The rapid growth of shared micromobility, such as e-scooters and e-bikes, has transformed urban transportation, bridging the gap between public transit and first/last-mile mobility. As users and cities need information on the status and usage of such micromobility vehicles, operators publish the data using General Bikeshare Feed Specification (GBFS)-compliant APIs. These feeds are extremely useful for operational transparency and enabling third-party integration into navigation apps. However, it has raised significant privacy concerns, particularly around the fine-grained spatiotemporal data sharing, which can reveal potentially sensitive information about travel patterns, even without explicit personal identifiers. For instance, by leveraging high-precision GPS coordinates, battery levels, and timestamps, malicious actors can infer trip origins and destinations, posing a risk of membership inference attacks. Despite efforts to mitigate such risks through dynamic vehicle IDs and GBFS guidelines, the potential for privacy leakage remains. In this paper, we investigate these privacy risks in the context of micromobility data, addressing four key research questions: (1) identifying vulnerable fields in GBFS data that can leak trip trajectories; (2) validating trip origin-destination inference attacks without access to ground truth data from operators; (3) assessing the generalizability of such attacks across different cities, operators and GBFS version; and (4) proposing effective mitigation strategies. We propose a heuristic method for reconstructing trip origins and destinations using only publicly available GBFS data, without relying on vehicle identifiers or auxiliary quasi-identifiers. Our empirical analysis, conducted on data from two cities with varying sizes, shows that a significant proportion of trips can be accurately recalled, with over 80% of trip source and destination pairs identified across both cities. Furthermore, our proposed anonymization techniques, such as data generalization and removal of quasi-identifiers, can prevent up to 97% of successful attacks, ensuring privacy without sacrificing data utility.

With the growing adoption of location-based services, ensuring user privacy without compromising data utility has become a critical research focus. In this paper, we investigate the impact of different statistical noise distributions-Laplace, Gaussian, Exponential, Cauchy, and Uniform, on trajectory anonymization across different transportation modes using Differential Privacy. We propose a modality-wise anonymization approach and assess its effectiveness through two down-stream tasks utility metric: (i) counting the number of unique users within a predefined area, and (ii) predicting modes of transport. Experimental results on the two public datasets Geolife and Roma taxi reveal that our modality based anonymization retains high utility, achieving up to 5% improvement in micro-F1 scores compared to single-noise based anonymization and preserves user counts (deviates by 4 users) closely as of original data. These findings emphasize that downstream tasks play a pivotal role in designing privacy-preserving mechanisms while maintaining the practical usability of mobility data.

In the past years, we have witnessed a remarkable surge in the integration of machine learning within pervasive computing, revolutionizing how we interact with technology daily and build pervasive systems.

The integration of machine learning (ML) approaches in sensor-based applications in the field of pervasive computing is becoming increasingly prominent due to the increasing number of sensor-based applications in general and the continuous adaption of ML approaches to new domains. Several ML models are used within a processing pipeline that operates on the same sensor data. Still, the cloud computing approach is a straightforward solution where all sensor data is sent to the cloud before processing, which is inefficient according to resource utilization. Appropriate management of the different processing tasks for ML models enhances resource utilization.This paper proposes an architecture for resource-aware classification empowered by an ML model management (MLMM) framework and a distributed data stream management system (DDSMS). First, the classification pipeline is decomposed and implemented as data stream operators. Second, ML models are retrieved from an MLMM framework considering preprocessing, segmentation, and feature alignment to enable an effective redundancy elimination. Finally, the classification pipeline is deployed using resource-aware operator placement optimization. The evaluation results on a real-world scenario of a sensor-based activity classification pipeline for dairy cows show that our approach can reduce network utilization up to 98.9%.

