CN110958612A - Edge calculation unloading period minimization method under multi-user scene - Google Patents

Edge calculation unloading period minimization method under multi-user scene Download PDF

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CN110958612A
CN110958612A CN201911017859.2A CN201911017859A CN110958612A CN 110958612 A CN110958612 A CN 110958612A CN 201911017859 A CN201911017859 A CN 201911017859A CN 110958612 A CN110958612 A CN 110958612A
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wireless sensor
calculation
unloading
energy
base station
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CN110958612B (en
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田贤忠
姚超
赵晨
丁军
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/06—Hybrid resource partitioning, e.g. channel borrowing
    • H04W16/08—Load shedding arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/20—Circuit arrangements or systems for wireless supply or distribution of electric power using microwaves or radio frequency waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/509—Offload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/08—Access point devices
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

A method for minimizing an unloading period of edge computing in a multi-user scene comprises the following steps: step 1, a base station is switched to an energy sending mode to send energy to wireless sensor equipment; step 2, the wireless sensor equipment is switched to an energy capture mode to capture energy from electromagnetic waves for storage, and then switched to a task calculation mode to sequentially carry out partial task data unloading and local calculation; step 3, the base station is switched to a data receiving mode to sequentially receive the data unloaded by the wireless sensor equipment and send the data to the edge cloud server for data calculation; and 4, the base station returns the calculation result to the wireless sensor equipment. The invention reasonably sets the node charging time, calculates the unloading proportion and reasonably dispatches the unloading, thereby minimizing the time delay of the whole calculation period.

