A Network Stack for Computation-Centric Vehicular Networking

A Network Stack for Computation-Centric Vehicular Networking Dennis Grewe*, Claudio Marxer‡, Christopher Scherb‡, Marco Wagner*, Christian Tschudin‡ * Robert Bosch GmbH, Corporate Sector Research and Advance Engineering, Renningen, Germany {dennis.grewe,marco.wagner3}@de.bosch.com ‡University of Basel, Department of Mathematics and Computer Science, Basel, Switzerland {claudio.marxer,christopher.scherb,christian.tschudin}@unibas.ch

ABSTRACT Recently, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity transitioned from a vision of the future to reality. Applications in such environments vary from local propagation of road conditions to large-scale traffic flow control systems. In this demo, we present a network stack for the data exchange in the automotive IoT, based on the Named Function Networking (NFN) principles. In NFN, the communication model is not restricted to propagation of static data but natively supports computation-offloading to other nodes. We present solutions and report on experiments with real cars on a test course. Figure 1: NFN on ETSI ITS-G5 protocol stack.

CCS CONCEPTS • Networks → In-network processing; Naming and addressing; Network protocol design;

KEYWORDS Connected Vehicles, Named Function Networking, Mobile Edge Computing, Information-Centric Networking ACM Reference Format: Dennis Grewe*, Claudio Marxer‡, Christopher Scherb‡, Marco Wagner*, Christian Tschudin‡. 2018. A Network Stack for Computation-Centric Vehicular Networking. In 5th ACM Conference on Information-Centric Networking (ICN ’18), September 21–23, 2018, Boston, MA, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3267955.3269015

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INTRODUCTION

Content-centric networks (CCN) offer the service to deliver information based on the content’s name [2]. This means, clients are released from holding server locations and request content in a hop-by-hop fashion. As an extension, Named Function Networking (NFN) allows users to request for derived, i.e. dynamically produced, data [7]. In NFN, named functions are published in the network, which can be applied to any available content on demand. For this purpose, a user composes a data name which encodes the application of named function(s) to certain data. It is the task of the network to compute and deliver the result. Behind the scenes, the Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). ICN ’18, September 21–23, 2018, Boston, MA, USA © 2018 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-5959-7/18/09. https://doi.org/10.1145/3267955.3269015

network implements a forwarding strategy which decides where a computation is executed [6]. Mobile Edge Computing with NFN. In challenged networks such as connected vehicle environments, Edge Computing (EC) has become popular to bring computational resources to the edge of the network to produce and manage data closer to the consumers. By combining NFN and EC, the computation of data is performed only few hops away from the consumer. In NFN, the identification of the computation location is realized by using so-called resolution strategies. As part of this work, the Find and Execute (FaX) forwarding strategy is used, originally designed for mobile IoT environments [5]. An exemplary presentation of the strategy can be described as follows: it is expected, that a result of a computation intensive operation can not always be shipped directly to a mobile consumer. In FaX, the closest computation-capable node starts the computation immediately, and checks adjacent computation nodes for a already computed result in parallel. As a result, FaX reduces the resolution time and increases data delivery success rate. This work contributes a full stack implementation of NFN for the 5th Generation of Intelligent Transportation Systems of the European Telecommunications Standards Institute (ETSI ITS-G5) connected vehicle protocol stack. Furthermore, the stack has been tested including real cars and a infrastructural deployment showing the offloading of computations from vehicles to the infrastructure.

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INTEGRATION OF NFN INTO ETSI ITS-G5 SYSTEMS

The stack developed in this work integrates the NFN implementation PiCN and enables vehicular units to exchange ITS-G5 as well as CCN messages between other vehicles and an infrastructure in parallel. Figure 1 illustrates the process of integrating NFN into

ICN ’18, September 21–23, 2018, Boston, MA, USA

D.Grewe, C.Marxer, C.Scherb, M.Wagner, C.Tschudin

Computation 5

Computation RSU2 Search for cached Result

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RSU1 2

Result 7 Result

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Interest: I1

Interest: I1

Car Movement

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Figure 2: Scenario of field experiments.

Figure 3: Vehicular unit hardware on the field test.

the stack. Existing safety-critical applications are still supported using the ETSI ITS-G5 stack, while computations can be offloaded to infrastructural components using the NFN layer.

offloading of computations to the infrastructure and demonstrates how a result can be fetched, in case the computation time is longer than the time the car is connected to a single RSU. For the demonstration of the automotive scenario, mobility is a key aspect and the FaX forwarding strategy is designed for highly mobile use cases. Since it is difficult to demonstrate mobility with moving vehicles due to space constraints in the demo session, in the second part this aspect will be presented with a video, a simulation and a poster. We report on experiments involving real cars on a test course.

The layers used in the stack include: • IEEE 802.11p access layer in the 5.9 GHz band through a Linux kernel modification [4]. As part of the prototype, the ITS-G5D band (5905MHz - 5925MHz frequency with 10MHz channel spacing) and the service channel G5-SCH5 are used which are reserved for non-safety future applications [1]. • ETSI ITS-G5 Network & Transport layer (including the Decentralized Congestion Ccontrol (DCC), Geo-Networking (GeoNet), CAM/DENM) based on OpenC2X [3]. • OpenC2X NFN module based on the NFN implementation PiCN 1 . The module implements the required CCN data structures as well as the FaX resolution strategy.

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SYSTEM DEMONSTRATION

This work demonstrates two contributions: (i) computation offloading supporting mobility using the novel network stack and (ii) a demonstration of the stack in a real world automotive deployment including road-site units (RSU) and vehicular units (VU) using ETSI ITS-G5. The feature set of the stack will be demonstrated on a RSU and a VU using IPC boards (NF36-2600 - 1GHz CPU, 1GB RAM). As part of the demonstration, the communication and the computation offloading capability are presented using NFN computations in a simple two node setup. Furthermore, we will present our field experiments used to validate the system (cf. Figure 3) The vehicle sends out NFN requests for a computation intensive operation during the journey (e.g., augmentation of a map) (Figure 2, part 1). A RSU (here: RSU1 ) starts the computation (Figure 2, part 2), while the car moves out of the communication range of the RSU1 (Figure 2, part 3). As soon as the car connects to RSU2 , it send out the NFN request again (Figure 2, part 4). Based on the FaX strategy, RSU2 starts the computation and searches for a cached result in the network (Figure 2, part 5). Since the previous RSU1 has finished the computation, the result can be directly transported to RSU2 (Figure 2, part 6). Finally, the result can be directly shipped to the car (Figure 2, part 7). This field experiment has shown the 1 https://github.com/cn-uofbasel/PiCN

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