IMDEA Networks Institute Digital Repository
Not a member yet
1915 research outputs found
Sort by
Two to Tango: Hybrid Light and Backscatter Networks for Next Billion Devices
Get PDFThe growth rate of Internet-of-Things (IoT) devices sold globally is constantly lower than the forecast. This deceleration is caused in part by the need for batteries and the scalability cost for their replacement. Backscatter has attracted significant interest over the past couple of years to enable sustainable sensing devices by eliminating batteries. IoT devices have been designed for transmitting sensed data with backscatter, but the question of efficient reception of data with battery-free devices is still open. As shown in this paper, classical low-power Radio Frequency (RF) envelope detectors are affected by low sensitivity, false detection alarms, and low energy efficiency. We argue that Light Fidelity (LiFi) can provide downlink and harvesting medium as LED lights are becoming pervasively deployed for illumination. We show, for the first time, that the advantages of LiFi and RF backscatter can be combined for battery-free communication. We design a low-power platform that leverages the complementary nature of these two mediums. We demonstrate that our platform removes energy-inefficiency in the downlink reception typical of RF backscatter, and significantly expands the deployment scenarios for battery-free tags when compared to conventional single-technology designs.TRUEpu
Update on ACM SIGCOMM CCR Reviewing Process: Towards a More Open Review Process
Get PDFThis editorial note aims to first inform the SIGCOMM community on the reviewing process in place currently at CCR, and second, share our plans to make CCR a more open and welcoming venue by making changes to the review process, adding more value to the SIGCOMM community.pu
Will the Proliferation of 5G Base Stations Increase the Radio-Frequency "Pollution"?
Get PDFThis paper received the Best Paper Award at the conference (VTC2020-Spring)A common concern among the population is that installing new 5G Base Stations (BSs) over a given geographic region may result in an uncontrollable increase of Radio-Frequency “Pollution” (RFP). To face this dispute in a way that can be understood by the layman, we develop a very simple model, which evaluates the RFP at selected distances between the user and the 5G BS locations. We then obtain closed-form expressions to quantify the RFP increase/decrease when comparing a pair of alternative 5G deployments. Results show that a dense 5G deployment is beneficial to the users living in proximity to the 5G BSs, with an abrupt decrease of RFP (up to three orders of magnitude) compared to a sparse deployment. We also analyze scenarios where the user equipment minimum detectable signal threshold is increased, showing that in such cases a (slight) increase of RFP may be experienced.TRUEpu
Tracking Fraudulent and Low-Quality Display Impressions
No full textDisplay advertising is traded in a complex market with multiple sides and intermediaries, where advertisers are exposed to several forms of potentially fraudulent behavior. Intermediaries often claim to implement measures to detect fraud but provide limited information about those measures. Advertisers are required to trust that self-regulation efforts effectively filter out low-quality ad impressions. In this article, we propose an approach for tracking key display impression metrics by embedding a light JavaScript code in the ad to collect the necessary information to help detect fraudulent activities. We explain these metrics using the campaign cost per thousand (CPT) and the number of impressions per publisher. We test the approach through six display ad campaigns. Our results provide a counterargument against the industry claim that it is effectively filtering out display fraud and show the utility of our approach for advertisers.pu
Identifying Sensitive URLs at Web-Scale
Get PDFSeveral data protection laws include special provisions for protecting personal data relating to religion, health, sexual orientation, and other sensitive categories. Having a well-defined list of sensitive categories is sufficient for filing complaints manually, conducting investigations, and prosecuting cases in courts of law. Data protection laws, however, do not define explicitly what type of content falls under each sensitive category. Therefore, it is unclear how to implement proactive measures such as informing users, blocking trackers, and filing complaints
automatically when users visit sensitive domains. To empower such use cases we turn to the Curlie.org crowdsourced taxonomy project for drawing training data to build a text classifier for sensitive URLs. We demonstrate that our classifier can identify sensitive URLs with accuracy above 88%, and even recognize specific sensitive categories with accuracy above 90%. We then use our classifier to search for sensitive URLs in a corpus of 1 Billion URLs collected by the Common Crawl project. We identify more than 155 millions sensitive URLs in more than 4 million domains. Despite their sensitive nature, more than 30% of these URLs belong to domains that fail to use HTTPS. Also, in sensitive web pages with third-party cookies, 87% of the third-parties set at least one persistent cookie.TRUEpu
A Walk Down Memory Lane: On Storage Capacity in Opportunistic Content Sharing Systems
Get PDFFloating Content (FC) is a paradigmatic example of opportunistic infrastructure-less content sharing system where information is spread upon mobile node encounters within an area which is called the replication zone. FC allows the probabilistic spatial storage of information, even in the case of unreliable communications, with no support from dedicated servers. Given the large amount of communication and storage resources typically required to guarantee content persistence despite node mobility, a major open issue for the practical viability of FC and of similar distributed storage systems is the characterization of their storage capacity, i.e., of the maximum amount of information which can be stored for a given set of system parameters. In this paper, we propose a simple yet powerful information theoretical model of the storage capacity of probabilistic distributed storage systems such as FC, based on a mean field model of opportunistic information exchange. We evaluate numerically our results, and validate the model by means of realistic simulations, showing the accuracy of our mean field approach and characterizing the properties of the FC storage capacity versus the main system parameters.TRUEpu
Don’t accept candies from strangers: An analysis of third-party SDKs
No full textMobile app developers often include third-party Software Development Kits (SDKs) in their software to externalize services and features, or monetize their apps through advertisements. Unfortunately, these development practices often come at a privacy cost to the end user. In this paper, we discuss the privacy damage that third-party SDKs can cause to end users due to limitations present in today’s mobile permission models, and the overall lack of transparency in the ecosystem. We combine static, dynamic and manual analysis of the SDKs embedded in the top 50 Google Play store’s applications to develop a taxonomy of hird-party libraries. We also provide insights about their data collection, and transparency issues.
