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In this discussion paper, we outline the motivations and the main principles of the Trusted Smart Statistics (TSS) concept that is under development in the European Statistical System. TSS represents the evolution of official statistics in response to the challenges posed by the new datafied society. Taking stock from the availability of new digital data sources, new technologies, and new behaviors, statistical offices are called nowadays to rethink the way they operate in order to reassert their role in modern democratic society. The issue at stake is considerably broader and deeper than merely adapting existing processes to embrace so-called Big Data. In several aspects, such evolution entails a fundamental paradigm shift with respect to the legacy model of official statistics production based on traditional data sources, for example, in the relation between data and computation, between data collection and analysis, between methodological development and statistical production, and of course in the roles of the various stakeholders and their mutual relationships. Such complex evolution must be guided by a comprehensive system-level view based on clearly spelled design principles. In this paper, we aim at providing a general account of the TSS concept reflecting the current state of the discussion within the European Statistical System.

The forthcoming 5G technology aims to provide massive device connectivity and ultra-high capacity with reduced latency and costs. These features will be enabled by increasing the density of the base stations, using millimeter-wave (mmWave) bands, massive multiple-input multiple-output systems, and non-orthogonal multiple access techniques. The ability to support a large number of terminals in a small area is in fact a great challenge to guarantee massive access. In this context, this paper proposes a new receiver model for the uplink of 5G mmWave cellular networks. The receiver, called Iterative Decoding and Interference Cancellation (IDIC), is based on the Slotted Aloha (SA) protocol and exploits the capture effect alongside the successive IC process to resolve packet collisions. A 5G propagation scenario, modeled according to recent mmWave channel measurements, is used to compare IDIC with the widely adopted Contention Resolution Diversity SA (CRDSA) scheme to show the performance gain of IDIC, when elements of practical relevance, like imperfect cancellation and receive power diversity, are considered. The impact of packet and power diversity is also investigated to derive the preferable uplink random access strategy that maximizes the system throughput according to the offered channel load.

The concept of present population is gaining increasing attention in official statistics. One possible approach to measure present population exploits data collected by Mobile Network Operators (MNO), from simple Call Detail Records (CDR) to more informative and complex signalling records. Such data, collected primarily for network operation processes, can be repurposed to infer patterns of human mobility. Two decades of research literature have produced several case studies, mostly focused on to CDR data, and a variety of ad-hoc methodologies tailored to specific datasets. Moving beyond the stage of explorative research, the regular production of official statistics across different MNO requires a more systematic approach to methodological development. Towards this aim, Eurostat and other members of the European Statistical System are working towards the definition of a general Reference Methodological Framework for processing MNO data for official statistics. In this contribution we report on the methodological aspects related to the estimation of present population density, for which we present a general and modular methodological structure that generalises previous proposals found in the academic literature. Along the way, we define a number of specific research problems requiring further attention by the research community. We stress the importance of comparing different methodological options at various points in the data workflow, e.g. in the geolocation of individual observations and in the inference method. Finally, we present illustrative results from a case-study based on real signalling data from a European operational network, complemented by numerical results from a simple simulation scenario.

Multiple packet reception (MPR) is becoming a viable reality for wireless random access protocols thanks to advances in the physical layer and new coding techniques. In the simplest $K$ -MPR model, a receiver can resolve up to $K\geq 1$ parallel transmissions. We extend the classical analysis of pure and slotted ALOHA to $K$ -MPR devices with arbitrary degree $K$ for fixed and variable packet size. Through a parsimonious modeling approach, we derive relatively simple analytical expressions. In this way, we gain insight and quantitative understanding on the fundamental tradeoffs between pure and slotted ALOHA and on the scaling of maximum achievable throughput with $K$ .

Mobile cellular networks can serve as ubiquitous sensors for physical mobility. We propose a method to infer vehicle travel times on highways and to detect road congestion in real-time, based solely on anonymized signaling data collected from a mobile cellular network. Most previous studies have considered data generated from mobile devices active in calls, namely Call Detail Records (CDR), an approach that limits the number of observable devices to a small fraction of the whole population. Our approach overcomes this drawback by exploiting the whole set of signaling events generated by both idle and active devices. While idle devices contribute with a large volume of spatially coarse-grained mobility data, active devices provide finer-grained spatial accuracy for a limited subset of devices. The combined use of data from idle and active devices improves congestion detection performance in terms of coverage, accuracy, and timeliness. We apply our method to real mobile signaling data obtained from an operational network during a one-month period on a sample highway segment in the proximity of a European city, and present an extensive validation study based on ground-truth obtained from a rich set of reference datasources - road sensor data, toll data, taxi floating car data, and radio broadcast messages.

