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An Optimal Scheme to Recharge Communication Drones
Get PDFThe adoption and integration of drones in commu- nication networks is becoming reality thanks to the deployment of advanced solutions for IoT and cellular communication relay schemes. However, using drones introduces new energy con- straints and scheduling issues in the dynamic management of the network topology, due to the need to call back and recharge, or substitute, drones that run out of energy. In this paper, we describe the design of a drone recharging scheme for realisti- cally limited flight time of drones, and leverage the presence of recharging stations. Indeed, drones need to be recharged periodically, and maximizing the operational time of drones is paramount to minimize the size of the fleet of drones to be devoted to a drone mission, hence its cost. We design Homogeneous RotatingRecharge(HRR), an optimal drone recharging scheduling that extends the coverage of a cellular network. HRR minimizes the number of back-up drones needed to guarantee a fixed number of operational drones, so as to support the operation of an underlying cellular network. Results show that operating a network of drones with our scheme provides reliable and stable performance over time.Comunidad de MadridTRUEinpres
Beam Searching for mmWave Networks with sub-6 GHz WiFi and Inertial Sensors Inputs: an experimental study
Get PDFBeam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional beams under mobility. In this work, we exploit the trend of multiple chipsets integrated in the same mobile device to study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mm-wave devices. We experimentally evaluate SLASH with commodity hardware, including a 60 GHz testbed, commercial sub-6 GHz WiFi APs and smartphones. SLASH can increase the median data rate by more than 22% for link establishment and 25% for link maintenance with respect to prior work.TRUEpu
Estimating the COVID-19 Prevalence in Spain with Indirect Reporting via Open Surveys
Get PDFDuring the initial phases of the COVID-19 pandemic, accurate tracking has proven unfeasible. Initial estimation methods pointed towards case numbers that were much higher than officially reported. In the CoronaSurveys project, we have been addressing this issue using open online surveys with indirect reporting. We compare our estimates with the results of a serology study for Spain, obtaining high correlations (R squared 0.89). In our view, these results strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic.pu
PassiveLiFi: Rethinking LiFi for Low-Power and Long Range RF Backscatter
Get PDFLight bulbs have been recently explored to design Light Fidelity (LiFi) communication to battery-free tags, thus complementing Radiofrequency (RF) backscatter in the uplink. In this paper, we show that LiFi and RF backscatter are complementary and have unexplored interactions. We introduce PassiveLiFi, a battery-free system that uses LiFi to transmit RF backscatter at a meagre power budget. We address several challenges on the system design in the LiFi transmitter, the tag and the RF receiver. We design the first LiFi transmitter that implements a chirp spread spectrum (CSS) using the visible light spectrum. We use a small bank of solar cells for communication and harvesting and reconfigure them based on the amount of harvested energy and desired data rate. We further alleviate the low responsiveness of solar cells with a new low-power receiver design in the tag. Experimental results with an RF carrier of 17 dBm show that we can generate RF backscatter with a range of 80.3 meters/W consumed in the tag, which is almost double with respect to prior work.European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 814215TRUEinpres
Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint
Get PDFWith the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference, we study the problem of offloading inference jobs by considering the following novel aspects: 1) in contrast to a typical computational job, the processing time of an inference job depends on the size of the ML model, and 2) recently proposed Deep Neural Networks (DNNs) for resource-constrained devices provide the choice of scaling the model size. We formulate an assignment problem with the aim of maximizing the total inference accuracy of n data samples available at the ED, subject to a time constraint T on the makespan. We propose an approximation algorithm AMR2, and prove that it results in a makespan at most 2T, and achieves a total accuracy that is lower by a small constant from optimal total accuracy. As proof of concept, we implemented AMR2 on a Raspberry Pi, equipped with MobileNet, and is connected to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR2 for image classification application
New methods, algorithms, and theoretical guarantees for algorithms in network element design
Get PDFA broad spectrum of services running on top of an exponentially growing number of interconnected de¬vices makes network operations more complex than ever. New complex network¬wide behaviors, the vari¬ability of desired objectives incorporating different intents into final decisions together with increasing scalability levels require network infrastructure to be more intelligent, expressive, and robust. Usually, these requirements lead to significant operational complexity and an increased cost of network infras¬tructure. Reducing a manageable network state by better exploiting an expensive network infrastructure without compromising flexibility can overcome new levels of operational complexity and scalability constraints.
