A Robust Zero-Calibration RF-based Localization System for Realistic Environments

Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with the used mobile device, changes of the transmit power of smart access points (APs), and dynamic changes in the environment; requiring re-calibration of the area of interest; which reduces the technology ease of use. In this paper, we present IncVoronoi: a novel system that can provide zero-calibration accurate RF-based indoor localization that works in realistic environments. The basic idea is that the relative relation between the received signal strength from two APs at a certain location reflects the relative distance from this location to the respective APs. Building on this, IncVoronoi incrementally reduces the user ambiguity region based on refining the Voronoi tessellation of the area of interest. IncVoronoi also includes a number of modules to efficiently run in realtime as well as to handle practical deployment issues including the noisy wireless environment, obstacles in the environment, heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on different Android phones using the iBeacons technology in a university campus. Evaluation of IncVoronoi with a side-by-side comparison with traditional fingerprinting techniques shows that it can achieve a consistent median accuracy of 2.8m under different scenarios with a low beacon density of one beacon every 44m2. Compared to fingerprinting techniques, whose accuracy degrades by at least 156%, this accuracy comes with no training overhead and is robust to the different user devices, different transmit powers, and over temporal changes in the environment. This highlights the promise of IncVoronoi as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201

Survey of Spectrum Sharing for Inter-Technology Coexistence

Increasing capacity demands in emerging wireless technologies are expected to be met by network densification and spectrum bands open to multiple technologies. These will, in turn, increase the level of interference and also result in more complex inter-technology interactions, which will need to be managed through spectrum sharing mechanisms. Consequently, novel spectrum sharing mechanisms should be designed to allow spectrum access for multiple technologies, while efficiently utilizing the spectrum resources overall. Importantly, it is not trivial to design such efficient mechanisms, not only due to technical aspects, but also due to regulatory and business model constraints. In this survey we address spectrum sharing mechanisms for wireless inter-technology coexistence by means of a technology circle that incorporates in a unified, system-level view the technical and non-technical aspects. We thus systematically explore the spectrum sharing design space consisting of parameters at different layers. Using this framework, we present a literature review on inter-technology coexistence with a focus on wireless technologies with equal spectrum access rights, i.e. (i) primary/primary, (ii) secondary/secondary, and (iii) technologies operating in a spectrum commons. Moreover, we reflect on our literature review to identify possible spectrum sharing design solutions and performance evaluation approaches useful for future coexistence cases. Finally, we discuss spectrum sharing design challenges and suggest future research directions

Simulating Opportunistic Networks: Survey and Future Directions

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works[EN] Simulation is one of the most powerful tools we have for evaluating the performance of opportunistic networks (OppNets). In this paper, we focus on available tools and mod- els, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art OppNet simulations and sketch out possible further development and lines of research. This paper is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.This work was supported in part by the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R, in part by the Universidad Laica Eloy Alfaro de Manabi, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion, Ecuador. (Corresponding author: Jens Dede.)Dede, J.; Förster, A.; Hernández-Orallo, E.; Herrera-Tapia, J.; Kuladinithi, K.; Kuppusamy, V.; Manzoni, P.... (2018). Simulating Opportunistic Networks: Survey and Future Directions. IEEE Communications Surveys & Tutorials. 20(2):1547-1573. https://doi.org/10.1109/COMST.2017.2782182S1547157320

BlueSeer: AI-Driven Environment Detection via BLE Scans

IoT devices rely on environment detection to trigger specific actions, e.g., for headphones to adapt noise cancellation to the surroundings. While phones feature many sensors, from GNSS to cameras, small wearables must rely on the few energy-efficient components they already incorporate. In this paper, we demonstrate that a Bluetooth radio is the only component required to accurately classify environments and present BlueSeer, an environment-detection system that solely relies on received BLE packets and an embedded neural network. BlueSeer achieves an accuracy of up to 84% differentiating between 7 environments on resource-constrained devices, and requires only ~12 ms for inference on a 64 MHz microcontroller-unit

Evaluating Machine Learning Techniques for Smart Home Device Classification

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, information leakage, and endangerment of senior leaders [1]. Recent research has shown that it is possible to model a subject’s pattern-of-life through data leakage from Bluetooth Low Energy (BLE) and Wi-Fi smart home devices [2]. A key step in establishing pattern-of-life is the identification of the device types within the smart home. Device type is defined as the functional purpose of the IoT device, e.g., camera, lock, and plug. This research hypothesizes that machine learning algorithms can be used to accurately perform classification of smart home devices. To test this hypothesis, a Smart Home Environment (SHE) is built using a variety of commercially-available BLE and Wi-Fi devices. SHE produces actual smart device traffic that is used to create a dataset for machine learning classification. Six device types are included in SHE: door sensors, locks, and temperature sensors using BLE, and smart bulbs, cameras, and smart plugs using Wi-Fi. In addition, a device classification pipeline (DCP) is designed to collect and preprocess the wireless traffic, extract features, and produce tuned models for testing. K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forests (RF) classifiers are built and tuned for experimental testing. During this experiment, the classifiers are tested on their ability to distinguish device types in a multiclass classification scheme. Classifier performance is evaluated using the Matthews correlation coefficient (MCC), mean recall, and mean precision metrics. Using all available features, the classifier with the best overall performance is the KNN classifier. The KNN classifier was able to identify BLE device types with an MCC of 0.55, a mean precision of 54%, and a mean recall of 64%, and Wi-Fi device types with an MCC of 0.71, a mean precision of 81%, and a mean recall of 81%. Experimental results provide support towards the hypothesis that machine learning can classify IoT device types to a high level of performance, but more work is necessary to build a more robust classifier

Block-Based Development of Mobile Learning Experiences for the Internet of Things

The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund