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A Bio-inspired Emergent Control Approach for Distributed Processes
Get PDFThe complexity of industrial processes has grown exponentially, with high degrees of dependency, non-linearity, imprecision, among other aspects, which motivates the interest in developing distributed control systems for their management. In this sense, this work proposes a bio-inspired distributed control approach, where control actions emerge from the component interactions. The distributed control approach is based on the response threshold model to solve the control problem by imitating the behavior of ants. Particularly, our approach is inspired by the way as the ants carry out the division of labor in a colony. Thus, our control approach based on the threshold response model refers to the possibility of reacting to stimuli associated with the distributed control tasks. It has the ability to stabilize the process in the presence of abrupt/successive changes and various initial conditions, with a minimum effort of the actuators to achieve the objectives. Also, it has shown its versatility in different operational contexts with the same parameter tuning. The bio-inspired control approach is proved in a quadruple tank process, a complex system due to its multivariate nature. In this way, our paper introduces a new domain of application of the response threshold model in industrial processes. Several experiments were carried out in different contexts to evaluate its stability, robustness, etc., and compare it with other similar works. In general, the control performance metrics show satisfactory results, which reflects its ability to adapt to changes in the dynamics of the process, which encourages additional studies.TRUEpu
A Study on 5G Performance and Fast Conditional Handover for Public Transit Systems
Get PDFFifth-generation (5G) networks are now in a stable phase in terms of commercial release. 5G design is flexible to support a diverse range of radio bands (i.e., low-, mid-, and high-band) and application requirements. Since its initial roll-out in 2019, extensive measurements studies have revealed key aspects of commercial 5G deployments (e.g., coverage, signal strength, throughput, latency, handover, and power consumption among others) for several scenarios (e.g., pedestrian and car mobility, mid-, and high-bands, etc.). In this paper, we make a twofold contribution. First, we carry out an in-depth measurement study of 5G in a large public bus transit system in a major European city. Second, based on the insights observed with the measurement study, we propose a new target cell selection criteria applicable to Fast Conditional Handover (FCHO), a 3GPP-specific 5G technique to foster reliable mobility. Our results are based on an extensive measurement campaign performed with several mobile phones connected to several mobile network operators totaling more than 1500 km over three months. The measurements reveal how flexible the network deployment is by analyzing Radio Resource Control (RRC) messages, mobility management and the suitability of our FCHO solution, and application performance.Comunidad de MadridMinisterio de Asuntos Económicos y Transformación DigitalMinisterio de Ciencia e InnovaciónTRUEpu
High-speed Machine Learning-enhanced Receiver for Millimeter-Wave Systems
Get PDFMachine Learning (ML) is a promising tool to design wireless physical layer (PHY) components. It is particularly interesting for millimeter-wave (mm-wave) frequencies and above, due to the more challenging hardware design and channel environment at these frequencies. Rather than building individual ML-components, in this paper, we design an entire ML-enhanced mm-wave receiver for frequency selective channels. Our ML-receiver jointly optimizes the channel estimation, equalization, phase correction and demapper using Convolutional Neural Networks. We also show that for mm-wave systems, the channel varies significantly even over short timescales, requiring frequent channel measurements, and this situation is exacerbated in mobile scenarios. To tackle this, we propose a new MLchannel estimation approach that refreshes the channel state information using the guard intervals (not intended for channel measurements) that are available for every block of symbols in communication packets. To the best of our knowledge, our MLreceiver is the first work to outperform conventional receivers in general scenarios, with simulation results showing up to 7dB gains. We also provide an experimental validation of the ML-enhanced receiver with a 60 GHz FPGA-based testbed with phased antenna arrays, which shows a throughput increase by a
factor of up to 6 over baseline schemes in mobile scenarios.Ministry of Economic Affairs and Digital TransformationMinistry of Economic Affairs and Digital TransformationMadrid Regional GovernmentTRUEinpres
Simulation of Tele-Operated Driving over 5G Using CARLA and OMNeT++
Get PDFTele-Operated Driving (ToD) allows a remote operator to drive a vehicle through the services provided by a mobile radio network. ToD can replace on-board driving in many different occasions, such as dangerous environments, but can also provide assistance to autonomous driving systems in difficult and unexpected situations. ToD is a bandwidth-demanding and latency-sensitive service, which requires transmitting a large
amount of sensor data from vehicle to operator, and driving instructions from operator to vehicle. The data exchange must comply with strict real-time requirements. The low latency and high bandwidth offered by 5G Radio Access Networks (RANs) open new opportunities for an effective deployment of ToD services in different contexts. However, the rapidly changing channel quality and network conditions can raise many
challenges in meeting bandwidth and latency requirements.
