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Blockchain-based cross-domain authentication in a multi-domain Internet of drones environment
Get PDFAs a new paradigm, the Internet of drones (IoD) is making the future easy with its flexibility and wide range of applications. However, these drones are prone to security attacks during communication because of this flexibility. The traditional authentication mechanism uses a centralized server which is a single point of failure to its network and a performance bottleneck. Also, privacy-preserving mechanisms involving a single authority are vulnerable to identity attacks if compromised. Moreover, cross-domain authentication schemes are getting more costly as the security requirements increase. So, this work proposes a blockchain-based cross-domain authentication scheme to make drone communication more secure and efficient. In this work, an elliptic curve digital signature algorithm (ECDSA) based message authentication scheme and a session key generation scheme are modeled. A two-phase pseudonym generation procedure is used to secure the identity of the drones. Hyperledger Fabric is used to implement the blockchain network, and the analysis is done using Hyperledger Caliper. Blockchain analysis through caliper shows the blockchain’s performance for various loads of transactions. Security analysis of the proposed scheme shows that the scheme is secure from various security attacks. The performance analysis shows that the proposed scheme is more lightweight and efficient than most similar authentication schemes.TRUEpu
A Joint Optimization Approach for Power-Efficient Heterogeneous OFDMA Radio Access Networks
Get PDFHeterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalating energy consumption poses sustainability challenges. In this paper a novel optimization approach for orthogonal heterogeneous networks is proposed to minimize transmission power while respecting individual users' throughput constraints. The problem is formulated as a mixed integer geometric program, and optimizes at once multiple system variables such as user association, working bandwidth, and base stations transmission powers. Crucially, the proposed approach becomes a convex optimization problem when user-base station associations are provided. Evaluations in multiple realistic scenarios from the production mobile network of a major European operator and based on precise channel gains and throughput requirements from measured data validate the effectiveness of the proposed approach. Overall, our original solution paves the road for greener connectivity by reducing the energy footprint of heterogeneous mobile networks, hence fostering more sustainable communication systems.TRUEpu
A Stacking Ensemble Machine Learning Strategy for COVID-19 Seroprevalence Estimations in the USA Based on Genetic Programming
Get PDFThe COVID-19 pandemic exposed the importance of research on the spread of epidemic diseases. In the case of COVID-19, official data about infection prevalence was based on PCR and antigen tests reports, which can be unreliable. In our work, we construct prediction models based on Genetic Programming to estimate the SARS-Co V-2 seroprevalence of a given population from multiple estimates of the COVID-19 prevalence (official prevalence data, estimates derived from wastewater data, and estimates obtained from massive surveys with different rules and ML methods). To do that, we propose the use of stacking techniques based on Genetic Programming to obtain Machine Learning Ensemble Methods. Our approach produces more accurate prediction models than conventional stacking techniques based on Linear Regression.TRUEpu
Quality of Experience in Video Streaming: Status Quo, Pitfalls, and Guidelines
Get PDFQuality of experience (QoE) becomes both the holy grail and a free-for-all in adaptive bitrate (ABR) video streaming. On the one hand, the design, operation, and evaluation of ABR algorithms increasingly rely on QoE. On the other hand, QoE frequently receives only cursory attention in this supporting role, with many of its important aspects treated with insufficient care. As a complex subjective notion, QoE is directly measurable through subjective tests, which incur evident overhead. While an objective QoE model represents a scalable automated means for QoE assessment, QoE models proliferate without consensus on their goodness due to numerous influence factors, construction methods, and usages. The model proliferation creates a false impression that proposing a new QoE model without a proper validation is acceptable. Because the multifaceted QoE problem involves separable and often separated tasks of test conducting, model building, and model using, this separation of concerns causes additional complications. By leveraging two large real datasets of individual QoE perception, this paper reviews the status quo in QoE, identifies various pitfalls, and offers guidelines for test conducting, model building, and model using, so as to foster high standards in future work on QoE in ABR video streaming.MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTRMCIN/AEI/10.13039/501100011033 and European Union ERDF “A way of making Europe”TRUEinpres