Description

Edge calculation unloading period minimization method under multi-user scene
Technical Field
The invention belongs to the technical field of edge computing unloading in a wireless sensor network, and particularly relates to a method for minimizing a computing unloading delay period of a wireless sensor.
Background
The latest development of Wireless Sensor Network (WSN) technology has brought a key step away from the realization of true intelligence and remote control of many important industrial and commercial systems such as environmental monitoring, smart grid, smart home, and the like. The wireless sensor network has the characteristics of low cost, low power consumption, distributed type, self-organization and the like, and is widely applied to social production and life.
In WSNs, a large number of sensor devices capable of wireless communication and data computation are deployed. Due to stringent device size constraints and manufacturing cost considerations, sensor devices typically have limited battery capacity carried by themselves. Limited device battery life does not support the transmission and calculation of wireless information by wireless sensor devices. In order to solve the above problems, the wireless sensor network based on energy capture is an effective solution to the problem of WSN battery capacity limitation. In the WSN, a node can capture radio frequency energy and convert the radio frequency energy into electric energy for data transceiving and calculation of a sensor. The scheme well solves the problem of continuous power supply of the wireless sensor equipment.
On the other hand, due to the limited computing capability of the wireless sensor device, many complex computing scenarios cannot be supported, such as some application scenarios like artificial intelligence, virtual reality and the like which require a large amount of data computation. One effective solution is edge computing, which increases the computing power of wireless sensor devices by offloading part of the computing task to a nearby edge cloud server, completing complex computations on the server.
In the energy capture wireless sensor network, if all data of the wireless sensor devices are unloaded to the edge cloud server for calculation, although the calculation speed is high, more energy is consumed due to calculation unloading, and even all calculation tasks cannot be completed due to insufficient energy captured by some wireless sensor devices; further, if all the wireless sensor devices simultaneously offload data to nearby edge clouds, problems such as network transmission congestion and data offloading conflicts may occur. Conversely, if all calculations are performed locally at the wireless sensor device, although power consumption may be reduced, the calculation speed may be slowed. How to balance the relationship between them is a problem solved by the present invention.
Disclosure of Invention
Aiming at the problems, the invention provides a method for minimizing the unloading period of the edge computing in the multi-user scene, which minimizes the time delay of the whole computing period by reasonably setting the node charging time, calculating the unloading proportion and reasonably scheduling multi-user unloading tasks.
The invention is applicable to the scenario shown in fig. 1. A plurality of wireless sensor devices are deployed around the base station, the base station is provided with an edge cloud server, and the working mode can supply power to peripheral wireless devices and also can receive computing tasks unloaded by the peripheral devices for computing; the energy of the wireless sensor equipment is obtained from electromagnetic waves transmitted by the base station, and the working mode can be calculated in the equipment and can also unload the calculation task to the base station. How to reasonably allocate the power supply time of the base station to the wireless sensor device, the local calculation task and the unloading task of the wireless sensor device, the transmission power of the wireless sensor device during unloading and the like, and the total time required by each wireless device to complete a certain amount of calculation is minimized.
In order to solve the technical problems, the invention provides the following technical scheme:
a method for minimizing an unloading period of edge computing in a multi-user scene comprises the following steps:
step 1: the base station is switched to an energy sending working mode, broadcasts and sends energy to nearby wireless sensor equipment within a certain time, and then is switched to an edge cloud server data receiving mode;
step 2: the wireless sensor equipment is switched to an energy capture mode, energy is captured and stored in the energy sending time of the base station, and then the wireless sensor equipment is respectively switched to a local calculation mode or a calculation unloading mode for calculation;
and step 3: the base station switches to a data receiving mode, sequentially receives the data unloaded by the wireless sensor equipment, and calculates;
and 4, step 4: and after finishing the calculation task unloaded by the wireless sensor equipment, the base station returns the calculation result to the wireless sensor equipment.
Further, the processing procedure of step 1 is as follows:
step 11: first, the base station switches to an energy transmission mode with a given power pBSCarrying out energy broadcast transmission, wherein the energy transmission time is α T;
step 12: and the base station switches to a data receiving mode to wait for receiving the unloading data of the wireless sensor device.
Still further, the processing procedure of step 2 is:
step 21, switching the wireless sensor equipment to an energy capture mode, capturing energy in electromagnetic waves broadcasted and sent from the base station, wherein the capture time is α T;
step 22: the wireless sensor devices are sequentially switched to a calculation unloading mode to finish respective calculation unloading, and the unloading time of each wireless sensor device is
Figure BDA0002246274090000021
Unloaded power is pi；
Step 23: meanwhile, when the device does not take the turn to computation unloading, the device carries out local computation, and the local computation time of each wireless sensor device is
Figure BDA0002246274090000022
Locally calculated CPU frequency of fi。
Further, the processing procedure of step 3 is as follows:
step 31: the base station is switched to a data receiving mode, and the data unloaded by the wireless sensor equipment are sequentially received;
step 32: and calculating data unloaded by the wireless sensor device by utilizing the edge cloud server.
Further, the processing procedure of step 4 is as follows:
step 41: the edge cloud server returns a calculation result to the wireless sensor equipment;
step 42: the wireless sensor device completes the calculation task.
The invention has the beneficial effects that: by reasonably setting the charging time of the nodes, calculating the unloading proportion and reasonably scheduling multi-user unloading tasks, the time delay of the whole calculation period is minimized
Drawings
FIG. 1 is an energy capture and edge computation offload model of a wireless sensor network and an internal structural model of a base station and wireless sensor devices;
FIG. 2 is a timing diagram of energy capture, computation offloading, and local computation for all wireless sensor devices;
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention considers an edge computing system, as shown in figure 1, comprising a single-antenna base station, an edge cloud server and N single-antenna wireless sensor devices, wherein the base station integrates radio frequency energy transmission and edge cloud server computing functions, each wireless sensor can capture energy from radio frequency signals transmitted by the base station and store the energy in a battery, and then the energy in the battery is used for selecting to unload a computing task part to the edge cloud server or complete computation locally through the base stationiThe proportion of the local calculation part task of the device i to the whole calculation task is 1-rhoi. To avoid signal collision caused by simultaneous unloading of multiple devices, it is specified that multiple devices cannot simultaneously perform calculation unloading in the same time periodLoading, so sequential alternate unloading is used here, as shown in FIG. 2; is additionally provided with fiCPU frequency, p, calculated locally for a wireless sensor deviceiTo calculate the power to offload the transmitted task data, all wireless sensor devices energy capture and the entire time period T of the task calculation.
