Publications

Conferences & Workshops

1. Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems

   J. Gao, I. Mavromatis, P. Li, P. Carnelli, and A. Khan

   In Proc. of IEEE PerCom Workshop on Pervasive and Resource-constrained AI (PeRConAI), Mar. 2024

   Federated learning (FL) systems face performance challenges in dealing with heterogeneous devices and non-identically distributed data across clients. We propose a dynamic global model aggregation method within Asynchronous Federated Learning (AFL) deployments to address these issues. Our aggregation method scores and adjusts the weighting of client model updates based on their upload frequency to accommodate differences in device capabilities. Additionally, we also immediately provide an updated global model to clients after they upload their local models to reduce idle time and improve training efficiency. We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data. The simulation results, using the FashionMNIST dataset, demonstrate over 10% and 19% improvement in global model accuracy compared to state-of-the-art methods PAPAYA and FedAsync, respectively. Our dynamic aggregation method allows reliable global model training despite limiting client resources and statistical data heterogeneity. This improves robustness and scalability for real-world FL deployments.

   @inproceedings{systemBias,
     author = {{Gao}, J. and {Mavromatis}, I. and {Li}, P. and {Carnelli}, P. and {Khan}, A.},
     title = {{Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems}},
     booktitle = {Proc. of IEEE PerCom Workshop on Pervasive and Resource-constrained AI (PeRConAI)},
     year = {2024},
     month = mar,
     keywords = {Machine Learning; Federated Learning; Scalability; Resource-constrained Devices; System Bias; Device Heterogeneity},
     address = {Biarritz, France},
   }

2. Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed

   P. Li, I. Mavromatis, and A. Khan

   In Proc. of IEEE PerCom Workshop on Pervasive Computing Challenges in Trustable Crowdsensing Systems (TrustSense), Mar. 2024

   Invited Paper

   UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices. This paper provides a guide to the implemented and prospective artificial intelligence (AI) capabilities of UMBRELLA in real-world IoT systems. Four existing UMBRELLA applications are presented in detail: 1) An automated streetlight monitoring for detecting issues and triggering maintenance alerts; 2) A Digital twin of building environments providing enhanced air quality sensing with reduced cost; 3) A large-scale Federated Learning framework for reducing communication overhead; and 4) An intrusion detection for containerised applications identifying malicious activities. Additionally, the potential of UMBRELLA is outlined for future smart city and multi-robot crowdsensing applications enhanced by semantic communications and multi-agent planning. Finally, to realise the above use-cases we discuss the need for a tailored MLOps platform to automate UMBRELLA’s model pipelines and establish trust.

   @inproceedings{umbrellaMLOps,
     author = {{Li}, P. and {Mavromatis}, I. and {Khan}, A.},
     title = {{Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed}},
     booktitle = {Proc. of IEEE PerCom Workshop on Pervasive Computing Challenges in Trustable Crowdsensing Systems (TrustSense)},
     year = {2024},
     month = mar,
     highlight = {Invited Paper},
     keywords = {IoT; Wireless Communication; Robotics; Edge Intelligence; Use Cases; MLOps},
     address = {Biarritz, France},
   }

3. FROST: Towards Energy-efficient AI-on-5G Platforms - A GPU Power Capping Evaluation

   I. Mavromatis, S. De Feo, P. Carnelli, R. J. Piechocki, and A. Khan

   In Proc. of IEEE Conference on Standards for Communications and Networking (CSCN), Nov. 2023

   The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, consumes 73% of its total energy. That makes it an ideal target for optimisation through the integration of Machine Learning (ML). However, the energy consumption of ML is frequently overlooked in such ecosystems. Our work addresses this critical aspect by presenting FROST - Flexible Reconfiguration method with Online System Tuning - a solution for energy-aware ML pipelines that adhere to O-RAN’s specifications and principles. FROST is capable of profiling the energy consumption of an ML pipeline and optimising the hardware accordingly, thereby limiting the power draw. Our findings indicate that FROST can achieve energy savings of up to 26.4% without compromising the model’s accuracy or introducing significant time delays.

   @inproceedings{frost,
     author = {{Mavromatis}, I. and {De Feo}, S. and {Carnelli}, P. and {Piechocki}, R. J. and {Khan}, A.},
     title = {{FROST: Towards Energy-efficient AI-on-5G Platforms - A GPU Power Capping Evaluation}},
     booktitle = {Proc. of IEEE Conference on Standards for Communications and Networking (CSCN)},
     year = {2023},
     month = nov,
     keywords = {Machine Learning; Power Profiling; Power Capping; O-RAN; 5G; Sustainable AI; Green AI},
     address = {Munich, Germany},
   }

4. Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study

   H. Friji, I. Mavromatis, A. Sanchez-Mompo, P. Carnelli, A. Olivereau, and A. Khan

   In Proc. of IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Nov. 2023

   With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play a crucial role in ensuring network security, actively identifying and mitigating malicious behaviours. However, the relentless advancement of cyber-threats has rendered traditional/classical approaches insufficient in addressing the sophistication and complexity of attacks. This paper proposes a novel 3-stage intrusion detection system inspired by a simplified version of the Lockheed Martin cyber kill chain to detect advanced multi-step attacks. The proposed approach consists of three models, each responsible for detecting a group of attacks with common characteristics. The detection outcome of the first two stages is used to conduct a feasibility study on the possibility of predicting attacks in the third stage. Using the ToN IoT dataset, we achieved an average of 94% F1-Score among different stages, outperforming the benchmark approaches based on Random-forest model. Finally, we comment on the feasibility of this approach to be integrated in a real-world system and propose various possible future work.

