WINGS ICT

Whilst traffic demand is increasing exponentially, network operators’ revenue remains flat. There is an urgent for data driven 4G/5G networks. In this project, we exploit heterogeneous big data analytics to optimize both the deployment and operations of wireless networks. We design protocols that enable future Data Aware Wireless Networks (DAWN) for enabling a new age of Internet of Everything (IoE). The proposal has been developed to address the following open issues in data driven flexible systems: • How to characterize user mobility and wireless data traffic patterns • How to infer user Quality-of-Experience (QoE) from combining data sets • How to use data analytics to assist cell planning • How to use data driven techniques to optimise the network using Self-Organising-Network (SON) algorithms • How to optimally cache data to accelerate and optimise data storage and transmission. The research objectives of the DAWN4IoE project are as follows: • Develop appropriate spatial-temporal structured filters to combine different data sets and infer both human location/mobility and digital data demand patterns. • Develop appropriate machine-learning techniques for unstructured natural language processing (NLP) to understand consumer experience for different service categories. • Design algorithms to integrate the new data analytics techniques with current state-of-the-art deployment techniques to assist HetNet planning, performance prediction, and deployment • Design mechanisms to integrate structured and unstructured data analytics to drive SON algorithms for radio resource management and smart antenna elements. • Design algorithms to optimally cache data leveraging on mobile edge computing (MEC). Achieving the above objectives will provide crucial inputs for 5G/B5G data-driven flexible wireless network design and both increase network capacity by 50% and decrease operation costs by 20-30% (compared with non-data driven networks).

As today “to out compute is to out compete”, the position of Europe in all industrial sectors and societal challenges is highly depending on the technological progress in computing. However, although computing has reached an unparalleled progress, it still remains a research topic as new challenges impose its transformative nature and adaptation, among others the evolution towards Cyber Physical Systems, the proliferation of devices and the big data they produce, the reduction of energy footpring, and the abstraction of infrastructure complexity. To that respect, PHANTOM wishes to deliver an economically and energetically sustainable solution for next generation computing systems, through a cross-layer design comprising reconfigurable multi-core and heterogeneous hardware platforms managed by a hardware-agnostic software platform that hides the complexity from the programmer and offers multi-dimensional optimization. Specifically, PHANTOM is structured in three layers. First, parallel programming and productivity tools are provided including application-driven APIs for programming and annotations, a parallelization toolset for maintaining intrinsically the code parallelization and model based testing techniques for early parallel program verification. Then, multi-dimensional optimization is addressed through an adaptive and multi-objective scheduler, deciding on where to execute each application component, which is supported by runtime monitoring/data analytics and security implementations. Last, low power, heterogeneous hardware platforms are built together with system software for enabling their management as a service. PHANTOM brings multi-disciplinary expertise through an ecosystem of academia, industries and a strong number of SMEs. The outcome will be validated in three use cases (automotive, telecoms, surveillance), in order to prove a cross-market approach, while documentation for developers will be delivered and reusability readiness evaluation will be run.

The main objective of the project is to design and implement a parameterized, knowledge-based, multi-target food sensitive mini-portable system, with heterogeneous micro-scale photonics for on-the-spot food quality sensing and shelf-life prediction. In particular, the miniaturized smart integrated system will be able to detect food hazards, spoilage (incl. early sign of spoilage) and food fraud through the combined bio-chemical data analysis and additionally will be able to perform food components/additives analysis, food identification and prediction of food shelf-life. The following use case will be addressed during the project: Use case 1: Detection of mycotoxins in various grains and nuts. Aflatoxins detection. A simple, convenient ultraviolet test makes it possible to detect the possible presence of aflatoxin. Use case 2: Detection of early sign of spoilage and spoilage in fruits, vegetables, meat, fish: combined with estimation on product expiration date. Use case 3: Detection of food fraud: Adulteration of alcoholic beverages, oil, milk and meat. 3 sensor devices will be integrated in the miniaturised smart sensor node: i) a MEMS-based near IR spectrometer (950-1900 nm), ii) a UV-VIS spectrometer (450-900 nm) and iii) a micro-camera. Moreover 3 light sources will also be integrated to support the sensing functionality: i) UV-LED, ii) white LED and iii) a miniaturised IR emitter. Smart signal processing of the spectrum images will be performed by an advanced microcontroller, integrated in the sensing device. The data will be communicated to a smartphone device, where the spectroscopy analysis will take place with the help of a cloud-base application connected to a reference database. Advanced detection algorithms will be deployed both in the level of cloud and the smartphone application. PhasmaFOOD system will enable common consumers for on the spot food quality sensing and shelf-life prediction.

Water management requires massive, low-cost monitoring means coping with differentiated and evolving requirements. However, the majority of multifunctional water sensors only supports predefined goals hindering interoperability, with a high cost, impeding large scale deployments. Addressing this, PROTEUS aims at offering x10 reduction in both size and unit function cost compared to state of the art. To this end, an increased number of functions will be integrated at a reduced cost and PROTEUS will deliver a reconfigurable microfluidic-and nano-enabled sensor platform for cognitive water quality monitoring. Innovative embedded software will provide reconfigurability of the sensing board to support several differentiated applicative goals while cognitive capabilities will manage evolving requirements during exploitation. Energy autonomy will be made by harvesting water flow energy. In addition, low cost of additional sensing components will enable redundancy increasing life span of the systems. The main challenge relates to the heterogeneous integration into a monolithic, microfluidic sensing chip of carbon-nanotubes-based resistive chemical sensors, of MEMS physical and rheological resistive sensors and of a multifunctional adaptive deep-submicron CMOS system on chip. Upstream, high level system design addressing industrial use cases, manufacturability and cost-effectiveness, packaging, energy budget and interfaces between building blocks, will enable consistency and efficiency of the whole approach. Downstream, system validation will be carried out at different levels: benchmarking, reliability assessment to guarantee service time, model deployments and field testing. The consortium brings together renowned actors along the whole value chain, including system integration and end users. This will contribute to post-project exploitation prepared by ensuring appropriate inclusion of business requirements within the system design.

Given the inability of Highly-Distributed-Application-Developers to foresee the changes as well as the heterogeneity on the underlying infrastructure, it is considerable crucial the design and development of novel software paradigms that facilitate application developers to take advantage of the emerging programmability of the underlying infrastructure and therefore develop Reconfigurable-by-Design applications. In parallel, it is crucial to design solutions that are scalable, support high performance, are resilient-to-failure and take into account the conditions of their runtime environment. Towards this direction, the ARCADIA project aims to design and validate a Novel Reconfigurable-By-Design Highly Distributed Applications Development Paradigm over Programmable Infrastructure. The proposed framework will rely on the development of an extensible Context Model which will be used by developers directly at the source-code level. Proper Context-Model will be assisted and validated by IDE-plugins (for many IDEs) in order to re-assure that the generated executable files contain meaningful semantics. According to ARCADIA’s vision, the generated executables should be on-boarded by a Smart Controller which will undertake the tasks of translating annotations to optimal infrastructural configuration. Such a controller will enforce an optimal configuration to the registered programmable resources and will pro-actively adjust the configuration plan based on the Infrastructural State and the Application State. The Context-Model and the aforementioned ARCADIA toolset will be complemented by a Development Methodology that will assure that developed Highly Distributed Applications are Reconfigurable-By-Design. The framework is planned to be validated and evaluated on three use cases that will be deployed over testbeds that host heterogeneous programmable infrastructure.