# US 11,445,400 B2 · Energy-efficient optimized computing offloading method for vehicular edge computing network and system thereof

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- Number: US 11,445,400 B2
- Document ID: US11445400B2
- Status: Granted
- Kind: B2
- Application number: 17/437,120
- Filed: 2020-08-04
- Published: 2022-06-30
- Granted: 2022-09-13
- Earliest claimed: 2020-07-15
- Assignees: Nantong University
- Applicants: Nantong University
- Inventors: Guoan Zhang; Xiaohui Gu; Li Jin; Jinyuan Gu; Chen Ji; Yancheng Ji; Wei Duan
- Web page: https://patentgrep.com/patent/US11445400B2

## CPC

- H04W 28/0226: {based on location or mobility (handoff or reselection H04W36/00; mobile application services making use of the location of users or terminals H04W4/02)}
- H04L 41/0826: for reduction of network costs (H04L41/0833 takes precedence)
- H04L 41/0833: for reduction of network energy consumption
- H04L 43/0852: Delays
- H04L 43/0894: Packet rate
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- H04L 67/59: Providing operational support to end devices by off-loading in the network or by emulation, e.g. when they are unavailable
- H04W 4/40: for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W 4/44: for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
- H04W 28/22: Negotiating communication rate
- H04W 52/146: {Uplink power control}
- H04W 52/283: {Power depending on the position of the mobile}
- H04W 52/343: {taking into account loading or congestion level}
- H04L 41/142: using statistical or mathematical methods
- H04L 41/145: {involving simulating, designing, planning or modelling of a network}
- H04L 41/16: using machine learning or artificial intelligence
- H04W 52/0287: {changing the clock frequency of a controller in the equipment}
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

The present disclosure relates to an energy-efficient optimized computing offloading method for a vehicular edge computing network and a system thereof; the method comprises: calculating the energy efficiency cost EEC of local computing; calculating the energy efficiency cost EEC of mobile edge computing; determining an optimal offloading decision based on the energy efficiency cost of local computing and the energy efficiency cost of mobile edge computing; determining an optimal CPU frequency and an optimal transmit power of the vehicle based on the optimal offloading decision; and determining the optimal offloading time of the vehicle based on the optimal CPU frequency and the optimal transmit power of the vehicle. The method of the present disclosure can improve the computing offloading efficiency.