   @inproceedings{multiStageIDS,
     author = {{Friji}, H. and {Mavromatis}, I. and {Sanchez-Mompo}, A. and {Carnelli}, P. and {Olivereau}, A. and {Khan}, A.},
     title = {{Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study}},
     booktitle = {Proc. of IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)},
     year = {2023},
     month = nov,
     keywords = {Intrusion Detection, Artificial Intelligence, Cyber Kill Chain, Graph Neural Network, Multi-step Attacks},
     address = {Exeter, UK},
   }

5. Federated Deep Learning for Intrusion Detection in IoT Networks

   O. Belarbi, T. Spyridopoulos, E. Anthi, I. Mavromatis, P. Carnelli, and A. Khan

   In Proc. of IEEE Global Communications Conference (GLOBECOM), Nov. 2023

   The vast increase of IoT technologies and the ever-evolving attack vectors and threat actors have increased cyber-security risks dramatically. Novel attacks can compromise IoT devices to gain access to sensitive data or control them to deploy further malicious activities. The detection of novel attacks often relies upon AI solutions. A common approach to implementing AI-based IDS in distributed IoT systems is in a centralised manner. However, this approach may violate data privacy and secrecy. In addition, centralised data collection prohibits the scale-up of IDSs. Therefore, intrusion detection solutions in IoT ecosystems need to move towards a decentralised direction. FL has attracted significant interest in recent years due to its ability to perform collaborative learning while preserving data confidentiality and locality. Nevertheless, most FL-based IDS for IoT systems are designed under unrealistic data distribution conditions. To that end, we design an experiment representative of the real world and evaluate the performance of two FL IDS implementations, one based on DNNs and another on our previous work on DBNs. For our experiments, we rely on TON-IoT, a realistic IoT network traffic dataset, associating each IP address with a single FL client. Additionally, we explore pre-training and investigate various aggregation methods to mitigate the impact of data heterogeneity. Lastly, we benchmark our approach against a centralised solution. The comparison shows that the heterogeneous nature of the data has a considerable negative impact on the model performance when trained in a distributed manner. However, in the case of a pre-trained initial global FL model, we demonstrate a performance improvement of over 20% (F1-score) when compared against a randomly initiated global model.

   @inproceedings{idsFL,
     author = {{Belarbi}, O. and {Spyridopoulos}, T. and {Anthi}, E. and {Mavromatis}, I. and {Carnelli}, P. and {Khan}, A.},
     title = {{Federated Deep Learning for Intrusion Detection in IoT Networks}},
     booktitle = {Proc. of IEEE Global Communications Conference (GLOBECOM)},
     year = {2023},
     month = nov,
     address = {Kuala Lumpur, Malaysia},
     keywords = {Federated Learning, Intrusion Detection System, Internet of Things, Deep Learning, Deep Belief Networks},
   }

6. Evaluating Concept Drift Detectors on Real-World Data

   U. Erol, F. Raimondo, J. Pope, S. Gunner, V. Kumar, I. Mavromatis, P Carnelli, T. Spyridopoulos, A. Khan, and G. Oikonomou

   In Proc. of ACM Embedded Wireless Systems and Networks (EWSN), Sep. 2023

   Cloud-IoT deployments are ubiquitous and employed in various application domains, including smart buildings. Often employed in public spaces, IoT devices are exposed to various security threats. One such attack is "anomalous concept drift". It occurs when an attacker tampers with a device causing it to report realistic sensor data that slowly deviates from the correct value. Evaluating concept drift detectors on real-world data is ideal. Though many indoor datasets exist, our real-world dataset provides a natural, long-term collection of indoor environmental sensor readings over six months. The dataset consists of environmental sensor samples collected via eight IoT devices in a real office setting. The dataset is particularly useful for evaluating concept drift detection algorithms as spatial aspects can be used along with the signals. The dataset has been made openly available, and in this paper we use it to inject malicious concept drifts and to evaluate the performance of several drift detection techniques. The injection tool’s source code is also publicly available.

   @inproceedings{multiSensorDataDrift,
     author = {{Erol}, U. and {Raimondo}, F. and {Pope}, J. and {Gunner}, S. and {Kumar}, V. and {Mavromatis}, I. and {Carnelli}, P and {Spyridopoulos}, T. and {Khan}, A. and {Oikonomou}, G.},
     title = {{Evaluating Concept Drift Detectors on Real-World Data}},
     booktitle = {Proc. of ACM Embedded Wireless Systems and Networks (EWSN)},
     year = {2023},
     month = sep,
     isbn = {},
     address = {Calabria, Italy},
   }

7. FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments

   T. Chow, U. Raza, I. Mavromatis, and A. Khan

   In Proc. of International Wireless Communications & Mobile Computing Conference (IWCMC) 2023, Jun. 2023

   Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices. In traditional cloud-centric approaches, raw data is transmitted to a central server for training and inference purposes. On the other hand, Federated Learning migrates both tasks closer to the edge nodes and endpoints. This allows for a significant reduction in data exchange while preserving the privacy of users. Trained models, though, may under-perform in dynamic environments due to changes in the data distribution, affecting the model’s ability to infer accurately; this is referred to as concept drift. Such drift may also be adversarial in nature. Therefore, it is of paramount importance to detect such behaviours promptly. In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model’s training behaviour and inference statistics, respectively. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods (over 5x reduction), is easily scalable to larger systems, and can successfully detect concept drift reactively with at least a 16x reduction in latency.