The availability of very cheap RTL-SDR "dongle" devices has unleashed the popularity of Software-Defined Radio (SDR) projects in the last years, both among academics and hobbyists. The main success factors are the very affordable price (

Recently, Secure-Multiparty Computation (SMC) has been proposed as an approach to enable inter-domain network monitoring while protecting the data of individual ISPs. The SMC family includes many different techniques and variants, featuring different forms of ‘‘security’’, i.e., against different types of attack (er), and with different levels of computation complexity and communication overhead. In the context of collaborative network monitoring, the rate and volume of network data to be (securely) processed is massive, and the number of participating players is large, therefore scalability is a primary requirement. To preserve scalability one must sacrifice other requirement, like verifiability and computational completeness that, however, are not critical in our context. In this paper we consider two possible schemes: the Shamir’s Secret Sharing (SSS), based on polynomial interpolation on prime fields, and the Globally-Constrained Randomization (GCR) scheme based on simple blinding. We address various system-level aspects and quantify the achievable performance of both schemes. A prototype version of GCR has been implemented as an extension of SEPIA, an open-source SMC library developed at ETH Zurich that supports SSS natively. We have performed a number of controlled experiments in distributed emulated scenarios for comparing SSS and GCR performance. Our results show that additions via GCR are faster than via SSS, that the relative performance gain increases when scaling up the data volume and/or number of participants, and when network conditions get worse. Furthermore, we analyze the performance degradation due to sudden node failures, and show that it can be satisfactorily controlled by containing the fault probability below a reasonable level.

Multilateration techniques have been proposed to verify the integrity of unprotected location claims in wireless localization systems. A common assumption is that the adversary is equipped with only a single device from which it transmits location spoofing signals. In this paper, we consider a more advanced model where the attacker is equipped with multiple devices and performs a geographically distributed coordinated attack on the multilateration system. The feasibility of a distributed multi-device attack is demonstrated experimentally with a self-developed attack implementation based on multiple COTS software-defined radio (SDR) devices. We launch an attack against the OpenSky Network, an air traffic surveillance system that implements a time-difference-of-arrival (TDoA) multi-lateration method for aircraft localization based on ADS-B signals. Our experiments show that the timing errors for distributed spoofed signals are indistinguishable from the multilateration errors of legitimate aircraft signals, indicating that the threat of multi-device spoofing attacks is real in this and other similar systems. In the second part of this work, we investigate physical-layer features that could be used to detect multi-device attacks. We show that the frequency offset and transient phase noise of the attacker's radio devices can be exploited to discriminate between a received signal that has been transmitted by a single (legitimate) transponder or by multiple (malicious) spoofing sources. Based on that, we devise a multi-device spoofing detection system that achieves zero false positives and a false negative rate below 1%.

The rigorous characterization of ultrahigh-frequency passive radio-frequency identification (RFID) tags is a challenging but mandatory task. Indeed, tags are the most critical devices in RFID systems: their performance should be adequately good, although stringent requirements in terms of compactness, used materials, and costs must be satisfied. Factors such as the goodness of the conjugate impedance matching between the chip and the antenna, the chip sensitivity, and the quality of the backscattered signal affect tag performance. Tag sensitivity and differential radar cross section (RCS) are the most significant metrics for tag characterization: they define the forward (from the reader to the tag) and the backward (from the tag to the reader) link reliability, respectively. Nevertheless, measurement of such metrics cannot be approached with conventional methods based on vector network analyzers or conventional RFID readers. Vice versa, commercially available instrumentation and solutions are very expensive and not totally flexible. In this paper, a novel approach for performance characterization of RFID tags is explored. To this end, we developed a very cheap (below $1000) and flexible tool based on software-defined radio, which enables measurement of tag sensitivity and differential RCS. An exhaustive experimental campaign has been carried out on ten commercial and four built-in laboratory RFID tags. Achieved results demonstrate the flexibility, accuracy, and appropriateness of the proposed approach.

Several scientific works have considered the possibility to build Wireless Mesh Networks (WMN) using multi-channel IEEE 802.11 architectures. At the basis of these works is the notion of ''non-overlapping'' channels, i.e. with a frequency separation equal or greater than 25MHz. It is now a common assumption that multiple independent transmissions over these channels can coexist without mutual interference even in physical proximity. In this work we demonstrate that this assumption does not hold in general. Through an extensive set of experiments we illustrate the presence of cross-channel interference between ''non-overlapping'' channels at relay nodes due to the ''near-far'' effect. We analyze in what manner the MAC layer reacts to such an interference and how this problem extends to higher layers, with detrimental effects on the global throughput. The central problem is that cross-channel interference is not handled adequately by the MAC layer, and in some cases single-channel multi-hop settings perform better than multi-channel. Our results highlight a serious mismatch between some routing and channel assignment schemes proposed recently by the research community, assuming full separation between non-overlapping channels, and what is achievable in practice. More generally, as the presence of cross-channel interference can not be neglected at relay nodes, our findings point to a fundamental problem in building Multi-channel Multi-hop WMN based on IEEE 802.11b/g technology.

