A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs

Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs

identifier:oai:t2r2.star.titech.ac.jp:5068660

Applying Time Series Analysis and Neighbourhood Voting in a Decentralised Approach for Fault Detection and Classification in WSNs

In pervasive computing environments, wireless sensor networks play an important infrastructure role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time in a decentralised manner; however, sensed data is often faulty. We thus design a decentralised scheme for fault detection and classification in sensor data in which each sensor node does localised fault detection. A combination of neighbourhood voting and time series data analysis techniques are used to detect faults. We also study the comparative accuracy of both the union and the intersection of the two techniques. Then, detected faults are classified into known fault categories. An initial evaluation with SensorScope, an outdoor temperature dataset, confirms that our solution is able to detect and classify faulty readings into four fault types, namely, 1) random, 2) malfunction, 3) bias, and 4) drift with accuracy up to 95%. The results also show that, with the experimental dataset, the time series data analysis technique performs comparable well in most of the cases, whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types.

Energy adaptive buildings:From sensor data to being aware of users

Energie besparen is fundamenteel voor het realiseren van een duurzame energievoorziening. Het besparen van energie draagt bij aan milieudoelstellingen, verbetert de zakelijke positie van landen, en levert werkgelegenheid. Er zijn tal van mogelijkheden voor het behalen van aanzienlijke energiebesparingen in gebouwen gezien individuen en bedrijven gebaat zijn bij energiebesparingen en daardoor zelf de verantwoordelijkheid nemen. Het is bewezen dat het gedrag van gebouwgebruikers een grote impact heeft op de verwarming en ventilatie van ruimtes, en op het energieverbruik van verlichting en huishoudelijke apparaten. Huidige gebouwautomatiseringssystemen kunnen niet overweg met veranderingen in het gedrag van gebruikers en zijn daardoor niet in staat om het energieverbruik terug te dringen met behoud van gebruikerscomfort. Mijn promotieonderzoek wordt gedreven door het doel om een dergelijk energy adaptive building te realiseren dat intelligent systemen aanstuurt en zich aanpast aan de gebruiker en gebruikersactiviteiten door deze te leren, terwijl energieverspilling wordt teruggedrongen. Mijn focus ligt op het ontwikkelen van een framework, beginnende bij de hardware infrastructuur voor sensoren en actuatoren, het verwerken en analyseren van de sensordata, en de nodige informatie over de omgeving en gebruikersactiviteiten verkrijgen zodat het gebouw aangestuurd kan worden. Onze oplossing kan 35% besparen op het totale energieverbruik van een gebouw. Als een succesverhaal, besparen de software systemen zelfs 80% op het energieverbruik van de verlichting in het restaurant van de Bernoulliborg. Wij commercialiseren de resultaten verkregen in ons onderzoek door het oprichten van de start-up SustainableBuildings, een spin-off bedrijf van onze universiteit, om onze oplossing aan te bieden aan kantoorgebouwen.Saving energy is the foundation for achieving a sustainable energy supply. Saving energy contributes to environmental objectives, improves the competitiveness of a country’s businesses, and boosts employment. There are numerous opportunities for achieving significant energy savings in buildings since individuals and businesses have an interest themselves in saving energy and will shoulder the responsibility for doing so.Occupant behaviour has shown to have large impact on space heating and cooling demand, energy consumption of lighting and appliances. Current building automation systems are unable to cope with changes caused by occupants’ behaviour and interaction with the environment, therefore they fail to reduce unnecessary energy consumption while preserving user comfort.My PhD research is driven by the aim of realising such energy adaptive buildings that facilitate intelligent control, that learn and adapt to the building users and their activities, while reducing energy waste. My particular focus is on a framework, going from the hardware infrastructure for sensing and actuating, to processing and analysing sensor data, providing necessary information about the environment and occupants’ activities for the system to produce adaptive control strategies, regulating the environment accordingly.Our solution can save 35% of energy for a single building. As a success story, the software system saves 80 percent on energy spent for lighting in the restaurant of the Bernoulliborg.We are commercialising the results of our research by creating the SustainableBuildings start-up, a spin-off from our university, to offer the solutions to non-residential buildings, first in the Netherlands, and later extending wider