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Very Small Neural Networks for Optical Classification of Fish Images and Videos
Get PDFThe task of visual classification, done until not long ago by specialists through direct observation, has recently benefited from advancements in the field of computer vision, specifically due to statistical optimization algorithms, such as deep neural networks. In spite of their many advantages, these algorithms require a considerable amount of training data to produce meaningful results. Another downside is that neural networks are usually computationally demanding algorithms, with millions (if not tens of millions) of parameters, which restricts their deployment on low-power embedded field equipment.
In this paper, we address the classification of multiple species of pelagic fish by using small convolutional networks to process images as well as videos frames. We show that such networks, even with little more than 12,000 parameters and trained on small datasets, provide relatively high accuracy (almost 42% for six fish species) in the classification task. Moreover, if the fish images come from videos, we deploy a simple object tracking algorithm to augment the data, increasing the accuracy to almost 49\% for six fish species. The small size of our convolutional networks enables their deployment on relatively limited devices.TRUEpu
CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics
Get PDFThe world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.TRUEpu
Network Slicing Meets Artificial Intelligence: an AI-based Framework for Slice Management
Get PDFNetwork slicing is an emerging paradigm in mobile networks that leverages Network Function Virtualization (NFV) to enable the instantiation of multiple virtual networks –named slices– over the same physical network infrastructure. The operator can allocate to each slice dedicated resources and customized functions that allow meeting the highly heterogeneous and stringent requirements of modern mobile services. Managing functions and resources under network slicing is a challenging task that requires making efficient decisions at all network levels, in some cases even in real-time, which can be achieved by integrating artificial intelligence (AI) in the network. We outline a general framework for AI-based network slice management, introducing AI in the different phases of the slice lifecycle, from admission control to dynamic resource allocation in the network core and at the radio access. A sensible use of AI for network slicing results in strong benefits for the operator, with expected performance gains between 25% and 80% in representative case studies.pu
Atomic Appends in Asynchronous Byzantine Distributed Ledgers
Get PDFA Distributed Ledger Object (DLO) is a concurrent object that maintains a totally ordered sequence of records, and supports two operations: APPEND, which appends a record at the end of the sequence, and GET, which returns the whole sequence of records. The work presented in this article is made up of two main contributions.
The first contribution is a formalization of a Byzantine-tolerant Distributed Ledger Object (BDLO), which is a DLO in which clients and servers processes may deviate arbitrarily
from their intended behavior (i.e. they may be Byzantine). The proposed formal definition is
accompanied by algorithms that implement BDLOs on top of an underlying Byzantine Atomic Broadcast service.
The second contribution is a suite of algorithms, based on the previous BDLO implementations, that solve the Atomic Appends problem in the presence of asynchrony, Byzantine clients and Byzantine servers. This problem occurs when clients have a composite record (set of basic records) to append to different BDLOs, in such a way that either each basic record is appended to its BDLO (and this must occur in good circumstances), or no basic record is appended. Distributed algorithms are presented, which solve the Atomic Appends problem when the clients (involved in the Atomic Appends) and the servers (which maintain the BDLOs) may be Byzantine.TRUEpu
Visible Light Communication Networks for IoT and its Applications
Get PDFVisible Light Communication(VLC) has emerged in the last years as a new way to communicate. Using the existing lighting infrastructure, it has great potential to provide high bandwidth and communication security, making it a strong alternative against conventional RF communications. Although VLC addresses several of the problems that RF communications have for specific scenarios, its potential for IoT applications must still be unleashed.
IoT deployments are, by nature, limited in some way. The limitation could be given by the hardware used, as the cost may need to be minimal to have dense realistic deployments; the energy available, which depends on the battery size of the device; and computing power available, which is given by the available energy and the processing power of the device, among others. Therefore, there is an interest in studying the advantages, drawbacks, and limitations of integrating VLCin IoT scenarios with the constraints mentioned above. This is especially necessary in the case of real-life deployments, mainly if the devices used are multi-purpose, and they need to perform other tasks, such as sensing, in addition to communicating.
First of all, VLCdeployments for IoT use the available dense lighting infrastructure to achieve communication on top of illumination in indoor scenarios. The advantage of such an approach is that it allows reusing the existing infrastructure, improving the coverage, and the energy consumed. Although VLC is energy efficient, it consumes more than just illuminating. If the luminaries are not correctly controlled, energy could be wasted by transmitting from a luminary with little or no effect into the receiver. In DenseVLC, we explore the energy consumption of luminaries in dense deployments, and we propose an approach to optimize the SINR given an energy budget. In order to do so, we propose to coordinate the transmission done by several independent devices concurrently. We introduce a novel synchronization method that uses the NLOS component of the signal to tackle this problem. Our approach can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, compared to existing solutions.
Secondly, even if the required infrastructure follows the design presented above, IoT deployments still need to face one fundamental problem: power management at deployed mobile devices. Having batteries increases the price of the device, its size, the maintenance required, and the ecological impact that the product has. Removing the battery while still being able to operate under realistic circumstances would be desired. In this work, we study what limitations such a system has. We then propose a new communication scheme, combining VLC and RF backscattering, that allows having continuous end-to-end communication with a custom-designed battery-free device. We design the hardware, software, and protocol that optimizes each aspect of the system to decrease the power requirement of each component. Finally, we evaluate our system and show that it can run with consumptions as low as 95uW, transmit continuously at 500 bits/second, and achieve more than 20 meters on backscattering distance, even with blockage elements as glass and walls covering the LOS.
