When it comes to conserving electricity, it is crucial for
users to understand how much electricity is used by single appliances. However, the rather technical feed-
back in pure numbers and intangible units of existing
energy feedback systems is not appropriate for most users. To improve this situation, we present PowerPe-
dia, a system that aims at providing action guiding
feedback beyond pure consumption values. PowerPedia
enables users to identify and compare the consumption of their residential appliances to those of others. It thus helps users to better assess their electricity consump-
tion and draw effective measures to save electricity.
Several problems exist in accurately quantifying the greenhouse gases (GHG) which result from the production, transportation, usage, and recycling of products. We review in this paper the current attempts at measuring GHG emissions and investigate the potential of UbiComp technologies in improving the state of the art in carbon footprint calculation and in communicating the result to
consumers. This has potential benefits on consumer awareness and behaviour and can also foster competition among companies towards higher energy and resource efficiency.
The strong effects of descriptive normative feedback (“how you compare to others”) on an individual’s electricity consumption have been documented in the IS literature. Here, we extend prior research on reference group effects (“whom to compare with”) by defining the relevance of the reference group in terms of similarity in contextual factors as opposed to personal characteristics. Specifically, we manipulate the spatial proximity of reference groups and test whether population density moderates the effects of feedback. In a field study with 560 energy customers, we find that reference groups that are close in terms of geographical proximity are more effective than more distant groups. However, population density does not moderate this effect. Designer of green information systems should therefore use reference groups that are close to the energy consumer with regard to geographical proximity, but they do not need to tailor the intervention to the energy consumer’s location.
This paper describes a field study to investigate whether and to what extent individuals conserve more electricity if they have the opportunity to signal their behavior to others in the online community. Moreover, the study intends to reveal how positive social sanctions (e.g., publicly rewarding people who reduce their energy consumption) and negative social sanctions (e.g., publicly warning people who increase their consumption) cause individuals to alter both their energy demand and their time dedicated to the online community. We discuss related work on identity disclosure in online communities, on promoting sustainable behavior with information systems, and on economic theory explaining the effects of prosocial motives on behavior. The study will be conducted as field experiment using an energy efficiency portal developed by us and operated by an Austrian utility company that currently facilitates 9,899 active users out of which 1,400 will be randomly selected as study participants.
There is an on-going trend towards turning paper documents that store personal information or other valuable data into machine-readable form. An example of this trend is the electronic passport that will become common in the near future. In this paper we show how the security of these machine readable documents could be improved by combining RFID with optical memory devices. We propose integrating an optical memory device into the RFID enabled smart document and present methods how these two storage media can be combined to secure the document against threats like illicit scanning, eavesdropping and forgery. The presented approaches make use of the optical document-to-reader channel which is more secure than the radio-frequency communication interface. To demonstrate the potential of our approaches we show how they could overcome a number of existing security and privacy threats of electronic passports.
Legislation, customer pressure, and energy costs are increasing the interest of enterprises in environmental performance indicators such as greenhouse gas emissions and energy usage. Currently, business users take decisions across the value chain, from product design to disposal, without the ability to compare the environmental impact of alternatives within their information systems, thus limiting the optimization potential. In this paper we consider procurement as an example business operation and show how capturing previously-unknown material parameters in the respective information system can significantly increase the achievable optimizations. We use apple procurement into the U.K. to illustrate the paper’s idea, and conduct Monte Carlo analysis to quantify the realizable impact reductions as each additional life cycle parameter is tracked. The results show that taking into account the production country alone achieves a decrease in energy consumption of around 1250MJ per ton of apples, equivalent to 28% reduction from the base case.
The personal transport sector constitutes an important target of energy conservation and emission reduction programs. In this context, eco-feedback technologies that provide information on the driving behavior have shown to be an effective means to stimulate changes in driving in favor of both, reduced costs and environmental impact. This study extends the literature on eco-feedback technologies as it demonstrates that a smartphone application can improve fuel efficiency even under conditions where monetary incentives are not given, i.e. where the drivers do not pay for fuel. The field test, which took place with 50 corporate car drivers, demonstrates an improvement in the overall fuel efficiency by 3.23%. The theoretical contribution underlines the assumption that context-related feedback can favorably influence behavior even without direct financial benefits for the agent. Given the large share of corporate cars, findings are also of high practical importance and motivate future research on eco-driving feedback technologies.
In today's business, there is a growing problem of product counterfeiting and piracy. Criminals have considerable expertise and resources that enable them to produce and sell counterfeits of products. The proposed solution aims at providing unique and secure authentication mechanisms of a given item, in order to distinguish between genuine products and counterfeits. As underlying technology, the approach utilizes RFID technology: transponders hold unique and cryptographically secured data that uniquely binds a given product to a given tag, and thus makes duplication or re-application of tags difficult
This work presents the hardware design of a dynamically reconfigurable function unit (RFU) to accelerate computation-intensive tasks in Medium Access Control (MAC) layers of WLANs. The function unit is integrated in a pipelined 32 bit RISC processor and provides full hardware support for the Advanced Encryption Standard (AES) as specified in upcoming WLAN standards such as IEEE 802.11i. Dynamic reconfiguration allows the processor to use arithmetic components and memory elements of the RFU not only for AES, but also for additional tasks common in the MAC-layer. With our approach it is possible to accelerate Reed-Solomon-Code generation, Cyclic Redundancy Checks as well as other encryption standards like SQUARE, Magenta and Twofish by supporting Galois Field multiplication and table look-ups. The integration of the reconfigurable unit in the processor core results in an architecture that can simultaneously support control-flow and data-flow oriented tasks. This architecture was prototyped onto a Virtex2 FPGA.
Detecting when a household is occupied by its residents is fundamental to enable a number of home automation applications. Current systems for occupancy detection usually require the installation of dedicated sensors, like passive infrared sensors, magnetic reed switches, or cameras. In this paper, we investigate the suitability of digital electricity meters -- which are already available in millions of households worldwide -- to be used as occupancy sensors. To this end, we have collected fine-grained electricity consumption data along with ground-truth occupancy information for 5 households during a period of about 8 months. Our results show that using common classification methods it is possible to achieve occupancy detection accuracies of more than 80%.