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

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.

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.

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Energy-efficient optimized computing offloading method for vehicular edge computing network and system thereof

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Energy-efficient optimized computing offloading method for vehicular edge computing network and system thereof

Life of this patent

Abstract

Claims5