Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

Towards adaptive actors for scalable iot applications at the edge

Traditional device-cloud architectures are not scalable to the size of future IoT deployments. While edge and fog-computing principles seem like a tangible solution, they increase the programming effort of IoT systems, do not provide the same elasticity guarantees as the cloud and are of much greater hardware heterogeneity. Future IoT applications will be highly distributed and place their computational tasks on any combination of end-devices (sensor nodes, smartphones, drones), edge and cloud resources in order to achieve their application goals. These complex distributed systems require a programming model that allows developers to implement their applications in a simple way (i.e., focus on the application logic) and an execution framework that runs these applications resiliently with a high resource efficiency, while maximizing application utility. Towards such distributed execution runtime, we propose Nandu, an actor based system that adapts and migrates tasks dynamically using developer provided hints as seed information. Nandu allows developers to focus on sequential application logic and transforms their application into distributed, adaptive actors. The resulting actors support fine-grained entry points for the execution environment. These entry points allow local schedulers to adapt actors seamlessly to the current context, while optimizing the overall application utility according to developer provided requirements

Realizing the Digital Twin Transition for Smart Cities

The digital twin transition for cities is expected to improve, among others, living quality, carbon footprint and generate new business opportunities across different organizations. However, as cities consist of many separate entities that are in close and frequent interaction with each other, it is not possible to simply apply digital twin concepts from the engineering and manufacturing domains in a silo-ed fashion for each entity. In this paper, we distill the requirements and challenges to develop digital twins for smart cities based on a typical smart city use case. We follow with a first systematic approach to address them in a data-driven fashion to realize the digital twin transition for cities

VersaMatch : ontology matching with weak supervision

Ontology matching is crucial to data integration for across-silo data sharing and has been mainly addressed with heuristic and machine learning (ML) methods. While heuristic methods are often inflexible and hard to extend to new domains, ML methods rely on substantial and hard to obtain amounts of labeled training data. To overcome these limitations, we propose VersaMatch, a flexible, weakly-supervised ontology matching system. VersaMatch employs various weak supervision sources, such as heuristic rules, pattern matching, and external knowledge bases, to produce labels from a large amount of unlabeled data for training a discriminative ML model. For prediction, VersaMatch develops a novel ensemble model combining the weak supervision sources with the discriminative model to support generalization while retaining a high precision. Our ensemble method boosts end model performance by 4 points compared to a traditional weak-supervision baseline. In addition, compared to state-of-the-art ontology matchers, VersaMatch achieves an overall 4-point performance improvement in F1 score across 26 ontology combinations from different domains. For recently released, in-the-wild datasets, VersaMatch beats the next best matchers by 9 points in F1. Furthermore, its core weak-supervision logic can easily be improved by adding more knowledge sources and collecting more unlabeled data for training