Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.

This paper presents the development of a parking occupancy simulator to support a smart parking system. The simulator uses an agent-based approach to model drivers who follow activity plans and who may or may not use the smart parking system. We illustrate how the process of developing our simulator helped in the design and implementation of the smart parking system components. The paper also shows how the simulator was used to study the possible usage of the smart parking system in a university campus, foreseeing (1) support for the smart parking system’s overall suitability, (2) reservation guarantee violation problems, and (3) the value of using total traveled distance as a metric for the smart parking evaluation. The experience presented in this paper may prove valuable to teams planning the development of a smart parking system for similar contexts.

Urban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental, mobile geo-game. Fifty-seven participants recorded bicycle trips during one week periods in three cities. By aggregating them, we extracted not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, participants recorded bicycle segments at lower speeds indicating a deviation from an ideal cycling scenario. We describe the potential frictions inhibiting bicycle commuting with regard to the distance to bicycle paths, surrounding infrastructure, and location in the urban area.

This paper centers on the deeper presentation of a new and publicly accessible dataset comprising of Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in an university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a Radio Map (RM), to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal Received Signal Strength IndicatorRSSI stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.