IMDEA Networks Institute Digital Repository
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An Analysis of Pre-installed Android Software
Get PDFThe open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first largescale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to thirdparty organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android’s open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.TRUEpu
ASUNA: A Topology Data Set for Underwater Network Emulation
No full textIn this article, we report the details of ASUNA, a freely shared data set for underwater network emulation. ASUNA tackles the time-consuming and costly logistics of multiple underwater networking sea trials by providing a benchmark database of time-varying network topologies recorded across multiple sea experiments, thus facilitating experiment replay and network emulation. The ASUNA database currently includes 20 diverse, time-varying topology structures, multimodal communication technologies, and different link quality measurements. With the aim of becoming a standard benchmark, ASUNA is open to extensions as new data become available from the underwater communications community. We provide the details of ASUNA's structure, the list of recorded topologies, as well as examples of how to use the database as part of an emulation system to test the performance of two scheduling protocols. We freely share the database and the emulation code, both through a web server and via the Code Ocean repository.pu
Chapter 32 - Crowdsensing architectures for smart cities
No full textSmart cities take advantage of information and communication technology developments to provide added value to existing public services and improve citizens' quality of life. Mobile crowdsensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, that is, the mobile devices consume energy for sensing and reporting operations. This chapter emphasizes the role of energy management in MCS by assessing the performance of multiple data-collection frameworks and presents the applicability of MCS in a smart public street lighting scenario.TRUEpu
Hide Me: Enabling Location Privacy in Heterogeneous Vehicular Networks
Get PDFIn order to support location-based services, vehicles share their location with a server to receive relevant data. Revealing a vehicle’s location compromises its privacy. One way to reduce this problem is obfuscating the vehicle’s location by adding artificial noise. However, this increases the area where the true location of the vehicle may be.Hence, under limited available bandwidth, the server will provide fewer data relevant to the vehicle’s true location, reducing the effectiveness of the location-based service. To compensate for this reduction, we allow that the data relevant to a vehicle is also shared through direct, ad hoc communication between neighboring vehicles. Through such Vehicle-to-Vehicle (V2V) cooperation, the impact of location obfuscation is mitigated. In this set up, and assuming that the data served may have different impact levels, we propose and study a game that determines the data subscription a vehicle should use, without explicit coordination among them. The aim is maximizing the expected impact of the data received, either directly from the server or via V2V. Our analysis and results show that the proposed V2V cooperation and derived strategy lead to significant performance increase compared to other uncoordinated approaches, and largely alleviates the impact of location obfuscation.TRUEpu
Routing in Generalized Geometric Inhomogeneous Random Graphs
Get PDFIn this paper we study a new random graph model that we denote (κ,π)-KG and new greedy routing algorithms (of deterministic and probabilistic nature). The (κ,π)-KG graphs have power-law degree distribution and small-world properties. (κ,π)-KG roots on the Geometric Inhomogeneous Random Graph (GIRG) model, and hence they both preserve the properties of the hyperbolic graphs and avoid the problems of using hyperbolic cosines. In order to construct (κ,π)-KG graphs, we introduce two parametersκandπin the process of building a (κ,π)-KG graph. With these parameters we can generate Kleinberg and power-law networks as especial cases of (κ,π)-KG. Also, we propose two new greedy routing algorithms to reduce the fail ratio and maintaining a good routing performance. The first algorithm is deterministic and the second is, in essence, a weighted random walk. We use simulation techniques to test our network model, and evaluate the new routing algorithms on the two graph models (GIRG and (κ,π)-KG). In our simulations, we evaluate the number of hops to reach a destination from a source and the routing fail ratio, and measure the impact of the parameters (κ and π) on the performance of the new routing algorithms. We observe that our graph model (κ,π)-KG is more flexible than GIRG, and the new routing algorithms have better performance than the routing algorithms previously proposed.TRUEpu
Modeling MTC and HTC Radio Access in a Sliced 5G Base Station
Get PDFIn this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) pro- cedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures.