The increasing availability of indoor and outdoor location sensors raises the interest for understanding the mobility in different places, e.g., in museums, train stations, or cities. One of the highly used approaches for understanding the mobility of individuals is to detect staypoints from trajectories. Although offline detection of staypoints fits best for long-term planning, some applications require online staypoint detection from trajectories, like online recommendation systems. Latency plays an important role here. In this paper, we propose a stream-based approach for staypoint detection, which can be realized by applying a Data Stream Management System. We claim that the proposed approach can detect staypoints with low latency from the high-frequency location update streams. To evaluate our approach, we compare it with a batch-based approach on real data from an indoor tracking system and Geolife dataset. The results demonstrate that the online approach detects staypoints with much lower latency compared to traditional approaches like offline and batch processing. Moreover, we prove that the accuracy of the stream-based approach is similar to the batch-based approach.

Abstract The recent evolution of the Internet of Things into a cyber-physical reality has spawned various challenges from a data management perspective. In addition, IoT platform designers are faced with another set of questions. How can platforms be extended to smoothly integrate new data management functionalities? Currently, data processing related tasks are typically realized by manually developed code and functions which creates difficulties in maintenance and growth. Hence we need to explore other approaches to integration for IoT platforms. In this paper we cover both these aspects: (1) we explore several emerging data management challenges, and (2) we propose an IoT platform integration model that can combine disparate functionalities under one roof. For the first, we focus on the following challenges: sensor data quality, privacy in data streams, machine learning model management, and resource-aware data management. For the second, we propose an information-integration model for IoT platforms. The model revolves around the concept of a Data-Sharing Market where data management functionalities can share and exchange information about their data with other functionalities. In addition, data-sharing markets themselves can be combined into networks of markets where information flows from one market to another, which creates a web of information exchange about data resources. To motivate this work we present a use-case application in smart cities.

For Bluetooth- or WIFI-based localization, fingerprinting data plays an important role. Mobile devices compare their received signals with recorded signals on a map (so-called reference points) and derive their most like location from that. Obviously, this method requires an elaborate offline phase to record the reference points. If this data set is too small and not dense enough, the localization accuracy is low, too. However, by just increasing the number of recorded data points, storage and comparison costs on the mobile device are also increased. Hence, the goal of this work is to find the best reference points within large-scale sets of fingerprinting data based on clustering. We present different novel algorithms for signal-based clustering and compare them with existing work. An extensive evaluation on real-world data sets shows that our approach can reduce the data set size up to 90% while keeping a mean accuracy of 1.0m in the experiments in the real world.

On-the-fly data integration, i.e. at query time, happens mostly in tightly coupled, homogeneous environments where the partitioning of the data can be controlled or is known in advance. During the process of data fusion, the information is homogenized and data inconsistencies are hidden from the application. Beyond this, we propose in this paper the Nexus metadata model and a processing approach that support on-the-fly data integration in a loosely coupled federation of autonomous data providers, thereby advancing the status quo in terms of flexibility and expressive power. It is able to represent data and schema inconsistencies like multi-valued attributes and multi-typed objects. In an open environment, this best suites the application needs where the data processing infrastructure is not able to decide which attribute value is correct. The Nexus metadata model provides the foundation for integration schemata that are specific to a given application domain. The corresponding processing model provides four complementary query semantics in order to account for the subtleties of multi-valued and missing attributes. In this paper we show that this query semantics is sound, easy to implement, and it builds upon existing query processing techniques. Thus the Nexus metadata model provides a unique level of flexibility for on-the-fly data integration.

The complexity of critical systems such as traffic management has dramatically increased over the last decades, since they involve more and more sensors to derive distributed situational awareness. Most existing systems require an a-priori configuration of sensors or need human intervention to adapt to changes. Furthermore, analysis of data quality or query plan reliability is often not possible and management of recorded data is done by hand. Our goal is to support the research, development, evaluation and demonstration of such systems. Within this study we analyze requirements and challenges for the data management of sensor based research environments and present a data stream based architecture which fulfills these requirements.

