.0, smart mobility and energy efficient buildings.
LoLiPoP IoT creates an ecosystem of developers, integrators and users to develop these platforms thinking about power/battery life, ease of installation and maintenance.
The project is driven by 12 laboratory- and field-based use cases to initially demonstrate their technical viability and then potential impact.
Expected impacts from the LoLiPoP IoT use cases include; a) typical battery life increase from ~18 months to >5-years, b) Reduced maintenance overhead of mobile and fixed assets from >30% to <15%, c) Reduced costs related to location of assets typ. by 100Ks PA per use case, d) Optimised flow, management and throughput of assets through the identification of bottlenecks, yielding reductions of >10% in production time, cycle time and inventory costs, e) Improved comfort levels and well-being of building occupants whilst reducing energy footprint by >20% and f) Revenues of >10M PA for LoLiPoP IoT industry partners offering asset tracking & condition monitoring services.
All of this is achieved by developing and integrating: a) Multi-source Energy Harvesting solutions (vibrational and photovoltaic transducers, PMICs and discrete circuits), b) Digital interfacing to contextually adapt mode settings of WSN devices and connected systems to minimise power drain, c) Ultra low power components for WSN, d) Innovative Architectures for wireless data collection that minimize battery power drain, e) Simulation Models to optimise component design and integration and f) embedded AI/ML in IoT devices, for lower latency and power consumption and higher robustness.
Taua Srl Societa' Benefit; Tecnologias Servicios Telematicos Ysistemas Sa; Stichting Imec Nederland; Luna Geber Engineering Srl; Csem Centre Suisse D'Electronique Et de Microtechnique Sa - Recherche Et Developpement; Depuy Ireland Unlimited Company; Fersa Innova S.L.U.; Mittuniversitetet; Noldus Information Technology Bv; Meshworks Wireless Oy; Ins Gmbh; Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev; Sim4Future Societa' A Responsabilita' Limitata Semplificata; Centria-Ammattikorkeakoulu Oy; Sysgo Gmbh; Infineon Technologies Ag; Cognitechna Sro; Boston Scientific Limited; Eesy-Innovation Gmbh; Vysoke Uceni Technicke V Brne; Bigtri Bilisim Anonim Sirketi; Arcelik A.S.; Consiglio Nazionale Delle Ricerche; Offcode Oy; Abatec Mikrosysteme Gmbh; Alma Mater Studiorum - Universita Di Bologna; Consorzio Nazionale Interuniversitario Per la Nanoelettronica; Zens Group Bv; Technische Hochschule Nurnberg Georg Simon Ohm; Swiss Airtainer Sa; Universita Degli Studi Di Perugia; Cnet Svenska Ab; Technische Universiteit Eindhoven; Signify Netherlands Bv; Miromico; University College Cork - National University of Ireland, Cork; Universidad Politecnica de Madrid; Marmara University; Yonga Teknoloji Mikroelektronik Arastirma Gelistirme Tic; Analog Devices International Unlimited Company
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