   @inproceedings{flare,
     author = {{Chow}, T. and {Raza}, U. and {Mavromatis}, I. and {Khan}, A.},
     title = {{FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments}},
     booktitle = {Proc. of International Wireless Communications & Mobile Computing Conference (IWCMC) 2023},
     year = {2023},
     month = jun,
     doi = {10.1109/IWCMC58020.2023.10182870},
     address = {Marrakesh, Morocco},
     keywords = {Federated Learning, Distributed deployment, Concept Drift, Model Robustness, Scalable IoT Inference},
   }

8. Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework

   I. Mavromatis, and A. Khan

   In Proc. of IEEE Consumer Communications & Networking Conference (CCNC), Jan. 2023

   This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of Things (IoT) sensor deployments. Our framework operates in a distributed manner, preserving data privacy while still being adaptable to new sensors with minimal online reconfiguration. Our framework currently supports multiple drift estimators for time-series IoT data and can easily be extended to accommodate new data types and drift detection mechanisms. This demo will illustrate the functionality of LE3D under a real-world-like scenario.

   @inproceedings{le3dDemo,
     author = {{Mavromatis}, I. and {Khan}, A.},
     title = {{Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework}},
     booktitle = {Proc. of IEEE Consumer Communications & Networking Conference (CCNC)},
     year = {2023},
     month = jan,
     doi = {10.1109/CCNC51644.2023.10060554},
     address = {Las Vegas, NV, USA},
     keywords = {Data Drift, IoT, Drift Detector, Resource-Constrained, Ensemble Learning},
   }

9. LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices

   I. Mavromatis, A. Sanchez-Mompo, F. Raimondo, J. Pope, M. Bullo, I. Weeks, V. Kumar, P. Carnelli, G. Oikonomou, T. Spyridopoulos, and A. Khan

   In Proc. of IEEE Consumer Communications & Networking Conference (CCNC), Jan. 2023

   Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding IoT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run.

   @inproceedings{le3dDataDrift,
     author = {{Mavromatis}, I. and {Sanchez-Mompo}, A. and {Raimondo}, F. and {Pope}, J. and {Bullo}, M. and {Weeks}, I. and {Kumar}, V. and {Carnelli}, P. and {Oikonomou}, G. and {Spyridopoulos}, T. and {Khan}, A.},
     title = {{LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices}},
     booktitle = {Proc. of IEEE Consumer Communications & Networking Conference (CCNC)},
     year = {2023},
     month = jan,
     doi = {doi.org/10.1109/CCNC51644.2023.10060415},
     address = {Las Vegas, NV, USA},
     keywords = {Data Drift, IoT, Drift Detector, Resource-Constrained, Ensemble Learning},
   }

10. Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs

    A. Katsenou, J. Mao, and I. Mavromatis

    In Proc. of Picture Coding Symposium (PCS), Dec. 2022

    The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video formats, and the newest, more complex video codecs increase the energy consumption in data centers and display devices. In this paper, we explore and compare the energy consumption across optimized state-of-the-art video codecs, SVT-AV1, VVenC/VVdeC, VP9, and x.265. Furthermore, we align the energy usage with various objective quality metrics and the compression performance for a set of video sequences across different resolutions. The results indicate that SVT-AV1 provides the best tradeoff between energy consumption and quality. The reported results aim to serve as a guide towards sustainable video streaming while not compromising the quality of experience of the end user.

    @inproceedings{erqTradeOffVideoCodecs,
      author = {{Katsenou}, A. and {Mao}, J. and {Mavromatis}, I.},
      title = {{Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs}},
      booktitle = {Proc. of Picture Coding Symposium (PCS)},
      year = {2022},
      month = dec,
      address = {San Jose, CA, USA},
      keywords = {Video Codecs, Energy Consumption, VVenC/VVdeC, SVT-AV1, VP9, x.265.},
      doi = {10.1109/PCS56426.2022.10017999},
    }

11. A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges

    D. Anand, I. Mavromatis, P. E. Carnelli, and A. Khan

    In Proc. of ACM MobiCom - Workshop on Al Empowered Mobile and Wireless Sensing (MORSE), Oct. 2022

    Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalised machine learning techniques for the classification task of the lampposts operation. Incorporating FL in such a scenario shows minimal performance reduction in terms of the classification task, but huge improvements in the communication cost and the privacy preserving. These outcomes strengthen FL’s viability and potential for IoT applications.

    @inproceedings{flStreetLightApplication,
      author = {{Anand}, D. and {Mavromatis}, I. and {Carnelli}, P. E. and {Khan}, A.},
      title = {{A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges}},
      booktitle = {Proc. of ACM MobiCom - Workshop on Al Empowered Mobile and Wireless Sensing (MORSE)},
      year = {2022},
      month = oct,
      doi = {10.1145/3556558.3558580},
      address = {Syndey, Australia},
      keywords = {Smart Cities, IoT, Infrastructure, Monitoring, Lamppost, Neural Networks, Federated Learning},
    }

12. Securing Synchronous Flooding Communications: An Atomic-SDN Implementation

    C. Lockie, I. Mavromatis, A. Stanoev, Y. Jin, and G. Oikonomou

    In Proc. of ACM Embedded Wireless Systems and Networks (EWSN), Oct. 2022

    Synchronous Flooding (SF) protocols can enhance the wireless connectivity between Internet of Things (IoT) devices. However, existing SF solutions fail to introduce sufficient security measures due to strict time synchronisation requirements, making them vulnerable to malicious actions. Our paper presents a design paradigm for encrypted SF communications. We describe a mechanism for synchronising encryption parameters in a network-wide fashion. Our solution operates with minimal overhead and without compromising communication reliability. Evaluating our paradigm on a real-world, large-scale IoT testbed, we have proven that a communication layer impervious to a range of attacks is established without sacrificing the network performance.