Smart urban spaces need traffic information beyond traditional vehicular traffic. Detailed data about bicycle traffic in a city is highly valuable to adapt traffic lights, plan traffic routes, or provide information about situational travel times. However, traditional induction loops do not work well for bikes, and they cannot give information about routes and travel times. This paper shows how infrastructure data (induction loops) and data from Bluetooth sensing can be fused to derive better information about bicycle traffic in a smart city. We present a novel approach to dynamically determine Bluetooth ratios of different traffic participants based on rare events, and we evaluate the approach with data from a real-world study with 97 registered users, 23,074 Bluetooth devices and more than 174,917 Bluetooth detections over one week in the City of Oldenburg.

Due to the fact that currently operating autonomous vehicles can observe only a limited area with their onboard sensors, safety regulations often dictate a very slow speed. However, as more and more sensors in the environment are available, we can fuse their information and provide extended information as a shared context model to support the autonomous vehicles. In this paper, we consider a scenario with a publicly accessible area that is populated with autonomous transport vehicles, human guided vehicles like trucks or bicycles, and pedestrians. We analyze requirements and challenges for highly dynamic context models in this scenario. Furthermore, we propose a comprehensive system architecture that can cope with these challenges, namely deterministic processing of multiple sensor updates with high throughput rates, prediction of moving objects, and on-line quality assessments, and demonstrate the feasibility of this approach by implementing the generic system architecture with laser scanners for object detection.

One of the main challenges in the development of traffic systems is to assure safety for all road users. Hence, especially expensive vehicles are equipped with advanced driver assistance systems (ADAS) that use data about the vehicle and information about objects in the proximity of the vehicle to execute the assistance function. These objects have to be detected by sensors and they have to be tracked over multiple scans to keep the object's state up-to-date. Usually, such ADAS are developed as proprietary systems that are tailored for the specific assistance function and the specific sensors in use. Indeed, that leads to a very efficient system. However, changing system properties, e. g. an exchange of sensors, is very expensive. In this case, very often at least some parts of the system code have to be reimplemented. To solve this problem of bad maintainability which arises especially during the development of new assistance functions in this work a new architecture for ADAS is presented. The relevant information for the assistance function is no longer provided by hard coded, predefined processes, but by flexible continuous operator plans in a datastream management system. These operator plans build up a dynamic context model of the vehicle's environment. The context model is kept up-to-date by object tracking operators in these operator plans and is then used as a data source to extract information for different assistance functions. This extraction is also done by operator plans that produce only relevant information and discard other information.

Semantic Web technologies, most notably RDF, are well-suited to cope with typical challenges in spatial data management including analyzing complex relations between entities, integrating heterogeneous data sources and exploiting poorly structured data, e.g., from web communities. Also, RDF can easily represent spatial relationships, as long as the location information is symbolic, i.e., represented by places that have a name. What is widely missing is support for geographic and geometric information, such as coordinates or spatial polygons, which is needed in many applications that deal with sensor data or map data. This calls for efficient data management systems which are capable of querying large amounts of RDF data and support spatial query predicates. We present a native RDF triple store implementation with deeply integrated spatial query functionality. We model spatial features in RDF as literals of a complex geometry type and express spatial predicates as SPARQL filter functions on this type. This makes it possible to use W3C's standardized SPARQL query language as-is, i.e., without any modifications or extensions for spatial queries. We evaluate the characteristics of our system on very large data volumes.

With the Web 2.0 trend and its participation of end-users more and more data and information services are online accessible, such as web sites, Wikis, or web services. The integration of this plethora of information is taken over by the community: so-called Mashups---web applications that combine data from more than one source into an integrated service---spring up like mushrooms, because they can be easily realized using script languages and web development platforms. Another trend is that mobile devices that get more and more powerful have ubiquitous access to the Web. Local sensors (such as GPS) can easily be connected to these devices. Thus, mobile applications can adapt to the current situation of the user, which can change frequently because of his or her mobility.

We present the design and implementation of a component for the preprocessing of position data taken from moving objects. The movement of mobile objects is represented by piecewise functions over time that approximate the real object movement and significantly reduce the initial data volume such that effcient storage and analysis of object trajectories can be achieved. The maximal acceptable deviation - an input parameter of our algorithms - of the approximations also includes the uncertainty of the position sensor measurements. We analyze and compare five different lossy preprocessing methods. Our results clearly indicate that even with simple approaches, a more than sufficient overall performance can be achieved.