To cope with a number of slices devoted to serve various co- deployed tenants, we derive a multi-class queueing model of the network processor. We then present (i) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and (ii) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications.pu
How shrinkage can be used for robust methods
Get PDFThis poster won the second prize of the Award for the three best posters of the 3rd BYMAT Conference.In this work, we propose a new methodology, based on the notion of shrinkage, for outlier
detection and robust regression. First, we define robust estimators of the location vector and the covariance matrix in case of multivariate data. Then, a robust Mahalanobis distance can be computed based on these estimators, for the task of outlier detection. Some properties are investigated, such as the affine equivariance and the breakdown value. The performance of the proposal is illustrated through the comparison to other robust techniques from the literature, in a simulation study and with a real example of breast cancer data. The robust alternatives are also reviewed, highlighting their advantages and disadvantages. The performance results as well as the significantly smaller computational time show the advantages of the proposal. With the proposed robust estimators, a robust regression approach is proposed as well. It is compared to the classical Ordinary Least Squares (OLS) approach and the robust alternatives from the literature. A real socio-economic dataset about the Living Environment Deprivation (LED) of areas in Liverpool (UK), is studied. The results from the simulations and the real dataset example show the advantages of the proposed robust estimator in regression. Furthermore, the proposed robust regression method has improved performance compared to other machine learning techniques previously used for this data, with the advantage of interpretability.FALSEpu
Underwater Localization via Wideband Direction-of-Arrival Estimation Using Acoustic Arrays of Arbitrary Shape
Get PDFUnderwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to estimate the direction of arrival of a desired signal. When the available equipment does not provide the required level of accuracy, it may be convenient to merge multiple transceivers into a larger acoustic array, in order to achieve better processing performance. In this paper, we name such a structure an “array of opportunity” to signify the often inevitable sub-optimality of the resulting array design, e.g., a distance between nearest array elements larger than half the shortest acoustic wavelength that the array would receive. The most immediate consequence is that arrays of opportunity may be affected by spatial ambiguity, and may require additional processing to avoid large errors in wideband direction of arrival (DoA) estimation, especially as opposed to narrowband processing. We consider the design of practical algorithms to achieve accurate detections, DoA estimates, and position estimates using wideband arrays of opportunity. For this purpose, we rely jointly on DoA and rough multilateration estimates to eliminate spatial ambiguities arising from the array layout. By means of emulations that realistically reproduce underwater noise and acoustic clutter, we show that our algorithm yields accurate DoA and location estimates, and in some cases it allows arrays of opportunity to outperform properly designed arrays. For example, at a signal-to-noise ratio of –20 dB, a 15-element array of opportunity achieves lower average and median localization error (27 m and 12 m, respectively) than a 30-element array with proper λ/2 element spacing (33 m and 15 m, respectively). We confirm the good accuracy of our approach via emulation results, and through a proof-of-concept lake experiment, where our algorithm applied to a 10-element array of opportunity achieves a 90th-percentile DoA estimation error of 4 ∘ and a 90th-percentile total location error of 5 m when applied to a real 10-element array of opportunity.pu
Optimizing mmWave Wireless Backhaul Scheduling
Get PDFMillimeter wave (mmWave) communication not only provides ultra-high speed radio access but is also ideally suited for efficient and flexible wireless backhauling. Specifically for dense deployments, a mmWave macro base station (MBS) that serves a large number of mmWave micro base stations (µBSs) is much more cost effective than legacy cellular architectures which connect µBSs to the core network through fibers. In addition, µBSs can cooperate with each other by acting as relay nodes. The directional nature of mmWave communication allows for spatial reuse, even in the presence of interference, which can be exploited to optimize mmWave wireless backhaul performance. The optimization opportunistically prioritizes the use of good connections at the MBS and further leverages compact and concurrent transmissions between µ BS. Relays and directional antennas speed up communication, but increase the complexity of the scheduling problem. In this work, we study the mmWave backhaul scheduling problem and derive an MILP formulation for it as well as upper and lower bounds. We prove that the problem is NP-hard and can be approximated, but only if interference is negligible. By means of numerical simulations, we compare theoretical results with heuristics in small system sizes. Results validate the analysis and demonstrate the high performance of our heuristics in realistic cellular settings.pu
Contributions to real-time monitoring and analysis of heterogeneous learning environments
No full textThe ubiquity and flexibility of heterogeneous learning environments allows gathering a huge amount of data from students’ interactions. Applying learning analytics and data mining to these data, as well as a self-regulated
learning criterion, is a well-accepted method to learn students’ behavior and ultimately predict their learning outcomes. In order to enrich the learning experience, the prediction should be done before the failure occurs. Thus, the thesis proposed in this paper aims to contribute with several prediction algorithms based in students’ interactions gathered through events in a real-time basis. This could be used to early detect students at risk and help them to succeed.TRUEpu