In this paper, we report on the development of an elaborate simulation framework combining the realism of vehicle dynamics simulated by CARLA and the detailed network models provided by OMNeT++. We demonstrate the capabilities of the simulation framework by describing results about the feasibility
of ToD services in a simple scenario under different network and application configurations. We simulate the implementation of the ToD service in a slice of a 5G RAN, with varying application and network parameters, also considering a variable amount of background traffic. Our simulation results show that the ToD
service performance is heavily impacted by the amount and shape (i.e., the selected 5G NR numerology) of radio resources allocated to the 5G slice.TRUEinpres
Orchestration Procedures for the Network Intelligence Stratum in 6G Networks
Get PDFThe quest for autonomous mobile networks introdu- ces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stratum. The NI stratum is responsible for the full automation of the NI operation in the network, including the management of the life-cycle of NI algorithms, in a way that is synergic with traditional network management and orchestration framework. In this regard, the NI stratum must accommodate the unique requirements of NI algorithms, which differ from the ones of, e.g., virtual network functions, and thus plays a critical role in the native integration of NI into current network architectures. In this paper, we leverage the recently proposed concept of Network Intelligence Orchestrator (NIO) to (i) define the specific requirements of NI algorithms, and (ii) discuss the procedures that shall be supported by an NIO sitting in the NI stratum to effectively manage NI algorithms. We then (iii) introduce a reference implementation of the NIO defined above using cloud- native open-source tools.European Union Horizon 2020 research and innovation program under grant agreement no. 101017109 “DAEMON”TRUEpu
Characterizing sub-THz MIMO channels in practice: a novel channel sounder with absolute time reference
Get PDFPendingTRUEinpres
Evaluation of the Level of Digital Transformation in MSMEs Using Fuzzy Cognitive Maps Based on Experts
Get PDFThe concept of digital transformation involves exploiting digital technologies to generate new ways of doing things in organizations, including the creation of new processes, models, and services that produce value based on the digitization of data and processes. The application of digital technologies enables organizations to develop capabilities for innovation, automation, etc., utilizing both established and emerging technologies, including the widespread use of artificial intelligence. In this paper is proposed the implementation of a fuzzy cognitive map based on experts for the evaluation of the level of digital transformation in MSMEs (Micro, Small & Medium Enterprises). The main variables of digital transformation used to define our fuzzy cognitive map were classified into five types: i) Organization and Culture variables related to strategies, way of working, and ecosystems, ii) Customer variables related to services and digital channels and products, iii) Operations and Internal Processes variables related to supply chain, suppliers, business model, iv) Information Technologies variables related to innovation, digitization, data and analytic. Finally, the fifth type of variable is the target, which indicates the level of digital transformation of the organization. Our model managed to specify with 99.4% the level of digital transformation of the organization. Thus, our fuzzy cognitive map can predict and analyze the factors associated with digital transformation in MSMEs.Best paper of the "SYSTEMS IN PRACTICE" trackTRUEpu
Efficiency of Distributed Selection of Edge or Cloud Servers Under Latency Constraints
Get PDFWe consider a set of network nodes that generate latency-constrained service requests requiring the execution of computing tasks on servers located either in the cloud or at the network edge. We explore the efficiency of a distributed server selection strategically performed by individual nodes. In an earlier analysis, we argued for a stateless centralized allocation based on a probabilistic selection between edge and cloud servers. In that proposal, the optimal share of edge and cloud tasks was computed according to static network characteristics, with no knowledge of the actual network state. In this new study, we perform an analysis based on game theory, where we compare the globally optimal allocation performed at a central level against a distributed server selection driven by the selfish objectives of individual nodes. The inefficiency of the selfish allocation can be computed as the price of anarchy, which is shown to be very small, thus justifying a distributed strategic implementation of stateless policies. This insight is precious for designing algorithms for server selection and quantitatively proves the efficiency of distributed selfish approaches.Ministry of Economic Affairs and Digital Transformation and the European Union NextGeneration-EU in the framework of the Spanish Recovery, Transformation and Resilience PlanTRUEpu
Towards a Human-Centric Data Economy
Get PDFSpurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming a key production factor, comparable in importance to capital, land, or labour in an increasingly digital economy. In spite of an ever-growing demand for third-party data in the B2B market, firms are generally reluctant to share their information. This is due to the unique characteristics of “data” as an economic good (a freely replicable, non-depletable asset holding a highly combinatorial and context-specific value), which moves digital companies to hoard and protect their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise the provision of innovative services built upon them. As a result, most of those valuable assets still remain unexploited in corporate silos nowadays.