We address the problem of localising a mobile terminal ("blind" node) in unknown position from a set of "anchor" nodes in known positions. The proposed method does not require any form of node synchronisation nor measurement (or control) of the transmission times, which is difficult or anyway costly to achieve in practice. It relies exclusively on reception timestamps collected by the anchor nodes, according to their local clocks, that overhear packets transmitted by the blind node and by (at least one) other transmitting node(s) in known position, e.g., other anchors. The clock differences between the nodes are not eliminated ex ante through clock synchronisation, as in traditional ToA and TDoA methods. Instead, they are counteracted ex post, during the data processing stage, leveraging the data redundancy that is intrinsic to the multiple reception of the same packet by different (anchor) nodes. We validate the proposed method in different experimental settings, indoor and outdoor, using exclusively low-cost Commercial-Off-The-Shelf WiFi devices, achieving sub-meter accuracy in full Line-of-Sight conditions and meter-level accuracy in mild Non-line-of-sight environment. The proposed method does not require that the blind node participate actively to the localisation procedure and can use "opportunistically" any legacy signal or packet available over-the-air for communication purposes. Considering the very minimal requirement on the system - basically, only that anchors in known positions are able to collect and share reception timestamps - the proposed approach can enable practical adoption of opportunistic and/or cooperative localisation on top of existing radio communication systems., Comment: To appear in Computer Networks

We consider the problem of estimating instantaneous rates for a set of independent arrival flows from (possibly incomplete) passive observations. We introduce a hierarchical Bayesian model with an unknown hyperparameter, whose estimation yields in turn the minimum mean square error (MMSE) estimate of arrival rates for each flow. Such an approach is able to leverage the information from the ensemble of flows in order to improve the local estimate. Since hyperparameter estimation is not available in closed-form, we propose a much simpler estimator based on the best linear unbiased predictor (BLUP) that is computationally comparable to the conventional approach. Simulation results show that our scheme improves the estimation accuracy compared to conventional estimation based on the raw cumulative sum of the arrivals at each flow, especially for small sample sizes, and performs extremely close to the (much more complex) optimal MMSE estimator.

Wireless sensor networks are drawing much attention as an effective means to enable the Internet of Things. In this context, energy efficiency is an important requirement in applications where battery-powered sensing devices are exploited. Battery power saving can be addressed using many techniques, concerning different layers of the OSI model. This work addresses energy consumption in MAC protocols from a transceiver usage perspective. We first describe a set of protocols that includes IEEE 802.15.4 and IEEE 802.15.4e DSME. Then, we analyze their energy consumption and propose a set of Enhancements for Low-Power Instrumentation DSME Applications. Finally, given a wireless network topology, we compare the performance of the above mentioned protocols by simulations. Obtained results show that, for end devices, the proposed approach allows an energy consumption reduction up to a factor of 9 with respect to the IEEE 802.15.4, while enabling for higher throughput, and up to a factor of 7 with regard to native IEEE 802.15.4e DSME.

The bandwidth demand of today's mobile applications is permanently increasing. This requires more frequent upgrades of the mobile network capacity in the radio access as well as in the backhaul section. In such quickly evolving scenario, the risk of capacity bottleneck is increased, therefore network operators need tools to promptly detect capacity bottlenecks or, conversely, validate the current network state.To this end, we propose to exploit the passive observation of individual TCP connections. Being a closed loop protocol, the performances of every TCP connection depend on the status of the whole end-to-end path. Leveraging on this property, we propose a method to infer the presence of a capacity bottleneck along the path of an individual TCP connection by passively monitoring the DATA and ACK packets at a single monitoring point. We validate our approach with test traffic in a real 3G/4G operational network. The realized monitoring algorithm offers a powerful tool to network operators for on-line performance assessment and network troubleshooting.

A novel approach to opportunistic positioning based on timing measurements is presented. The asynchronous radio transmissions from fixed stations and from mobile GPS-equipped nodes are jointly exploited to cooperatively localize a blind node. To this end, a weighted least-squares (WLS) estimator is proposed; its effectiveness is illustrated via simulations in a realistic scenario.

