Multi-Staged Framework for Safety Analysis of Offloaded Services in Distributed Intelligent Transportation Systems

TL;DR

This paper proposes a multi-staged safety analysis framework for offloaded services in distributed intelligent transportation systems, enhancing reliability and security for connected autonomous vehicles.

Contribution

It introduces a novel multi-staged safety analysis framework integrated into service offloading for ITS, addressing data reliability and security concerns.

Findings

Framework effectively detects corrupted remote data.

Inclusion of safety analysis improves reliability of offloaded services.

Energy savings achieved through function offloading are maintained.

Abstract

The integration of service-oriented architectures (SOA) with function offloading for distributed, intelligent transportation systems (ITS) offers the opportunity for connected autonomous vehicles (CAVs) to extend their locally available services. One major goal of offloading a subset of functions in the processing chain of a CAV to remote devices is to reduce the overall computational complexity on the CAV. The extension of using remote services, however, requires careful safety analysis, since the remotely created data are corrupted more easily, e.g., through an attacker on the remote device or by intercepting the wireless transmission. To tackle this problem, we first analyze the concept of SOA for distributed environments. From this, we derive a safety framework that validates the reliability of remote services and the data received locally. Since it is possible for the autonomous driving task to offload multiple different services, we propose a specific multi-staged framework for safety analysis dependent on the service composition of local and remote services. For efficiency reasons, we directly include the multi-staged framework for safety analysis in our service-oriented function offloading framework (SOFOF) that we have proposed in earlier work. The evaluation compares the performance of the extended framework considering computational complexity, with energy savings being a major motivation for function offloading, and its capability to detect data from corrupted remote services.