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Building Byzantine-tolerant Systems with Distributed Ledgers and Distributed Sets
No full textIn order to formalize Distributed Ledger Technologies and their interconnections, a recent line of research work has formulated the notion of Distributed Ledger Object (DLO), which is a concurrent object that maintains a totally ordered sequence of records, abstracting blockchains and distributed ledgers. It has been shown that solving consensus is required to implement a multi-writer DLO. However, it is possible to implement Byzantine-tolerant atomic single-writer DLOs without consensus in a distributed system of n servers, of which f<n/3 can be Byzantine.
In the same line, the Distributed Grow-only Set object (DSO) has been proposed, which maintains a set (instead of a sequence) of records in an immutable way: only Add and Get operations are provided. This object is inspired by the Grow-only Set (G-Set) data type which is part of the Conflict-free Replicated Data Types. There are consensus-free Byzantine-tolerant implementations of the DSO that guarantee eventual consistency.
Fundamental coordination challenges in these objects are the Atomic Appends and Atomic Adds problems, by which multiple records are added to distinct DLOs or DSOs in an atomic way.
In this talk we introduce these objects and problems, and we describe how they can be implemented and solved in Byzantine distributed systems.FALSEpu
DAEMON: A Network Intelligence Plane for 6G Networks
Get PDFWhile there is a clear trend towards network automation through the usage of Artificial Intelligence (AI) and Machine Learning (ML) solutions, the major reference network architectures are still not natively including all the mechanisms needed to handle Network Intelligence (NI). This paper introduces a novel architecture proposed within the EU-funded DAEMON project, which includes a Network Intelligence Plane (NIP) that natively integrates NI into the network operation, management, and orchestration procedures. We do so by analyzing the gaps in current reference architectures and designing a Network Intelligence Orchestration (NIO) that handles the most important NI-related mechanisms such as lifecycle management, coordination, and data management.European CommissionTRUEpu
An Offloading Algorithm for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System
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 from the data samples collected at the EDs, we study the problem of offloading inference jobs by considering the following novel aspects: in contrast to a typical computational job 1) both inference accuracy and processing time of an inference job increase with the size of the ML model and 2)recently proposed Deep Neural Networks (DNNs) for resource-constrained EDs provide the choice of scaling down the model size by trading off the inference accuracy. Therefore, we consider that multiple small-size ML models are available at the ED and a powerful large-size ML model is available at the ES, and study a general assignment problem with the objective of maximizing the total inference accuracy for the data samples at the ED subject to a time constraint T on the makespan. Noting that the problem is NP-hard, we propose an approximation algorithm: Accuracy Maximization using LP-Relaxation and Rounding (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 the optimal total accuracy. As proof of concept, we implemented AMR2 on a Raspberry Pi, equipped with MobileNets, that is connected via LAN to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR2 for image classification.TRUEpu
AX-CSI: Enabling CSI extraction on commercial 802.11ax Wi-Fi platforms
Get PDFChannel state information (CSI) is paramount to modern Wi-Fi communication systems, as it allows for proper equalization of frames at the receiver side and enables advanced signal processing techniques such as beamforming and MIMO. Given that the CSI can accurately mirror physical changes in the wireless channel, CSI analysis has become a valuable resource to many wireless sensing applications based on the opportunistic use of Wi-Fi signals. Since CSI can usually not be accessed by users directly, several CSI extraction tools have been published over the last few years for various Wi-Fi chipsets. In this paper, we present the first system ever capable of extracting CSI from 802.11ax consumer devices using the Broadcom 43684 Wi-Fi chipset. This platform can extract up to 160 MHz-wide CSI using 4x4 MIMO, and it is compatible with the latest HE PHY. We make our CSI extraction tool available to the research community to foster further work on this emerging topic.TRUEpu
Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models
Get PDFCell densification through the installation of small- cells and femtocells in indoor environments is an emerging solution to enhance the operation of wireless networks. The deployment of new components within the heart of the radio access network calls for expedient tools that assist and ensure their optimal placement within the existing network infrastructure. In this paper, we introduce metrics that can characterize indoor wireless network performance (IWNP) in terms of coverage and capacity, and we evaluate them via physics-based propagation models. In particular, we exploit a deterministic propagation model, i.e., a ray-tracer, as well as a novel machine learning-based propagation model. We demonstrate that data-driven propagation models can be leveraged for the rigorous evaluation of the IWNP metrics, yielding a remarkable computational efficiency compared to the conventional deterministic models. The use of physics-based site- specific propagation models allows for the particularities of each indoor geometry to be taken into account, and also makes feasible the consideration of uncertainties related to the indoor environment. In this case, the IWNP metrics are expressed as stochastic quantities and a stochastic solution is derived through an efficient polynomial chaos expansion representation, enabling on-the-fly computation of the IWNP metrics statistics.European CommissionTRUEpu
RGB LED bulbs for communication, harvesting and sensing