## Claims

### Claim 1

An energy-efficient optimized computing offloading method for a vehicular edge computing network, comprising:
calculating an energy efficiency cost EEC of local computing, wherein the calculating comprises:
calculating a local computing latency;
determining an energy consumption of local computing based on the local computing latency; and
determining an energy efficiency cost EEC of local computing based on the energy consumption and the local computing latency;
calculating an energy efficiency cost EEC of mobile edge computing, wherein the calculating comprises:
calculating a distance between a vehicle n and a base station BS;
determining a channel gain between the vehicle n and the base station based on the distance;
determining a real-time transmission rate from the vehicle n to the base station based on the channel gain;
determining a task offloading time based on the real-time transmission rate;
calculating a computing time of an MEC server;
determining a total latency of mobile edge computing based on the task offloading time and the computing time of the MEC server;
calculating an energy consumption of mobile edge computing; and
determining the energy efficiency cost EEC of mobile edge computing based on the energy consumption of mobile edge computing and the total latency of mobile edge computing;
determining an optimal offloading decision based on the energy efficiency cost of local computing and the energy efficiency cost of mobile edge computing, wherein the determining adopts the following formula:
a
n
*
=
{
1
,
if
⁢
Cost
n
o
<
Cost
n
l
&
⁢
T
n
o
<
c
n
0
,
otherwise
where a*n represents the optimal offloading decision,
c
n
=
△
R
max
2
-
D
2
-
x
n
v
n
represents a maximum communication time between the vehicle and the base station, Rmax represents a maximum communication coverage of the base station BS, D represents a vertical distance between the base station and a road, xn represents an initial position of the vehicle n on the road, vn represents a moving speed of the vehicle
n
,
Cost
n
l
=
△
Z
n
l
+
λ
n
⁢
L
n
⁢
C
n
f
n
l
represents a computing cost of local computing,
Cost
n
o
=
△
Z
n
o
+
λ
n
(
t
n
ot
+
L
n
⁢
C
n
f
MEC
)
represents a computing cost of mobile edge computing, λn represents a Lagrange multiplier corresponding to the latency constraint (1−an)Tnl+anTno≤Tn,max, an represents a decision variable, Tn,max represents a maximum tolerable latency, Ln represents a data size of a task Rn, Cn represents the computational complexity of the task Rn, fnl represents a computing speed of the vehicle n, Tnl represents the local computing latency, Tno represents the total latency of mobile edge computing, Zno represents the energy efficiency cost EEC of mobile edge computing, and Znl represents the energy efficiency cost EEC of local computing;
determining an optimal CPU frequency and an optimal transmit power of the vehicle based on the optimal offloading decision, where the determining comprises:
when a*n=0, determining the optimal CPU frequency of the vehicle by the following formula:
f
n
l
*
=
{
β
n
T
+
λ
n
2
⁢
β
n
E
⁢
k
3
,
if
⁢
0
≤
β
n
T
+
λ
n
2
⁢
β
n
E
⁢
k
3
≤
f
n
,
max
l
f
n
,
max
l
,
otherwise
where fn,maxl represents a maximum CPU frequency of the vehicle n, fnl*-represents the optimal CPU frequency of the vehicle, βnT represents a latency weight parameter, λn represents a Lagrange multiplier corresponding to a latency constraint, βnE represents an energy consumption weight factor, and k represents an effective switched capacitor coefficient,
when a*n=1, determining the optimal transmit power of vehicle n by the following formula:
p
n
*
=
{
0
,
if
⁢
p
^
n
<
0
p
^
n
,
if
⁢
0
≤
p
^
n
≤
p
n
,
max
p
n
,
max
,
if
⁢
p
^
n
>
p
n
,
max
where pn,max represents a maximum transmit power of the vehicle n, {circumflex over (p)}n is the unique solution of the equation βnEtnot−χnφ′(pn,tnot)=0, χn represents a Lagrange multiplier corresponding to the constraint
a
n
⁢
L
n
≤
φ
⁡
(
p
n
,
T
n
ot
)
,
φ
⁡
(
p
n
,
t
n
ot
)
⁢
=
Δ
⁢
∫
0
t
n
ot
⁢
r
n
⁡
(
τ
)
⁢
d
⁢
⁢
τ
,
⁢
φ
′
⁡
(
p
n
,
t
n
ot
)
⁢
=
Δ
⁢
∂
φ
⁡
(
p
n
,
t
n
ot
)
∂
p
n
,
t
n
ot
represents a task transmission time, pn>0 represents a transmit power of the vehicle n, and rn(t) represents a real-time transmission rate from the vehicle n to the base station;
determining an optimal offloading time of the vehicle based on the optimal CPU frequency and the optimal transmit power of the vehicle, wherein determining the optimal offloading time comprises:
determining the cost function; wherein the cost function is the energy efficiency cost function for the vehicle to complete the computing task, and wherein determining the cost function adopts the following formula:
ζ
⁡
(
t
n
ot
)
=
∑
n
=
1
N
⁢
{
β
n
T
⁡
[
(
1
-
a
n
*
)
⁢
L
n
⁢
C
n
f
n
l
*
+
a
n
*
⁡
(
t
n
ot
+
L
n
⁢
C
n
f
MEC
)
]
+
β
n
E
⁢
⁡
[
(
1
-
a
n
*
)
⁢
kL
n
⁢
C
n
⁡
(
f
n
l
*
)
2
+
a
n
*
⁢
p
n
*
⁢
t
n
ot
]
}
;
where βnE represents an energy consumption weight factor, p*n represents the optimal transmit power of vehicle, and fMEC represents a computing capacity of the MEC server; and
determining the optimal offloading time of the vehicle using a one-dimensional linear search method based on the cost function.

### Claim 2 (depends on 1)

The energy-efficient optimized computing offloading method for a vehicular edge computing network according to claim 1,
wherein calculating the local computing latency adopts the following formula:
T
n
l
=
L
n
⁢
C
n
f
n
l
where fnl represents a CPU frequency of the vehicle n, Ln represents a data size of the task Rn, and Cn represents a computational complexity of the task Rn;
wherein determining the energy consumption of local computing based on the local computing latency adopts the following formula:
Enl=kTnl(fnl)3=kLnCn(fnl)2
where k represents effective switching capacitance coefficient, Tnl represents the local computing latency, fnl represents the CPU frequency of the vehicle n, Ln represents the data size of the task Rn, and Cn represents the computational complexity of the task Rn; and
wherein determining the energy efficiency cost EEC of local computing based on the energy consumption and latency of local computing adopts the following formula:
Znl=βnTTnl+βnEEnl
where 0≤βnT≤1 and 0≤βnE≤1 represent weight factors of latency and energy consumption, respectively, Tnl represents the latency of local computing, and Enl represents the energy consumption of local computing.