In the era of Software-Defined Networking, the behavior of network elements is defined by packet processing programs. Due to efficiency constraints, these programs consist of one or multiple packet classifiers. In the first part of the thesis, we propose efficient representations of multifield packet classifiers. In particular, we develop efficient combined representations of multiple packet classifiers, approximate classifiers allowing to trade the classification accuracy for additional memory reduction, and methods constructing ternary representations of range-based packet classifiers. In the second part of the thesis, we show how to use resources of the whole network for traffic monitoring problem that allows to address local resource constraints.Computer ScienceNational Research University Higher School of Economics, Moscow, Russiapu
Blocklist Babel: On the Transparency and Dynamics of Open Source Blocklisting
Get PDFBlocklists constitute a widely-used Internet security
mechanism to filter undesired network traffic based on IP/domain reputation and behavior. Many blocklists are distributed in open source form by threat intelligence providers who aggregate and process input from their own sensors, but also from third-party feeds or providers. Despite their wide adoption, many open-source blocklist providers lack clear documentation about their structure, curation process, contents, dynamics, and inter-relationships with other providers. In this paper, we perform a transparency and content analysis of 2,093 free and open source blocklists with the aim of exploring those questions. To that end, we perform a longitudinal 6-month crawling campaign yielding more than 13.5M unique records. This allows us to shed light on their nature, dynamics, inter-provider relationships, and transparency. Specifically, we discuss how the lack of consensus
on distribution formats, blocklist labeling taxonomy, content focus, and temporal dynamics creates a complex ecosystem that complicates their combined crawling, aggregation and use. We also provide observations regarding their generally low overlap as well as acute differences in terms of liveness (i.e., how frequently records get indexed and removed from the list) and the lack of documentation about their data collection processes, nature and
intended purpose. We conclude the paper with recommendations in terms of transparency, accountability, and standardization.pu
Salient brain entities labelled in P2rx7-EGFP reporter mouse embryos include the septum, roof plate glial specializations and circumventricular ependymal organs
Get PDFThe purinergic system is one of the oldest cell-to-cell communication mechanisms and exhibits relevant functions in the regulation of the central nervous system (CNS) development. Amongst the components of the purinergic system, the ionotropic P2X7 receptor (P2X7R) stands out as a potential regulator of brain pathology and physiology. Thus, P2X7R is known to regulate crucial aspects of neuronal cell biology, including axonal elongation, path-finding, synapse formation and neuroprotection.
Moreover, P2X7R modulates neuroinflammation and is posed as a therapeutic target in inflammatory, oncogenic and degenerative disorders. However, the lack of reliable technical and pharmacological approaches to detect this receptor represents a major hurdle in its study. Here, we took advantage of the P2rx7-EGFP reporter mouse, which expresses enhanced green fluorescence protein (EGFP) immediately downstream of the P2rx7 proximal promoter, to conduct a detailed study of its distribution. We performed a comprehensive analysis of the pattern of P2X7R expression in the brain of E18.5 mouse embryos revealing interesting areas within the CNS. Particularly, strong labelling was found in the septum, as well as along the entire neural roof plate zone of the brain, except chorioidal roof areas, but including specialized circumventricular roof formations, such as the subfornical and subcommissural organs (SFO; SCO). Moreover, our results reveal what seems a novel circumventricular organ, named by us postarcuate organ (PArcO). Furthermore, this study sheds light on the ongoing debate regarding the specific presence of P2X7R in neurons and may be of interest for the elucidation of additional roles of P2X7R in the idiosyncratic histologic development of the CNS and related systemic functions.pu
Single Access Point Localization System
No full textThis paper presents a Single Access Point indoor localization system that can be easily deployed with commodity WiFi
infrastructure and that estimates both Angle of Arrival (AOA) and ranges to commodity smartphones. Our system consists of an
enrichment of Spring localization method including appropriate improvements to the SpotFi algorithm for AOA estimation. First, a new
Channel State Information (CSI) calibration approach is proposed in order to eliminate the existing bias caused by hardware problems.
Second, we improve the AOA resolution by showing the benefits of changing the dimensions of the smoothed CSI matrix and applying
MUSIC as initial step before the smoothing algorithm. Third, we propose an alternative approach to the Sanitization algorithm of SpotFi
in order to improve the time resolution too. Finally, an analysis of different clustering techniques takes place, showing that the DBSCAN
is the most appropriate one, thus giving a more accurate calculation of AOA. As for the estimation of distance, Fine Time
Measurements (FTM) procedure is used, and we apply an appropriate method in order to filter these measurements. Our system
works with only one AP, and it achieves an 80-th percentile error of 1.67 and 4.1 meters in two different testbeds.Telematics EngineeringUniversidad Carlos III de Madrid, Spai