Big Automotive Data Preprocessing: A Three Stages Approach

Poster (2019, October 08)

The automotive industry generates large datasets of various formats, uncertainties and frequencies. To exploit Automotive Big Data, the data needs to be connected, fused and preprocessed to quality ... [more ▼]

The automotive industry generates large datasets of various formats, uncertainties and frequencies. To exploit Automotive Big Data, the data needs to be connected, fused and preprocessed to quality datasets before being used for production and business processes. Data preprocessing tasks are typically expensive, tightly coupled with their intended AI algorithms and are done manually by domain experts. Hence there is a need to automate data preprocessing to seamlessly generate cleaner data. We intend to introduce a generic data preprocessing framework that handles vehicle-to-everything (V2X) data streams and dynamic updates. We intend to decentralize and automate data preprocessing by leveraging edge computing with the objective of progressively improving the quality of the dataflow within edge components (vehicles) and onto the cloud. [less ▲]

SDN-based Pseudonym-Changing Strategy for Privacy Preservation in Vehicular Networks

in 15th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob'19) (2019, October)

The pseudonym-changing approach is the de-factolocation privacy solution proposed by security standards toensure that drivers are not tracked during their journey. SeveralPseudonym Changing Strategies ... [more ▼]

The pseudonym-changing approach is the de-factolocation privacy solution proposed by security standards toensure that drivers are not tracked during their journey. SeveralPseudonym Changing Strategies (PCSs) have been proposed tosynchronize Pseudonym Changing Processes (PCPs) between con-nected vehicles. However, most of the existing strategies are static,rigid and do not adapt to the vehicles’ context. In this paper, weexploit the Software Defined Network (SDN) paradigm to proposea context-aware pseudonym changing strategy (SDN-PCS) whereSDN controllers orchestrate the dynamic update of the securityparameters of the PCS. Simulation results demonstrate that SDN-PCS strategy outperforms typical static PCSs to perform efficientPCPs and protect the location privacy of vehicular network users [less ▲]

Multi-flow congestion-aware routing in software-defined vehicular networks

in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) Proceedings (2019, September 22)

5G-enabled vehicular networks will soon allow their users to exchange safety and non-safety related information over heterogeneous communication interfaces. Routing vehicular data flows over multi-hop ... [more ▼]

5G-enabled vehicular networks will soon allow their users to exchange safety and non-safety related information over heterogeneous communication interfaces. Routing vehicular data flows over multi-hop Vehicle-to-Vehicle (V2V) communications is one of the hardest challenges in vehicular networking, and it has been tackled in literature by using distributed algorithms. The distributed approach has shown significant inefficiencies in such dynamic vehicular scenarios, mainly due to poor network congestion control. To overcome the complexity of the envisioned architecture, and the inefficiency of distributed routing algorithms, we hereby propose to leverage the coordination capabilities of Software-Defined Networking (SDN) to determine optimal V2V multi-hop paths and to offload traffic from the Vehicle-to-Infrastructure-to-Vehicle (V2I2V) to the V2V communications, using both cellular and Wi-Fi technologies. In order to achieve this goal, we propose Multi-Flow Congestion-Aware Routing (MFCAR), a centralized routing algorithm that relies on graph theory to choose short and uncongested V2V paths. Realistic simulations prove that MFCAR outperforms well- established centralized routing algorithms (e.g. Dijkstra's) in terms of Packet Delivery Ratio (PDR), goodput and average packet delay, up to a five-fold performance gain. [less ▲]

IoT Device Fingerprinting: Machine Learning based Encrypted Traffic Analysis

in The IEEE Wireless Communications and Networking Conference (WCNC) (2019, April 19)

Even in the face of strong encryption, the spectacular Internet of Things (IoT) penetration across sectors such as e-health, energy, transportation, and entertainment is expanding the attack surface ... [more ▼]

Even in the face of strong encryption, the spectacular Internet of Things (IoT) penetration across sectors such as e-health, energy, transportation, and entertainment is expanding the attack surface, which can seriously harm users’ privacy. We demonstrate in this paper that an attacker is able to disclose sensitive information about the IoT device, such as its type,by identifying specific patterns in IoT traffic. To perform the fingerprint attack, we train machine-learning algorithms based on selected features extracted from the encrypted IoT traffic.Extensive simulations involving the baseline approach show that we achieve not only a significant mean accuracy improvement of 18.5% and but also a speedup of 18.39 times for finding the best estimators. Obtained results should spur the attention of policymakers and IoT vendors to secure the IoT devices they bring to market. [less ▲]

How Road and Mobile Networks Correlate: Estimating Urban Traffic Using Handovers

in IEEE Transactions on Intelligent Transportation Systems (2019)

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network ... [more ▼]

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network signaling data. To achieve this, we estimate the vehicular density inside specific areas using a polynomial function of the inner and exiting mobile phone handovers performed by the base stations covering those areas. We can then use the aggregated handovers as flow proxies alongside the density proxy to directly estimate an average velocity within an area. We evaluate the model in a simulation study of Luxembourg city and generalize our findings using a real-world data set extracted from the LTE network of a Luxembourg operator. By predicting the real traffic states as measured through floating car data, we achieve a mean absolute percentage error of 11.12%. Furthermore, in our study case, the approximations of the network macroscopic fundamental diagrams (MFD) of road network partitions can be generated. The analyzed data exhibit low variance with respect to a quadratic concave flow-density function, which is inline with the previous theoretical results on MFDs and are similar when estimated from simulation and real data. These results indicate that mobile signaling data can potentially be used to approximate MFDs of the underlying road network and contribute to better estimate road traffic states in urban congested networks. [less ▲]