This situation is shaping the so-called data economy around a number of champions, and it is hampering the benefits of a global data exchange on a large scale. Some analysts have estimated the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking the value of data has become a central policy of the European Union, which also estimated the size of the data economy in 827€ billion for the EU27 in the same period. Within the scope of the European Data Strategy, the European Commission is also steering relevant initiatives aimed to identify relevant cross-industry use cases involving different verticals, and to enable sovereign data exchanges to realise them.
Among individuals, the massive collection and exploitation of personal data by digital firms in exchange of services, often with little or no consent, has raised a general concern about privacy and data protection. Apart from spurring recent legislative developments in this direction, this concern has raised some voices warning against the unsustainability of the existing digital economics (few digital champions, potential negative impact on employment, growing inequality), some of which propose that people are paid for their data in a sort of worldwide data labour market as a potential solution to this dilemma.
From a technical perspective, we are far from having the required technology and algorithms that will enable such a human-centric data economy. Even its scope is still blurry, and the question about the value of data, at least, controversial. Research works from different disciplines have studied the data value chain, different approaches to the value of data, how to price data assets, and novel data marketplace designs. At the same time, complex legal and ethical issues with respect to the data economy have risen around privacy, data protection, and ethical AI practices.
In this dissertation, we start by exploring the data value chain and how entities trade data assets over the Internet. We carry out what is, to the best of our understanding, the most thorough survey of commercial data marketplaces. In this work, we have catalogued and characterised ten different business models, including those of personal information management systems, companies born in the wake of recent data protection regulations and aiming at empowering end users to take control of their data. We have also identified the challenges faced by different types of entities, and what kind of solutions and technology they are using to provide their services.
Then we present a first of its kind measurement study that sheds light on the prices of data in the market using a novel methodology. We study how ten commercial data marketplaces categorise and classify data assets, which categories of data command higher prices, and what features are driving the prices of data in the market.
Next we turn to topics related to data marketplace design. Particularly, we study 1) how buyers can select and purchase suitable data for their tasks without requiring a priori access to such data in order to make a purchase decision, and 2) how marketplaces can distribute payoffs for a data transaction combining data of different sources among the corresponding providers, be they individuals or firms. The difficulty of both problems grows exponentially with the number of data providers involved, and hence it is further exacerbated in a human-centric data economy where buyers have to choose among data of thousands of individuals, and where marketplaces have to distribute payoffs to thousands of people contributing personal data to a specific transaction. Using large datasets of taxi rides from Chicago, Porto and New York we show that the value of data is different for each individual, and cannot be approximated by its volume, and we develop algorithms and tools to reduce the complexity of both problems and to make data purchasing more profitable for buyers and more efficient for data marketplaces.
We conclude with a number of open issues and propose further research directions that leverage the contributions and findings of this dissertation. These include monitoring data transactions to better measure data markets, and complementing market data with actual transaction prices to build a more accurate data pricing tool. A human-centric data economy would also require that the contributions of thousands of individuals to machine learning tasks are calculated daily. For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff calculation processes in data marketplaces. In that direction, we also point to some even more efficient alternatives beyond the ones presented in this dissertation. Finally, we discuss the challenges and potential technologies that will help with building a federation of standardised data marketplaces.
The data economy will develop fast in the upcoming years, and researchers from different disciplines will work together to unlock the value of data and make the most out of it. Maybe the proposal of getting paid for our data and our contribution to the data economy finally flies, or maybe it is other proposals such as the robot tax that are finally used to balance the power between individuals and tech firms in the digital economy. Still, we hope our work sheds light on the value of data, and contributes to making the price of data more transparent and, eventually, to moving towards a human-centric data economy.Telematics EngineeringUniversidad Carlos III de Madrid, Spai
Securing Federated Sensitive Topic Classification against Poisoning Attacks
Get PDFWe present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing sensitive content, i.e., content related to categories such as health, political beliefs, sexual orientation, etc. Although such a classifier addresses the limitations of previous offline/centralised classifiers, it is still vulnerable to poisoning attacks from malicious users that may attempt to reduce the accuracy for benign users by disseminating faulty model updates. To guard against this, we develop a robust aggregation scheme based on subjective logic and residual-based attack detection. Employing a combination of theoretical analysis, trace-driven simulation, as well as experimental validation with a prototype and real users, we show that our classifier can detect sensitive content with high accuracy, learn new labels fast, and remain robust in view of poisoning attacks from malicious users, as well as imperfect input from non-malicious ones.TRUEinpres