The behavior and performance of a UMTS network are governed by a number of parameter settings that are configured by the network operator, e.g., timeouts. In this letter we show that the actual value of such parameters can be inferred by a conceptually simple set of end-to-end measurements, without any cooperation with the network operator. In principle, such information can be used by researchers to define realistic network scenarios, e.g., for their simulations. Moreover, it can be used by a malicious attacker to fine-tune a large scale attack against the Radio Access Network, e.g., a paging attack.

In this paper we propose a novel method to detect a bottleneck in the core section of a mobile network using TCP related counters. The dataset used in this work consists of the full TCP statistics for two different UMTS SGSNs over four different one-day periods: one day in March 2006 and three consecutive days in September 2006. We derived the number of packets and the number of retransmissions per individual user during the peak hours. These two quantities were put into scatterplots to derive “footprints” of global TCP behavior in the SGSN areas. One of the traces contained a capacity bottleneck on an SGSN link. This trace was taken as the reference footprint for bottleneck presence. Based on such reference we developed a method to infer the presence of future bottlenecks based on footprint similarity. We tested two different distance metrics. It turns out that the simple correlation performs similar to a much more sophisticated function based on a ratio of symmetriezed Kullback-Leibler distances.

Mobile devices became the main repository of personal data and source of user-generated contents as well as the principal controller of our social networked life. In this scenario, malicious applications try to take advantage of all the possibilities left open by users and operating systems. In this paper, we propose SecureDroid: an extension of the Android security framework able to enforce flexible and declarative security policies at run-time, providing a fine-grained access control system. In particular, we focus on context dependent policies that allow the user to specify the way in which applications work according to current context.

We present the software-defined radio (SDR) implementation of a Radio Frequency IDentification (RFID) Listener, a passive receive-only device that decodes the signals exchanged between the RFID Reader and the interrogated Tag following the Electronic Product Code Class-1 Generation-2 standard. Our RFID Listener is based on the open-source project GNU Radio. It provides complete flexibility and full control over the entire protocol stack down to the physical layer. As such, it can be used as a powerful but inexpensive tool for testing and measuring the performance of RFID systems in real operating conditions. In this paper, we leverage the SDR Listener for a number of experiments that, taken collectively, are illustrative of the potential of such tool for RFID research. First, we use it to test comparatively different timing recovery schemes for the reception of the Tag signal in uplink, through the analysis of bit-error statistics measured experimentally. We find that the scheme proposed by Harris and Rice based on polyphase filter bank displays excellent performance in this context, very close to the theoretical bound and with a gain of 5 dB over the more common Mueller and Muller scheme. Second, we use the Listener to evaluate the impact of frequency nulls in the uplink RFID channel for different indoor scenarios. We find that frequency nulls are pronounced particularly in the presence of metal objects and obstructions of the Fresnel zone, but frequency hopping is effective in counteracting the problem. Our experiments show that the signal backscattered by a passive Tag can be correctly received up to a distance of 35 m, with low-cost equipment and without highly directional antennas.

In this Chapter we address the problem of detecting "anomalies" in the global network traffic produced by a large population of end-users. Empirical distributions across users are considered for several traffic variables at different timescales, and the goal is to identify statistically-significant deviations from the past behavior. This problem is casted into the framework of hypothesis testing. We first address the methodology for dynamically identifying a reference for the null hypothesis ("normal" traffic) that takes into account the typical non-stationarity of real traffic in volume and composition. Then, we illustrate two general distribution-based detection approaches based on both heuristic and formal methods. We discuss also operational criteria for dynamically tuning the detector, so as to track the physiological variation of traffic profiles and number of active users. The Chapter includes a final evaluation based on the analysis of a dataset from an operational 3G network, so as to show in practice the detection of real-world traffic anomalies.

In this work we consider a recently proposed variant of the classical Framed Slotted-ALOHA where slot selection is based on a pseudo-random function of the message to be transmitted and of the frame index. We couple this feature with convolutional encoding, that allows to perform Inter-frame Soft Combining (ISoC) of multiple (re)transmission attempts of the same payload across different frames. The ISoC scheme, proposed here for the first time, requires less memory usage and computational complexity at the receiver digital signal processor compared to existing techniques based on inter-frame signal cancellation (instead of combining). Numerical simulation results show that the ISoC scheme brings a noticeable throughput gain over traditional schemes in a dense RFID scenario with multiple concurrent Tag transmissions.