Thirdly, we explore one of the multiple applications that the designed VLC systems for IoT allow to implement; device positioning. The majority of the literature requires to have multiple transmitters and/or receivers to achieve localization. The objective of this work is to localize with the minimum amount of necessary hardware, which is critical for IoT applications. We investigate how VLCsystems could be used for positioning in dynamic scenarios. Exploiting the fact that, in our scenario, the transmitter and receiver are relatively moving, we propose a mathematical solution that, just using one VLC transmitter and one receiver both equipped with a compass, computes the correct relative position. We then implement our solution in a modified version of OpenVLC and achieve accuracies with less than 5 cm of error. Nevertheless, in this work we assume that the NLOS is non-existant, which may not always be the case.
Finally, we try to overcome the problem mentioned above of NLOS reflections for device positioning with a low resource consumption NLOS component detector. Similar work try to solve this problem computing the CIR, but for systems with limited resources this is impractical because 1) The VLC front-end may not be fast enough for acquiring required data for the CIR calculation or 2) IoT boards are not able to run computationally expensive algorithms in real time. In this thesis, we propose a solution that in complex environments, reduces the localization error using LEDs up to 93%.
In order to perform experimental research in VLC for IoT, a research platform is needed. In this thesis, we also present the latest version of an open-source, software-based, VLC platform, OpenVLC. OpenVLC was first introduced as part of the thesis of Dr. Qing Wang. During this thesis, the platform has been re-designed on both hardware and software. The throughput improved more than 23 times and the transmission distance increased by a factor of 4. In this thesis, OpenVLC, parts of it, or modified versions have been used as a framework to create new IoT systems and explore the practical side of VLC.
As a summary, in this work, we explore how VLC can be leveraged for IoT deployments. We study the features of such real-world deployments from different perspectives in a variety of scenarios, and we show that realistic implementations of VLC systems are not only possible but doable, enabling new features that IoT developer can exploit.Telematics EngineeringUniversidad Carlos III de Madrid, Spainpu
Angel or Devil? A Privacy Study of Mobile Parental Control Apps
Get PDFAndroid parental control applications are used by parents to monitor and limit their children’s mobile behaviour (e.g., mobile apps use, Internet browsing, calls, and text messages). In order to offer this service, parental control apps require access to sensitive data and system resources which may significantly reduce the dangers associated with kids’ online activities, but it also raises important privacy concerns which are overlooked by European security centers providing recommendations to the public. We conduct the first in-depth study of the Android parental control applications ecosystem from a privacy and regulatory point of view. We exhaustively study 46 apps which have a combined 20M installs in the Google Play Store. Using a combination of static and dynamic analysis we find that, among others: these apps are on average more permission-hungry than the top 150 apps in the Google Play Store, and tend to request more dangerous permissions with new releases; 11% of the apps transmit personal data in the clear; 34% of the apps gather and send personal information without appropriate consent; and 72% of the apps share data with third parties (including online advertising and analytics services) without mentioning their presence in the apps’ privacy policies. In summary, parental control applications lack of transparency and lack of compliance with regulatory requirements can have severe implications for children’s privacy. Therefore, it is necessary to develop stricter auditing tools that incorporate transparency and privacy risk analysis before recommending their use to concerned parents.TRUEpu
Mis-shapes, Mistakes, Misfits: An Analysis of Domain Classification Services
Get PDFDomain classification services have applications in multiple areas, including cybersecurity, content blocking, and targeted advertising. Yet, these services are often a black box in terms of their methodology to classifying domains, which makes it difficult to assess their strengths, aptness for specific applications, and limitations. In this work, we perform a large-scale analysis of 13 popular domain classification services on more than 4.4M hostnames. Our study empirically explores their methodologies, scalability limitations, label constellations, and their suitability to academic research as well as other practical applications such as content filtering. We find that the coverage varies enormously across providers, ranging from over 90% to below 1%. All services deviate from their documented taxonomy, hampering sound usage for research. Further, labels are highly inconsistent across providers, who show little agreement over domains, making it difficult to compare or combine these services. We also show how the dynamics of crowd-sourced efforts may be obstructed by scalability and coverage aspects as well as subjective disagreements among human labelers. Finally, through case studies, we showcase that most services are not fit for detecting specialized content for research or content-blocking purposes. We conclude with actionable recommendations on their usage based on our empirical insights and experience. Particularly, we focus on how users should handle the significant disparities observed across services both in technical solutions and in research.TRUEpu
Privacy in trajectory micro-data publishing: a survey
Get PDFWe survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.pu
A Novel Methodology for the Automated Detection and Classification of Networking Anomalies
Get PDFThe active growth and dynamic nature of cellular networks makes challenging accommodating end-users with flawless quality of service. Identification of network problems leveraging on machine learning has gained a lot of visibility in the past few years, resulting in dramatically improved cellular network services. In this paper, we present a novel methodology to automate the fault identification process in a cellular network and to classify network anomalies, which combines supervised and unsupervised machine learning algorithms. Our experiments using real data from operational commercial mobile networks show that our method can automatically identify and classify networking anomalies, so to enable timely and precise troubleshooting actions.TRUEpu
Millimeter‐Wave Meets D2D: A Survey
Get PDFDevice-to-device (D2D) and millimeter-wave (mmW) communications play an important role in the design of future wireless communication systems, since they are mature and resource-efficient technologies. Integrating both technologies while retaining the benefit they offer in isolation is challenging. In this survey, we review the literature on mmW-based D2D proposals to enable network applications and service extensions that span from network-controlled use cases to opportunistic solutions for direct data exchange, caching and relay. The survey unveils that, although a large effort has been devoted to the study of D2D with mmW, we are still far from a full analytical and experimental characterization of the system. More effort is needed in view of 5G and beyond, to consider the integration of network computing elements and to protect mmW-based D2D against security threats.TRUEpu