The base station transmits radio frequency energy with power pBSThe duration of the continuous transmission of the radio frequency energy is α T, and the energy captured by the wireless sensor i is:
Ei＝μpBShiαT (1)
wherein EiFor the energy captured by each wireless sensor device during α T, μ is the energy conversion efficiency, hiFor channel gain, pBSIs the broadcast transmit power of the base station.
Let D be the calculation task to be completed by each wireless sensor in one periodiThe proportion of the tasks unloaded due to calculation to the total tasks is (1-rho)i) Then the computation amount offloaded to the edge cloud server is (1- ρ)i)DiThe calculated unload time for each wireless sensor device is expressed as:
Figure BDA0002246274090000041
each wireless sensor device offloads a portion of the task data (1- ρ @)i)DiEnergy consumption required for arriving at edge cloud server
Figure BDA00022462740900000414
Comprises the following steps:
Figure BDA0002246274090000042
wherein p isiTransmitting power, p, for wireless sensor devices in offloading task dataiA transmission power range of
Figure BDA0002246274090000043
RiComputing offload for wireless sensor devicesThe data transmission rate in time, expressed as:
Figure BDA0002246274090000044
in order to prevent conflict and excessive network load when a plurality of wireless sensor devices simultaneously calculate and unload, the same time period is ensured
Figure BDA0002246274090000045
Only one wireless sensor device can perform calculation unloading, and the following constraints are obtained:
Figure BDA0002246274090000046
in local calculation, the CPU calculation frequency of the wireless sensor device needs to be at the maximum value fi maxIn addition, the local computation time for each wireless sensor device is expressed as:
Figure BDA0002246274090000047
each wireless sensor device locally calculates ρiDiEnergy consumed by task data
Figure BDA0002246274090000048
Comprises the following steps:
Figure BDA0002246274090000049
where v is the effective switched capacitor, ZiNumber of CPU cycles required to calculate data per bit, fiComputing power calculated locally for a wireless sensor device, fiComputing power range of [0, fi max]。
The wireless sensor device calculates the total energy consumed by the whole task as
Figure BDA00022462740900000410
The following energy constraints were obtained:
Figure BDA00022462740900000411
the wireless sensor device calculates the total time required for the whole task as
Figure BDA00022462740900000412
The following time constraints are obtained:
Figure BDA00022462740900000413
the task is to select proper charging time proportion, CPU frequency, transmission power, calculate task unloading proportion and the like, and minimize the time delay of the whole period under the conditions of meeting energy constraint, unloading conflict constraint and the like, wherein a specific model is as follows:
Figure BDA0002246274090000051
the above problem (10) is a convex optimization problem, so it can be solved by lagrange duality, (10) expressed as the lagrange function:
Figure BDA0002246274090000052
Figure BDA0002246274090000053
is a set of lagrange multipliers.
(10) The dual problem of (a) is expressed as:
Figure BDA0002246274090000054
the solving problem (10) can be solved through a dual problem (12) of the (10), an internal minimization problem is firstly solved according to omega, a Lagrange multiplier set is solved through a sub-gradient descent method,
Ω(x+1)＝[Ω(x)-ψ(x)d(Ω(x))]+(13)
wherein
Figure BDA0002246274090000055
When Ω converges to the optimal solution, the optimal solution of the problem (10) can be found.
According to the Lagrange multiplier and the Lagrange function (11), and substituting (6) and (7) into (11) to obtain the corresponding fiThe minimization problem of (2):
Figure BDA0002246274090000061
the problem (14) is a convex optimization problem, so the lagrange function KKT condition can be used to get the optimal solution:
Figure BDA0002246274090000062
similarly, (2), (3) and (4) can be substituted into (11) to obtain the relation piThe minimization problem of (2):
Figure BDA0002246274090000063
(16) each wireless sensor device in (1) is independent and irrelevant, so the above problem translates into the problem of optimal transmission power of each wireless sensor device, namely:
Figure BDA0002246274090000064
(17) is a convex optimization problem, so the lagrangian dual can be used to solve the problem, and the lagrangian function of (17) is expressed as:
Figure BDA0002246274090000065
Figure BDA0002246274090000066
is the lagrange multiplier set of (18).
(17) The dual problem of (a) is expressed as:
Figure BDA0002246274090000067
solving the internal minimization problem of problem (19) an optimal solution of (17) can be obtained by (18) on piCalculating a partial derivative to obtain:
Figure BDA0002246274090000068
according to (20) making
Figure BDA0002246274090000071
Optimization software can then be used to find the interval
Figure BDA0002246274090000072
Optimal solution in (1)
Figure BDA0002246274090000073
Due to T, α and rho in (11)iAll are first order polynomials and substituting (1) (2) (3) (4) (6) (7) into the original problem, which is a convex optimization problem, the optimum for α can be found by (11) applying the partial derivative for T:
Figure BDA0002246274090000074
order to
Figure BDA0002246274090000075
To obtain
Figure BDA0002246274090000076
Obtaining the following by the same method:
Figure BDA0002246274090000077
order to
Figure BDA0002246274090000078
To obtain
Figure BDA0002246274090000079
Trade-off ratio ρ of task computation offload and local computation workload with respect to wireless sensor deviceiIn the original problem, the relationship is accumulated, so that the rho of each wireless sensor device is expandediAnd solving to obtain:
Figure BDA00022462740900000710
same as above
Figure BDA00022462740900000711
Can be expressed in the same form as equation (23), and then let
Figure BDA00022462740900000712
Solving each rho by a multi-equation simultaneous equationi。
According to the parameters obtained above, the specific implementation steps of the invention are as follows:
step 1, the base station is firstly switched to a working mode of radio frequency energy transmission, transmission energy is broadcasted to surrounding wireless sensor equipment within α T, and the energy transmission power of the base station is pBS；
Step 2, the wireless sensor equipment is switched to an energy capture mode, energy transmitted by the base station is captured within α T time and stored in a battery of the wireless sensor equipment, and the total captured energy E can be obtained according to the formula (1)i. Then each device is switched to a calculation unloading mode in turn, and unloaded power p is adoptediA part (1-rho) of the total task to be completedi)DiUnloading to the edge cloud server through the base station, wherein the unloading time of each device can be obtained according to the formulas (2) and (3)
Figure BDA0002246274090000081
The energy consumed for unloading is
Figure BDA0002246274090000082
Switching to local computation when the wireless sensor device is not in turn computation offload, each wireless sensor locally computing a partial task ρiDiThe local calculation time is
Figure BDA0002246274090000083
Calculated CPU frequency of fiThe energy required for local calculation is
Figure BDA0002246274090000084
The total energy consumed by each wireless sensor device in the stage is less than or equal to the energy captured by the device EiThe total calculation time required by each device is less than or equal to (1- α) T;
and step 3: the base station switches a data receiving mode, sequentially receives data tasks unloaded by the wireless sensor equipment, and then calculates partial task data unloaded by the wireless sensor equipment by using the edge cloud server;
and 4, step 4: and the base station is switched to a data sending mode, the data result calculated by the edge cloud server is returned and sent to each wireless sensor device, and each wireless sensor device receives the calculation result of the edge cloud server to complete all task calculations.