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 2012

We address the problem of detecting "anomalies" in the network traffic produced by a large population of end-users following a distribution-based change detection approach. In the considered scenario, different traffic variables are monitored at different levels of temporal aggregation (timescales), resulting in a grid of variable/timescale nodes. For every node, a set of per-user traffic counters is maintained and then summarized into histograms for every time bin, obtaining a timeseries of empirical (discrete) distributions for every variable/timescale node. Within this framework, we tackle the problem of designing a formal Distribution-based Change Detector (DCD) able to identify statistically-significant deviations from the past behavior of each individual timeseries.For the detection task we propose a novel methodology based on a Maximum Entropy (ME) modeling approach. Each empirical distribution (sample observation) is mapped to a set of ME model parameters, called "characteristic vector", via closed-form Maximum Likelihood (ML) estimation. This allows to derive a detection rule based on a formal hypothesis test (Generalized Likelihood Ratio Test, GLRT) to measure the coherence of the current observation, i.e., its characteristic vector, to the given reference. The latter is dynamically identified taking into account the typical non-stationarity displayed by real network traffic. Numerical results on synthetic data demonstrates the robustness of our detector, while the evaluation on a labeled dataset from an operational 3G cellular network confirms the capability of the proposed method to identify real traffic anomalies.

Road traffic can be monitored by means of static sensors and derived from floating car data, i.e., reports from a sub-set of vehicles. These approaches suffer from a number of technical and economical limitations. Alternatively, we propose to leverage the mobile cellular network as a ubiquitous mobility sensor. We show how vehicle travel times and road congestion can be inferred from anonymized signaling data collected from a cellular mobile network. While other previous studies have considered data only from active devices, e.g., engaged in voice calls, our approach exploits also data from idle users resulting in an enormous gain in coverage and estimation accuracy. By validating our approach against four different traffic monitoring datasets collected on a sample highway over one month, we show that our method can detect congestions very accurately and in a timely manner.

Web browsers are a common platform for delivering cross-platform applications. However, they currently fail to provide consistent access control for security and privacy sensitive JavaScript APIs, such as geolocation and local storage. This problem is exacerbated by new HTML5 APIs and the increasing number of personal devices people own and use. In this paper we present the webinos platform which aims to provide a single, cross-device policy system for web applications on a wide range of web-enabled devices including TVs, smartphones, in-car systems and PCs. webinos solves the existing deficiencies in web authorisation by introducing the concept of a personal zone, the set of all devices and services owned by a particular user. All devices in this zone can synchronize their access control policies through interoperable middleware and can create flexible rules which may refer to an individual user, device or the entire zone. We provide details of the architecture and explain how our experience during design highlighted several conceptual challenges.

M2M represents a future where billions to trillions of everyday objects and the surrounding environment are connected and managed through a range of devices, communication networks, and cloud-based servers. In particular, the Web of Things (WoT) approach exploits some common standard web technologies to address the problem of access and connectivity of such devices. In this paper the usage of the "browser paradigm" within M2M is explored proposing a solution to provide a secure, multi user and cross domain approach to the WoT: the new webinos platform is an effective way of addressing some of the most common issues of these kind of networks. Furthermore, we show how the standard and open source essence of the platform can encourage WoT actors to adopt webinos as a common middleware to increase interoperability across such heterogeneous ecosystem of devices.

The signaling traffic of a cellular network is rich of information related to the movement of devices across cell boundaries. Thus, passive monitoring of anonymized signaling traffic enables the observation of the devices' mobility patterns. This approach is intrinsically more powerful and accurate than previous studies based exclusively on Call Data Records as significantly more devices can be included for investigation, but it is also more challenging to implement due to a number of artifacts implicitly present in the network signaling. In this study we tackle the problem of estimating vehicular trajectories from 3G signaling traffic with particular focus on crucial elements of the data processing chain. The work is based on a sample set of anonymous traces from a large operational 3G network, including both the circuit-switched and packet-switched domains. We first investigate algorithms and procedures for preprocessing the raw dataset to make it suitable for mobility studies. Second, we present a preliminary analysis and characterization of the mobility signaling traffic. Finally, we present an algorithm for exploiting the refined data for road traffic monitoring, i.e., route detection. The work shows the potential of leveraging the 3G cellular network as a complementary "sensor" to existing solutions for road traffic monitoring.

Discussions of how to measure the performance of computer networks for various applications have been ongoing for over twenty years in the area of network research. The continual increase of data traffic volume has reached a point at which solving any network problem by over-provisioning is not suitable. The quest for alternatives makes it vital to have well-defined metrics for evaluating and sustaining the performance of networks.

