Cross-domain interoperability using federated interoperable semantic IoT/Cloud testbeds and applications: The FIESTA-IoT approach

This work is funded by the European Commission under the EU-H2020 Project Grant ”Federated Interoperable Semantic IoT/cloudTestbeds andApplications (FIESTA)” with the Grant Agreement No. CNECT-ICT-643943

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

A Test Methodology for Evaluating Cognitive Radio Systems

The cognitive radio field currently lacks a standardized test methodology that is repeatable, flexible, and effective across multiple cognitive radio architectures. Furthermore, the cognitive radio field lacks a suitable framework that allows testing of an integrated cognitive radio system and not solely specific components. This research presents a cognitive radio test methodology, known as CRATM, to address these issues. CRATM proposes to use behavior-based testing, in which cognition may be measured by evaluating both primary user and secondary user performance. Data on behavior based testing is collected and evaluated. Additionally, a unique means of measuring secondary user interference to the primary user is employed by direct measurement of primary user performance. A secondary user pair and primary user radio pair are implemented using the Wireless Open-Access Research platform and WARPLab software running in MATLAB. The primary user is used to create five distinct radio frequency environments utilizing narrowband, wideband, and non-contiguous waveforms. The secondary user response to the primary user created environments is measured. The secondary user implements a simple cognitive engine that incorporates energy-detection spectrum sensing. The effect of the cognitive engine on both secondary user and primary user performance is measured and evaluated

Efficient multi-objective optimization of wireless network problems on wireless testbeds

A large amount of research focuses on experimentally optimizing performance of wireless solutions. Finding the optimal performance settings typically requires investigating all possible combinations of design parameters, while the number of required experiments increases exponentially for each considered design parameter. The aim of this paper is to analyze the applicability of global optimization techniques to reduce the optimization time of wireless experimentation. In particular, the paper applies the Efficient Global Optimization (EGO) algorithm implemented in the SUrrogate MOdeling (SUMO) toolbox inside a wireless testbed. The proposed techniques are implemented and evaluated in a wireless testbed using a realistic wireless conference network problem. The performance accuracy and experimentation time of an exhaustively searched experiment is compared against a SUMO optimized experiment. In our proof of concept, the proposed SUMO optimizer reaches 99.51% of the global optimum performance while requiring 10 times less experiments compared to the exhaustive search experiment

CorteXlab: A Facility for Testing Cognitive Radio Networks in a Reproducible Environment

International audience—While many theoretical and simulation works have highlighted the potential gains of cognitive radio, several technical issues still need to be evaluated from an experimental point of view. Deploying complex heterogeneous system scenarios is tedious, time consuming and hardly reproducible. To address this problem, we have developed a new experimental facility, called CorteXlab, that allows complex multi-node cognitive radio scenarios to be easily deployed and tested by anyone in the world. Our objective is not to design new software defined radio (SDR) nodes, but rather to provide a comprehensive access to a large set of high performance SDR nodes. The CorteXlab facility offers a 167 m 2 electromagnetically (EM) shielded room and integrates a set of 24 universal software radio peripherals (USRPs) from National Instruments, 18 PicoSDR nodes from Nutaq and 42 IoT-Lab wireless sensor nodes from Hikob. CorteXlab is built upon the foundations of the SensLAB testbed and is based the free and open-source toolkit GNU Radio. Automation in scenario deployment, experiment start, stop and results collection is performed by an experiment controller, called Minus. CorteXlab is in its final stages of development and is already capable of running test scenarios. In this contribution, we show that CorteXlab is able to easily cope with the usual issues faced by other testbeds providing a reproducible experiment environment for CR experimentation

Middleware for wireless sensor network virtualization

Sensor and network virtualization technology are used in smart home, smart grid, smart city and many other applications of Internet of Things (IoT) that deploy Wireless Sensor Network (WSN) to facilitate multiple sensor data transmission over multiple networks. Existing WSNs are designed for a specific application running on low data rate network. The challenge is how to ensure multiple sensor data for multiple applications be transmitted over multiple heterogeneous networks having different transmission rates while ensuring Quality-of-Service (QoS). The research has developed a middleware that provides sensor and network virtualization with guaranteed QoS. The middleware was designed comprising of two layers: Application Dependent Layer Middleware (ADLM) and Network Dependent Layer Middleware (NDLM). The ADLM combined multiple sensor data to form services based of Service Oriented Application (SOA). It is comprised of service handling manager that combines various sensor data and form services, QoS manager that assigns priority and service scheduling manager that forwards the service frames. The NDLM facilitated seamless transmissions of various service data over multiple heterogeneous networks. It consists of hypervisor which is composed of flowvisor and the powervisor. The flowvisor is madeup of transmit and routing managers responsible for routing and transmitting service packets. The powervisor consists of a resource manager that determines and selects the node with the highest battery power. The middleware was implemented and evaluated on a real experimental testbed. The experimental results showed that the middleware increased throughput by 8.7% and reduced the numbers of packets transmissions from the node by 68.7% compared to proxy middleware using SOA. In addition, end-to-end transmission delay was reduced by 85.2% when compared to SenShare using SOA. The flowvisor at the gateway decreased the waiting time of packets in the queue by 59.8%, when the flowvisor raised the output rate up to 2.5 times the maximum arrival rate of WSN packets. The powervisor increased the node’s life time by 17.6% when compared to VITRO by limiting the transmission power to the existing battery voltage level. In brief, the middleware has provided guaranteed QoS by increasing throughput, reducing end-to-end delay and minimizing energy consumption. The middleware is highly recommended for IoT applications such as smart city and smart grid