Controlled Emission Zone Pollution Resource Management in

5G C-ITS

Tomasz Mach and Wei Guo

Samsung R&D Institute UK, Communications House, South Street, Staines-upon-Thames, U.K.

Keywords: Vehicular Pollution Control, Controlled Emission Zone, C-ITS, Pollution Grant, Pollution Grant Scheduler,

Pollution Grant Signalling, 5G Wireless Communications, MEC, Internet of Things, Smart Cities.

Abstract: An innovative pollution resource management scheme is proposed to tackle air pollution. The scheme

introduces a novel concept of a pollution grant, a centralised pollution grant scheduler and accompanying

pollution grant signalling between the scheduler and controlled polluting vehicle or a stationary source in a

co-operative ITS environment. The scheme is analysed and discussed qualitatively as it can be effectively

applied to controlled emission zones in cities and as a result, can improve the pollution control fairness,

effectiveness and efficiency. The scheme can be implemented as a new pollution resource management

function in 5G wireless base station within MEC architecture to leverage its low latency capabilities in

parallel to its traditional radio resource management role.

1 INTRODUCTION

Air pollution from vehicles has been one of the most

challenging aspects of the urban transport systems.

Toxic gaseous and particulate matter emissions from

vehicle internal combustion engines such as carbon

oxides, nitrogen oxides, particulate matters etc.

contribute a significant part to the urban atmospheric

pollutants. To protect public health, the total amount

of generated pollution is controlled by the

legislation. E.g. the hourly NO

concentrations in the

air are regulated by EU and UK legislations. The

soft concentration limit is 200µg/m

measured

hourly and the hard concentration limit is less than

18 excesses per year (Air Quality Expert Group,

2004).

Local city authorities are required to meet air

pollution regulations to ensure the city air quality.

Traffic pollution charge schemes are introduced with

the aim of reducing tailpipe emissions. The schemes

are typically based on a simplified version of ‘pay as

you pollute’ principle. A typical example is London

Low Emission Zone (LEZ) (Transport for London,

retrieved 2017. Watkiss, 2003. Chapman, 2010).

Such LEZs are currently common across Europe and

beyond (European Commission, retrieved 2017.

Weinmann, 2014. Transport and Travelling

Research Ltd., 2006). London authority has even

stepped up the measure by introducing Ultra Low

Emission Zone (ULEZ) (Transport for London,

retrieved 2017) in the most polluted city centre. The

zone itself is a geographically defined area where

access by certain polluting vehicles is restricted or

deterred with the aim of improving the air quality.

Only vehicles not conforming to higher emission

standard (e.g. EURO 4 for lorries) are charged.

Other vehicles not covered by the scheme enter the

zone for free.

The amount of vehicle generated pollutants in a

controlled emission zone like LEZ mainly depends

on:

 Distance travelled in the zone

 Time spent in the zone

 Vehicle speed in the zone

 Alternative engine power source used (for

hybrid vehicles) in the zone.

Existing controlled emission zone traffic

pollution charge schemes are not based on accurate

amount of the actually generated vehicle emissions.

This limitation is mainly caused the by the lack of

efficient methods to directly control actual emissions

from every vehicle accurately. The charging model

based on a flat fee when entering the LEZ fee is not

linked to the actually generated vehicle pollution and

as a result, it could be argued that the charging

model is not fair. Therefore, a more efficient traffic

pollution control approach to fairly implement “pay

Mach, T. and Guo, W.

Controlled Emission Zone Pollution Resource Management in 5G C-ITS.

DOI: 10.5220/0006670402830289

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 283-289

ISBN: 978-989-758-293-6

283

as you pollute” model is required in such controlled

emission zones like LEZ.

In this paper, we propose a new scheme of

Pollution Resource Management (PRM) for

controlled emission zones which can be used to

improve the fairness, efficiency and effectiveness of

such controlled emission zones.

The remaining of the paper is organised as

follows: section two specifies the proposed PRM

scheme in details; section three gives some

alternative implementations of the scheme; and in

the last section we discuss the scheme and provide

conclusions.

2 SPECIFICATION OF THE PRM

SCHEME

PRM defines pollution as a controllable and

shareable common user resource by using regulatory

limits in space and time, i.e. the difference between

regulatory pollutant limit and actual mass of

pollutant per cubic meter at precise location during

predefined period of time. It aims to improve the

controlled emission zone scheme by leveraging

Cooperative Intelligent Transport Systems (C-ITS)

architecture (European Telecommunications

Standards Institute, retrieved 2017). Each polluting

vehicle, or other stationary pollution source, is a C-

ITS Station supporting proposed PRM

functionalities. Each active vehicle in the controlled

emission zone is continuously supervised by a

centralised scheduler. The vehicle reports its

emission characteristics status and corresponding

data to the scheduler, and in turn is informed about

the current environmental pollution level in the

predefined space and time unit and how much

pollutant it can emit in this unit (i.e. pollution grant).