    @inproceedings{atomicencryption2022,
      author = {{Lockie}, C. and {Mavromatis}, I. and {Stanoev}, A. and {Jin}, Y. and {Oikonomou}, G.},
      title = {{Securing Synchronous Flooding Communications: An Atomic-SDN Implementation}},
      booktitle = {Proc. of ACM Embedded Wireless Systems and Networks (EWSN)},
      year = {2022},
      month = oct,
      doi = {10.5555/3578948.3578988},
      isbn = {},
      address = {Linz, Austria},
      keywords = {IoT, Synchronous Flooding, Wireless Security, Bluetooth, CCM},
    }

13. Reliable IoT Firmware Updates: A Large-scale Mesh Network Performance Investigation

    I. Mavromatis, A. Stanoev, A. J. Portelli, C. Lockie, M. Ammann, Y. Jin, and M. Sooriyabandara

    In Proc. of IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2022

    Internet of Things (IoT) networks require regular firmware updates to ensure enhanced security and stability. As we move towards methodologies of codifying security and policy decisions and exchanging them over IoT large-scale deployments (security-as-a-code), these demands should be considered a routine operation. However, rolling out firmware updates to large-scale networks presents a crucial challenge for constrained wireless environments with large numbers of IoT devices. This paper initially investigates how the current state-of-the-art protocols operate in such adverse conditions by measuring various Quality-of-Service (QoS) Key Performance Indicators (KPIs) of the shared wireless medium. We later discuss how Concurrent Transmissions (CT) can extend the scalability of IoT protocols and ensure reliable firmware roll-outs over large geographical areas. Measuring KPIs such as the mesh join time, the throughput, and the number of nodes forming a network, we provide great insight into how an IoT environment will behave under a large-scale firmware roll-out. Finally, we conducted our performance investigation over the UMBRELLA platform, a real-world IoT testbed deployed in Bristol, UK. This ensures our findings represent a realistic IoT scenario and meet the strict QoS requirements of today’s IoT applications.

    @inproceedings{umbrellawireless2022,
      author = {{Mavromatis}, I. and {Stanoev}, A. and {Portelli}, A. J. and {Lockie}, C. and {Ammann}, M. and {Jin}, Y. and {Sooriyabandara}, M.},
      title = {{Reliable IoT Firmware Updates: A Large-scale Mesh Network Performance Investigation}},
      booktitle = {Proc. of IEEE Wireless Communications and Networking Conference (WCNC)},
      year = {2022},
      month = apr,
      isbn = {978-1-6654-4266-4},
      doi = {10.1109/WCNC51071.2022.9771708},
      address = {Austin, Texas},
      keywords = {IoT, Bluetooth, Large-scale Testbed, Firmware Update, IEEE 802.15.4},
    }

14. Dataset: Container Escape Detection for Edge Devices

    J. Pope, F. Raimondo, V. Kumar, R. McConville, R. Piechocki, G. Oikonomou, T. Pasquier, B. Luo, D. Howarth, I. Mavromatis, P. Carnelli, A. Sanchez-Mompo, T. Spyridopoulos, and A. Khan

    In Proc. of ACM Embedded Networked Sensor Systems (SenSys), Nov. 2021

    Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

    @inproceedings{data2021,
      author = {{Pope}, J. and {Raimondo}, F. and {Kumar}, V. and {McConville}, R. and {Piechocki}, R. and {Oikonomou}, G. and {Pasquier}, T. and {Luo}, B. and {Howarth}, D. and {Mavromatis}, I. and {Carnelli}, P. and {Sanchez-Mompo}, A. and {Spyridopoulos}, T. and {Khan}, A.},
      title = {{Dataset: Container Escape Detection for Edge Devices}},
      booktitle = {Proc. of ACM Embedded Networked Sensor Systems (SenSys)},
      year = {2021},
      month = nov,
      doi = {10.1145/3485730.3494114},
      isbn = {9781450390972},
      address = {Coimbra, Portugal},
      keywords = {Container Escape, Cybersecurity, Anomaly Detection, Datasets},
    }

15. UMBRELLA Collaborative Robotics Testbed and IoT Platform

    T. D. Farnham, S. Jones, A. Aijaz, Y. Jin, I. Mavromatis, U. Raza, A. Portelli, A. Stanoev, and M. Sooriyabandara

    In Proc. of IEEE Workshop on Communication and Networking for Swarms Robotics (RoboCom), Jan. 2021

    This paper provides details of the collaborative robotics testbed platform that has been developed within the UMBRELLA project. The testbed is part of a larger open Industrial IoT testbed which is currently being deployed in the UK. The aim of the testbed is to permit flexible experimentation using different end devices (including smart city sensing and robot nodes) to evaluate algorithms or new practical application scenarios. For the collaborative robotics testbed this relates to warehouse robotics, which can move pallets of different sizes and shapes. The testbed also includes simulator facilities for validation of algorithms prior to deployment on the robot nodes. The nodes support a rich set of sensors, actuators, and wireless communication technologies. The software architecture is based on Docker containers and ROS2 DDS middleware for flexible and extensible evolution to support future sensors or network technologies. This will be provided as an open testbed to support research, experimentation and evaluation of swarm robotics and other Industrial IoT use-cases.

