![](https://i0.wp.com/assist-iot.eu/wp-content/uploads/2023/01/viber_image_2023-01-20_16-14-53-419.jpg?resize=350%2C300&ssl=1)
ASSIST-IoT Advisory Board meeting
ASSIST-IoT Advisory Board meeting took place on January 20th! Partners had the chance to present the pilots status and the technical advancements to 6 Advisory Board members.
ASSIST-IoT Advisory Board meeting took place on January 20th! Partners had the chance to present the pilots status and the technical advancements to 6 Advisory Board members.
IoT Week 2023 will take place in Berlin! IoT Week 2023 invites you to discover the latest developments with innovative technologies such as Artificial Intelligence, edge computing, extended reality and metaverse, Blockchain, Quantum Computing, Robotics, Data Sovereignty, data spaces, etc.
ASSIST-IoT project will be present at HiPEAC (High Performance Embedded Architecture and Compilation) with VEDLIoT project. The HiPEAC conference is the premier European forum for experts in computer architecture, programming models, compilers and operating systems for general-purpose, embedded and cyber-physical systems. Areas of focus and integration include safety-critical dependencies, cybersecurity, energy efficiency and machine learning.
The HiPEAC 2023 conference will take place in Toulouse, France. Read more “HiPEAC conference 2023”
During 14-16 December 2022, a technical workshop took place in Valencia (UPV premises) concerning ASSIST-IoT Pilot 3b “Vehicle condition monitoring”. UPV, PRO and TWOTRONIC partners attended.
ASSIST-IoT Pilot 2 partners did a preliminary evaluation of the localization and edge computing hardware developed for ASSIST-IoT (Gateway Edge Node (GWEN), Ultra Wideband localization system, and the Location Tracking enabler). The test was done on an active construction site. Find out the video here: https://www.youtube.com/watch?v=Mr3i8v2SkLM&t=5s
Read more “ASSIST-IoT: Testing UWB localization on a construction site”
This survey is part of the project “Architecture for Scalable, Self-*, human-centric,
Intelligent, Secure, and Tactile next generation IoT’ — ‘ASSIST-IoT’”, funded by the European Union’sHorizon 2020 research and innovation programme under grant agreement no. 957258.
Read more ““Adoption Barriers of NGIoT technologies” Survey”
Ιnsighio, a Greek SME, ASSIST-IoT Open Call #1 winner, presented RAZOR project (funded by ASSIST-IoT) during DigiFed event on November 30th 2022.
DigiFed is one of the 16 EU funded H2020 projects under the umbrella of SAE and is dedicated to supporting EU industries to digitalize their product & services and reach new markets enabled by Cyber Physical Systems (CPS) & Embedded Systems.
Read more “Ιnsighio ASSIST-IoT Open Caller Presentation at DigiFed”
UPV represented ASSIST-IoT on the event Transport Research Arena (TRA) 2022 held in Lisbon during the days 14th to 16th November 2022. There, ASSIST-IoT was pitched at the stand of the European Commission in the main exhibitors pavilion, counting with the presence of members of the EC. In addition, ASSIST-IoT dissemination material was actively shared with many attendees thanks to the joint participation in a booth sponsored by the projects DataPorts and MobiDataLab.
The eighth issue of ASSIST-IoT newsletter presents the project activities throughout August -October 2022 period. This issue focuses on the ASSIST-IoT Open Call #2, the communication and the dissemination activities of the period, the project deliverables, the interaction with the NGIoT association, and the 5th GA Meeting in Warsaw.
Artificial intelligence (AI) algorithms can provide an effective solution for dynamic and automated network resource management in Software Defined Networking (SDN). In this contribution, we propose an auto-configuration enabler inside the next-generation Internet of Things (IoT) architecture proposed in the ASSIST-IoT project, for network resource allocation. The AI algorithm is responsible for controlling intent-based routing in an SDN network. This paper focuses on the problem of optimal intent switching between two designated paths using a Deep-Q-Learning approach based on an artificial neural network. Read more “AI Application in Next Generation Programmable Networks”