The scheme is implemented by a feedback loop

between the vehicle and the scheduler, enabling

dynamic data sharing and permitted emission

scheduling. The communication between the

controlled C-ITS station and the pollution scheduler

is facilitated by a complete pollution grant signalling

message set (request/response) implemented by a

wireless technology.

2.1 High-Level Overview

The three pivotal components of PRM are

centralised pollution scheduler, dynamically adapted

pollution grant and reliable low latency pollution

grant signalling. Figure 1 illustrates a typical

example how the PRM scheme works, incorporating

the three pivotal components. A vehicle equipped

with a C-ITS station travelling in the controlled

emission zone is granted the pollution allowance. It

communicates with the centralised pollution

scheduler all the time during its presence in the

zone. Figure 2 presents the data flow diagram with

more details of the system implementing the

proposed scheme.

Figure 1: Example scenario showing pollution scheduler, grant signalling and pollution resource.

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C-ITS Station (e.g. vehicle)

supporting PRM

Requests pollution grant

Vehicle type and

characteristics, trip plan,

ambient pollutants

concentration,

requested pollution

amount

PRM Scheduler

Calculates difference between

current pollution level and

regulatory limits,

predicts next time slot pollution

level,

works out the available shared

resources and the allowance for

each requesting vehicle

Measured pollution data

Air quality monitoring sites

Collects current atmospheric

pollutant data with sensors

Pollution grant

Weather conditions Meteorological sensors

Figure 2: Detailed flow of data in a system implementation of PRM.

2.2 Pollution Scheduler

The centralised PRM scheduler is similar to

frequency/time resource scheduler in the LTE

mobile telecommunication network entity eNB (base

station). The PRM Scheduler periodically receives

pollution related information (ideally in real time),

from various sources in different locations using

wireless communication (e.g. high throughput low

latency communication may be required for almost

real time pollution information exchange e.g.

provided by 5G systems):

 Generated pollution (e.g. [g] or [g/s]) -

vehicles, power stations, business premises,

households, and other sources

 Pollution measurements (e.g. concentration

limit [g/m

]) - from available pollution sensors

in the zone (e.g. reusing pollution stations in

cities or pollution sensors integrated with

pollution sources i.e. vehicles)

 Weather conditions – factors impacting

pollution dispersion (e.g. wind direction and

strength, temperature)

Based on the received feedback aggregated from

all sources, PRM Scheduler could build a precise

pollution concentration map [g/m

] in spatial domain

and model the pollution dispersion in time domain

(e.g. caused by the wind) and use the interpolation

where the information is not available. Scheduler

tracks pollutant concentration changes in a spatial

domain, possibly with a cubic meter used as a

concentration unit mapped to a square meter on the

map to render the isometric representation (see

Figure 1), and compares it with the regulatory limit

(concentration limit per second rather than hour or

similar needs to be defined). The main goal of the

scheduler is to keep the actual mass of the pollutant

per cubic meter (or similar unit) at the precise

location during a predefined period of time below

the regulatory pollutant limit (soft or hard) while

‘fairly’ sharing the observable difference i.e. [g] or

[g/s] between the users of the common resource by

using pollution grants. The actual definition of

fairness in this context could be an implementation

specific and could be related to the communicated

characteristics of the pollution source (see pollution

grant and signalling described in next sections for

more details).

2.3 Pollution Grant

PRM controls the amount of generated pollution in a

spatial domain by scheduling pollution grants to

pollution sources (e.g. vehicles or other stationary

objects). Pollution grant controls how much

pollution (mass) the source could generate per

spatial unit or air volume (in cubic meters) in its

location to keep the concentration level below the

limit. Scheduling decision frequency may depend on

the frequency of received pollution feedback (in

seconds or tens of seconds). Scheduling grant could

be communicated to sources by a wireless

communication technology (e.g. cellular, V2X). The

communication does not necessarily need to happen

in the controlled emission zone. It can be initiated by

a vehicle outside of the zone to facilitate the trip

planning when in the zone. If inside the zone, every

pollution source after receiving the grant

continuously monitors its emissions and makes sure

its pollution emission does not exceed the grant to

avoid consequential penalty from the local

environment authority (enforcement).