Claims (5)

1. A method for minimizing an unloading period of edge computing in a multi-user scene is characterized by comprising the following steps:
step 1: the base station switches to an energy sending working mode, and broadcasts and transmits radio frequency energy to nearby wireless sensor equipment within a certain time;
step 2: the wireless sensor equipment is switched to an energy capture mode, energy is captured from the radio frequency signals transmitted by the base station and stored, and then the wireless sensor equipment is respectively switched to a local calculation mode or a calculation unloading mode to calculate data;
and step 3: the base station is switched to a data receiving mode to sequentially receive the calculation data unloaded by the wireless sensor equipment, and data calculation is carried out through the edge cloud server;
and 4, step 4: and after the edge cloud server completes the data calculation unloaded by the wireless sensor equipment, returning the calculation result to the wireless sensor equipment to complete the calculation task.
2. The method for minimizing an unloading period of edge computing in a multi-user scenario according to claim 1, wherein the processing procedure of step 1 is:
step 11, switching the base station to an energy sending mode, broadcasting and transmitting radio frequency energy to nearby wireless sensing equipment by using given power, wherein the energy sending time is α T;
step 12: and the base station switches to a data receiving mode to wait for receiving the unloading data of the wireless sensor device.
3. The method for minimizing the unloading period of the edge computing in the multi-user scenario according to claim 1 or 2, wherein the processing procedure of step 2 comprises the following sub-steps:
step 21, switching the wireless sensor equipment to an energy capture mode, capturing energy in electromagnetic waves broadcast and transmitted from the base station, wherein the capture time is α T;
step 22: the wireless sensor devices are sequentially switched to a calculation unloading mode to finish respective calculation unloading, and each wireless sensor device is unloaded for time
Figure FDA0002246274080000011
Unloaded power is pi。
Step 23: meanwhile, when the device does not take the turn to computation unloading, the device carries out local computation, and the local computation time of each wireless sensor device is
Figure DEST_PATH_BDA0002246274090000083
Locally calculated CPU frequency of fi。
4. The method for minimizing the unloading period of the edge computing in the multi-user scenario according to claim 1 or 2, wherein the processing procedure of the step 3 is as follows:
step 31: the base station is switched to a data receiving mode, and the data unloaded by the wireless sensor equipment are sequentially received;
step 32: and calculating task data unloaded by the wireless sensor equipment by utilizing the edge cloud server.
5. The method for minimizing the unloading period of the edge computing in the multi-user scenario according to claim 1 or 2, wherein the processing procedure of the step 4 is as follows:
step 41: the edge cloud server returns a calculation result to the wireless sensor equipment through the base station;
step 42: the wireless sensor device completes the calculation task.
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