    @inproceedings{umbrellaRobot,
      author = {{Farnham}, T. D. and {Jones}, S. and {Aijaz}, A. and {Jin}, Y. and {Mavromatis}, I. and {Raza}, U. and {Portelli}, A. and {Stanoev}, A. and {Sooriyabandara}, M.},
      title = {{UMBRELLA Collaborative Robotics Testbed and IoT Platform}},
      booktitle = {Proc. of IEEE Workshop on Communication and Networking for Swarms Robotics (RoboCom)},
      year = {2021},
      month = jan,
      doi = {10.1109/CCNC49032.2021.9369615},
      isbn = {978-1-7281-9794-4},
      address = {Las Vegas, NV, USA},
      keywords = {Swarm robotics,Testbed,IoT Platform},
    }

16. DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems

    I. Mavromatis, R. Piechocki, M. Sooriyabandara, and A. Parekh

    In Proc. of IEEE Symposium on Computers and Communications (ISCC), Jul. 2020

    In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.

    @inproceedings{driveSimulator,
      author = {{Mavromatis}, I. and {Piechocki}, R. and {Sooriyabandara}, M. and {Parekh}, A.},
      booktitle = {Proc. of IEEE Symposium on Computers and Communications (ISCC)},
      title = {{DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems}},
      year = {2020},
      month = jul,
      doi = {10.1109/ISCC50000.2020.9219683},
      keywords = {C-ITS, CAV, Digital Twin, Simulation Framework, SUMO, Urban Mobility},
      address = {Rennes, France},
    }

17. On Urban Traffic Flow Benefits of Connected and Automated Vehicles

    I. Mavromatis, A. Tassi, R. J. Piechocki, and M. Sooriyabandara

    In Proc. of IEEE Vehicular Technology Conference (VTC2020-Spring), May 2020

    Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

    @inproceedings{urbanTrafficFlow,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Sooriyabandara}, M.},
      booktitle = {Proc. of IEEE Vehicular Technology Conference (VTC2020-Spring)},
      title = {{On Urban Traffic Flow Benefits of Connected and Automated Vehicles}},
      year = {2020},
      pages = {1-7},
      keywords = {road traffic;road traffic control;road vehicles;traffic engineering computing;urban traffic flow benefits;Intelligent Transportation Systems;future mobility services;self-driving vehicles;Level 4&5 Automated Vehicles;environmental perception tasks;Connected;sensor information sharing;vehicle group;traffic congestion;average flow speeds;human-driven vehicles;Roads;Urban areas;Mathematical model;Sensors;Autonomous vehicles;Layout;ITS;CAV;AV;SUMO;Simulation Framework;Urban Mobility.},
      doi = {10.1109/VTC2020-Spring48590.2020.9128758},
      issn = {2577-2465},
      month = may,
      address = {Antwerp, Belgium},
    }

18. Location Anomalies Detection for Connected and Autonomous Vehicles

    X. Wang, I. Mavromatis, A. Tassi, R. Santos-Rodriguez, and R. J. Piechocki

    In Proc. of IEEE Connected and Automated Vehicles Symposium (CAVS), Sep. 2019

    Future Connected and Automated Vehicles (CAVs), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres will have access to suitably anonymized CAV mobility information. Safe and secure operations will then be contingent on early detection of anomalies. In this paper, a novel unsupervised learning model based on deep autoencoder is proposed to detect the self-reported location anomaly in CAVs, using vehicle locations and the Received Signal Strength Indicator (RSSI) as features. Quantitative experiments on simulation datasets show that the proposed approach is effective and robust in detecting self-reported location anomalies.

    @inproceedings{anomaliesDetection,
      author = {{Wang}, X. and {Mavromatis}, I. and {Tassi}, A. and {Santos-Rodriguez}, R. and {Piechocki}, R. J.},
      booktitle = {Proc. of IEEE Connected and Automated Vehicles Symposium (CAVS)},
      title = {{Location Anomalies Detection for Connected and Autonomous Vehicles}},
      year = {2019},
      pages = {1-5},
      keywords = {intelligent transportation systems;mobile computing;road traffic;unsupervised learning;ITS;RSSI;CAV mobility information;Internet of Vehicles;connected and automated vehicles;location anomalies detection;received signal strength indicator;unsupervised learning model;traffic centres;traffic flows;Anomaly detection;Transmitters;Training;Received signal strength indicator;Rician channels;Buildings;Data models},
      doi = {10.1109/CAVS.2019.8887778},
      month = sep,
      address = {Hawaii, USA},
    }

19. Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation

    I. Mavromatis, A. Tassi, and R. J. Piechocki

    In Proc. of IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Sep. 2019

    Future Connected and Autonomous Vehicles (CAVs) will be equipped with a large set of sensors. The large amount of generated sensor data is expected to be exchanged with other CAVs and the road-side infrastructure. Both in Europe and the US, Dedicated Short Range Communications (DSRC) systems, based on the IEEE 802.11p Physical Layer, are key enabler for the communication among vehicles. Given the expected market penetration of connected vehicles, the licensed band of 75 MHz, dedicated to DSRC communications, is expected to become increasingly congested. In this paper, we investigate the performance of a vehicular communication system, operated over the unlicensed bands 2.4 GHz-2.5 GHz and 5.725 GHz-5.875 GHz. Our experimental evaluation was carried out in a testing track in the centre of Bristol, UK and our system is a full-stack ETSI ITS-G5 implementation. Our performance investigation compares key communication metrics (e.g., packet delivery rate, received signal strength indicator) measured by operating our system over the licensed DSRC an the considered unlicensed bands. In particular, when operated over the 2.4 GHz-2.5 GHz band, our system achieves comparable performance to the case when the DSRC band is used. On the other hand, as soon as the system, is operated over the 5.725 GHz-5.875 GHz band, the packet delivery rate is 30% smaller compared to the case when the DSRC band is employed. These findings prove that operating our system over unlicensed ISM bands is a viable option. During our experimental evaluation, we recorded all the generated network interactions and the complete data set has been publicly available.

    @inproceedings{itsg5Unlicensed,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J.},
      booktitle = {Proc. of IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)},
      title = {{Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation}},
      year = {2019},
      pages = {1-7},
      keywords = {performance evaluation;vehicular ad hoc networks;city-scale performance evaluation;dedicated short range communications systems;IEEE 802.11p physical layer;expected market penetration;vehicular communication system;full-stack ETSI ITS-G5 implementation;packet delivery rate;unlicensed ISM bands;sensor data generation;ITS-G5 DSRC communications;CAV;connected and autonomous vehicles;Bristol;UK;received signal strength indicator;frequency 5.725 GHz to 5.875 GHz;frequency 2.4 GHz to 2.5 GHz;frequency 75.0 MHz;Data Offloading;ITS;CAV;V2X;DSRC;ITS-G5},
      doi = {10.1109/PIMRC.2019.8904214},
      issn = {2166-9589},
      month = sep,
      address = {Istanbul, Turkey},
    }

20. Secure Data Offloading Strategy for Connected and Autonomous Vehicles

    A. Tassi, I. Mavromatis, R. J. Piechocki, and A. Nix

    In Proc. of IEEE Vehicular Technology Conference (VTC2019-Spring), Apr. 2019

    Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road - thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs’ communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks.

    @inproceedings{secureOffloading,
      author = {{Tassi}, A. and {Mavromatis}, I. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of IEEE Vehicular Technology Conference (VTC2019-Spring)},
      title = {{Secure Data Offloading Strategy for Connected and Autonomous Vehicles}},
      year = {2019},
      doi = {10.1109/VTCSpring.2019.8746698},
      issn = {2577-2465},
      month = apr,
      address = {Kuala Lumpur, Malaysia},
    }

21. Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs

    A. Tassi, I. Mavromatis, R. Piechocki, A. Nix, C. Compton, T. Poole, and W. Schuster

    In Proc. of IEEE Vehicular Technology Conference (VTC2019-Spring), Apr. 2019

    IEEE Best Paper Award

    Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area. This paper develops a Fog Computing-based infrastructure for future Intelligent Transportation Systems (ITSs) enabling an agile and reliable off-load of CAV data. Since CAVs are expected to generate large quantities of data, it is not feasible to assume data off-loading to be completed while a CAV is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be in the range of an RSU only for a limited amount of time, necessitating data reconciliation across different RSUs, if traditional approaches to data off-load were to be used. To this end, this paper proposes an agile Fog Computing infrastructure, which interconnects all the RSUs so that the data reconciliation is solved efficiently as a by-product of deploying the Random Linear Network Coding (RLNC) technique. Our numerical results confirm the feasibility of our solution and show its effectiveness when operated in a large-scale urban testbed.

    @inproceedings{agileOffloading,
      author = {{Tassi}, A. and {Mavromatis}, I. and {Piechocki}, R. and {Nix}, A. and {Compton}, C. and {Poole}, T. and {Schuster}, W.},
      booktitle = {Proc. of IEEE Vehicular Technology Conference (VTC2019-Spring)},
      title = {{Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs}},
      year = {2019},
      pages = {1-6},
      doi = {10.1109/VTCSpring.2019.8746302},
      issn = {2577-2465},
      month = apr,
      highlight = {IEEE Best Paper Award},
      address = {Kuala Lumpur, Malaysia},
    }

22. Efficient Millimeter-Wave Infrastructure Placement for City-Scale ITS

    I. Mavromatis, A. Tassi, R. J. Piechocki, and A. Nix

    In Proc. of IEEE Vehicular Technology Conference (VTC2019-Spring), Apr. 2019

    Millimeter Waves (mmWaves) will play a pivotal role in the next-generation of Intelligent Transportation Systems (ITSs). However, in deep urban environments, sensitivity to blockages creates the need for more sophisticated network planning. In this paper, we present an agile strategy for deploying road-side nodes in a dense city scenario. In our system model, we consider strict Quality-of-Service (QoS) constraints (e.g. high throughput, low latency) that are typical of ITS applications. Our approach is scalable, insofar that takes into account the unique road and building shapes of each city, performing well for both regular and irregular city layouts. It allows us not only to achieve the required QoS constraints but it also provides up to 50% reduction in the number of nodes required, compared to existing deployment solutions.

