Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.

Abstract:WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

The Border Gateway Protocol (BGP) has been used for decades as the de facto protocol to exchange reachability information among networks in the Internet. However, little is known about how this protocol is used to restrict reachability to selected destinations, e.g., that are under attack. While such a feature, BGP blackholing, has been available for some time, we lack a systematic study of its Internet-wide adoption, practices, and network ecacy, as well as the prole of blackholed destinations. In this paper, we develop and evaluate a methodology to automatically detect BGP blackholing activity in the wild. We apply our method to both public and private BGP datasets. We nd that hundreds of networks, including large transit providers, as well as about 50 Internet exchange points (IXPs) oer blackholing service to their customers, peers, and members. Between 2014-2017, the number of blackholed prexes increased by a factor of 6, peaking at 5K concurrently blackholed prexes by up to 400 Autonomous Systems. We assess the eect of blackholing on the data plane using both targeted active measurements as well as passive datasets, nding that blackholing is indeed highly eective in dropping trac before it reaches its destination, though it also discards legitimate trac. We augment our ndings with an analysis of the target IP addresses of blackholing. Our tools and insights are relevant for operators considering oering or using BGP blackholing services as well as for researchers studying DDoS mitigation in the Internet.

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only k-means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

Intentional interference in satellite navigation is becoming an increasing threat for modern systems relying on Global Navigation Satellite Systems (GNSS). In particular, critical applications such as aviation can be severely affected by undetected and un-mitigated interference and therefore interference management solutions are crucial to be employed. Methods to cope with such intentional interference enclose interference detection, interference mitigation, interference classification, and interference localization. This paper offers a comprehensive survey of interference management methods developed in the last four decades by the research community. After reviewing the main concepts of GNSS-based navigation, the interference and interference management solutions are classified, with a particular focus on the two major threats in GNSS navigation, namely jamming and spoofing. Mathematical models, comparative tables for various interference management solutions, such as detection, localization, mitigation, and classification, as well as comparative numerical results based on several selected algorithms are also presented. We especially focus on algorithms relying on omnidirectional antennas, which do not require additional specific antennas to be installed on the aircraft and thus reduce the costs of retrofit and installation.