Real-life performance of protocol combinations for wireless sensor networks

Wireless sensor networks today are used for many and diverse applications like nature monitoring, or process and wireless building automation. However, due to the limited access to large testbeds and the lack of benchmarking standards, the real-life evaluation of network protocols and their combinations remains mostly unaddressed in current literature. To shed further light upon this matter, this paper presents a thorough experimental performance analysis of six protocol combinations for TinyOS. During these protocol assessments, our research showed that the real-life performance often differs substantially from the expectations. Moreover, we found that combining protocols is far from trivial, as individual network protocols may perform very different in combination with other protocols. The results of our research emphasize the necessity of a flexible generic benchmarking framework, powerful enough to evaluate and compare network protocols and their combinations in different use cases

Distributed spectrum sensing in a cognitive networking testbed

In this demonstration, we show how the IBBT w-iLab.t wireless testbed, combined with multiple spectrum sensing engines designed by imec, can be used for experimentally-supported design and evaluation of cognitive networking protocols. Functionalities include the advanced characterization of the behavior of a cognitive solution under test, and characterization of the wireless experimentation environment itself

FORGE enabling FIRE facilities for the eLearning community

International audienceMany engineering students at third-level institutions across the world will not have the advantage of using real-world experimentation equipment, as the infrastructure and resources required for this activity are too expensive. This paper explains how the FORGE (Forging Online Education through FIRE) FP7 project transforms Future Internet Research and Experimentation (FIRE) testbed facilities into educational resources for the eLearning community. This is achieved by providing a framework for remote experimentation that supports easy access and control to testbed infrastructure for students and educators. Moreover, we identify a list of recommendations to support development of eLearning courses that access these facilities and highlight some of the challenges encountered by FORGE

Deploying Learning Analytics for Awareness and Reflection in Online Scientific Experimentation

Recent trends in online learning, most notably Massive Open Online Courses (MOOCs) and Learning Analytics, are changing the landscape in the education sector by offering learners with access to free learning materials of high quality, as well as with the means to monitor their progress and reflect on their learning experiences. This part presents FORGE, a European initiative for online learning and experimentation via interactive learning resources. FORGE provides learners and educators with access to world-class experimentation facilities and high quality learning materials. Additionally, the deployment of Learning Analytics in the FORGE learning resources aims at supporting awareness and reflection both for learners and educators

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

BONFIRE: benchmarking computers and computer networks

The benchmarking concept is not new in the field of computing or computer networking. With “benchmarking tools”, one usually refers to a program or set of programs, used to evaluate the performance of a solution under certain reference conditions, relative to the performance of another solution. Since the 1970s, benchmarking techniques have been used to measure the performance of computers and computer networks. Benchmarking of applications and virtual machines in an Infrastructure-as-a-Service (IaaS) context is being researched in a BonFIRE experiment and the benchmarking of wired and wireless computer networks is put forward as a research topic in the research projects CREW and OneLab2. In this paper, we elaborate on the interpretation of the term “benchmarking” in these projects, and answer why research on benchmarking is still relevant today. After presenting a high-level generic benchmarking architecture, the possibilities of benchmarking are illustrated through two examples: benchmarking cloud services and benchmarking cognitive radio solution