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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.

This paper proposes a procedure based on time-difference of arrival measurements to localize a blind node in an asynchronous network where a set of nodes with known position is present. The proposed method computes the time-difference of arrival of each transmitted signal to any pair of receiving nodes in order to get rid of the unknown transmission time. A range-based localization procedure is implemented: first a least-squares estimator is used to compute a set of pseudo-ranges involving the blind node, then an iterative least-squares method is used to localize the blind node. The effectiveness of the proposed scheme is illustrated via simulations.

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.

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.

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.

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.

In this paper we present a Maximum Likelihood (ML) trajectory estimation of a mobile node from Received Signal Strength (RSS) measurements. The reference scenario includes a number of nodes in fixed and known positions (anchors) and a target node (blind) in motion whose instantaneous position is unknown. We first consider the dynamic estimation of the channel parameters from anchor-to-anchor measurements, statistically modeled according to the well-known Path-Loss propagation model. Then, we address the ML estimation problem for the position and velocity of the blind node based on a set of blind-to-anchor RSS measurements. We compare also the algorithm with a ML-based single-point localization algorithm, and discuss the applicability of both methods for slowly moving nodes. We present simulation results to assess the accuracy of the proposed solution in terms of localization error and velocity estimation (modulus and angle). The distribution of the localization error on the initial and final point is analyzed and closed-form expressions are derived.

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.

We consider the problem of RSS-based indoor localization with Maximum Likelihood (ML) estimation techniques in low-cost Wireless Sensor Networks (WSN). In the perspective of fully automated methods, we consider the problem of channel and position estimation as coupled problems. We compare via simulations the approaches of separate and joint ML estimation, plus a third method based on multi-lateration. We find that channel estimation via simple linear regression combined with ML localization has the potential to achieve good accuracy while keeping a very low level of computational and implementation complexity. We also find that in 3D localization the vertical error on the z-axis is considerably larger than the horizontal error on the xy-plane. This is due to the limited vertical offset that can be imposed to anchor beacons in “flat” buildings where the height is considerably smaller than the horizontal dimensions.

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 work we present a novel scheme for statistical-based anomaly detection in 3G cellular networks. The traffic data collected by a passive monitoring system are reduced to a set of per-mobile user counters, from which time-series of unidimensional feature distributions are derived. An example of feature is the number of TCP SYN packets seen in uplink for each mobile user in fixed-length time bins. We design a changedetection algorithm to identify deviations in each distribution time-series. Our algorithm is designed specifically to cope with the marked non-stationarities, daily/weekly seasonality and longterm trend that characterize the global traffic in a real network. The proposed scheme was applied to the analysis of a large dataset from an operational 3G network. Here we present the algorithm and report on our practical experience with the analysis of real data, highlighting the key lessons learned in the perspective of the possible adoption of our anomaly detection tool on a production basis.

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.

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 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.

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.

Passive network monitoring is required for the operation and maintenance of communication networks as well as to detect frauds and attacks. Typically, raw packet-level traffic traces are collected using suitable traffic probe devices and fed to monitoring applications (IDSs, antivirus, etc.) for analysis, with potential risks for the legitimate privacy rights of the customers. This paper aims to discuss the technical feasibility and the underlying research challenges of a two-tiered privacy-preserving network monitoring system, where carefully designed data protection mechanisms can coexist with suitably adapted monitoring applications.

In this paper we address the problem of optimal data gathering in wireless sensor networks (WSN). The goal of this work is to develop algorithms and techniques in order to minimize the data delivery latency and at the same time balance the energy consumption among the nodes, so as to maximize the network lifetime. Following an incremental-complexity approach, several mathematical programming problems are proposed with focus on different network aspects. First, the static routing problem is formulated for large and dense WSNs. Then, an accurate network model is proposed that captures the tradeoff between the data delivery latency and the network energy consumption by modeling the interactions among the routing, medium access control and physical layers. Finally, we consider dynamic rerouting and scheduling. For each problem we provide extensive simulations results for reference scenarios. The proposed models provide a deeper insight into the problem of timely and energy efficient data gathering. Along with the simulation results reported here they provide useful guidelines for the design of effective WSNs.

We present a preliminary analysis of mobility data collected from an operational GPRS network. The input data are time-series counting the number of mobile stations present in each of 126 sample routing areas at equally spaced instants (5 min) during one full week. The time-series were extracted from packet-level traces captured by passively monitoring a subset of the Gb links of the network of mobilkom austria AG & Co KG during October 2004. We apply the principal component analysis (PCA) to this dataset. The PCA offers a simple method for classifying the routing areas into two main groups, residential and business areas, plus a few "atypical" ones. Additionally, we address the problem of robustness of the PCA to temporary local gaps in the input data.

Sensor networks are emerging wireless technologies; their integration with the existing 2.5G, 3G mobile networks is a key issue to provide advanced services, e.g., health control. However this integration poses new challenges in the design and simulation of the involved embedded systems since it requires the cooperation of simulation tools that model hardware, software, and network aspects and their interactions. We present the modeling and simulation of a network scenario, core of a telecom provider's future portfolio, in which an ARM-based mobile handset is used as the gateway between a wireless sensor network (WSN) and remote users through a wide area network (WAN). Initially, the gateway and the WSN are modeled at system level with SystemC while the wide area network is modeled with NS-2. Then, HW/SW partitioning is performed on the gateway and an instruction set simulator of the ARM processor is used for the cycle-accurate execution of the RTOS and the application software.

