Hydra is affilliated with the following programs and organisations:
The Hydra coordinater FhG FIT is a member of ARTEMISIA, the association for R&D actors in the field of ARTEMIS: Advanced Research & Technology for EMbedded Intelligence and Systems.
The Hydra middleware allows developers to create inclusive applications with a high degree of accessibility for all. The Hydra project supports the Commissions campaign: eInclusion - be part of it!
The Hydra project is part of the Cluster of European projects on the Internet of Things. The Cluster aims to promote a common vision of the Internet of Things.
Why not see the on-line Hydrademo? You can turn on and off devices and follow the energy consumption in real time. Just click on the picture and you see it!
The SENSE project (Smart
Embedded Network of Sensing Entities) will develop methods, tools and a
test platform for the design, implementation and operation of smart
adaptive wireless networks of embedded sensing components. The network
is an ambient intelligent system, which adapts to its environment,
creates ad-hoc networks of heterogeneous components, and delivers
reliable information to its component sensors and the user. The sensors
cooperate to build and maintain a coherent global view from local
information. Newly added nodes automatically calibrate themselves to the
environment, and share knowledge with neighbours. The network is
scalable due to local information processing and sharing, and
self-organizes based on the physical placement of nodes.
A test
platform for a civil security monitoring system will be developed as a
test application, composed of video cameras and microphones. The test
platform will be installed in an airport, to yield real data and
performance goals from a realistic test environment. Each sensor is a
stand-alone system consisting of multiple embedded components: video
system, audio system, central processor, power source and wireless
networking. The security application will implement object/scenario
recognition (e.g. baggage left unattended). Nodes will recognize local
objects, using a combination of video and audio information, and
neighbouring nodes will exchange information about objects in a
self-organizing network. The result is a global overview of current
objects and events observed by the network.
Relevance to HYDRA:
The key innovative aspects are the methods by which
the network perceives its environment, fuses these perceptions using
local message passing to achieve local and global object recognition,
and calibrates itself based on its environment. Challenges include
perception, adaptation, and learning, as well as tools to diagnose and
maintain a self-adapting distributed network of embedded components.