Edge-Based Control of Multi-Platoons
Get PDFVehicle platooning is expected to significantly increase road utilization while reducing transport cost and driver fatigue. Of course, the larger the platoon size the higher the efficiency. However, long platoons generate challenging road conditions when, for instance, platoons must allow for cross traffic at intersections, roundabouts and highway junctions. In this paper, we study the performance of platoons that split and merge in response to the traffic and road context. We propose PLATO, an edge-based control system to efficiently manage the resulting set of coordinated sub-platoons, which acts as a platoon of platoons, i.e., a multi-platoon . We analyze costs and benefits of a multi-platoon by means of analysis and realistic simulations. We show that what is most critical for good performance is the coherence of the instructions received by individual platoons, and the optimal adaptation of the number of sub-platoons in response to the changing road and traffic conditions.Ministry of Economic Affairs and Digital Transformation, European Union NextGeneration-EUTRUEpu
ARTEMIS: Adaptive bitrate ladder optimization for live video streaming
Get PDFThe conference acronym is USENIX NSDI 2024.Live streaming of segmented videos over the Hypertext Transfer Protocol (HTTP) is increasingly popular and serves heterogeneous clients by offering each segment in multiple representations. A bitrate ladder expresses this choice as a list of bitrate-resolution pairs. Whereas existing solutions for HTTP-based live streaming use a static bitrate ladder, the fixed ladders struggle to appropriately accommodate the dynamics in the video content and network-conditioned client capabilities. This paper proposes ARTEMIS as a practical scalable alternative that dynamically configures the bitrate ladder depending on the content complexity, network conditions, and clients' statistics. ARTEMIS seamlessly integrates with the end-to-end streaming pipeline and operates transparently to video encoders and clients. We develop a cloud-based implementation of ARTEMIS and conduct extensive real-world and trace-driven experiments. The experimental comparison vs. existing prominent bitrate ladders demonstrates that live streaming with ARTEMIS outperforms all baseline solutions, reduces encoding computation by 25%, end-to-end latency by 18%, and increases the quality of experience by 11%.Spanish Ministry of Science and InnovationAustrian Federal Ministry for Digital and Economic Affairs, National Foundation for Research, Technology and Development, and Christian Doppler Research AssociationTRUEinpres
User-Plane Algorithms for Stateless and Stateful Inference in Programmable Networks
Get PDFOver the past decade, network complexity has grown exponentially to support the emergence of new and innovative applications. This increased sophistication has rendered most human-in-the-loop approaches to network management tasks obsolete, calling for more automation and flexibility in the network. The advent of Software Defined Networking (SDN) with a programmable control plane was a huge step in the right direction, giving rise to a variety of network automation applications running in the SDN control plane. With the vulgarization of Machine Learning (ML) in recent years, many network automation applications use ML techniques to address network problems like intrusion detection, routing optimization, quality of service prioritization, and fault detection. Yet, as these applications run in the control plane, many of them cannot respond in real-time to network issues and incur a response delay in the order of milliseconds to seconds, which is undesirable in ultra-low-latency applications that will abound in 6G networks.
Recent advances in user-plane programmability have led to the current availability of off-the-shelf programmable user-plane equipment like Intel Tofino switches, alongside compatible network programming languages like P4. This has sparked a strong interest in in-network computation, with efforts to offload ML models from the control plane to the user plane to reduce their response time and enable inference at line rate with low latency and high throughput. Most work on user-plane inference has focused on programmable switches due to their ubiquitous presence in the network and the availability of multiple high-speed ports. However, switches are highly constrained in terms of available memory, support for mathematical operations, and the number of allowed operations per packet. This makes it impossible to train ML models in the switch and shifts the focus to deploying trained models into user-plane switches. The constraints above also make complex models like Neural Networks (NN) less feasible for in-switch deployment. Instead, most prior works have deployed tree-based models like Decision Trees (DT) and Random Forests (RF) for in-switch inference due to their simple logical structure and few operations required at inference, which make them ideal for constrained environments. Yet, these works have several limitations such as limited scalability and adaptability which translate into performance barriers when handling complex inference tasks.