The pollution source takes into account its

characteristics and adapts its behaviour to use the

grant efficiently to reduce emissions:

Controlled Emission Zone Pollution Resource Management in 5G C-ITS

285

 Mobile vs. static source - Vehicle will receive

different grant as it moves to different location

in the zone, while power stations will receive

updated grant as time proceeds

 Behaviour change - Vehicle limits maximum

speed, reduces engine power (e.g. switches off

some engine cylinders), switches to electric or

alternative power source (hybrid)

Pollution grant validity could be limited by

 a configurable timer

 mobile pollution source (vehicle) leaving

predefined area (location based)

 new grant received from the pollution

scheduler (update)

2.4 Pollution Grant Signalling

Pollution grant signalling is based on a request

message(s) from the pollution source (grant request)

and response message(s) from the pollution

scheduler (grant approval). Figure 3 illustrates the

corresponding message sequence diagram.

Pollution Request Message could be sent when

the current pollution grant was used (grant invalid)

or periodically and could include parameters which

the scheduler takes into account:

 Measured ambient pollutant concentration at

source area location (if available). Source area

location size could vary (meters to hundreds

of meters) depending on the granularity of the

pollution resource definition in PRM

Scheduler (increased granularity may increase

amount of exchanged data and as a result, high

performance wireless communication

providing high throughput and low latency

may be required e.g. based on 5G system)

 Amount of planned emitted pollution

requested to be approved by the scheduler,

e.g. based on other in zone trip related factors

 current location

 distance to be travelled in the zone

 planned time spent in the zone

 average speed

 capability to temporarily reduce

emissions (e.g. alternative engine power

source, reduced engine power)

Scheduler Response Message based on the

knowledge of pollution resource usage in current

location (pollution emission amount-mass to stay

below the regulatory limit) may grant either

 a full pollution amount requested by the

source or

 a reduced pollution amount (local pollution

high or close to the limit) to keep the overall

concentration below the limit

Overriding pollution scheduler grant decision

(generating more pollution than approved) is

possible but

 must be communicated back to the scheduler

(by using another message or piggybacked on

the next grant request message from the

vehicle) to make sure the scheduler controls

the overall pollution

 may generate additional charging fee (“pay as

you pollute” principle etc.)

3 ALTERNATIVE

IMPLEMENTATIONS

3.1 Distributed Pollution Scheduler

within MEC Architecture

Pollution scheduler can be implemented in a

distributed architecture – centralised pollution

scheduler controlling predefined geographical area

can communicate (e.g. wirelessly) with other

schedulers in the neighbourhood geographical areas

to share information and coordinate the pollution

level changes at the boundaries (i.e. due to the wind

impact). This makes PRM a suitable use case for

Mobile Edge Computing (or Multi-access Edge

Computing, MEC) (ETSI, Multi-access Edge

Computing, retrieved 2017). MEC has a primary use

case of Active Device Location Tracking (Huawei et

al. 2014). PRM can be integrated with the MEC use

case using the location service. Figure 4 illustrates

an example of the PRM implementation within the

MEC network architecture. The example assumes a

5G network deployment scenario, to leverage

superior 5G capability, where the PRM can be

regarded as a user scenario of massive Machine

Type Communication (mMTC). The PRM

application may be also using 4G LTE or 3G UMTS

technologies for communication.

To allow continuous and almost real time

information exchange (low latency), the pollution

scheduler functionality can be implemented as a new

application integrated into cellular base station

architecture (5G gNB or 4G LTE eNB) in parallel to

its normal radio resource management function

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286

Figure 3: Pollution grant signalling scenario example.

Figure 4: Example of PRM implementation within MEC network architecture.

supported by high performance wireless

communications. The new application is denoted as

PRM Scheduler in Figure 4. It will use the cloud

resources centralised and provided at gNB-

Centralised Unit (gNB-CU, or Baseband Unit, BBU)

in a typical 5G Centralised Radio Access Network

(or Cloud RAN, C-RAN). On the vehicle side, each

5G User Equipment (UE) is also a C-ITS Station,

communicating directly with a Distributed Unit

(gNB-DU, or Remote Radio Head, RRH).

Periodically, the vehicle will send its location

and polluting emission information to the gNB and

Controlled Emission Zone Pollution Resource Management in 5G C-ITS

287

the data is centralised and locally pre-processed at

the gNB. It is worth also noting, although not

depicted in Figure 4, that the sensed atmospheric

environmental pollution data can also be collected in

the same way as the vehicle data is done.

The controlled emission zone supported by a low

latency radio access and the PRM application

available at the network edge coincides with the 5G

small cell network deployment in a geographical

sense, providing a great potential for highly efficient

pollution controlled scheme in the local

neighbourhood of the small cell. Depending on the

PRM deployment policy and the controlled emission

zone granularity, the small cells can be aggregated

or divided to be mapped into each controlled zone.

