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Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems

Aidin Ferdowsi, Ursula Challita, Walid Saad

arXiv:1712.04135v1cs.ITstat.ML

TL;DR

ITS analytics requires reliable, ultra-low-latency processing of heterogeneous sensor data beyond what cloud-centric approaches provide. The paper proposes a deep-learning-enabled edge architecture using vehicle and passenger devices, with preliminary results including a 0.1-second cloud security-check delay and a conclusion that the approach can support reliable and secure transportation.

  • Problem

    Cloud-centric ITS processing can impose high communication and computation latency, while edge analytics introduces security risks in vehicle-to-vehicle links.

  • Method

    The paper proposes an ITS edge architecture that runs deep-learning techniques on passenger mobile devices and intra-vehicle processors for heterogeneous data, control, platooning, and security tasks.

  • Results

    0.1 seconds is the reported delay for the security check at the cloud, while the proposed architecture is concluded to optimize ITS computation, latency, reliability, and operation.

  • Takeaways & Limitations

    Processing vehicle data at the edge and transmitting summaries or basic results to the cloud is presented as a basis for reliable, secure, and smart transportation operation.

Abstract

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Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.

I. INTRODUCTION

ITS edge analytics addresses the latency, reliability, and computational limits of cloud-centric processing by moving deep-learning-enabled analytics to vehicles and intra-vehicle devices. The paper investigates challenges across heterogeneous data, autonomous control, platooning, driver behavior, and security.

  • Motivation: Cloud-centric processing can create high delays, network congestion, and computational overload when massive mobile-sensor data is transmitted remotely.Delayed decisions may cause congestion, travel delays, and accidents.
  • Motivation: Passenger mobile devices and intra-vehicle processors can serve as edge processors, but require artificial-intelligence algorithms for effective analytics.The paper motivates deep learning as a way to provide optimized edge analytics.
  • Contribution: The paper introduces an ITS edge analytics architecture that incorporates deep learning at passenger mobile devices and intra-vehicle processors.The architecture is presented as a response to ITS latency and reliability challenges.
  • Contribution: Proposed techniques target heterogeneous data processing, path planning, platoon control, driver behavior prediction, and cyber-physical security while reducing cloud communication and computation load.The techniques are intended for implementation at the ITS edge.
  • Novelty: Unlike existing deep-learning and ITS surveys, this paper proposes an ITS edge analytics framework focused on reliability and latency.The architecture transmits summaries or basic results to the cloud instead of entire generated datasets.

II. MOBILE EDGE ANALYTICS IN ITSS: OPPORTUNITIES AND CHALLENGES

ITS edge analytics must handle high-volume sensor data under ultra-low-latency and reliability requirements. Edge processing with capable vehicle and passenger devices is proposed to reduce cloud dependence and improve these constraints.

  • Challenges: ITS sensors generate high volumes of data that must be processed with low latency for reliable operation.Ultra-low latency can require shorter packets, while reliability requires additional communication resources.
  • Challenges: Uploading all vehicle and roadside-sensor data to a remote cloud creates high end-to-end latency from communication and computation loads.The paper identifies this as a limitation of conventional cloud processing.
  • Opportunity: An edge architecture using intra-vehicle processors and passenger mobile equipment can potentially improve latency and reliability while reducing reliance on the remote cloud.The proposed arrangement performs ITS analytics closer to the data sources.

A. Heterogeneous Data Sources

ITS applications must process heterogeneous, high-volume measurements while planning routes and controlling vehicle dynamics in real time. Cloud-centric control is constrained by bandwidth and communication latency, motivating per-vehicle edge algorithms.

  • Heterogeneous Data Sources: Autonomous ITSs combine heterogeneous measurements from environmental, vehicle, fuel-consumption, and driver-fatigue sensors.These real-time data streams require reliable, ultra-low-latency information sharing.
  • Path Planning and Autonomous Control: Self-driving vehicles must plan routes and control speed, direction, and acceleration despite dense traffic and unpredictable human-driver decisions.These requirements make path planning and autonomous control challenging.
  • Path Planning and Autonomous Control: Cloud-centric path planning requires vehicle tracking and location transmission, increasing bandwidth demands and vehicle-to-cloud latency.The paper therefore calls for online algorithms implemented individually by each vehicle.

C. Vehicular Platoon Control

Vehicular platooning requires real-time decisions from each vehicle while accommodating both autonomous and human-controlled traffic. Edge analytics must also address security risks because inter-vehicle links can be vulnerable to attack.

  • Vehicular Platoon Control: Platooning can improve highway safety, traffic stability, and fuel consumption, but each vehicle must make real-time dynamics decisions with minimal effect on platoon stability.Vehicles use measurements and images from their environment for these decisions.
  • Vehicular Platoon Control: Semi-autonomous ITSs must coordinate self-driving vehicles with driver-controlled vehicles whose behavior can be unpredictable.This coexistence complicates control and navigation decisions.
  • ITS Security: Edge analytics can increase exposure to cyber-physical attacks because adversaries may more easily target links between vehicles than the cloud.This creates a security boundary for distributed ITS processing.
  • ITS Security: Faulty edge measurements or injected information can cause accidents, injuries, and non-optimal platoon control stability.The paper calls for authenticated inter-vehicle and vehicle-cloud communications plus anomaly and collision detection.
  • Analytics Requirements: Computer vision extracts image features and surrounding objects, while heterogeneous-data processing extracts correlations across signals from different sources.The passage also identifies conventional encryption as insufficient for inter-vehicle communication protection.

III. DEEP LEARNING FOR MOBILE SENSING IN ITS

Deep neural networks provide hierarchical feature learning for complex, high-dimensional ITS sensor data, with different architectures suited to sensing, control, vehicle dynamics, and security.