Get PDFRGB LED bulbs have entered the market as a promising alternative to traditional phosphor coating LEDs to meet illumination standards. In this paper, we introduce and propose solutions to address the challenges of RGB LED lighting for the Internet of Things applications, performing communication, harvesting energy and sensing tasks. Through experiments, we demonstrate that we can use RGB LEDs for multiple tasks. We can achieve a bandwidth in the order of 50 kHz and distances of around 3.5 m using commercial RGB LED bulbs as transceivers, without using any dedicated photodetector. RGB LED links are composed of three main colours, and we show that red is the best colour both for communicating to another receiving RGB LED bulb, as well as for harvesting with a solar cell; green and blue can instead be exploited for standard-compliant lighting and/or sensing purposes. We evaluate the system in two proof of concepts and provide insights for operating RGB LEDs for multiple tasks.European UnionTRUEpu
A Comprehensive Analysis and Performance Enhancements for the IEEE 802.11ay Group Beamforming Protocol
Get PDFMillimeter-wave technology provides the necessary improvements in capacity and performance for the next generation of wireless networks. The new IEEE 802.11ay amendment extends IEEE 802.11ad to offer 100 Gbit/s connectivity in the unlicensed 60 GHz band through technical advancements such as Multiple-Input and Multiple-Output (MIMO), channel bonding and aggregation. Additionally, it offers improvements to the Beamforming Training (BFT) process in order to increase its efficiency and accuracy. One new technique defined by IEEE 802.11ay is Group Beamforming, which allows to simultaneously train all stations, and significantly reduces training overhead, especially in very dense networks. In this paper, we provide an implementation of IEEE 802.11ay in ns-3 and perform, to the best of our knowledge, the first detailed system-level evaluation of the performance of the novel IEEE 802.11ay protocol. We specifically study the performance of Group Beamforming and compare it against the legacy 802.11ad BFT. We explore how different BFT approaches scale in large networks, identify the possible problems and evaluate at how the BFT process influences the performance of the network overall. Our analysis shows that Group Beamforming can outperform the legacy approach, resulting in lower overhead and improved network performance.
However, we also found that the Access Point (AP) training is quite vulnerable to interference in dense networks, introducing severe limitations to the performance, especially in large rooms where precise BFT is crucial to maintain the communication link. Therefore, we propose several improvements to Group Beamforming that improve performance and provide robust beamforming even in very dense scenarios.European UnionTRUEpu
YourAdvalue: Measuring Advertising Price Dynamics without Bankrupting User Privacy
Get PDFTheRealTimeBidding(RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktop RTB prices in the wild, and compare them with desktop and mobile RTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how it preserves user privacy while reporting results back to a data-server for analysis. We deployed our system, disseminated its browser extension, and collected data from 200 users, including 12000 ad impressions over 11 months. By analyzing this dataset, we show that desktop RTB prices have grown 4.6× over desktop RTB prices measured in 2013, and 3.8× over mobile RTB prices measured in 2015. We also study how user demographics associate with the intensity of RTB ecosystem tracking, leading to higher ad prices. We find that exchanging data between advertisers and/or data brokers through cookie syncronization increases the median value of displayed ads by 19%. We also find that female and younger users are more targeted, suffering more tracking (via cookie synchronization) than male or elder users. As a result of this targeting in our dataset, the advertising value (i) of women is 2.4× higher than that of men, (ii) of 25-34 year-olds is 2.5× higher than that of 35-44 year-olds, (iii) is most expensive on weekends and early mornings.TRUEpu
Stateful Versus Stateless Selection of Edge or Cloud Servers Under Latency Constraints
Get PDFWe consider a radio access network slice serving mobile users whose requests imply computing requirements. Service is virtualized over either a powerful but distant cloud infrastructure or an edge computing host. The latter provides less computing and storage capacity with respect to the cloud, but can be reached with much lower delay. A tradeoff thus naturally arises between computing capacity and data transfer latency. We investigate the performance of this service model, discussing how service requests should be routed to edge or cloud servers. We look at the performance of various classes of online algorithms based on different levels of information about the system state. Our investigation is based on analytical models, simulations in OMNeT++, and a prototype implementation over operational cellular networks. First of all, we observe that distributing the load of service requests over edge and cloud is in general beneficial for performance, and simple to implement with a stateless online server selection policy that can be easily configured with near-optimal performance. Second, we shed light on the limited improvements that stateful polices can offer, notwithstanding they base their decisions on the knowledge of server congestion levels or round-trip latency conditions. Third, we unveil that stateful policies are dangerously prone to errors, which may make stateless policies preferable.Comunidad de MadridTRUEpu
Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
Get PDF6G networks are expected to face the daunting task of providing support to a set of extremely diverse services, each more demanding than those of previous generation networks (e.g., holographic communications, unmanned mobility, etc.), while at the same time integrating non-terrestrial networks, incorporating new technologies, and supporting joint communication and sensing. The resulting network architecture, component interactions, and system dynamics are unprecedentedly complex, making human-only operation impossible, and thus calling for AI-based automation and configuration support. For this to happen, AI solutions need to be robust and interpretable, i.e., network engineers should trust the way AI operates and understand the logic behind its decisions. In this paper, we revise the current state of tools and methods that can make AI robust and explainable, shed light on challenges and open problems, and indicate potential future research directions.European UnionTRUEpu