This thesis proposes efficient solutions for embedding ML models into production-grade programmable switches, thereby addressing the above limitations and advancing the state of the art in ML-based user-plane inference. To illustrate the evolution across the solutions presented in the thesis, a practical application of user-plane inference is considered to show how in-switch inference can enable rapid detection of cyberattacks on SDN-based Smart Grid (SG) networks. Current power grids are smart, with millions of electronic devices interconnected by data networks. This exposes them to many cyberattacks which could lead to power outages and data breaches with far-reaching impacts. Thus, the timely detection of cyberattacks is critical. ML models are widely used for cyberattack detection in SDN-based SGs, where the models either run in external servers or in-network but fully in the control plane or distributed between the control and user planes. In these cases, the models do not run at line rate and incur millisecond-level delays in attack detection. The application developed in this thesis explores how ML inference in programmable switches at Packet-Level (PL) can enable accelerated attack detection and mitigation in SGs at line rate with sub-microsecond delay. The proposed workflow brings the concept of user-plane inference to SDN-based SGs for the first time, and deploys a trained DT model into the switch pipeline for real-time inference on live traffic. Results produced in this thesis show how a pure user plane solution achieves up to 99% accuracy in attack detection and classification, while operating up to four orders of magnitude faster than solutions running entirely in the control plane.
The above solution and all earlier solutions for PL inference in the user-plane focus on flat classification, and have significant structural limitations that prevent them from scaling when handling complex inference tasks. To tackle these limitations, this thesis proposes Henna, the pioneer implementation of an in-switch multi-stage hierarchical classification system. The concept upon which Henna hinges is that of splitting a difficult classification task into easier cascaded inference tasks, which can then be addressed with separate resource-efficient tree-based classifiers. The design of Henna aligns with the internal organization of the Protocol Independent Switch Architecture (PISA), and integrates state-of-the-art strategies for mapping decision trees to switch hardware. Henna is then implemented into a real-world testbed with off-the-shelf Intel Tofino programmable switches using the P4 language. Experiments with a complex 21-category classification task based on measurement data exhibit how Henna improves the F1-score of an advanced single-stage model by 21%, while maintaining usage of switch resources at 8% on average.
Despite the improvements brought about by Henna, existing hardware-compatible in switch inference solutions are still either limited to only PL operation, lack support for rich statistical features, or are not scalable, hitting performance barriers in complex tasks involving large decision spaces. To address this limitation, Flowrest is presented as a first complete RF model implementation that operates at the level of individual flows in commercial switches. The proposed solution builds on (i) novel guidelines for tailoring RF models to operation in programmable switches right from the design phase, (ii) an original framework to embed flow-level (FL) machine learning models into programmable switch ASICs, and (iii) efficient strategies for maintaining state within switches to compute, store and employ FL features for inference. Flowrest is implemented in a hardware switch as an open-source software using the P4 language.
Flowrest sets a new standard for FL inference in the user plane. To validate this claim, a thorough evaluation of the proposed solution is conducted in an experimental platform based on Intel Tofino switches in two steps; (i) Flowrest is evaluated on unencrypted traffic, comparing it to major existing proposals for in-switch inference which all target unencrypted traffic, and (ii) it is then evaluated on encrypted traffic classification. Results from the evaluation with tasks of unprecedented complexity show how Flowrest achieves accuracy gains in the 10% − 39% range over previous approaches to implement DT and RF models in real-world equipment.
Despite the improved performance resulting from FL classification, a major dichotomy still exists between works for in-switch inference, based on whether they operate at PL or FL. The former relies on simple features from packet headers that are simple to implement but limit accuracy in challenging use cases; the latter exploits richer flow based statistical features to improve accuracy, but leaves early packets in each flow unclassified. To close this gap, this thesis presents Jewel, an in-switch ML solution based on a fully joint PL and FL design, which offers the best of both worlds by classifying early flow packets individually at PL and shifting to FL inference as soon as possible. The proposed solution involves (i) a single RF model trained to classify both packets and flows, and (ii) hardware-aware model selection and training techniques for resource footprint minimization. Jewel is implemented in P4 and deployed in a testbed with Intel Tofino switches, where extensive experiments are conducted with a variety of real world use cases. Results from experiments conducted in this thesis reveal how Jewel outperforms four state-of-the-art benchmarks, with absolute accuracy gains in the 2.0%−5.3% range, while consuming a modest amount of switch resources.