  • III. DEEP LEARNING FOR MOBILE SENSING IN ITS: DNNs use multiple hidden layers to learn hierarchical representations of complex, high-dimensional ITS data.Relevant inputs include camera, LIDAR, radar, and vehicle-dynamics readings.
  • III. DEEP LEARNING FOR MOBILE SENSING IN ITS: CNNs and restricted Boltzmann machines support heterogeneous data processing and automatic control.
  • III. DEEP LEARNING FOR MOBILE SENSING IN ITS: RNNs support vehicle-dynamics estimation, while GANs and LSTMs support ITS security applications.

B. Deep Learning for ITS Edge Analytics and Mobile Sensing

Mobile sensing in ITS requires combining heterogeneous sensor streams for perception and control at the vehicle edge. The proposed architecture combines offline maps, online sensor inputs, CNNs, and LSTMs for faster path decisions.

  • B. Deep Learning for ITS Edge Analytics and Mobile Sensing: Self-driving vehicles combine light, camera, ultrasound, and other sensor readings to assess their environment.
  • B. Deep Learning for ITS Edge Analytics and Mobile Sensing: Path planning combines offline environmental maps with online sensor inputs that detect dynamic objects.
  • B. Deep Learning for ITS Edge Analytics and Mobile Sensing: A CNN-LSTM network can identify objects and support online edge decisions for self-driving vehicles.The architecture is intended to reduce decision delay and the probability of accidents or congestion events while increasing fuel efficiency.

3) CNNs and Vehicular Communication for Platoon Control:

Vehicular platoons can reduce duplicated processing by sharing a leading vehicle’s environmental decisions, while CNNs support similarity detection and adaptive V2X resource allocation.

  • 3) CNNs and Vehicular Communication for Platoon Control:: In a platoon, the leading vehicle can process shared environmental information and transmit results to followers through V2V links.
  • 3) CNNs and Vehicular Communication for Platoon Control:: CNNs compare follower observations with the leader’s results so similar observations can reuse the leader’s processing output.
  • 3) CNNs and Vehicular Communication for Platoon Control:: CNN-based reinforcement learning can dynamically adapt V2X power control and spectrum allocation to vehicle operation.
  • 3) CNNs and Vehicular Communication for Platoon Control:: 250 Mbps of data is generated before 75 seconds when all three vehicles independently observe a non-steady-state environment.After 75 seconds, a steady-state follower can use the leader’s computational result instead of recording environmental images.
  • 3) CNNs and Vehicular Communication for Platoon Control:: An edge RNN can predict a leading driver’s future actions from observed decisions, reducing sensor readings needed by a following vehicle.

5) GAN and LSTM for ITS Security:

GAN and LSTM models address ITS security by detecting stealthy or unauthenticated data, with LSTM fingerprint checks introducing a reported 0.1-second cloud delay. Figure 5 summarizes the proposed deep-learning solutions for mobile sensing.

  • 5) GAN and LSTM for ITS Security:: A GAN discriminator at ITS edge components can distinguish adversarially generated data from real data.
  • 5) GAN and LSTM for ITS Security:: LSTMs can fingerprint vehicle data sequences and detect unauthenticated messages through fingerprint mismatch.
  • 5) GAN and LSTM for ITS Security:: 0.1 seconds is the reported delay for the cloud security check comparing extracted and authenticated fingerprints.The paper states that edge implementation can provide better accuracy with lower delay than traditional cloud-based security architecture.
  • 5) GAN and LSTM for ITS Security:: Figure 5 provides an overview of the proposed deep-learning solutions for mobile sensing in ITS.

C. Future Research Challenges in DNNs for ITS Edge Analytics

Future ITS edge analytics must address data quality and the limited capabilities of distributed edge devices. Accurate, heterogeneous training data and reliable edge processing remain central requirements.

  • Data Accuracy: Deep learning requires large, accurate datasets, while inaccurate training data can produce erroneous artificial neural networks.
  • Data Accuracy: ITS decision-making depends on precisely supervised heterogeneous data, including vehicle position, speed, inter-vehicle spacing, and roadside-object distances.
  • Data Accuracy: Distributed processing across vehicles, roadside sensors, and mobile devices can make results more error prone because edge devices have limited capabilities.

2) Unpredictable Situations:

ITS edge analytics must handle unpredictable driver and environmental behavior while remaining resilient when accidents damage edge devices. These conditions can increase reaction delays and require fallback processing.

  • Unpredictable Situations: Random driver behavior and environmental effects can create unpredictable situations that delay ITS reactions.
  • Unpredictable Situations: Deep learning methods should learn driver behavior to reduce delays caused by reacting to drivers’ actions.
  • Unpredictable Situations: Security and memory constraints prevent storing driving profiles at the edge, increasing the number of queries sent to the cloud.
  • Unpredictable Situations: After accidents damage edge devices, ITS processing must recover quickly by relying on neighboring devices or reverting to cloud processing.

5) Robustness:

Robust ITS edge analytics must address cyberattacks and training overhead while preserving reliable operation. The paper positions its architecture as a framework for applying deep learning across these challenges.

  • Robustness: Cyber attackers can deceive deep neural networks by injecting intentional noise, such as altering pixels in traffic signs.
  • Robustness: Moving intelligence to the network edge increases cyberattack threats, so edge deep learning methods require appropriate security.
  • Robustness: DNN training can be time consuming, making testing and adding new features difficult in some scenarios.
  • Robustness: The proposed architecture applies deep learning to heterogeneous measurements, path planning, vehicle and platoon control, semi-autonomous ITSs, and cyber-physical security.
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