### Claim 3 (depends on 1)

The energy-efficient optimized computing offloading method for a vehicular edge computing network according to claim 1,
wherein calculating the distance between the vehicle n and the base station BS adopts the following formula:
d
n
⁡
(
t
)
=
H
2
+
D
2
+
(
x
n
+
v
n
⁢
t
)
2
where H represents an antenna height of the base station, D represents the vertical distance between the base station and the road, xn represents the initial position of the vehicle n on the road, and vn represents the moving speed of the vehicle n;
wherein determining the channel gain between the vehicle n and the base station based on the distance adopts the following formula:
G
n
⁡
(
t
)
=
β
0
⁢
d
n
⁡
(
t
)
-
θ
=
β
0
[
H
2
+
D
2
+
(
x
n
+
v
n
⁢
t
)
2
]
θ
2
where β0 represents a gain at a reference distance d0=1 m, and θ represents a path loss factor of a V2I link;
wherein determining the real-time transmission rate from the vehicle n to the base station based on the channel gain adopts the following formula:
r
n
⁡
(
t
)
=
W
⁢
⁢
log
2
⁡
(
1
+
p
n
⁢
G
n
⁡
(
t
)
σ
2
)
=
W
⁢
⁢
log
2
(
1
+
p
n
⁢
ρ
0
[
H
2
+
D
2
+
(
x
n
+
v
n
⁢
t
)
2
]
θ
2
)
where W represents a channel bandwidth, pn>0 represents the transmit power of the vehicle n, ρ0=β0/σ2, σ2 represents a noise power of a BS receiver, and Gn(t) represents the channel gain between the vehicle n and the base station;
wherein determining task offloading time based on the real-time transmission rate adopts the following formula:
∫0tnotrn(t)dt=Ln
where tnot represents the task offloading time, Ln represents the data size of the task Rn, and rn(t) represents the real-time transmission rate from the vehicle n to the base station;
wherein calculating the computing time of the MEC server adopts the following formula:
t
n
oe
=
L
n
⁢
C
n
f
MEC
where fMEC represents the computing capacity of the MEC server, and Cn represents the computational complexity of the task Rn;
wherein determining the total latency of mobile edge computing based on the task offloading time and the computing time of the MEC server adopts the following formula:
Tno=tnot+tnoe
where tnoe represents the computing time of the MEC server, and tnot represents the task offloading time;
wherein calculating the energy consumption of mobile edge computing adopts the following formula:
Eno=pntnot; and
wherein determining the energy efficiency cost EEC of mobile edge computing based on the energy consumption of mobile edge computing and the total latency of mobile edge computing adopts the following formula:
Zno=βnTTno+βnEEno
where Tno represents the total latency of mobile edge computing, Eno represents the energy consumption of mobile edge computing, βnT represents a latency weight factor, and βnE represents the energy consumption weight factor.

### Claim 4 (depends on 1)

The energy-efficient optimized computing offloading method for a vehicular edge computing network according to claim 1, wherein
determining the optimal offloading time of the vehicle using a one-dimensional linear search method based on the cost function adopts the following formula:
min
f
n
ot
⁢
ζ
⁡
(
t
n
ot
)
s
.
t
.
⁢
0
≤
t
n
ot
≤
c
n
where cn represents the maximum communication time between the vehicle and the BS, tnot represents the task offloading time, and ζ(tnot) represents the energy efficiency cost function for the vehicle to complete the calculation task.

