Pseudo-random Aloha for Enhanced Collision-recovery in RFID

In this letter we motivate the need to revisit the MAC protocol used in Gen2 RFID system in order to leverage receiver structures with Collision Recovery capabilities at the PHY layer. To this end we propose to consider a simple variant of the Framed Slotted Aloha with pseudo-random (deterministic) slot selection as opposite to the classical random selection. Pseudo-random access allows naturally to implement Inter-frame Successive Interference Cancellation (ISIC) without changing the PHY modulation and coding format of legacy RFID standard. By means of simulations we show that ISIC can bring 20-25% gain in throughput with respect to traditional intra-frame SIC. Besides that, we elaborate on the potential of leveraging pseudo-random access protocols in combination with advanced PHY techniques in the context of RFID applications.Comment: This manuscript has been submitted to IEEE on the 19th September 201

Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)

Privacy-preserving techniques for distributed computation have been proposed recently as a promising framework in collaborative inter-domain network monitoring. Several different approaches exist to solve such class of problems, e.g., Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC) based on Shamir's Secret Sharing algorithm (SSS). Such techniques are complete from a computation-theoretic perspective: given a set of private inputs, it is possible to perform arbitrary computation tasks without revealing any of the intermediate results. In fact, HE and SSS can operate also on secret inputs and/or provide secret outputs. However, they are computationally expensive and do not scale well in the number of players and/or in the rate of computation tasks. In this paper we advocate the use of "elementary" (as opposite to "complete") Secure Multiparty Computation (E-SMC) procedures for traffic monitoring. E-SMC supports only simple computations with private input and public output, i.e., it can not handle secret input nor secret (intermediate) output. Such a simplification brings a dramatic reduction in complexity and enables massive-scale implementation with acceptable delay and overhead. Notwithstanding its simplicity, we claim that an E-SMC scheme is sufficient to perform a great variety of computation tasks of practical relevance to collaborative network monitoring, including, e.g., anonymous publishing and set operations. This is achieved by combining a E-SMC scheme with data structures like Bloom Filters and bitmap strings.Comment: This is an extended version of the paper presented at the Third International Workshop on Traffic Monitoring and Analysis (TMA'11), Vienna, 27 April 201

Report from the 6th PhD School on Traffic Monitoring and Analysis (TMA)

This is a summary report by the organizers of the 6th TMA PhD school held in Louvain-la-Neuve on 5-6 April 2016. The insight and feedback received about the event might turn useful for the organization of future editions and similar events targeting students and young researchers

Estimating population density distribution from network-based mobile phone data

In this study we address the problem of leveraging mobile phone network-based data for the task of estimating population density distribution at pan-European level. The primary goal is to develop a methodological framework for the collection and processing of network-based data that can be plausibly applied across multiple MNOs. The proposed method exploits more extensive network topology information than is considered in most state-of-the-art literature, i.e., (approximate) knowledge of cell coverage areas is assumed instead of merely cell tower locations. A distinguishing feature of the proposed methodology is the capability of taking in input a combination of cell-level and Location Area-level data, thus enabling the integration of data from Call Detail Records (CDR) with other network-based data sources, e.g., Visitor Location Register (VLR). Different scenarios are considered in terms of input data availability at individual MNOs (CDR only, VLR only, combinations of CDR and VLR) and for multi-MNO data fusion, and the relevant tradeoff dimensions are discussed. At the core of the proposed method lies a novel formulation of the population distribution estimation as a Maximum Likelihood estimation problem. The proposed estimation method is validated for consistency with synthetically generated data in a simplified simulation scenario.JRC.H.6-Digital Earth and Reference Dat

Revisiting an old friend: On the observability of the relation between Long Range Dependence and Heavy Tail

International audienceTaqqu's Theorem plays a fundamental role in Internet traffic modeling, for two reasons: First, its theoretical formulation matches closely and in a meaningful manner some of the key network mechanisms controlling traffic characteristics; Second, it offers a plau- sible explanation for the origin of the long range dependence property in relation with the heavy tail nature of the traffic components. Numerous attempts have since been made to observe its predictions empirically, either from real Internet traffic data or from numerical simulations based on popular traffic models, yet rarely has this resulted in a satisfactory quantitative agreement. This raised in the literature a number of comments and questions, ranging from the adequacy of the theorem to real world data to the relevance of the statistical tools involved in practical analyses. The present contribution aims at studying under which conditions this fundamental theorem can be actually seen at work on real or simulated data. To do so, numerical simulations based on standard traffic models are analyzed in a wavelet framework. The key time scales involved are derived, enabling a discussion of the origin and nature of the difficulties encountered in attempts to empirically observe Taqqu's Theorem

Nanosecond-precision Time-of-Arrival Estimation for Aircraft Signals with low-cost SDR Receivers

Precise Time-of-Arrival (TOA) estimations of aircraft and drone signals are important for a wide set of applications including aircraft/drone tracking, air traffic data verification, or self-localization. Our focus in this work is on TOA estimation methods that can run on low-cost software-defined radio (SDR) receivers, as widely deployed in Mode S / ADS-B crowdsourced sensor networks such as the OpenSky Network. We evaluate experimentally classical TOA estimation methods which are based on a cross-correlation with a reconstructed message template and find that these methods are not optimal for such signals. We propose two alternative methods that provide superior results for real-world Mode S / ADS-B signals captured with low-cost SDR receivers. The best method achieves a standard deviation error of 1.5 ns.Comment: IPSN 201

In Pursuit of Aviation Cybersecurity: Experiences and Lessons From a Competitive Approach

The passive and independent localization of aircraft has been the subject of much cyberphysical security research. We designed a multistage open competition focusing on the offline batch localization problem using opportunistic data sources. We discuss setup, results, and lessons learned