    @inproceedings{mmwaveInfrastructureITS,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of IEEE Vehicular Technology Conference (VTC2019-Spring)},
      title = {{Efficient Millimeter-Wave Infrastructure Placement for City-Scale ITS}},
      year = {2019},
      pages = {1-5},
      keywords = {intelligent transportation systems;millimetre wave communication;mobile computing;quality of service;telecommunication network planning;vehicular ad hoc networks;city-scale ITS;Intelligent Transportation Systems;deep urban environments;blockages;road-side nodes;dense city scenario;Quality-of-Service constraints;irregular city layouts;network planning;QoS constraints;millimeter-wave infrastructure placement;Urban areas;Roads;Quality of service;Buildings;Genetic algorithms;Tools;Computational geometry},
      doi = {10.1109/VTCSpring.2019.8746518},
      issn = {2577-2465},
      month = apr,
      address = {Kuala Lumpur, Malaysia},
    }

23. Poster: Parallel Implementation of the OMNeT++ INET Framework for V2X Communications

    I. Mavromatis, A. Tassi, R. J. Piechocki, and A. Nix

    In Proc. of IEEE Vehicular Networking Conference (VNC), Dec. 2018

    IEEE Popularity Award

    The field of parallel network simulation frameworks is evolving at a great pace. That is also because of the growth of Intelligent Transportation Systems (ITS) and the necessity for cost-effective large-scale trials. In this contribution, we will focus on the INET Framework and how we re-factor its single-thread code to make it run in a multi-thread fashion. Our parallel version of the INET Framework can significantly reduce the computation time in city-scale scenarios, and it is completely transparent to the user. When tested in different configurations, our version of INET ensures a reduction in the computation time of up to 43%.

    @inproceedings{parallelOmnet,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of IEEE Vehicular Networking Conference (VNC)},
      title = {{Poster: Parallel Implementation of the OMNeT++ INET Framework for V2X Communications}},
      year = {2018},
      doi = {10.1109/VNC.2018.8628429},
      issn = {2157-9865},
      month = dec,
      address = {Taipei, Taiwan},
      highlight = {IEEE Popularity Award},
    }

24. A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges

    I. Mavromatis, A. Tassi, R. Piechocki, and A. Nix

    In Proc. of Ad-hoc, Mobile, and Wireless Networks, Aug. 2018

    As we move towards autonomous vehicles, a reliable Vehicle-to-Everything (V2X) communication framework becomes of paramount importance. In this paper we present the development and the performance evaluation of a real-world vehicular networking testbed. Our testbed, deployed in the heart of the City of Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe the testbed architecture and its operational modes. Then, we will provide some insight pertaining the firmware operating on the network devices. The system performance has been evaluated under a series of large-scale field trials, which have proven how our solution represents a low-cost high-quality framework for V2X communications. Our system managed to achieve high packet delivery ratios under different scenarios (urban, rural, highway) and for different locations around the city. We have also identified the instability of the packet transmission rate while using single-core devices, and we present some future directions that will address that.

    @inproceedings{cityScaleITSG5,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. and {Nix}, A.},
      title = {{A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges}},
      booktitle = {Proc. of Ad-hoc, Mobile, and Wireless Networks},
      year = {2018},
      publisher = {Springer International Publishing},
      pages = {53-63},
      isbn = {978-3-030-00247-3},
      doi = {10.1007/978-3-030-00247-3_5},
      month = aug,
      address = {Saint-Malo, France},
    }

25. Efficient V2V Communication Scheme for 5G mmWave Hyper-Connected CAVs

    I. Mavromatis, A. Tassi, R. J. Piechocki, and A. Nix

    In Proc. of IEEE International Conference on Communications Workshops (ICC Workshops), May 2018

    Connected and Autonomous Vehicles (CAVs) require continuous access to sensory data to perform complex high-speed maneuvers and advanced trajectory planning. High priority CAVs are particularly reliant on extended perception horizon facilitated by sensory data exchange between CAVs. Existing technologies such as the Dedicated Short Range Communications (DSRC) are ill-equipped to provide advanced cooperative perception service. This creates the need for more sophisticated technologies such as the 5G Millimetre-Waves (mmWaves). In this work, we propose a distributed Vehicle-to-Vehicle (V2V) mmWaves association scheme operating in a heterogeneous manner. Our system utilises the information exchanged within the DSRC frequency band to bootstrap the best CAV pairs formation. Using a Stable Fixtures Matching Game, we form V2V multipoint-to-multipoint links. Compared to more traditional point-to-point links, our system provides almost twice as much sensory data exchange capacity for high priority CAVs while doubling the mmWaves channel utilisation for all the vehicles in the network.

    @inproceedings{efficientV2V,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of IEEE International Conference on Communications Workshops (ICC Workshops)},
      title = {{Efficient V2V Communication Scheme for 5G mmWave Hyper-Connected CAVs}},
      year = {2018},
      pages = {1-6},
      keywords = {5G mobile communication;millimetre wave communication;vehicles;vehicular ad hoc networks;mmWaves channel utilisation;trajectory planning;Dedicated Short Range Communications;distributed Vehicle-to-Vehicle mmWaves association scheme;DSRC frequency band;CAV pairs formation;V2V multipoint-to-multipoint links;sensory data exchange capacity;V2V communication;5G mmWave hyper-connected CAV},
      doi = {10.1109/ICCW.2018.8403780},
      issn = {2474-9133},
      month = may,
      address = {Kansas, USA},
    }

26. Agile Calibration Process of Full-stack Simulation Frameworks for V2X Communications

    I. Mavromatis, A. Tassi, R. J. Piechocki, and A. Nix

    In Proc. of IEEE Vehicular Networking Conference (VNC), Nov. 2017

    Computer simulations and real-world car trials are essential to investigate the performance of Vehicle-to-Everything (V2X) networks. However, simulations are imperfect models of the physical reality and can be trusted only when they indicate agreement with the real-world. On the other hand, trials lack reproducibility and are subject to uncertainties and errors. In this paper, we will illustrate a case study where the interrelationship between trials, simulation, and the reality-of-interest is presented. Results are then compared in a holistic fashion. Our study will describe the procedure followed to macroscopically calibrate a full-stack network simulator to conduct high-fidelity full-stack computer simulations.

