TY - CHAP A1 - Melzer, Matthias A1 - Dünnweber, Jan A1 - Baumann, Timo T1 - Towards Smart Home Data Interpretation Using Analogies to Natural Language Processing T2 - 2022 IEEE International Conference on Smart Internet of Things: IEEE SmartIoT 2022, proceedings, 19-21 August 2022, Suzhou, China, hybrid conference (onsite and virtual) N2 - Recent advances in the development of smart homes have led to the availability of a wide variety of devices providing a high level of convenience via gesture and speech control or fully automated operation. Many smart home appliances also address the aspects of safety and electricity savings by automatically powering themselves off after not being used for a while. However, many devices remain in a typical household that are not themselves "smart", or are not primarily electric (such as heating systems). We address the savings aspect by identifying processes involving the use of multiple devices in the electrical flow data, as captured by a smart meter in a modern household, rather than focusing on a single appliance. Therefore, we introduce a novel approach to usage pattern analysis based on the idea that a pattern of device usages as a result of a resident's 'routine' (such as making breakfast) can be interpreted similarly to a natural language 'sentence'; Natural Language Processing (NLP) algorithms can then be used for interpreting the residents' behavior. We introduce the notion of bag-of-devices (BoD), derived from the bag-of-words model used in document classification. In an experiment, we show how we use this model to infer predictions about the inhabitants from device usage, such as the resident leaving for the day or just to fetch the newspaper. KW - IoT KW - AI KW - NLP KW - Smart City Y1 - 2022 SN - 978-1-6654-7952-3 U6 - https://doi.org/10.1109/SmartIoT55134.2022.00020 SP - 65 EP - 71 PB - IEEE ER - TY - CHAP A1 - Völkl, Jakob A1 - Melzer, Matthias A1 - Dünnweber, Jan A1 - Sarkar, Amitrajit T1 - Dynamic Route Planning for a Data Collecting Luggage Transport Service T2 - 18th IEEE International Conference on Control & Automation, June 18-21, 2024, Reykjavík, Iceland N2 - Control and Automation of services of the urban infrastructure offered to citizens and tourists are elementary parts of a smart city. But both rely on a stable supply of data from sensors spread across the whole city, e. g., the fill level sensors of waste bins needed for a waste management tool which we developed in a collaboration with the Regensburg city council for the on-demand collection of waste bins. Europe has a lot of historic cities like Regensburg with narrow streets and huge building walls, some made from granite and fieldstones, which often represents an insurmountable obstacle to wireless data transmission. The reduction of the road traffic volume poses an additional challenge for city planners. By means of networked planning and simulation software, the situation, state and efficiency of citywide logistic services can be monitored and optimized. In the course of such optimizations, we propose the combination of digital and logistic services. As an example, we show that monitoring state information, such as the waste bin fill levels, can be accomplished using the same vehicles and the same planning software, that is used for luggage transportation. Moreover, we describe how we adapted a solver for a variant of the TSP, namely the prize-collecting traveling salesman, to optimize the route planning dynamically. Y1 - 2024 PB - IEEE ER - TY - CHAP A1 - Huang, Wenfei A1 - Melzer, Matthias A1 - Dünnweber, Jan T1 - Optimizing Smart Retail by Experiment Using an Online AI Model Exploration Interface T2 - 2024 Winter Simulation Conference (WSC), 15-18 December 2024, Orlando, FL, USA N2 - Smart retail technologies save grocery store operators a lot of work. At the same time, these technologies produce valuable data for building sustainable and economical inventory management strategies. AI models can be trained for sales forecasting using the data. The forecasts support the provisioning of fresh food over the whole week and help reducing food waste. In this paper, we present a Web portal which we developed to allow grocery store operators experiments with AI models revealing interrelations between observed and anticipated customer behavior. Clickable diagrams facilitate the exploration of data sets combining historical data and synthetically generated data. Pricing and ordering can be adapted accordingly to the simulated forecasts. By means of a case study, we show that our simulations are not only useful for predicting future sales but for other smart retail tasks as well. Y1 - 2024 U6 - https://doi.org/10.1109/WSC63780.2024.10838897 SP - 2631 EP - 2642 PB - IEEE ER -