The scope of the E-FLIGHT TTX proposal is to implement energy consumption optimization strategies for dramatically improving the performance, and consequently, the industrial and commercial impact of LEONARDO, the smart blood and hemocomponents delivery system through autonomous drones.

On request, the TTX brief description will be published at the end of the TTX.

On request, the TTX brief description will be published at the end of the TTX.

IoT devices can be improved by running multi-layer neural networks. Machine-learning models show impressive accuracy. We plan to improve our wearable with 1) a better direction detection accuracy 2) lower power consumption 3) latest generation BLE chipsets and microcontrollers.

On request, the TTX brief description will be published at the end of the TTX.

The SWAN objective is to provide a proof-of-concept of a novel Decision Support System solution for storm and flood hazard based on short-term predictions in the Mediterranean countries. Artys’ technology is an IoT-driven rainfall monitoring system that will be transferred to Meteorage by means of a licensing agreement. SWAN is an unprecedented solution for the resilience of communities, industria.

This TTX is about transferring miniaturized low-energy radar hardware and firmware that prevents drones and helicopters to crash into obstacles, birds and each other. Radar allows (auto)pilots to see through bad visibility conditions. The airspace thus will become safer and the transferred technology might open up possibilities for electric flights over shorter distances throughout the EU.

Task automation is an emerging technology for enabling self-configuration and adaptation of services and sensors and it is one of the enabling technologies for IoT. Location based services are one of the most popular services that take advantage of both task automation as well as positioning services. This project aims at providing a Task Automation Platform based on Visible Light Positioning.

On request, the TTX brief description will be published at the end of the TTX.

The main objective of this project is to apply new techniques based on eXplainable Deep Fuzzy Learning for the detection of fatigue and predictive maintenance of rotary elements in Industry 4.0 based on their vibrations collected by multiple low-power and low- cost IIoT devices. This approach will allow not only to predict future failures in rotary elements, but also to explain the causes.

MEATRACK aims to increase the smartness of refrigerators by developing and testing minature gas sensors for tracking real time freshness of meat (beef in this project) using a patented gas sensing technology from IM2NP and combined with AI tools of NVISION and IoT software platform of S&C. NANZOZ will develop the required electronics for sensor and fabricating the prototypes.

This TTX will transfer the University of Salento existing IP on RFID-based CLEC to the SME BAGATIN by defining, supported by the Institute of Clinical Physiology of CNR- Italy, a new RFID sensor/computational tag enabling the unobtrusive monitoring of patients to tailor medical treatments based on real-time data. SME ION Solutions will perform productization/expansion in other market segments.

The brief description of this experiment will be published at the end of its implementation phase, as requested by the TTX proposers.

LV-EmbeDL will extend the EMBEDL AB’s Deep Learning (DL) optimizer (EmbeDL) with BSC’s aggressive undervolting technology (supply voltage underscaling below the nominal level) to maximize the energy efficiency of embedded FPGA-based DL systems. LV-EmbeDL will be utilized by the industry partner’s customers in domains like autonomous cars, telecom, and IoT.

The RAINWAVE transfer of technology project aims at providing a proof-of-concept of a novel Decision Support system solution for environmental monitoring in France by transferring the Artys’ IPs and knowledge on IoT low-power wide-area network of sensors to NOVIMET by means of a licensing agreement. Artys and NOVIMET will also agree on the exclusivity option in some specific states.

Over 50% of Diesel fuel is supplied via mobile and non-commercial industrial tank systems which in their vast majority do not offer any monitoring infrastructure. Key reasons are the very heterogeneous tank constructions, whose components are provided by numerous suppliers, and a sensor technology as key element that is designed for very specific use cases concerning tank form, size, or material.

Today, artificial intelligence (AI) and deep learning (DL) methods are exploited in a wide gamut of products. DL models are trained on GPGPU systems, achieving 5-40x speedup wrt CPU-based servers. ANDREAS develops advanced scheduling solutions optimizing DL training run- time performance and their energy consumption in disaggregated GPGPU clusters. 2x speed-up and 50% energy savings are expected.

LEMON system allows to study existing city infrastructure and patterns of bicycle usage by cyclists to improve city micro-mobility. In addition, it counts with an anti-theft functioning mode. Adaptation of the system to Narrowband-IoT networks will allow to boost the battery autonomy from 40 days to 10-12 months in active tracking mode. The enhanced product will be tested in a pilot in Spain.

BLEeper is a patent-pending approach that utilizes low-cost solar-powered water-proof Bluetooth beacons across geo-fenced shoreline areas (land and on coastal water buoys), in order to deliver high-accuracy location tracking for persons equippedwith any type of low-cost Bluetooth wearable (e.g. wristbands, life-vests). BLEeper delivers superior coastal surveillance, compared to any other available solution, leading to significantly increased levels of safety during shoreline activities.

This TTX considers soil humidity prediction system based on the Received Signal Strength Indicator (RSSI) generated from the underground LoRa beacon and related Machine Learning techniques. As a consequence, given energy efficient solution is cost effective and easier to maintain due to the prolonged battery lifetime. Such robust, but simple technology is a great candidate for becoming a strong competitor in the smart agriculture market.

