Doreen Boehnstedt

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.

Electricity-Metering in a Connected World: Virtual Sensors for Estimating the Electricity Consumption of IoT Appliances

Due to rising electricity prices, there is an increasing incentive to save energy. Therefore, more and more large organizations intend to reduce their energy consumption. Often, their plans cannot be realized due to missing insights into the causes energy consumption. Centralized energy meters provide no information at which appliances the energy is spent and the installation of thousands of distributed meters is often not feasible from an economic point of view. To simplify the energy metering in large scale, we propose to make Internet of Things (IoT) appliances aware of their own electricity consumption using on software based virtual energy sensors. We demonstrate how to automatically generate those energy models for nearly arbitrary networked devices with a high accuracy. Our purely software based energy metering solution approximates the energy consumption of common office equipment with an error between 2.19% and 10.8%. Using our approach, IoT appliances become aware of their own energy expenditure. This greatly simplifies energy metering on device level granularity, giving appropriate user feedback and developing more energy-efficient appliances. All these benefits are achieved without the need for installing additional hardware sensors.

ProbSense.KOM: A Probabilistic Sensing Approach for Gathering Vehicular Sensed Data

Advanced driver assistance systems (ADAS) improve safety, energy efficiency and driver comfort. Such systems are commonly based on sensor data, however, sensor range is physically limited. A way to extend the sensing range is to share sensor reading with others, i.e., vehicles and infrastructure services. Since direct vehicle communication is not widely deployed and vehicles are often not driving in direct communication range, communication has to be realized via cellular networks. Due to high costs for cellular communication, the transmission of sensed data has to be efficient and the amount of transmitted data must be minimized. As possible solution, we introduce a concept of probabilistic data transmission for vehicular sensed data. The system divides the map into geographic cells, and a probabilistic model is managed for each geographic cell individually. We are able to achieve a reduction in data transmission volume of up to 50% in comparison to opportunistic approaches.

Towards Virtual Sensors for Estimating the Electricity Consumption of Networked Appliances.

In modern environments, more and more smart appliances exist. Those devices are equipped with sensors to measure their internal state and environmental variables, with processing power, and also with networking capabilities. To make these appliances aware of their own electricity expenditure we propose the concept of virtual electricity sensors. Instead of adding dedicated hardware sensors, we use the device integrated sensors in conjunction with an energy model to estimate the actual power draw based on the current device state. First results indicate that this approach leads to an accuracy of up to 98% for various smart appliances. Our approach leads to cost-efficient fine grained electricity metering for future smart appliances.

Towards using situational information to detect an individual's perceived stress level

The advent of state-of-the-art telecommunication devices like smartphones has led to a considerable increase in the amount of electronic communication exchanged. While the improved availability increases personal flexibility—reducing rigidity in time and place of communication—it comes at a price. The ‘anytime, anyplace’-accessibility, which has become the norm in todays (working) society, can cause adverse effects to an individuals mood and emotions, and especially raise the stress level. Consequently, in this paper, we discuss methods for measuring stress via mobile devices, analyzing their pros and cons. Subsequently, we analyze which situational information— comprising the prevailing context of the users and their usage of communication devices—is useful for the quantification of the psychological stress perceived by users. Based on a field study with 27 participants, we observe that, while longer working hours and higher number of appointments have a positive correlation with an increased stress level, there is no one-fits-all method for stress measurement. In turn, we take the first steps towards non-intrusive methods to identify stressful situations and thus, lay the foundation for future research on stress mitigation.

Horizon.KOM: A First Step Towards an Open Vehicular Horizon Provider

Modern vehicles are commonly equipped with several sensors to gather information about the direct environment. Due to the physical limitation of these sensors, the detection range and field of view are limited to the direct vicinity. This limits the functionality of driver assistance systems. A way to overcome this issue is to use digital road maps to generate a so called electronic horizon as virtual sensor about the environment ahead. All known electronic horizon providers are closed source and owned by companies. In the work at hand we present our concept of an electronic horizon provider that we have published as open source.