CN110958612B - Edge calculation unloading period minimization method under multi-user scene - Google Patents
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- H—ELECTRICITY
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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
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 power 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. The base station is provided with an edge cloud server, and the working mode can supply power to peripheral wireless equipment and also can receive computing tasks unloaded by the peripheral equipment 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 distribute the power supply time of the base station to the wireless sensor equipment, the local calculation task and the unloading task of the wireless sensor equipment, the transmission power of the wireless sensor equipment during unloading and the like, and the total time required by each wireless equipment for completing 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 p BS Carrying out energy broadcast transmission, wherein the energy transmission time is alpha T;
step 12: and the base station is switched to a data receiving mode to wait for receiving the unloading data of the wireless sensor equipment.
Still further, the processing procedure of step 2 is:
step 21: the wireless sensor equipment is switched to an energy capture mode, energy in electromagnetic waves broadcasted and sent from the base station is captured, and the capture time is alpha T;
step 22: the wireless sensor equipment is sequentially switched to a calculation unloading mode to finish respective calculation unloading, and the unloading time of each wireless sensor equipment is
Unloaded power is p i ；
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
Locally calculated CPU frequency of f i 。
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 beneficial effects of the invention are as follows: 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 fig. 1, which is composed of a single-antenna base station, an edge cloud server and N single-antenna wireless sensor devices. 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 the energy in the battery is used for selecting to unload a computing task part to the edge cloud server through the base station or complete computing locally. The wireless power transmission and the communication are on the same frequency band, and in order to avoid mutual interference between energy capture and calculation unloading of the wireless sensor, a time division multiplexing mode is adopted to separate the time for transmitting and receiving unloading data of the radio frequency energy of the base station. The execution time period of each time of the system is T, the execution time period comprises base station energy sending time alpha T and calculation completion time (1-alpha) T of all wireless sensors, and alpha is a proportion and represents the proportion of wireless equipment energy capture time in the whole period. The wireless sensor equipment can complete calculation by adopting two modes of local calculation and calculation unloading, namely rho is set i The proportion of the tasks of the local computing part of the device i to the whole computing task is 1-rho i . In order to avoid signal collision caused by simultaneous unloading of multiple devices, it is specified that the multiple devices cannot simultaneously perform computation unloading in the same time period, so sequential alternate unloading is adopted here, as shown in fig. 2; is additionally provided with f i CPU frequency, p, calculated locally for a wireless sensor device i To calculate the power to offload the sending of task data, the entire time period T for all wireless sensor device energy capture and task calculation.
The base station transmits radio frequency energy with power p BS The continuous transmission radio frequency energy time is α T, and the energy captured by the wireless sensor i is:
E i ＝μp BS h i αT (1)
wherein E i For each wireless sensor device within an alpha T timeCaptured energy, μ is energy conversion efficiency, h i For channel gain, p BS Is the broadcast transmit power of the base station.
Let D be the calculation task to be completed by each wireless sensor in one period i The 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 )D i The calculated unload time for each wireless sensor device is expressed as:
each wireless sensor device offloads a portion of the task data (1- ρ @) i )D i Energy consumption required for arriving at edge cloud server
Comprises the following steps:
wherein p is i Transmitting power, p, for wireless sensor devices in offloading task data i A transmission power range of
R i Calculating a data transfer rate at the time of offloading for the wireless sensor device, expressed as:
in order to prevent collision and overhigh network load when a plurality of wireless sensor devices simultaneously calculate and unload, the same time period is ensured
Only one wireless sensor device can be used for countingCalculating the load, the following constraints are obtained:
in local calculation, the CPU calculation frequency of the wireless sensor device needs to be at the maximum value f i max In addition, the local computation time for each wireless sensor device is expressed as:
locally calculating p for each wireless sensor device i D i Energy consumed by task data
Comprises the following steps:
where v is the effective switched capacitor, Z i Number of CPU cycles required to calculate data per bit, f i Computing power calculated locally for a wireless sensor device, f i The computing power range is [0, f i max ]。
The wireless sensor device calculates the total energy consumed by the whole task as
The following energy constraints were obtained:
the wireless sensor device calculates the total time required for the whole task as
The following time constraints are obtained:
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:
the above problem (10) is a convex optimization problem, so it can be solved by lagrange duality, (10) expressed as the lagrange function:
(10) The dual problem of (a) is expressed as:
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
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 f i The minimization problem of (2):
the problem (14) is a convex optimization problem, so the lagrange function KKT condition can be used to get the optimal solution:
similarly, (2), (3) and (4) can be substituted into (11) to obtain the relation p i The minimization problem of (2):
(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:
(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:
(17) The dual problem of (a) is expressed as:
solving the internal minimization problem of problem (19) an optimal solution of (17) can be obtained by (18) on p i Obtaining a partial derivative:
according to (20) making
Optimization software can then be used to determine the interval +>
Optimal solution in (1)
