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Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks
No full textFALSEpu
Indoor Localization Using Commercial Off-The-Shelf 60 GHz Access Points
Get PDFThe very large bandwidth available in the 60 GHz band allows, in principle, to design highly accurate positioning systems. Integrating such systems with standard protocols (e.g., IEEE 802.11ad) is crucial for the deployment of location-based services, but it is also challenging and limits the design choices. Another key problem is that consumer-grade 60 GHz hardware only provides coarse channel state information, and has highly irregular beam shapes due to its cost-efficient design. In this paper, we explore the location accuracy that can be achieved using such hardware, without modifying the 802.11ad standard. We consider a typical 802.11ad indoor network with multiple access points (APs). Each AP collects the coarse signal-to-noise ratio of the directional beacons that clients transmit periodically. Given the irregular beam shapes, the challenge is to relate each beacon to a set of transmission angles that allows to triangulate a user. We design a location system based on particle filters along with linear programming and Fourier analysis. We implement and evaluate our algorithm on commercial off-the-shelf 802.11ad hardware in an office scenario with mobile human blockage. Despite the strong limitations of the hardware, our system operates in real-time and achieves sub-meter accuracy in 70% of the cases.TRUEpu
Collaboration models for Large-scale Spectrum Monitoring
No full textWith the explosion of wireless devices, there is a growing number of applications that requires a deep understanding of the actual spectrum usage. New technologies are needed that go beyond or can complement classical high-end spectrum analyzers. Electrosense is the first initiative that exploits the paradigms of low-cost programmable spectrum sensors, crowdsourcing to users and big data architecture to gather and make available spectrum data and events to scientists, practitioners and stakeholders. In this talk we will review the main design concepts of the Electrosense network, the main research findings and how the scientific community can contribute to the network.FALSEpu
Opportunistic Timing Signals for Pervasive Mobile Localization
Get PDFThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1.7 and 2.4 meters.
The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems
based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different self-positioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings.
These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization
technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce
an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the
subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in
standalone GPS technology.
All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and
respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.Telematics EngineeringUniversidad Carlos III de Madrid, Spainpu
A Protocol-Ignorance Perspective on Incremental Deployability of Routing Protocols
Get PDFNew protocols for Internet inter-domain routing struggle to get widely adopted. Because the Internet consists of more than 50,000 autonomous systems (ASes), deployment of a new routing protocol has to be incremental. In this work, we study such incremental deployment. We first formulate the routing problem in regard to a metric of routing cost. Then, the paper proposes and rigorously defines a statistical notion of protocol ignorance that quantifies the inability of a routing protocol to accurately determine routing prices with respect to the metric of interest. The proposed protocol-ignorance model of a routing protocol is fairly generic and can be applied to routing in both inter-domain and intra-domain settings, as well as to transportation and other types of networks. Our model of protocol deployment makes our study specific to Internet inter-domain routing. Through a combination of mathematical analysis and simulation, we demonstrate that the benefits from adopting a new inter-domain protocol accumulate smoothly during its incremental deployment. In particular, the simulation shows that decreasing the routing price by 25% requires between 43% and 53% of all nodes to adopt the new protocol. Our findings elucidate the deployment struggle of new inter-domain routing protocols and indicate that wide deployment of such a protocol necessitates involving a large number of relevant ASes into a coordinated effort to adopt the new protocol.TRUEpu
Optimal Joint Routing and Scheduling in Millimeter-Wave Cellular Networks
Get PDFMillimeter-wave (mmWave) communication is a promising technology to cope with the expected exponential increase in data traffic in 5G networks. mmWave networks typically require a very dense deployment of mmWave base stations (mmBS). To reduce cost and increase flexibility, wireless backhauling is needed to connect the mmBSs. The characteristics of mmWave communication, and specifically its high directionality, imply new requirements for efficient routing and scheduling paradigms. We propose an efficient scheduling method, so-called schedule-oriented optimization, based on matching theory that optimizes QoS metrics jointly with routing. It is capable of solving any scheduling problem that can be formulated as a linear program whose variables are link times and QoS metrics. As an example of the schedule-oriented optimization, we show the optimal solution of the maximum throughput fair scheduling (MTFS). Practically, the optimal scheduling can be obtained even for networks with over 200 mmBSs. To further increase the runtime performance, we propose an efficient edge coloring based approximation algorithm with provable performance bound. It achieves over 80% of the optimal max-min throughput and runs 5 to 100 times faster than the optimal algorithm in practice. Finally, we extend the optimal and approximation algorithms for the cases of multi-RF-chain mmBSs and integrated backhaul and access networks.TRUEpu
Panel: 5G Testbeds
No full textPanel Moderator: Douglas Castor (Senior Director, Interdigital, USA)
Panel Members: John Kaewell (Senior Director, Interdigital CTO, US-NSF PAWR Initiative, USA), Arturo Azcorra (Vicepresident 5TONIC Laboratory, Spain), Monique Calisti (Managing Director, Martel Innovate, Switzerland), Howard Tsao (Technical Director ITRI, Taiwan)FALSEpu
How to implement complex policies on existing network infrastructure
Get PDFTransport networks satisfy requests to forward data in a
given topology. At the level of a network element, forwarding decisions are defned by flows. To implement desired data properties during forwarding, a network operator imposes economic models by applying policies to flows, ideally without dealing with underlying resource constraints. Policy splitting over multiple network elements under resource constraints is a hard optimization problem [6, 7]. We discuss limitations of the proposed methods and existing Boolean minimization techniques. The major contribution of this work is an optimal solution with linear time complexity at the price of a single bit forwarded in every packet. The results are supported by a comprehensive evaluation study that compares previous and currently proposed methods.TRUEpu
Distributed Counting along Lossy Paths without Feedback
Get PDFNetwork devices need packet counters for a variety of applications. For a large number of concurrent flows, on-chip memories can be too small to support a separate counter per flow. While a single network element might struggle to implement flow accounting on its own, in this work we study alternatives leveraging underutilized resources elsewhere
in the network and implement flow accounting on multiple network devices. This paper takes the first step towards understanding the design principles for robust network-wide accounting with lossy unidirectional channels without feedback.TRUEpu
Deploying Small Cells in Traffic Hot Spots: Always a Good Idea?
Get PDFWe look at a very simple RAN (Radio Access Network) configuration comprising one macro cell and one small cell, the latter being strategically positioned to absorb the traffic peaks that occur in some time periods in a portion of the area covered by the macro cell. We study this two-cell system with a simple model based on a network of two queues, and we examine the system performance for variable parameter values, showing that some of the emerging behaviors can be critical. In particular, we see that when the handover rate out of the small cell increases, the blocking probability in the macro cell also increases, quickly reaching unacceptable levels. This can be a problem, since high handover rates correspond to limited dimensions of the small cell with respect to the macro cell, which is what is normally expected, unless the small cell is deployed in an area of very slow end user mobility. These behaviors (although possibly not applicable to all small cell scenarios) can have an important impact on the deployment of small cells, which are expected to become increasingly popular because of the need to provide additional capacity in RANs through densification of the cell layout.TRUEpu