The goal of the project is to implement a low energy IoT solution for smart home automation. The proposed solution has advantages such as lower price, lower energy consumption, many features, higher reliability, etc. andcould be used for many applications. Our first use case is to develop a smart solar heating controller using ultra low energy processor, cutting-edge techniques and know-how for additional saving.

EnergySequence is an energy efficiency web intelligence platform delivering data driven energy conservation measures for the industry sector, ReMoni’s smart sensors will power up real time performance by customizing its edge computing capabilities, and extend deployment features through an ad-hoc wireless sensor network for heat, electricity and flow, with energy harvesting capabilities.

NplusT intends to extend its product line with a new technology, targeting the high-speed characterization of non-volatile memory (NVM) devices. This feature is essential for mass storage (SSD, ...) makers in the optimization of their products. PCBDesign will transfer its high-speed communication technology, in order to follow the NVM roadmap: increased density, higher speed, lower voltages.

Effective power measurement is critical for the efficient development of Customized Low-Energy Computing (CLEC) systems. The NIPOLECS project will reduce the selling price of NTNU’s non-intrusive Lynsyn power measurement board by 3X and thereby create a competitive product to be sold by Sundance.

The TTX brief description will be published at the end of the TTX.

MIP is a vision of a magnetic data storage technology tailored for industrial applications and consumer products, to be used as a universal rewritable data carrier. This brings an advantage over competing technologies, such as barcodes, etc. Stored information in components can be updated during their life cycle. The information is not visible, and the medium has high temperature and chemical resistances. Hence, the stored data is inherently safe, reliable and suitable in many applications.

This project presents the REMAP IP, a hardware manager for low-latency chip-to-chip interconnection, targeting low-energy disaggregated platforms and datacenters. REMAP enables on- demand system scaling; processing nodes can dynamically hot- plug remote resources (e.g. memory, accelerators), following application needs on performance and / or dataset sizes. The IP is already tested, has a very light resource / energy footprint, and thus easily fitting into small low-power MPSoCs.

Tooff.in is an IoT integrated platform consisting of a NBIoT multimodal sensoring device for the characterization and position tracking of construction tools. By attaching a universal sensor to the outer casing of any construction tool, we can forecast failures, enforce preventive maintenance and prevent thefts. A SaaS model will be examined that provides progressive tariff billing, therefore incentivizing clients to gradually adopt our service and severely minimize their operational costs.

Based on IIoT Infrastructure and Big Data Analytics technology our portable software solutions enable for highly sensitive process monitoring and optimization of industrial automated (e. g. manufacturing) processes. The implemented tools are based on generic algorithms for data driven modelling and additional process specific services and use information extracted from existing control and process layer data.

This project aims to build urban networks based in intercommunicated nodes made of BLE (Bluetooth Low Energy) trackers to deploy multiple services based in geolocation at low cost and extremely consumption.

The main objective of the Eco-Rural-IoT project is the reduction of the consumption of power, water and even phytosanitary treatments through the research and innovation in techniques and intelligent algorithms which will be incorporated to the unified Nebusens' Rural-IoT platform, aimed at the management of farming resources in the agriculture and livestock sector by means of IoT devices.

NOx emissions are a societal challenge in Europe due to the adverse health effects it causes. Currently the monitoring of such gas is performed by expensive and stationary sensor modules. Nox.Box will offer a mobile low cost, low-power consuming alternative to monitor NOx on large scales and with high temporal and spatial resolution and an online control platform.

Tomappo is a digital gardening assistant enabling anyone to grow their own vegetables. Within Carrots, Lifely’s social sensors will be customized for use with Tomappo. This will add new dimension to Tomappo leading to a better product for users and new revenue stream for company receiving technology, while also benefit the owner of the technology by providing a new use-case for their sensors.

The simulation environment enables the full integration of UPMEM’s DPU in a complete DRAM/Controller simulation framework. This is an essential key to maintain a seamless transfer of the RTL based DPU design into DRAM technology.

EVErMORE TTX experiment aims at developing the next generation GAP-8 IoT processor from GreenWaves Technologies. Exploiting the adaptive management architecture for process and temperature compensation developed at University of Bologna, coupled to the low-voltage capabilities of 22nm FD-SOI technology, is expected to improve the energy efficiency of current generation GreenWaves Technology processors by 6x, enabling new applications and opening new market opportunities.

In the scope of the proposed TTX project, an upgraded system prototype will be implemented, with an actual SAVVY®ECG sensor, by connecting the accelerometer sensor to the SAVVY processor through an available serial interface, by integrating the accelerometer firmware into the SAVVY software and by incorporation the posture and activity data into the existing ECG radio packets.

Integration of the Montr Narrowband IoT equipped emergency button in the Smarthelp ecosystem. With this integration emergency messaging will reach a next level in terms of speeding up the process of alerting emergency services with all the specific information they need.

This TTX will transfer the University of Salento recognized skills on wirelessly-powered Computational-RFID technology for IoT to the SME Spica, by defining a new cost-effective battery-less CLEC-based tag enabling the smart traceability of fresh and frozen fish. By performing computation, communication, and sensing to check the food product integrity, the solution will improve the SME business.