69,702 research outputs found
An IoT-based solution for monitoring a fleet of educational buildings focusing on energy efficiency
Raising awareness among young people and changing their behaviour and habits concerning energy usage iskey to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examinesways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both theusers (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizenÅ› behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system's high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies andservices in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer newapp-based solutions that can be used either for educational purposes or for managing the energy efficiency ofthebuilding. The system is replicable and adaptable to settings that may be different than the scenarios envisionedhere (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity
Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments
This paper presents MACI, the first bespoke framework for the management, the
scalable execution, and the interactive analysis of a large number of network
experiments. Driven by the desire to avoid repetitive implementation of just a
few scripts for the execution and analysis of experiments, MACI emerged as a
generic framework for network experiments that significantly increases
efficiency and ensures reproducibility. To this end, MACI incorporates and
integrates established simulators and analysis tools to foster rapid but
systematic network experiments.
We found MACI indispensable in all phases of the research and development
process of various communication systems, such as i) an extensive DASH video
streaming study, ii) the systematic development and improvement of Multipath
TCP schedulers, and iii) research on a distributed topology graph pattern
matching algorithm. With this work, we make MACI publicly available to the
research community to advance efficient and reproducible network experiments
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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