Page 59 - ITU Journal Future and evolving technologies Volume 3 (2022), Issue 2 – Towards vehicular networks in the 6G era

ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 2

GENERATION RATE CONTROL WITH AOI UNDER TRAFFIC HOLE PROBLEM IN VEHICULAR
NETWORKS

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Jiqing Gu , Chao Song , Siqi Liao , Hongwei Li , Ming Liu , and Jie Wu 2
1 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu,
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Sichuan, China, Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
NOTE: Corresponding author: Chao Song, chaosong@uestc.edu.cn
Abstract – In 6G mobile networks, vehicular networks will signi icantly bene it from extremely high network throughput
and capacity. For Internet of Things (IoT) within a vehicular network, the sensor data as an update is delivered from each
sensor source to a nearby gateway, by Vehicle‑to‑Vehicle (V2V) and Vehicle to Roadside unit (V2R) communications. The
mobility of the vehicles is not only affected by the vehicle itself, but also by external means, such as the signal operations of
traf ic lights. The red light stops the vehicles at the intersection, which will increase the time it takes for updates carried by
the vehicle to be delivered. On the other hand, the red light can also increase the opportunities of vehicles moving behind
to catch up with the waiting vehicles in forwarding updates. This is termed as the traf ic hole problem by the traf ic lights
in vehicular networks. In this paper, we investigate the in luence of traf ic lights in vehicular networks using the metric of
Age of Information (AoI). We discuss the optimal generation rate at the source by considering the trade‑off between AoI and
transmission cost. We propose a Total Average Cost aware generation rate Algorithm (TACA) for the generation interval time
at the sensor source. Our intensive simulations verify the proposed algorithm and evaluate the in luence of the traf ic lights
on AoI.

Keywords – Age of information, generation rate control, traf ic hole, vehicular network

1. INTRODUCTION information at the monitor/controller, the instantaneous
age at any time is de ined as the difference between the
The deep integration of 6G and vehicular networks current time and the generation time of the last update
spawns future vehicular networks that have the poten‑ that has been successfully received [7, 4]. In general, the
tial to support autonomous driving and other advanced AoI is the average of the instantaneous age. Ubiquitous
vehicular applications. A wide range of spectrums, such
smart devices and high‑quality wireless networks enable
as microwave, millimeter wave, Terahertz (THz) wave,
workers to participate in Spatial Crowdsourcing (SC) easi-
and visible light, is used for transmitting data generated
ly [8], which refers to assigning location‑based tasks to
by many types of on‑board sensors [1]. Over the past
moving workers, has drawn increasing attention. Many
several years, the Department of Transportation (DOT)
real‑world SC applications (e.g., Uber [9], and Waze [10])
and its operating administrations have engaged in nu‑
require vehicular networks for task sensing and data de‑
merous activities related to connected vehicles, including
livery, the impact of traf ic lights on the quality of SC can
Vehicle‑to‑Vehicle (V2V), Vehicle‑to‑Infrastructure (V2I),
be measured by AoI. That is, the traf ic lights can affect the
and Vehicle‑to‑Pedestrian (V2P) communications, collec‑
vehicular network directly, and affect the quality of SC in‑
tively referred to as “V2X” communications [2]. Vehicle‑ directly.
to‑Vehicle (V2V) communication technology can increase
the performance of vehicle safety systems and help save
lives [3]. Major applications include environment moni‑ However, the mobility of vehicles is not only affected by
toring, safety messages, and multimedia sharing [4]. Due the vehicle itself, but also by external means such as the
to the increasing demands of various applications on ve‑ signal operations of traf ic lights. Therefore, traf ic lights
hicles, both academic researchers and automotive indus‑ can affect data delivery in vehicular networks. For exam‑
tries are paying a lot of attention to vehicular networks. ple, while a vehicle carries an update to move along a path,
This is a particular type of mobile sensor network [5], in it may stop at a red light. We call this situation a traf‑
which the vehicle‑mounted sensors send harvesting data ic hole [11]. On the other hand, the vehicles stopped by
to a remote sink via vehicular networks. the red light must wait with the vehicles moving behind,
which could increase the opportunities for vehicles mo-
In vehicular networks, timely and lossless multi‑hop data ving behind to catch up in data forwarding. As shown in
delivery among vehicles is essential. A new metric called Fig. 1, the sensor source generates the updates with the
Age of Information (AoI) was introduced in [6] to cap‑ interval time , and the nearby vehicles will deliver
ture the requirement for timely status updating. To for‑ them to the next Roadside Unit (RSU) as a gateway for
mally model and capture the concept of the freshness of uploading to a server by V2V and V2R communications.

© International Telecommunication Union, 2022 47