Aggregation of MQTT Topics over Integrated Satellite Terrestrial Networks

in ACM SIGMETRICS Performance Evaluation Review (2019, January)

The MQTT application protocol was originally designed for monitoring a oil pipeline through the desert by collecting sensor data via satellite link. Thus, by design MQTT is very suitable for data ... [more ▼]

The MQTT application protocol was originally designed for monitoring a oil pipeline through the desert by collecting sensor data via satellite link. Thus, by design MQTT is very suitable for data collection over integrated satellite-terrestrial networks. Leveraging on the MQTT Bridge functionality, in this work we propose a novel architecture with two MQTT Brokers located at the satellite terminal and the satellite gateway. By using the topic pattern option, supported by the bridge, a subscriber can request several topics within a single request. To reduce the amount of traffic ex-changed over the satellite return channel, we introduce in the architecture a new entity, namely MQTT message aggregation filter, which aggregates all the MQTT topics matching the topic pattern in the same response. [less ▲]

Multipathing Traffic to Reduce Entry Node Exposure in Onion Routing

Poster (2019)

Advancing the Security of Trustworthy Self-IoT (Position Paper)

in International Conference on Smart Applications, Communications and Networking (SmartNets) (2018, November)

The Internet of Things (IoT) encompasses many aspects of our daily life, from connected homes and cities through connected vehicles and roads to devices that collaborate independently to achieve a ... [more ▼]

The Internet of Things (IoT) encompasses many aspects of our daily life, from connected homes and cities through connected vehicles and roads to devices that collaborate independently to achieve a specific purpose. Being an example of a largescale self-organizing systems, the IoT should present imperative properties such as autonomy and trustworthiness. However, compared to classical self-organizing systems, IoT has intrinsic characteristics (wide deployment, resource constraints, uncertain environment, etc.) that open up several security challenges. These challenges cannot be solved by existing Autonomic and Organic Computing techniques and therefore new techniques adapted to self-organizing IoT, (that we call Self-IoT) peculiarities are needed. To this end, this paper studies related work in the area of self-organizing IoT, identifies and describes the key research challenges for trustworthy secure Self-IoT and proposes new and tailored existing solutions. [less ▲]

Revisiting Gaussian Mixture Models for Driver Identification

in Proceedings of IEEE International Conference on Vehicular Electronics and Safety (ICVES) (ICVES 2018) (2018, September)

The increasing penetration of connected vehicles nowadays has enabled driving data collection at a very large scale. Many telematics applications have been also enabled from the analysis of those datasets ... [more ▼]

The increasing penetration of connected vehicles nowadays has enabled driving data collection at a very large scale. Many telematics applications have been also enabled from the analysis of those datasets and the usage of Machine Learning techniques, including driving behavior analysis predictive maintenance of vehicles, modeling of vehicle health and vehicle component usage, among others. In particular, being able to identify the individual behind the steering wheel has many application fields. In the insurance or car-rental market, the fact that more than one driver make use of the vehicle generally triggers extra fees for the contract holder. Moreover being able to identify different drivers enables the automation of comfort settings or personalization of advanced driver assistance (ADAS) technologies. In this paper, we propose a driver identification algorithm based on Gaussian Mixture Models (GMM). We show that only using features extracted from the gas pedal position and steering wheel angle signals we are able to achieve near 100 accuracy in scenarios with up to 67 drivers. In comparison to the state-of-the-art, our proposed methodology has lower complexity, superior accuracy and offers scalability to a larger number of drivers. [less ▲]

Performance Analysis of CoAP under Satellite Link Disruption

in Performance Analysis of CoAP under Satellite Link Disruption (2018, June)

Internet of Things (IoT) devices connectivity is steadily increasing in both heterogeneity and sophistication. However, classical and emerging technology (Wi-Fi, Zigbee, LoRa, etc.) are not able to ... [more ▼]

Internet of Things (IoT) devices connectivity is steadily increasing in both heterogeneity and sophistication. However, classical and emerging technology (Wi-Fi, Zigbee, LoRa, etc.) are not able to support well IoT applications, when terrestrial networks are no longer available (e.g., in remote not habitable areas, in the occurrence of calamities). Subsequently, the only way forward is to transmit IoT data over satellite. The integrated satellite-terrestrial networks are emerging as a promising solution to ensure ubiquitous IoT connectivity, higher throughput and reliability. Being different by design, IoT protocols’ tuning is needed to integrate terrestrial and satellite segments. In the current paper, we evaluate the performances of CoAP, the well-known lightweight application protocol for IoT in an integrated scenario, taking into account the satellite link disruption. The key findings of our study, conducted using the OpenSAND simulator, show that decreasing the value of congestion control parameters proposed by the standard [1], mainly ACK_TIMEOUT and ACK_RANDOM_FACTOR, is crucial to achieve lower end-to-end delays and higher packet delivery ratio. [less ▲]