Intelligent outdoor lighting systems

Cities understand the advantages of branding themselves as unique, beautiful and secure places. Lighting plays a special part in establishing that identity. In 2014, TU/e Intelligent Lighting Institute, Philips Research and ST Microelectronics are collaborating in an EIT ICT Labs project called ‘Intelligent Outdoor Lighting Systems (IOLS)’ towards an integrated intelligent outdoor lighting luminaire solution that will allow improved energy efficiency, user experience and safety feeling in cities. The partners integrate their technologies of lighting control, environment sensing and data analytics in a smart urban lighting luminaire, that is capable of intelligent scene classification and real-time dynamic actuation of outdoor lights

Lighting control and interaction for the future

Ever since the light bulb was first discovered, we have turned lights on and off with a switch. Today, the intelligent lighting technology allows many opportunities ranging from autonomous lighting control to advanced user interaction styles. If researchers in the No Switches Allowed program get their way, radical change is on the way

In-plane user positioning indoors

Indoor positioning is a service required by many smart environment applications for various purposes, such as activity classification, indoor navigation and context awareness. In this paper, we present a novel approach to the user positioning problem based on in-plane detection enabled by a set of infrared light emitters and sensors placed horizontally along the walls. The simulation results show that the proposed system is able to determine locations of multiple users inside the room with high precision and accuracy

A first step towards a dependability framework for smart environment applications

Smart environments will consist of a large number of heterogeneous devices that communicate to collaboratively perform various tasks for users. We propose a novel dependability framework to increase availability and reliability of smart environment applications. We argue that the key step in achieving high dependability is to predict faults before they occur. Many statistical fault prediction techniques have been proposed for smart environment applications. Selecting the best one among these techniques involves performance assessment and detailed comparison on given metrics. We present a linear regression-based prediction model to predict the remaining battery lifetime of a device to prevent faults due to low battery. Further, we discuss the proposed dependability framework, the basic approaches and the corresponding mechanisms to achieve our long-term research goal. We envision that dependability framework will reduce maintenance costs of large-scale smart environments and increase the dependability of smart environment applications

Beyond the switch: explicit and implicit interaction with light

The commercial introduction of connected lighting that can be integrated with sensors and other devices is opening up new possibilities in creating responsive and intelligent environments. The role of lighting in such systems goes beyond simply functional illumination. In part due to the large and established lighting network, and with the advent of the LED, new types of lighting output are now possible. However, the current approach for controlling such systems is to simply replace the light switch with a somewhat more sophisticated smartphone-based remote control. The focus of this workshop is to explore new ways of interacting with light where lighting can not only be switched on or off, but is an intelligent system embedded in the environment capable of creating a variety of effects. The connectivity between multiple systems and other ecosystems, for example when transitioning from your home, to your car and to your office, will also be explored during this workshop as a part of a connected lifestyle between different contexts. Keywords: connected lighting; lighting control; user experienc

Bright environments vision of the Intelligent Lighting Institute (ILI)

The Bright Environments research program of the Eindhoven University of Technology Intelligent Lighting Institute aims to find new methods of intelligent lighting control and human interaction. We present a summary of the institute’s work on this research field and the research vision of the Bright Environments program as well as an overview of the related research projects. Keywords: Intelligent lighting; Layered light control; Smart spaces; User interaction for lightin

Bright environments vision of the Intelligent Lighting Institute (ILI)

The Bright Environments research program of the Eindhoven University of Technology Intelligent Lighting Institute aims to find new methods of intelligent lighting control and human interaction. We present a summary of the institute’s work on this research field and the research vision of the Bright Environments program as well as an overview of the related research projects. Keywords: Intelligent lighting; Layered light control; Smart spaces; User interaction for lightin

Using artificial neurons in evidence based trust computation

\u3cp\u3eThis paper proposes an alternative approach for evidence based trust computation where the relationship between evidence and trust is learned using an artificial neuron, making it possible to automatically adapt trust computation to different use cases. Computational trust aims to quantify trust based on ever increasing evidence on observations. In the literature a trust value is seen as a posterior subjective probability, computed using Bayesian inference on evidence, a prior and a weight of the prior. This provides a fixed mapping between evidence and trust, which may not be suitable for every case study, e.g. when positive and negative evidences are not equally important. The proposed solution is also a first step towards our future work to replace complex and case-specific trust fusion operators proposed in the literature with a generic case-independent artificial neural network solution. Our experiments on example cases of coin toss prediction and occupancy detection show that for sufficiently large data sets, i.e. given sufficient evidence based on a history of observations, the proposed learning approach yields comparable results and in some cases beats the existing approach.\u3c/p\u3

Fault-prevention in smart environments for dependable applications

The functionality and the performance of smart environment applications can be hampered by faults. Fault tolerance solutions aim to achieve graceful performance degradation in the presence of faults, ideally without leading to application failures. This is a reactive approach and, by itself, gives little flexibility and time for preventing potential failures. We propose a proactive fault-prevention framework, which predicts potential low-level hardware, software and network faults and tries to prevent them via dynamic adaptation. We envision that the proposed framework will provide better control over performance degradation of smart environment applications, increased reliability and availability, and a reduced number of manual user interventions

Distributed fault detection in smart spaces based on trust management

Application performance in a smart space is affected by faulty behaviours of nodes and communication networks. Detection of faults helps diagnosis of problems and maintenance can be done to restore performance, for example, by replacing or reconfiguring faulty parts. Fault detection methods in the literature are too complex for typical low-resource devices and they do not perform well in detecting intermittent faults. We propose a fully distributed fault detection method that relies on evaluating statements about trustworthiness of aggregated data from neighbors. Given one or more trust statements that describe a fault-free state, the trustor node determines for each observation coming from the trustee whether it is an outlier or not. Several fault types can be explored using different trust statements whose parameters are assessed differently. The trustor subsequently captures the observation history of the trustee node in only two evidence variables using evidence update rules that give more weight to recent observations. The proposed method detects not only permanent faults but also intermittent faults with high accuracy and low false alarm rate