    @inproceedings{agileCalibration,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of IEEE Vehicular Networking Conference (VNC)},
      title = {{Agile Calibration Process of Full-stack Simulation Frameworks for V2X Communications}},
      year = {2017},
      pages = {89-96},
      keywords = {calibration;computer networks;digital simulation;traffic engineering computing;vehicular ad hoc networks;vehicle-to-everything networks;V2X networks;high-fidelity full-stack computer simulations;full-stack network simulator;agile calibration process;Veins;Calibration;Roads;Conferences;Quality of service;Linux;Kernel;Connected Autonomous Vehicles;V2X;IEEE 802.11p/DSRC;Full-Stack Simulator;VEINS;Validation process},
      doi = {10.1109/VNC.2017.8275604},
      issn = {2157-9865},
      month = nov,
      address = {Torino, Italy},
    }

27. MmWave System for Future ITS: A MAC-Layer Approach for V2X Beam Steering

    I. Mavromatis, A. Tassi, R. J. Piechocki, and A. Nix

    In Proc. of IEEE Vehicular Technology Conference (VTC-Fall), Sep. 2017

    Millimetre Waves (mmWave) systems have the potential of enabling multi-gigabit-per-second communications in future Intelligent Transportation Systems (ITSs). Unfortunately, because of the increased vehicular mobility, they require frequent antenna beam realignments - thus significantly increasing the in-band Beamforming (BF) overhead. In this paper, we propose Smart Motion-prediction Beam Alignment (SAMBA), a MAC-layer algorithm that exploits the information broadcast via DSRC beacons by all vehicles. Based on this information, overhead-free BF is achieved by estimating the position of the vehicle and predicting its motion. Moreover, adapting the beamwidth with respect to the estimated position can further enhance the performance. Our investigation shows that SAMBA outperforms the IEEE 802.11ad BF strategy, increasing the data rate by more than twice for sparse vehicle density while enhancing the network throughput proportionally to the number of vehicles. Furthermore, SAMBA was proven to be more efficient compared to legacy BF algorithm under highly dynamic vehicular environments and hence, a viable solution for future ITS services.

    @inproceedings{macLayerBeamSteering,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of IEEE Vehicular Technology Conference (VTC-Fall)},
      title = {{MmWave System for Future ITS: A MAC-Layer Approach for V2X Beam Steering}},
      year = {2017},
      pages = {1-6},
      keywords = {access protocols;array signal processing;beam steering;intelligent transportation systems;millimetre wave communication;vehicular ad hoc networks;wireless LAN;information broadcast;DSRC beacons;overhead-free BF;SAMBA;sparse vehicle density;legacy BF algorithm;highly dynamic vehicular environments;future ITS services;MmWave system;MAC-layer approach;V2X beam steering;Millimetre Waves systems;future Intelligent Transportation Systems;ITSs;frequent antenna beam realignments;in-band Beamforming overhead;Smart Motion-prediction Beam Alignment;MAC-layer algorithm;vehicular mobility;Delays;Array signal processing;Antennas;Heuristic algorithms;Vehicle dynamics;Sensors;Resource management},
      doi = {10.1109/VTCFall.2017.8288267},
      issn = {},
      month = sep,
      address = {Toronto, Canada},
    }

28. Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

    I. Mavromatis, A. Tassi, R. J. Piechocki, and A. Nix

    In Proc. of Antennas, Propagation RF Technology for Transport and Autonomous Platforms, Feb. 2017

    Invited Paper

    Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.

    @inproceedings{beamAlignment,
      author = {{Mavromatis}, I. and {Tassi}, A. and {Piechocki}, R. J. and {Nix}, A.},
      booktitle = {Proc. of Antennas, Propagation   RF Technology for Transport and Autonomous Platforms},
      title = {{Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles}},
      year = {2017},
      pages = {1-8},
      keywords = {array signal processing;data communication;intelligent transportation systems;millimetre wave antennas;millimetre wave propagation;minimisation;mobile radio;mobile robots;motion estimation;motion measurement;radio links;radionavigation;wireless LAN;beam alignment;millimetre wave links;motion prediction;intelligent transportation systems;ultra-low end-to-end delays;multigigabit-per-second data transmission;mmWaves communications;connected and autonomous vehicles;beamforming algorithm;overhead-free beamforming training;CAV sensory data;dedicated short range communications;DSRC beacons;CAV motion estimation;CAV position estimation;position error minimisation;distinct error components;network performance;antenna beamwidth;IEEE 802.11ad approach;ITS applications;ITS services;Array signal processing;Training;Antennas;Delays;Global Positioning System;Autonomous vehicles;Algorithm design and analysis;Connected Autonomous Vehicles;mmWave;Beamwidth Optimisation;Beamforming;Heterogeneity;MAC layer;Vehicle-to-Infrastructure},
      doi = {10.1049/ic.2017.0023},
      month = feb,
      address = {Birmingham, UK},
      highlight = {Invited Paper},
    }