We also discuss different ways to tackle current challenges, like increasing developer’s awareness or changing the permission model of mobile phone to clearly state the purpose of permissions and to separate permissions requested by the app itself and third-party libraries, as well as mechanisms to ease certification and regulatory enforcement efforts.TRUEpu
Deep Learning solutions for next generation slicing-aware mobile networks
Get PDFIt is now commonly agreed that future 5G Networks will build upon the network slicing concept. Network slicing is an emerging paradigm in mobile networks that leverages Network Function Virtualization (NFV) to enable the instantiation of multiple logically independent copies -named slices- of a 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 and in real-time, which can be achieved by integrating Artificial Intelligence (AI) in the network.
This thesis investigates the potential of AI for sliced mobile networks. In particular it focuses on resource allocation and orchestration for network slices. This involves two steps: (i)Admission Control that is responsible to decide which slices can be admitted to the network, and (ii) Network resource orchestration that dynamically allots to the admitted slices the necessary resources for their operation.
Network Slicing will have an impact on the models that sustain the business ecosystem opening the door to new players: the Infrastructure Provider (InP), which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the InP to deliver specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this thesis we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. This includes (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure provider’s revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.
Dynamically orchestrate network resources is both a critical and challenging task in upcoming multi-tenant mobile networks, which requires allocating capacity to individual network slices so as to accommodate future time-varying service demands. Such an anticipatory resource configuration process must be driven by suitable predictors that take into account all the sources of monetary cost associated to network capacity orchestration. Legacy models that aim at forecasting traffic demands fail to capture these key economic aspects of network operation. To close this gap in the second part of this thesis, we first present DeepCog, a first generation deep neural network architecture inspired by advances in image processing and trained via a dedicated loss function in order to deal with monetary cost due to overprovisioning or underprovisioning of networking capacity. Unlike traditional traffic volume predictors, DeepCog returns a cost-aware capacity forecast, which can be directly used by operators to take short- and long-term reallocation decisions that maximize their revenues. Extensive performance evaluations with real-world measurement data collected in a metropolitan-scale operational mobile network demonstrate the effectiveness of our proposed solution, which can reduce resource management costs by over 50% in practical case studies. Then we introduce AZTEC, a second generation data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and reconfiguration, as well as by denied traffic demands. Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration.
At the time of writing DeepCog and AZTEC are, to the best of our knowledge, the only works where a deep learning architecture is explicitly tailored to the problem of anticipatory resource orchestration in mobile networks.Telematics EngineeringUniversidad Carlos III de Madrid, Spainpu
POLAR: Passive object localization with IEEE 802.11ad using phased antenna arrays
Get PDFMillimeter-wave systems not only provide high data
rates and low latency, but the very large bandwidth also allows for highly accurate environment sensing. Such properties are extremely useful for smart factory scenarios. At the same time, reusing existing communication links for passive object localization is significantly more challenging than radar-based approaches due to the sparsity of the millimeter-wave multi-path
environment and the weakness of the reflected paths compared to the line-of-sight path. In this paper, we explore the passive object localization accuracy that can be achieved with IEEE 802.11ad devices. We use commercial Access Points (APs) whereas the station design is based on a full-bandwidth 802.11ad compatible FPGA-based
platform with a phased antenna array. The stations exploit the preamble of the beam training packets of the APs to
obtain Channel Impulse Response (CIR) measurements for all antenna patterns. With this, we determine distance and angle information for the different multi-path components in the environment to passively localize a mobile object. We evaluate our system with multiple APs and a moving robot with a metallic surface. Our system operates in real-time and achieves 6.5cm mean error accuracy and sub-meter accuracy in 100% of the cases.TRUEpu
When Less is More: Core-Restricted Container Provisioning for Serverless Computing
Get PDFCloud applications are exposed to workloads whose intensity can change unpredictably over time. Hence, the ability to quickly scale the amount of computing resources provisioned to applications is essential to minimize costs while providing reliable services. In this context,containers are deemed to be a promising technology to enable fast elasticity in resource allocation schemes.In this paper, we propose and experimentally test an efficient container-based cloud computing provisioning system. First, we address the container deployment problem and discuss how to manage container provisioning and scaling. Second, we devise are source management mechanism leveraging on both admission control and auto-scaling techniques. We propose to drive auto-scaling decisions through a Q-Learning algorithm, which is agnostic to the specific computing environment, and proceeds based only on the load of the physical processors assigned to a container. We evaluate our solution in two experimental setups,and show that it yields significant advantages when compared to popular container managers such as Kubernetes.TRUEpu