### Claim 5

An energy-efficient optimized computing offloading system in a vehicular edge computing network, the system comprising:
a module for calculating energy efficiency cost of local computing, which is configured to:
calculate a local computing latency;
determine an energy consumption of local computing based on the local computing latency; and
determine the energy efficiency cost EEC of local computing based on the energy consumption of local computing;
a module for calculating energy efficiency cost of mobile edge computing, which is configured to:
calculate a distance between a vehicle n and a base station BS;
determine a channel gain between the vehicle n and the base station based on the distance;
determine a real-time transmission rate from the vehicle n to the base station based on the channel gain;
determine task offloading time based on the real-time transmission rate;
calculate a computing time of an MEC server;
determine a total latency of mobile edge computing based on the task offloading time and the computing time of the MEC server;
calculate an energy consumption of mobile edge computing; and
determine the energy efficiency cost EEC of mobile edge computing based on the energy consumption of mobile edge computing and the total latency of mobile edge computing;
an optimal offloading decision determining module, which is configured to:
determine an optimal offloading decision based on the energy efficiency cost of local computing and the energy efficiency cost of mobile edge computing according to the following formula:
a
n
*
=
{
1
,
if
⁢
⁢
Cost
n
o
<
Cost
n
l
&
T
n
o
<
c
n
0
,
otherwise
where a*n represents the optimal offloading decision,
c
n
⁢
=
Δ
⁢
R
max
2
-
D
2
-
x
n
v
n
represents a maximum communication time between the vehicle and the base station, Rmax represents a maximum communication coverage of the base station BS, D represents a vertical distance between the base station and a road, xn represents an initial position of the vehicle n on the road, vn represents a moving speed of the vehicle n,
Cost
n
l
⁢
=
Δ
⁢
Z
n
l
+
λ
n
⁢
L
n
⁢
C
n
f
n
l
represents a computing cost of local computing,
Cost
n
o
⁢
=
Δ
⁢
Z
n
o
+
λ
n
⁡
(
t
n
ot
+
L
n
⁢
C
n
f
MEC
)
represents a computing cost of mobile edge computing, λn represents a Lagrange multiplier corresponding to the latency constraint (1−an)Tnl+anTno≤Tn,max, an represents a decision variable, Tn,max represents a maximum tolerable latency, Ln represents a data size of the task Rn, Cn represents a computational complexity of the task Rn, fnl represents a computing speed of the vehicle n, Tnl represents the local computing latency, Tno represents the total latency of mobile edge computing, Zno represents the energy efficiency cost EEC of mobile edge computing, and Znl represents the energy efficiency cost EEC of local computing;
an optimal CPU frequency and optimal transmit power determining module, which is configured to:
determine an optimal CPU frequency and an optimal transmit power of the vehicle based on the optimal offloading decision, wherein:
when a*n=0, determine the optimal CPU frequency of the vehicle by the following formula:
f
n
l
*
=
{
β
n
T
+
λ
n
2
⁢
β
n
E
⁢
k
3
,
if
⁢
⁢
0
≤
β
n
T
+
λ
n
2
⁢
β
n
E
⁢
k
3
≤
f
n
,
max
l
f
n
,
max
l
,
otherwise
where fn,maxl represents a maximum CPU frequency of the vehicle n, fnl* represents the optimal CPU frequency of the vehicle, βnT represents a latency weight parameter, λn represents a Lagrange multiplier corresponding to a latency constraint, βnE represents an energy consumption weight factor, and k represents an effective switched capacitor coefficient;
when a*n=1, determine the optimal transmit power of vehicle n by the following formula:
p
n
*
=
{
0
⁢
⁢
if
⁢
⁢
⁢
p
n
^
<
0
p
n
^
,
if
⁢
⁢
0
≤
⁢
p
n
^
≤
p
n
,
max
⁢
p
n
,
max
,
if
⁢
⁢
⁢
p
n
^
>
p
n
,
max
where pn,max represents a maximum transmit power of the vehicle n, {circumflex over (p)}n is a unique solution of the equation βnEtnot−χnφ′(pn, tnot)=0, χn represents a Lagrange multiplier corresponding to the constraint anLn≤φ(pn, Tnot), φ(pn, tnot)∫0tnotrn(τ)dτ,
φ
′
⁡
(
p
n
,
t
n
ot
)
⁢
=
Δ
⁢
∂
φ
⁡
(
p
n
,
t
n
ot
)
∂
p
n
,
tnot represents a task transmission time, pn>0 represents a transmit power of the vehicle n, and rn(t) represents a real-time transmission rate from the vehicle n to the base station;
an optimal offloading time determining module, which is configured to:
determine the optimal offloading time of the vehicle based on the optimal CPU frequency and the optimal transmit power of the vehicle;
determine the cost function, wherein the cost function is the energy efficiency cost function for the vehicle to complete the calculation task, and wherein determining the cost function adopts the following formula:
ζ
⁡
(
t
n
ot
)
=
∑
n
=
1
N
⁢
{
β
n
T
⁡
[
(
1
-
a
n
*
)
⁢
L
n
⁢
C
n
f
n
l
*
+
a
n
*
⁡
(
t
n
ot
+
L
n
⁢
C
n
f
MEC
)
]
+
β
n
E
⁢
⁡
[
(
1
-
a
n
*
)
⁢
kL
n
⁢
C
n
⁡
(
f
n
l
*
)
2
+
a
n
*
⁢
p
n
*
⁢
t
n
ot
]
}
;
where βnT represents a latency weight factor, p*n represents the optimal transmit power of the vehicle, and fMEC represents a computing capacity of the MEC server; and
determine the optimal offloading time of the vehicle using a one-dimensional linear search method based on the cost function.