To support context-aware applications, it is beneficial to maintain shared context models that contain different types of information, like mobile objects, stationary objects, or spatially related digital information. This demonstration is about the NexusEditor, a graphical user interface to maintain spatial context models, interactively create queries, send them to a server and visualize the results. The contribution here is to show how schema awareness can improve such a tool: the NexusEditor dynamically parses the underlying data model and provides additional syntactic and semantic checks and short-cuts based on the schema information. Also, it supports export to existing information spaces like GoogleEarth.

In this workshop summary we describe the motivation for continued discussion in Human-Centered Computing, giving an outline of the articles presented at the workshop, its expected outcomes, and future activities. We emphasize the reasoning behind a non-traditional format for the workshop, which builds on the previous workshops on "Human-Centered Multimedia" held in conjunction with ACM Multimedia 2007 and 2006.

Rule-based context reasoning is an expressive way to define situations, which are crucial for the implementation of many context-aware applications. Along the scenario of the Conference Guard application we show how this reasoning can be done both by leveraging an efficient context management realized by the Nexus platform) and by a generic rule based service. We present the architecture of the Nexus semantic service, which uses the underlying definition of a low-level context model (the Nexus Augmented World Model) to carry out rules given in first order logic. We realize this service in a straight forward manner by using state-of-the-art softwarecomponents (the Jena 2 framework) and evaluate the number of instances this approach can handle. Our first experiences show that a pre-selection of instances is necessary if the semantic service should work on a largescale context model.

The usage of workflow technology to model and execute business processes is widespread in many enterprises and within the software industry. With the growing maturity of sensors, wireless communication, and distributed computing environments, we can enhance this approach to enable smart workflows, which are business processes crossing the boundary to the physical world. Applications for such processes can be found in many application domains, like logistics or in smart factory environments. To realize smart workflows, workflow engines can be coupled with existing context provisioning systems. However, there is a gap between the rather low- level provisioning of context (e.g., sensor data and stock information) and the concepts needed in smart workflows (e.g., "is a spare tool available?"). The main contribution of this paper is to bridge this gap: we show how integration processes can be used to provide context information at different semantical levels for smart workflows.

Location-based services (LBS) are typically mobile applications that adapt their behavior to the spatial context of the user, e.g. by providing maps and navigational information of the user's current position. Existing location-based applications rely on spatial data that is gathered and preprocessed especially for them and that is stored by particular data providers. Location-based applications can benefit from World Wide Web and additional information source, if, in a preprocessing step, Web pages are mapped to locations. A model for this is virtual information towers (VIT), spatial Web portals with a location and a visibility area that represents the region where the information is relevant. DCbot processes HTML pages in the WWW like a crawler of a search engine. It analyses the pages using pre-defined rules and spatial knowledge and maps them to locations.

In this paper, we address the data management aspect of large-scale pervasive computing systems. We aim at building an infrastructure that simultaneously supports many kinds of context-aware applications, ranging from room level up to nation level. This all-embracing approach gives rise to synergetic benefits like data reuse and sensor sharing. We identify major classes of context data and detail on their characteristics relevant for efficiently managing large amounts of it. Based on that, we argue that for large-scale systems it is beneficial to have special-purpose servers that are optimized for managing a certain class of context data. In the Nexus project we have implemented five servers for different classes of context data and a very flexible federation middleware integrating all these servers. For each of them, we highlight in which way the requirements of the targeted class of data are tackled and discuss our experiences.

In the vision of pervasive computing smart everyday objects communicate and cooperate to provide services and information to users. Interoperability between devices and applications not only requires common protocols but also common context management. In this paper we discuss requirements on the context management based on the Georgia Tech's Aware home environment and the global context management perspective of the Nexus project. Our experiences with integrating the aware home spatial service into the Nexus platform show how federation concepts and a common context model can provide applications with uniform context information in different administrative and application domains.