In summary, this thesis proposes novel solutions for inference in programmable network user planes. Technical details on the design and implementation of the proposed solutions are described first, followed by thorough experimental evaluations that shed light on the merits of each solution in comparison to prior work. Through these contributions, this thesis sets new standards in user-plane ML inference and makes steps towards enabling and encouraging the pervasive adoption of user-plane inference in programmable networks by making all the solutions open-source.Project PCI2022-133013 (ECOMOME), funded by MICIU/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTREuropean Union’s Horizon Europe research and innovation programme under Marie Skłodowska-Curie grant agreement no. 860239 "BANYAN"Telematics EngineeringUniversidad Carlos III de Madrid, Spai
DeepMEND: Reliable and Scalable Network Metadata Geolocation from Base Station Positions
Get PDFMetadata geolocation, i.e., mapping information collected at a cellular Base Station (BS) to the geographical area it covers, is a central operation in the production of statistics from mobile network measurements. This task requires modeling the probability that a device attached to a BS is at a specific location, and is presently addressed with simplistic approximations based on Voronoi tessellations. As we show, Voronoi cells exhibit poor accuracy compared to real-world geolocation data, which can, in turn, reduce the reliability of research results. We propose a new approach for data-driven metadata geolocation based on a teacher-student paradigm that combines probabilistic inference and deep learning. Our DEEPMEND model: (i) only needs BS positions as input, exactly like Voronoi tessellations; (ii) produces geolocation maps that are 56% and 33% more accurate than legacy Voronoi and their state-of-the-art VoronoiBoost calibration, respectively; and, (iii) generates geolocation data for thousands of BSs in minutes. We assess its accuracy against real-world multi-city geolocation data of 5, 947 BSs provided by a network operator, and demonstrate the impact of its enhanced metadata geolocation on two applications use casesComunidad de MadridFrench National Research AgencyOnassis Foundation and the Foundation for Education and European CultureTRUEpu
5G Positioning with Software-defined Radios
Get PDFPositioning is a key focus in 5G standardization, starting with 3GPP Release 16. However, most of the effort from the research community and work presented in the technical standardization has been limited mainly to simulation studies. This paper explores the use of software-defined radios (SDRs) platforms for 5G positioning, presenting an overview of the current state-of-the-art and available open-source platforms. Utilizing an advanced SDR-based multi-gNodeBs (gNBs) synchronized testbed, the paper conducts a series of time-based, over-the-air measurements. Results offer insights into the impact of various real-world system parameters, such as the number of gNBs, transmission bandwidth, signal processing
techniques, and localization algorithms on positioning accuracy and Time to First Fix (TTFF). These findings provide a pathway for the cost-effective and efficient implementation of high-precision 5G localization systems. The paper contributes to advancing both theoretical understanding and practical applications, serving as a guide for the development of 5G positioning technology.TRUEpu
Integrated Sensing and Communications With MIMO-OTFS: ISI/ICI Exploitation and Delay-Doppler Multiplexing
Get PDFOrthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency-division multiplexing (OFDM) for high-mobility communications. We propose a novel multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system based on OTFS modulation. We begin by deriving new sensing and communication signal models for the proposed MIMO-OTFS ISAC system that explicitly capture inter-symbol interference (ISI) and inter-carrier interference (ICI) effects. We then develop a generalized likelihood ratio test (GLRT) based multi-target detection and delay-Doppler-angle estimation algorithm for MIMO-OTFS radar sensing that can simultaneously mitigate and exploit ISI/ICI effects, to prevent target masking and surpass standard unambiguous detection limits in range/velocity. Moreover, considering two operational modes (discovery/track), we propose an adaptive MIMO-OTFS ISAC transmission strategy. For the discovery mode, we introduce the concept of delay-Doppler (DD) multiplexing, enabling omnidirectional probing of the environment and large virtual array at the OTFS radar receiver. For the track mode, we pursue a directional transmission approach and design an OTFS ISAC optimization algorithm in spatial and DD domains, seeking the optimal trade-off between radar signal-to-noise ratio (SNR) and achievable rate. Simulation results verify the effectiveness of the proposed sensing algorithm and reveal valuable insights into OTFS ISAC trade-offs under varying communication channel characteristics.TRUEpu