Distributed Geo-Spatial LISP Blackboard for Automotive
draft-barkai-lisp-nexagon-00

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LISP Working Group                                                    S. Barkai
Internet-Draft                                                B. Frenandez-Ruiz
Intended status: Experimental                                        O. Serfaty
Expires: September 4, 2019                                            Nexar Inc.
                                                             A. Rodriguez-Natal
                                                                       F. Maino
                                                                  Cisco Systems
                                                           A. Cabellos-Aparicio
                                              Technical University of Catalonia
                                                               February 4 2019

          Distributed Geo-Spatial LISP Blackboard for Automotive
                            draft-barkai-lisp-nexagon-00

Abstract

   This document specifies the use of LISP Blackboard for distributed
   Geo-Spatial Publish/Subscribe automotive applications.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Requirements Language . . . . . . . . . . . . . . . . . . . .   3
   3.  Deployment Assumptions  . . . . . . . . . . . . . . . . . . .   3
   4.  Nexagon Publish Procedure . . . . . . . . . . . . . . . . . .   4
   5.  Nexagon Subscribe Procedure  . . . . .  . . . . . . . . . . .   5
   6.  XTR Sharding-Handover tunnel . . . . . .  . . . . . . . . . .   7
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .   8
   8.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .   8
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   8
   10. Normative References  . . . . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

  (1) The Locator/ID Separation Protocol (LISP) [RFC6830] splits current IP
  addresses in two different namespaces, Endpoint Identifiers (EIDs) and
  Routing Locators (RLOCs).
  LISP uses a map-and-encap approach that relies on (1) a Mapping System
  (basically a distributed database) that stores and disseminates EID-RLOC
  mappings and on (2) LISP tunnel routers (xTRs) that encapsulate and
  decapsulate data packets based on the content of those mappings.

  (2) H3 is a geospatial indexing system using a hexagonal grid that can be
  (approximately) subdivided into finer and finer hexagonal grids,
  combining the benefits of a hexagonal grid with hierarchical subdivisions.
  H3 supports sixteen resolutions. Each finer resolution has cells with one
  seventh the area of the coarser resolution. Hexagons cannot be perfectly
  subdivided into seven hexagons, so the finer cells are only approximately
  contained within a parent cell. Each cell is identified by a 64bit int.

  (3) The Berkeley Deep Drive (BDD) Industry Consortium investigates state-of-
  the-art technologies in computer vision and machine learning for automotive
  applications. BDD based taxonomy of published automotive scene classification.

  These standards are combined to create an in-network key-value blackboard -
  reflecting the state of each 1sqm hexagon tile of road. The lisp network maps
  traffic form vehicle endpoint IP identifiers (EID) to the routing location
  (RLOC) of h3 hexagon identifier (HID).

  Th lisp network blackboard bridges the time-space gap between vision & sensory
  (publishers) - and - driving apps/smart-infrastructure (subscribers).
  Drivers (EID) communicate with blackboard tiles (HID), EID<=> RLOC <=> HID,
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