Due to T, alpha and rho in (11) i All are first order polynomials and substituting (1) (2) (3) (4) (6) (7) into the original problem, which is a convex optimization problem, the optimum value for α can be found by (11) deriving T as a partial derivative:
order to
Get->
Obtaining the following by the same method:
order to
Get->
Trade-off ratio ρ of task computation offload and local computation workload with respect to wireless sensor device i In the original problem, the relationship is accumulated, so that the rho of each wireless sensor device is expanded i And solving to obtain:
same as above
Can be expressed in the same manner as equation (23) and then be caused to @>
Solving each rho by using multi-equation simultaneous equations i 。
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, the transmission energy is broadcasted to the surrounding wireless sensor equipment within the alpha T time, and the energy transmission power of the base station is p BS ；
Step 2: the wireless sensor equipment is switched to an energy capture mode, energy transmitted by the base station is captured within the time alpha T 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 adopted i A part (1-rho) of the total task to be completed i )D i Unloading 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)
The energy consumed for unloading is
Switching to local computation when the wireless sensor device is not in turn computation offload, each wireless sensor locally computing a partial task ρ i D i Local count time of->
Calculated CPU frequency of f i The energy required for the local calculation is->
The total energy consumed by each wireless sensor device in the stage is less than or equal to the energy captured by the device E i The total calculation time required by each device is less than or equal to (1-alpha) T;
and 3, 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 (2)
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;
base station transmitting radio frequency energyPower of p B The continuous transmission radio frequency energy time is α T, and the energy captured by the wireless sensor device i is:
E i ＝μp B h i αT (1)
wherein E i The energy captured by each wireless sensor device in the period of alpha T, T is the period of each execution time of the system, alpha is a proportion representing the proportion of the energy capture time of the wireless device in the whole period, mu is the energy conversion efficiency, h i For channel gain, p B A broadcast transmit power for a base station;
let ρ be i The proportion of the local calculation part task of the wireless sensor device i to the whole calculation task is 1-rho i Let D be the calculation task to be completed by each wireless sensor in one cycle i The 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 )D i The calculated unload time for each wireless sensor device is expressed as:
each wireless sensor device offloads a portion of the task data (1- ρ @) i )D i Energy consumption required for arriving at edge cloud server
Comprises the following steps:
wherein p is i Transmitting power, p, for wireless sensor devices in offloading task data i A transmission power range of
R i Calculating a data transfer rate at the time of offloading for the wireless sensor device, expressed as:
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
Only one wireless sensor device can perform calculation unloading, and the following constraints are obtained:
in local calculation, the CPU calculation frequency of the wireless sensor device needs to be at the maximum value f i max In addition, the local computation time for each wireless sensor device is expressed as:
locally calculating p for each wireless sensor device i D i Energy consumed by task data
Comprises the following steps:
where v is the effective switched capacitor, Z i Number of CPU cycles required to calculate data per bit, f i Computing power calculated locally for a wireless sensor device, f i The computing power range is [0, f i max ]；
The wireless sensor device calculates the total energy consumed by the whole task as
The following energy constraints were obtained:
the wireless sensor device calculates the total time required for the whole task as
The following time constraints are obtained:
the task is to select proper charging time proportion, CPU frequency, transmission power and calculate task unloading proportion, and minimize the time delay of the whole period under the conditions of meeting energy constraint, unloading conflict constraint and the like, and the model is as follows:
the above problem (10) is a convex optimization problem, which is solved by lagrange duality, and (10) is expressed by lagrange function as:
(10) The dual problem of (a) is expressed as:
the solving problem (10) is 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
When Ω converges to the optimal solution, the optimal solution of the problem (10) can be solved;
according to the Lagrange multiplier and the Lagrange function (11), and substituting (6) and (7) into (11) to obtain the corresponding f i The minimization problem of (2):
the problem (14) is a convex optimization problem, and the optimal solution is obtained by using the Lagrangian function KKT condition:
similarly, substituting (2), (3) and (4) into (11) to obtain the relation p i The minimization problem of (2):
(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:
(17) Is a convex optimization problem, and uses Lagrangian dual to solve the problem, and the Lagrangian function (17) is expressed as:
(17) The dual problem of (a) is expressed as:
solving the internal minimization problem of problem (19) an optimal solution of (17) can be obtained by (18) on p i Calculating a partial derivative to obtain:
according to (20) making
Then the optimization software is used to calculate the interval->
Is not determined by the optimum solution in (4)>
Due to T, alpha and rho in (11) i All are first order polynomials, substituting (1), (2), (3), (4), (6) and (7) into the original question, which is a convex optimization questionTherefore, the optimum value for α is found by (11) devitalizing T:
order to
Get->
Obtaining the following by the same method:
order to
Get->
Trade-off ratio ρ of task computation offload and local computation workload with respect to wireless sensor device i In the original problem, the relationship is accumulated, so that the rho of each wireless sensor device is expanded i And solving to obtain:
same as above
Can be expressed in the same manner as equation (23) and then be caused to @>
Solving each rho by a multi-equation simultaneous equation i ；
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: the base station is switched to an energy sending mode, radio frequency energy is broadcast and transmitted to nearby wireless sensing equipment by using given power, and the energy sending time is alpha 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.
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