Frank Englert

Capacitive near-field communication for ubiquitous interaction and perception

Smart objects within instrumented environments offer an always available and intuitive way of interacting with a system. Connecting these objects to other objects in range or even to smartphones and computers, enables substantially innovative interaction and sensing approaches. In this paper, we investigate the concept of Capacitive Near-Field Communication to enable ubiquitous interaction with everyday objects in a short-range spatial context. Our central contribution is a generic framework describing and evaluating this communication method in Ubiquitous Computing. We prove the relevance of our approach by an open-source implementation of a low-cost object tag and a transceiver offering a high-quality communication link at typical distances up to 15 cm. Moreover, we present three case studies considering tangible interaction for the visually impaired, natural interaction with everyday objects, and sleeping behavior analysis.

How to auto-configure your smart home?: high-resolution power measurements to the rescue

Most current home automation systems are confined to a timer-based control of light and heating in order to improve the users comfort. Additionally, these systems can be used to achieve energy savings, e.g., by turning the appliances off during the users absence. The configuration of such systems, however, represents a major hindrance to their widespread deployment, as each connected appliance must be individually configured and assigned an operation schedule. The detection of active appliances as well as their current operating mode represents an enabling technology on the way to truly smart buildings. Once appliance identities are known, the devices can be deactivated to save energy or automatically controlled to increase the users comfort. In this paper, we propose an approach to have buildings informed about the presence and activity of electric appliances. It relies on distributed high-frequency measurements of electrical voltage and current and feature extraction process that distills the collected data into distinct features. We utilize a supervised machine learning algorithm to classify readings into the underlying device type as well as its operation mode, which achieves an accuracy of up to 99.8%.

Do you hear what I hear? Using acoustic probing to detect smartphone locations

Many context-aware smartphone applications depend on specific conditions for gathering data, e.g., specific phone locations or orientations. As a result, the significant overhead of keeping all this information in mind is imposed on their users. Besides averting the interest of potential application users, these requirements defeat one of the main purposes of these mobile data collection, namely simplifying life through mobile sensing applications. This is not a problem that solely affects the users, but the developers of the applications alike. As even the most diligent users often do not manage to follow the strict data collection guidelines at all times, errors in the collected data may ultimately lead to the provision of wrong services and thus to degraded application quality. In this paper, we thus present a solution to determine the location of a phone in order to support context-aware applications. It offers the possibility to detect the position of the phone with an accuracy of 97 %, as well as being able to correlate it with the type of the location of the user. Our system can be used to improve existing mobile sensing applications by facilitating various services that depend on the phone location, e.g., seamlessly adapting the ringtone volume or setting a phones flight mode.

Enhancing user privacy by preprocessing distributed smart meter data

The increasing presence of renewable sources requires power grid operators to continuously monitor electricity generation and demand in order to maintain the grids stability. To this end, smart meters have been deployed to collect realtime information about the current grid load and forward it to the utility in a timely manner. High resolution smart meter data can however reveal the nature of appliances and their mode of operation with high accuracy, and thus endanger user privacy. In this paper, we investigate the impact on user privacy when the consumption data collected by distributed smart metering devices are preprocessed prior to their usage. We therefore assess the impact on the successful classification of appliances when sensor readings are (1) quantized, (2) down-sampled at a lower sampling rate, and (3) averaged by means of an FIR filter. Our evaluation shows that a combination of these preprocessing steps can provide a balanced trade-off that is in the interests of both users (privacy protection) and utilities (near real-time information).

Extracting Human Behavior Patterns from Appliance-level Power Consumption Data

In order to provide useful energy saving recommendations, energy management systems need a deep insight in the context of energy consumption. Getting those insights is rather difficult. Either exhaustive user questionnaires or the installation of hundreds of sensors are required in order to acquire this data. Measuring the energy consumption of a household is however required in order to find and realize saving potentials. Thus, we show how to gain insights in the context of energy consumption directly from the energy consumption profile. Our proposed methods are capable of determining the user’s current activity with an accuracy up to 98% as well as the user’s current place in a house with an accuracy up to 97%. Furthermore, our solution is capable of detecting anomalies in the energy consumption behavior. All this is mainly achieved with the energy consumption profile.