The benefit of this mapping is that the pollution

resource quota can be allocated to each distributed

PRM scheduler residing on the gNB-CU. The

scheduler monitors the pollution level in its local

zone and schedules the grants to each vehicle in the

zone, under the coordination of the central PRM

service, which is located in the Core network.

The gNB edge processed information is then

filtered and the much reduced refined information is

exchanged with the central PRM service, which will

coordinate the whole controlled wider area emission

zone entailing all the distributed PRM scheduler

controlled zones. The information between the C-

RAN and Core Network central service comprises of

the subscriber ID of the vehicle, pollution data in the

controlled emission zone and other environmental

information. That information will be logged into

and updated at the central service user account and

zone database. In the opposite direction, the central

service will coordinate the pollution resource grant

and synchronise all the distributed user data all over

the zone. To summarise, the ‘centralised unit’ of

gNB centralises all the resources in the 5G C-RAN,

whilst PRM schedulers are distributed into each C-

RAN, under the coordination of the central service.

3.2 Pollution Grant Signalling

It is also possible that the same default pollution

grant value is first automatically broadcasted to all

users (e.g. this may be implemented as part of the

system information broadcast messaging typically

used in the mobile cellular networks if the pollution

scheduler is integrated with the 5G base station,

gNB) and then an additional amount is only

requested by those users who found the grant

insufficient for their use (see Figure 3).

The proposed pollution grant signalling can be

then implemented in a new pollution supporting

protocol running between the base station scheduler

application and corresponding applications

integrated with the pollution sources. This may be

realized in the future Internet of Things / Smart City

architectures by the broader integration of the

pollution monitoring and control network (sensors

and monitoring stations) with the communication

infrastructure (cellular wireless network).

4 DISCUSSION AND

CONCLUSIONS

PRM defines the pollution as a controllable and

shareable user resource by using regulatory limit in

space and time. The difference between the

regulatory pollutant limit and the actual mass of the

pollutant per cubic meter at a precise location during

a predefined period of time is viewed as the

pollution resource. If the pollutant concentration is

under the limit, then the resource is available and the

air can be polluted in a controllable manner with

PRM. If the concentration is above the limit, then

the resource is in deficit and as a result, immediate

measure should be taken with PRM.

PRM Scheduler provides proactive continuous

pollution emissions management based on the

central entity which aims to minimise the difference

between a regulatory pollutant limit and the actual

mass of the pollutant per cubic meter at a precise

location during a predefined period of time.

PRM provides continuous proactive control and

sharing of the pollution resource to be used between

users in a fairer way by means of the pollution grant.

Each vehicle or polluting source equipped with

communication capabilities and PRM functionalities

contributes to the fairer pollution management and

charging scheme. The flat charging will be replaced

with a “pay as you pollute” approach with an

accurate customised charging. According to the

vehicle type, the polluting grant is adjusted and

allocated fairly to each vehicle / source.

PRM signalling is based on a user asking for the

pollution emission allowance in advance (grant

request message) and receiving the pollution grant

(response). The polluting vehicle may be informed

about the possible consequence of travelling through

the the controlled emission zone at the trip planning

stage even before it enters the zone.

In summary, there are three novel elements

proposed in this paper related to the pollution:

 Grant - defining pollution as a controllable

and shareable user resource by using

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regulatory limit in space/time (i.e. difference

between regulatory pollutant limit and the

actual mass of the pollutant per cubic meter at

a precise location during a predefined period

of time)

 Scheduler - proactive continuous pollution

emissions management based on the central

entity which aims to keep the difference

between regulatory pollutant limit and the

actual mass of pollutant per cubic meter at a

precise location during a predefined period of

time

 Signalling - based on a user asking for the

pollution emission allowance in advance

(grant request message) and receiving the

pollution grant (response).

Furthermore, the other innovative aspect is the

continuous, proactive and dynamic control of the

pollution in the controlled emission zone (e.g. in a

large city) based on the current air quality, weather

forecast and polluting sources status. The dynamic

control through the interaction between the polluting

source (e.g. vehicle) and the centralised PRM

scheduler is achieved by restricting the pollution

allowed for each vehicle. This can be enforced by

charging users extra for overusing the grant and

causing excessive tailpipe emissions. Instead of

unfairly punishing some fraction of the vehicles in

the zone (typically heavy goods vehicles or buses),

every polluting source is involved in the scheme,

which takes the environmental awareness to a new

level. Although th proposed PRM scheme may

require some infrastructure investments, it can

improve the fairness, effectiveness and efficiency of

the controlled emission zones in cities. Finally, the

proposed scheme extends the 5G communications

technology application to the new vertical domain of

the pollution control by leveraging the capabilities of

5G to provide the continuous air pollution control in

the future Internet of Things and Smart Cities

environments.

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