This work presents our effort in developing a complete, real-time, Software-Defined Radio implementation of an OFDM transceiver, compliant with the IEEE 802.11 Physical Layer specifications. All the baseband components in both the transmitter and the receiver are implemented with fast software functions, running on a General Purpose CPU. Real-time operation is achieved on a modern CPU by means of extensive code optimization, mostly using the SIMD instruction sets which are widely available on almost every modern CPU.

Performance evaluation of passive Radio Frequency IDentification (RFID) tags is a challenging task. In fact, tag performance depends on multiple factors such as the goodness of the conjugate matching between chip and antenna, the chip sensitivity, the strength and quality of the backscattered signal. Commercially available solutions for tag testing are very expensive and not totally flexible. In this work, we propose a novel approach for precise characterization of RFID tags based on Software-Defined Radio (SDR). We show how a cheap (below 1000$) and flexible SDR-based RFID reader can be turned into an accurate tool for measuring the tag sensitivity and differential radar cross-section. We test our platform by analyzing the performance of two built-in-lab tags: measurements show a strong agreement with theoretical and simulation results.

Performance evaluation of passive Radio Frequency Identification (RFID) tags is a crucial but challenging task. In fact, tag performance depends on multiple factors like the goodness of the conjugate matching between chip and antenna, the chip sensitivity, and the strength and quality of backscattered signal. Moreover, passive RFID tags lack wired connectors and therefore cannot be directly interfaced with conventional measurement instruments. Some solutions for tag testing and performance evaluation are commercially available on the market, but they are expensive and not totally flexible. In this work we propose a novel approach to the problem of tag performance analysis based on Software-Defined Radio (SDR). We show that a SDR tool for UHF RFID reading allows the characterization of tag performance by any metric and for any operating frequency, with a total equipment cost below 1000 USD. We present the methodology to measure tag sensitivity, differential radar cross-section and Signal-to-Noise ratio, and provide numerical results for five different tags. © 2011 IEEE.

Conventional RFID readers combine transmission (to the tag) and reception (from the tag) functions in a single physical device. In this paper we discuss the design and potential applications of a receive-only device, called "RFID listener", that decodes the signals from both the tag and the reader. This enables augmented RFID systems where one transmitter coexists with multiple listeners offering reception redundancy and diversity. We present a Software-Defined Radio (SDR) implementation of an RFID listener compliant with Gen2 standard, which can serve as a research tool for experimenting "on air" novel augmented RFID systems. Moreover, our listener can be used as a flexible and cheap protocol analyzer for conventional reader/tag systems. We present a test-bed setting where our listener and a conventional SDR reader are used in conjunction to measure separately the maximum downlink and uplink range. © 2011 IEEE.

—In this work we describe a methodology to estimate one-way packet loss from IPFIX or NetFlow flow records collected at two monitoring points. The proposed method does not require tight synchronization between the two monitoring points, nor it relies upon external routing information. It can run online or offline, and can work on legacy IPFIX/NetFlow traces which were not collected for the specific purpose of loss estimation. In this preliminary work we describe the estimation procedure and present early validation results from a real testbed.

The aim of DEMONS is to significantly improve the trustworthiness of today's Internet by empowering the operators' ability to detect and react to global-scale incidents and malicious activity. To this end, DEMONS follows an integrated approach to network monitoring whose key elements are the distribution of programmable monitoring tasks across cooperative monitoring nodes, and the deployment of inter-domain collaborative mechanisms that preserve the privacy of customers' and operators' data.

Current Radio-Frequency Identification (RFID) technology involves two types of physical devices: tags and reader. The reader combines in a single physical device transmission (to the tags) and reception (from the tags) functions. In this paper we discuss an alternative approach, where receive functions are performed by a separate device called "RFID listener". This allows distributed tag-sensing schemes where one transmitter coexists with multiple listeners. We discuss pros and cons of both approaches and present our implementation of a passive RFID listener on GNU Radio. Our implementation is a basis for experimenting with future distributed listener-based systems, but it can be also used as a cheap and flexible protocol analyzer for currently available commercial RFID readers.

Future intelligent transportation systems (ITS) will necessitate wireless vehicle-to-infrastructure (V2I) communications. This wireless link can be implemented by several technologies, such as digital broadcasting, cellular communication, or dedicated short-range communication (DSRC) systems. Analyses of the coverage and capacity requirements are presented when each of the three systems is used to implement the V2I link. We show that digital broadcasting systems are inherently capacity limited and do not appropriately scale. Furthermore, we show that the Universal Mobile Telecommunications System (UMTS) can implement the V2I link using either a dedicated channel (DCH) or a multimedia broadcast/multicast service (MBMS), as well as a hybrid approach. In every case, such V2I systems scale well and are capacity limited. We also show that wireless access in vehicular environment (WAVE) systems scale well, provide ample capacity, and are coverage limited. Finally, a direct quantitative comparison of the presented systems is given to show their scaling behavior with the number of users and the geographical coverage.