SMARTENERGY.KOM: An intelligent system for energy saving in smart home

Over the last twenty years, energy conservation has always been of great importance to individuals, societies and decision makers around the globe. As a result, IT researchers have shown a great interest in providing efficient, reliable and easy-to-use IT services which help users saving energy at home by making use of the current advances in Information and Communications Technology (ICT). Driven by the aforementioned motivation, we developed SMARTENERGY.KOM, our framework for realizing energy efficient smart homes based on wireless sensor networks and human activity detection. Our work is based on the idea that most of the user activities at home are related to a set of electrical appliances which are necessary to perform these activities. Therefore, we show how it is possible to detect the users current activity by monitoring his fine-grained appliance-level energy consumption. This relation between activities and electrical appliances makes it possible to detect appliances which could be wasting energy at home. Our framework is organized in two components. On one hand, the activity detection framework which is responsible for detecting the users current activity based on his energy consumption. On the other hand, the EnergyAdvisor framework which utilizes the activity detection for the purpose of recognizing the appliances which are wasting energy at home and informing the user about optimization potential.

Empirical investigation of the effect of the door's state on received signal strength in indoor environments at 2.4 GHz

Due to the wide deployment of indoor wireless local area networks (WLANs), the indoor planning became a research of interest for IT as well as networking researchers. As a result of this wide deployment, many IT applications and services started relying on the ready implemented WLAN infrastructure. Therefore, there is a need for reliable propagation models which are able to predict the WLAN signal strength in indoor environments before starting the real world deployment which leads to an efficient and cost aware deployment process. In this paper we develop an empirical propagation model which focuses mainly on the effect of the door state on the propagated WLAN signal in indoor environments. The measurements were compared to other simulated results in literature. A new empirical parameter based on empirical measurements was introduced for a better estimation of the received signal strength (RSS).

Switching Push and Pull: An Energy Efficient Notification Approach

An increasing number of modern smartphone applications are dependent on information updates from the cloud. To realize such information updates mainly two communication approaches are common, namely push- and pull. Due to different communication patterns both approaches differ in their energy consumption and notification latency. The energy constrained nature of mobile devices entails a sensible selection of the appropriate notification approach. In this paper we provide an evaluation of the energy consumption of both communication approaches. Based on this we provide a transition approach that is able to use the best of both, low latency and low energy consumption. Our results show that energy savings of up to 7% of the total smartphone battery per day can be achieved by switching between both approaches, depending on the context.

Hybrid communication architecture for emergency response — An implementation in firefighter's use case

Emergency response is a critical mission. In an emergency situation, coordination and information dissemination are two of the most important tasks. The rise of the smart mobile devices leads to the tendency to use these devices to facilitate the aforementioned tasks. Despite of that, communication based on mobile devices in an emergency situation is not always straight forward since parts of the networks might not be available at the same time. Nevertheless, it is of utmost importance to keep the information synchronized across multiple stakeholders. In this work, we design and implement a hybrid communication system to support the relief work of the responders, in particular, coordination and information synchronization among the stakeholders through the usage of mobile devices. Our focus lies on the relief works carried out by the firefighters. Our designed system is evaluated regarding its performance and its usability through a field test to show its applicability in practice.

A concept for vehicle internet connectivity for non-safety applications

Internet access to multimedia content in vehicles today is only possible via cellular networks which offer insufficient bandwidth. By using additional V2X technology in a hybrid manner, vehicles can benefit from additional bandwidth to receive enhanced internet connectivity. We introduce a holistic concept that pursues the vision of an optimal use of available access networks in a vehicular environment combined with a managed resource utilization in a user-centric way to result in maximum user experience and economic efficiency. The resulting internet connection introduces further opportunities for value-added services that maximize Quality of Experience and allows further personalization.