The presence of "unwanted" (or background) traffic in the Internet is a well-known fact. In principle any network that has been engineered without taking into account the presence of unwanted traffic might experience troubles during periods of massive exposure to it, e.g. large-scale infections. A concrete example was provided by the spreading of Code-Red-II in 2001, which caused several routers crashes worldwide. Similar events might take place in 3G networks as well, with further potential complications due to the functional complexity and the scarcity of radio resources. In this explorative paper, we show that unwanted traffic is present also in GPRS/UMTS, mainly due to the widespread use of 3G connect cards for laptops. Based on a mixture of real-world measurements and theoretical speculations, we investigate the potential impact of such traffic onto the underlying network. We show that under certain hypothetical network configuration settings unwanted traffic, and specifically scanning traffic from infected mobile stations, can cause large-scale wastage of logical resources, and in extreme cases starvation. We urge the research community and network operators to consider the issue of 3G robustness to unwanted traffic as a prominent research area. The goal of this paper is to trigger interest and at the same time move a first pioneering step in this direction.

We address the problem of inferring the presence of a bottleneck from passive measurement in a 3G core network. The work is based on two weeks of packet traces collected in the core network of mobilkom austria AG&CoKG, the leading mobile telecommunications provider in Austria, EU. During the measurement period a bottleneck link in the 3G core network was revealed and removed, therefore the traces enable the accurate analysis and comparison of the traffic behavior in the two network conditions. The proposed diagnosis approach is based on the marginal rate distribution of the aggregate traffic. Since TCP is the dominant component in the overall traffic mix, the presence of a bottleneck strains the aggregate rate distribution, compressing it against the bottleneck capacity limit during the peak hour. The analysis of the trajectories of the first central moments (variance, skewness) can reveal such strain without any a priori knowledge of the provisioned capacity.

Mobile terminals are embedded systems with growing elaboration capacity, even still limited, which are continuously connected to a worldwide communication network. Thus, the idea of embedding in mobile terminals remote computing services seems attractive and it is more and more exploited. This paper evaluates a middleware platform based on the simple object access protocol (SOAP) to develop distributed applications on mobile terminals.

In this paper we present some observations about TCP RTT as captured in the live traffic of an operational GPRS/UMTS network. RTT samples are extracted from traces collected by passive monitoring the Gn interfaces of one of the major providers in Austria, EU. We compare results for GPRS and UMTS, and expose some methodological issues involved in performing this type of analysis, for instance the potential bias introduced by heavy users. We also explore the correlation of RTT with time-of-day. The primary motivation for this research is to gain a better understanding of the dominant causes that shape the behaviour and the distribution of RTT across the TCP connections that populate a GPRS/UMTS network. The results presented here are useful for the scientific community, for instance to set more realistic simulation scenarios for other research activities. In addition, RTT measurements as obtained by large-scale passive monitoring can be used in combination with other indicators to build a summary indicator of the performances experienced by the TCP users at the scale of the whole network, i.e. a global RTT-based KPI (key performance indicator).

This paper proposes Adaptive Multi-Path routing (AMP) as a simple algorithm for dynamic traffic engineering within autonomous systems. In contrast to related multipath routing proposals, AMP does not employ a global perspective of the network in each node. It restricts available information to a local scope, which opens the potential of reducing signaling overhead and memory consumption in routers. Having implemented AMP in ns-2, the algorithm is compared to standard routing strategies for a realistic simulation scenario. The results demonstrate the stability of AMP as well as the significant performance gains achieved.

We consider the problem of routing bandwidth-guaranteed flows with time-varying bandwidth profiles on an MPLS network. We assume that each demand is routed in a fixed LSP, and that the amount of bandwidth that must be reserved along the LSP varies during the day according to a piece-wise mask which is known in advance. Such profiles can be explicitly declared by the VPN customers in the SLA, or, alternatively, predicted by the ISP based on past measurements. We propose a simple on-line algorithm based on shortest-path computation with link weights influenced by the residual peak bandwidth. We also provide an ILP formulation for the associated off-line problem, and adopt it as a reference performance bound for the on-line algorithm. The results presented show that the proposed algorithm, despite its simplicity, closely approximates the optimal solution. The paper shows that the a priori knowledge of the per-demand traffic profiles, still within a fixed routing framework, can be exploited to achieve a sensible bandwidth saving, and/or to differentiate bandwidth provisioning (and billing) on a per-hour basis.

The differentiated service architecture is proposed as a scalable approach to QoS for IP networks. Therefore it is based on the aggregate (per class) scheduling of packets, but it aims at providing QoS to single flows. In particular the expedited forwarding (EF) per hop behavior (PHB) and the related premium service have been defined in order to provide determinist QoS guarantees to IP flows: zero loss and very low delay and jitter. Hence a possible approach to characterize and to dimension a network using the EF PHB is the worst-case analysis. We propose a worst case analysis which provides bounds to the queuing delay for a class of network topologies. Our results are compared with similar available results, which provide "looser" bounds. The comparison with simulation results shows that the bound is not enough tight to be of practical use. We justify the reason for this behavior and indicate directions for further improvements.

Proposes HD/sup 2/BIST, a complete hierarchical framework for BIST scheduling, data patterns delivering, and diagnosis of a complex system including embedded cores with different test requirements as full scan cores, partial scan cores, or BIST-ready cores. The main goal of HD/sup 2/BIST is to maximize and simplify the reuse of the built-in test architectures, giving the chip designer the highest flexibility in planning the overall SoC test strategy. HD/sup 2/BIST defines a test access method able to provide a direct "virtual" access to each core of the system, and can be conceptually considered as a powerful complement to the P1500 standard, whose main target is to make the test interface of each core independent from the vendor.