In this work we address the problem of estimating the network-wide packet loss rate across the radio access section of a 3G cellular network. The reference scenario consists of a passive monitoring probe located in the Core Network. The probe counts the number of TCP packets directed to each individual mobile terminal and, from the analysis of (un)acknowledged packets, infers the loss ratio for each individual terminal. The problem is then to derive a synthetic indicator representative of the network-wide packet loss, which can be used to detect large- scale performance drifts and network anomalies. We show that common simplistic indicators like the total rate of lost packets (across all terminals) and the average per-terminal loss rate do not work well in the general case. The key problem is the large disparity of traffic volume across individual terminals. In this contribution we formulate the problem in terms of optimal statistical inference and provide a set of robust near-optimum estimators that are relatively simple to implement. We validate the proposed estimators with simulations in synthetic scenarios and provide results from a real operational 3G network. Complex network infrastructures like third-generation (3G) cellular networks are exposed to errors and attacks. Passive packet-level monitoring can play an important role in support- ing the operation and troubleshooting of such systems. Sum- mary statistics of network-layer performance variables (delay, RTT, loss, throughput etc.) can be extracted from passive probes and used as synthetic indicators for the network health status. Sudden deviations in the otherwise regular temporal behavior of such indicators might point to network problems. Change-point detection methods can be used to automatically identify anomalies and raise timely alarms. In this work we focus on the use of network-wide packet loss statistics obtained from the passive analysis of TCP packets at a single measurement point. The reference scenario consists of a passive monitoring probe located in the Core Network (ref. Fig. 1). The probe counts the number of TCP packets directed to each individual mobile terminal and, from the analysis of (un)acknowledged packets, infers the number of packet loss events. The problem is then to derive a synthetic indicator representative of the network-wide packet loss. The ultimate goal is to use such indicator to detect network problems and large-scale performance drifts. A common simple approach is to use as the summary indicator the ratio of loss events over the total transmitted packets across all the terminals. Another possible method is to compute the individual per-terminal loss ratios and take the arithmetic average. We show that such simplistic approaches do not always work well in practical scenarios. The key issue is the large disparity found in the distribution of the number of packets per terminal, which is due to the typically large disparity in traffic intensity of individual users: many users generate very low traffic, while a few ones generate very high volumes. For the two simplistic summary indicators introduced above, such disparity translates directly into an increased variance of the estimate. In this work we formalize the problem at hand as an optimal estimation problem. The optimal solution can be found via Bayesian approach (9), but at the cost of a relatively high resolution complexity which might hamper the practical adoption of such schemes. In this contribution we focus on the derivation of simple yet robust near-optimum estimators.

In this paper we present a road traffic estimation system built on top of the cellular network infrastructure. Based on the concept that many road users are also customers of a cellular operator, we show that specific road conditions map to certain signaling patterns in the cellular core network. In order to estimate the road traffic, signaling is collected from the core network of an operational mobile network. We explore the feasibility of using mobility-related signaling for pantomiming local-loop sensors, i.e. counting the number of vehicles crossing a specific road section. In this work we present the main system component and discuss a number of practical issues to be considered in the deployment of such system. Based on the explorative analysis of real signaling data, we show how normal and abnormal road conditions (e.g. accidents) map into mobility signaling in a real cellular network.

This paper introduces a new method to detect and track Skype traffic and users by exploiting cross layer information available within 3G mobile cellular networks. In a 3G core network all flows can be analyzed on a per user basis. A detected Skype message is therefore related to a specific user. This information enables user profiles that provide a relationship between the mobile station and the characteristics of the corresponding Skype instance, which remain unchanged for long periods of time. Based on this information, our computationally lightweight method is able to classify Skype flows accurately. Moreover, the method is, by design, robust against false positives. Based on test traces from a live network, our new method achieves a similar detection performance as publicly available tools, yet with much less complexity.

In this study we present network-wide measurements of Round-Trip-Time (RTT) from an operational 3G network, separately for GPRS/EDGE and UMTS/HSxPA sections. The RTTs values are estimated from passive monitoring based on the timestamps of TCP handshaking packets. Compared to a previous study in 2004, the measured RTT values have decreased considerably. We show that the network-wide RTT percentiles in UMTS/HSxPA are very stable in time and largely independent from the network load. Additionally, we present separate RTT statistics for handsets and laptops, finding that they are very similar in UMTS/HSxPA. During the study we identified a problem with the RTT measurement methodology -- mostly affecting GPRS/EDGE data -- due to early retransmission of SYNACK packets by some popular servers.

In this contribution we address the problem of using cellular network signaling for inferring real-time road traffic information. We survey and categorize the approaches that have been proposed in the literature for a cellular-based road monitoring system and identify advantages and limitations. We outline a unified framework that encompasses UMTS and GPRS data collection in addition to GSM, and prospectively combines passive and active monitoring techniques. We identify the main research challenges that must be faced in designing and implementing such an intelligent road traffic estimation system via third-generation cellular networks.

In this paper we investigate the dynamics of one-way delays in an operational mobile core network. Our ultimate motivation is to develop anomaly detection schemes for the packet delay process in order to reveal network and equipment problems. This requires an online measurement system capable of collecting and processing delay statistics in real-time. We present an experimental deployment of such a measurement system in an operational General Packet Radio System (GPRS)/Universal Mobile Telecommunications System (UMTS) network and elaborate on some practical implementation issues. We present some measurement results for the Serving GPRS Support Nodes (SGSN) of UMTS and GPRS. We find that the delay at a UMTS-SGSN is moderately influenced by user mobility, while flow control and user mobility are considerably impacting the delay process at a GPRS-SGSN. We show that simple summary indicators can be extracted from the delay statistics, as a combination of percentiles and threshold-crossing probabilities. Such indicators can be used for the purpose of detecting abnormal delay deviations, pointing to problems in the network equipments.

In this paper we investigate the dynamics of one-way delays in an operational mobile core network. Our final goal is to develop anomaly detection schemes for the packet delay process in order to reveal network and equipment problems. This requires a preliminary exploration of the delay distribution in the core network, which we undertake in this study. We present one-way delay measurements extracted from passive traces captured at an operational General Packet Radio System (GPRS)/Universal Mobile Telecommunications System (UMTS) network. We find that queuing is the only source of delay at Gateway GPRS Support Nodes (GGSN), while for Serving GPRS Support Nodes (SGSN) the mobility and flow-control further complicate the characterization of the ldquonormalrdquo delay behavior. Moreover, the presence of unwanted traffic has an impact on the delay statistics and should be taken into account. We find that information about actual bandwidth conditions in the Radio Network may be inferred by investigating packet delays in the core network. Our explorative results are promising about the possibility of leveraging one-way delay measurement for troubleshooting in such networks.

In this paper we evaluate four different metrics for non intrusive bottleneck detection based on TCP counters. This work is based on the full TCP statistics recorded on five days spread over the last one and a half year within the core network of a mobile network operator in Austria. Scatterplots, so called ldquofootprintsrdquo, were generated counting the number of packets and the number of retransmission for each user during the peak hours. Two of the datasets had a known capacity bottleneck in place. Based on those datasets we benchmarked the different metrics for the detection of a bottleneck event. We preprocessed the traces in order to remove the traffic increase. After this step all metrics were able to detect the special bottleneck case. Even traces separated for more than one year deliver a clear result. The performance of a PSNR metric was similar to the other metrics based on more sophisticated functions.

In this work we explore the feasibility of implementing infrastructure-to-vehicle (I2V) communication through the UMTS infrastructure. We identify the problems that arise when providing I2V services on top of legacy UMTS networks as conceived in 3GPP Release 5 and consider an I2V architecture that exploits a new feature introduced by 3GPP Release 6, namely the multimedia broadcast/multicast services (MBMS). By means of an analytical model, we quantify the performance achievable with both releases in some realistic scenarios. We show that MBMS is able to provide I2V services efficiently on top of the UMTS network.

Nowadays a number of services and applications delivered on mobile terminals need to be secured in order to guarantee the user privacy, the business stakeholders and the commercial and legal issues related to security threads.

We report on the traffic and delay patterns observed at short time-scales at the edge of a cellular mobile network. We find that high-rate sequential scanners in the Internet are a common source of traffic impulses and introduce "noise" in the one-way delay measurements.

In this paper we present results from a measurement campaign evaluating the web performance of different mobile Internet access technologies with and without caching. We measured in GPRS, EDGE, UMTS and HSDPA radio access networks of a mobile operator in Austria. The operator has implemented a web-proxy for performance improvement. To evaluate the impact on the different technologies we ran the setup with and without the web-proxy. We defined the download time of web pages as the performance index for our work. We developed a tool to capture this parameter and derived the index for a set of web sites frequently used by the customers in the network. Beginning with an over-all comparison we will present detailed analysis for special cases. We evaluated the impact of the proxy system to different site configurations using the performance index. Finally we compared the performance numbers for HTTP with results collected from FTP transfers.