CN111163519A - Wireless body area network resource allocation and task unloading algorithm with maximized system benefits - Google Patents
Wireless body area network resource allocation and task unloading algorithm with maximized system benefits Download PDFInfo
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W72/50—Allocation or scheduling criteria for wireless resources
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Abstract
The algorithm is based on a classic bargaining and bargaining game model in a game theory, an edge server providing computing resources is used as a resource seller in the model, a wireless body area network distributed in the service range of the server is used as a resource buyer, bargaining is carried out on CPU resources and wireless channel resources by the aid of the edge server and the wireless body area network according to a selfish behavior mode by taking the maximum benefit as a target, and finally an optimal resource allocation scheme and a task offloading scheme of the whole system are obtained. The timeliness and the reliability of the wireless body area network data processing can be improved.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a wireless body area network resource allocation and task unloading algorithm for maximizing system benefits.
Background
A Wireless Body Area Network (WBAN) is a Wireless Network which takes a human Body as a center and consists of a center node and a plurality of sensor nodes attached to or implanted in the human Body, is used for monitoring various physiological data of the human Body in real time, can effectively relieve medical problems caused by shortage of medical resources and imbalance of medical situations, and promotes the development of electronic medical treatment. However, limited by the limited battery capacity and data processing capability of the WBAN, how to provide users with services with long endurance, high data reliability and personalized customization is a challenge in the related research of WBAN.
Meanwhile, Mobile/Multi-access Edge Computing (MEC) has become a research focus. The MEC aims to solve the problem that the centralized cloud computing capability cannot be matched with the explosive growth of mass edge data in the time of the Internet of things, and partial high-computing-density computing tasks are unloaded from a local computer (a mobile phone, a tablet computer or Internet of things equipment) to an edge server which is rich in computing resources and is closer to a mobile user in physical position for execution, so that the execution time delay and the energy loss are reduced, and the endurance time, the data processing capability and the use experience of the user of the mobile equipment are improved.
Most of the existing calculation unloading strategies aim at singly reducing the processing time delay or energy consumption of the mobile equipment, a few unloading decision making algorithms for balancing the time delay and the energy consumption only consider how to optimize the task processing mode of the mobile equipment, and few algorithms study user equipment and a server providing calculation resources as a whole, which often causes great pressure on the server and a wireless channel in the current environment after the requirements of the mobile equipment on the time delay and the energy consumption are met. When the number of users reaches a certain number, the influence on the whole system is more obvious.
Secondly, research on the wireless body area network focuses more on how to optimize information transmission in the network, but does not consider that the computing capacity and the battery capacity of the wireless body area network are limited, and a large amount of physiological data from the sensor nodes cannot be processed in real time, so that the effectiveness of a processing result is influenced, and the user experience is reduced.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a wireless body area network resource allocation and task unloading algorithm with maximized system benefits, a bargaining and price-repaying game model is used, an edge server and a wireless body area network are taken into consideration as an integral system, the aim of optimizing execution delay and system energy consumption is fulfilled, and timeliness and reliability of data processing of the wireless body area network are improved.
The invention provides a wireless body area network resource allocation and task unloading algorithm with maximized system benefits, which comprises the following steps:
step 1: the wireless body area network generates a plurality of tasks with different priorities and data volumes according to the priority specification of the wireless body area network of IEEE802.15.6 at a certain moment, and each task calculates the value of the task according to the priority and the data volume of the task;
step 2: the task can be selected to be processed locally or unloaded to an edge server for processing;
and step 3: calculating the total time delay and the total energy consumption of the wireless body area network processing task according to the time delay and the energy consumption of the wireless body area network local processing task and the time delay and the energy consumption of the unloading processing task;
and 4, step 4: respectively quantifying the total time delay and the total energy consumption of a certain task processed by the wireless body area network into time cost and energy consumption cost through unit time cost and unit energy consumption cost, introducing a time delay factor and an energy consumption factor to express the ratio of the time cost to the energy consumption cost, and determining the total cost for processing the task;
and 5: tasks unloaded by the wireless body area network rent the edge server resources and need to pay certain fee, and the two transaction parties determine the amount and the fee of the leased resources through a full-information bilateral price auction game to respectively obtain the profits of the wireless body area network and the edge server;
step 6: respectively obtaining the total income of the wireless body area network and the edge server according to the value of the task, the cost of processing the task and the cost of unloading the task, and further converting the total income into the problem of solving the system income maximization;
and 7: the method comprises the steps that a double-layer optimization idea is adopted to decompose an original system profit maximization problem into two embedded problems, wherein the two embedded problems comprise the distribution problem of network communication resources in the process of computing resources and unloading tasks of a wireless body area network and an edge server, and the unloading decision problem of an optimal task;
and 8: solving the distribution problem of the computing resources and the communication resources of the edge server and the wireless body area network by adopting a Lagrange multiplier algorithm, wherein the Lagrange multiplier is subjected to iterative solution by a sub-gradient iteration method;
and step 9: for the unloading decision problem, substituting the optimal resource allocation result about the unloading decision variable obtained in the resource allocation problem into the original system profit maximization problem formula to obtain a 0-1 shaping planning problem only about the task unloading decision, and then solving the problem by means of a genetic algorithm to obtain the optimal unloading decision of the task.
The wireless body area network resource allocation and task unloading algorithm with maximized system benefits at least has the following beneficial effects:
1. the invention applies the unloading decision technology to the wireless body area network, unloads part of the calculation tasks to the edge server for execution, relieves the calculation pressure on the central node of the wireless body area network, relieves the problems of high time delay and low efficiency of data processing caused by the limitation of hardware resources of the wireless body area network, and improves the data processing speed and accuracy.
2. The invention innovatively combines the bargaining game model with the calculation unloading algorithm, optimizes the wireless body area network and the edge server as an integral system, and avoids the condition of meeting the user requirements and generating a large amount of system overhead.
3. The invention adopts a two-layer iterative algorithm, combines the genetic algorithm and the Lagrangian method for use, and can solve the optimal unloading strategy and the resource allocation strategy in a shorter time.
Drawings
Fig. 1 is a flow chart of the wireless body area network resource allocation and task offloading algorithm for system revenue maximization of the present invention.
Detailed Description
As shown in fig. 1, the resource allocation and task offloading algorithm for wireless body area network with maximized system benefit of the present invention includes:
step 1: the wireless body area network generates a plurality of tasks with different priorities and data volumes at a certain moment, and each task calculates the value of the task according to the own priority and data volume; the step 1 specifically comprises the following steps:
wireless body area network generates N tasks with different priorities and data volumes at a certain time
The value of the task, i.e., the revenue payment, may be expressed as follows:
Rn＝r×(1+Kn)2×log2(1+Dn)
wherein ,RnRepresenting the value of the task, r representing the unit task value constant, DnData volume, K, representing the tasknIndicating its priority and complying with the ieee802.15.6 wireless body area network priority specification.
Step 2: if the task can be processed locally, directly processing locally, otherwise, unloading to an edge server for processing, where the step 2 specifically is:
according to the resource limitation condition and task attribute in the current scene, the wireless body area network can select local processing or unloading to an edge server for processing for a certain task, and introduces unE {0,1} represents the offloading decision of the nth task in the wireless body area network when unWhen 0 indicates that the task is executed locally, unAnd 1 indicates that the task is unloaded to the edge server for execution.
And step 3: calculating the total time delay and the total energy consumption of the wireless body area network processing task according to the time delay and the energy consumption of the wireless body area network local processing task and the time delay and the energy consumption of the unloading processing task, wherein the step 3 is calculated according to the following formula:
wherein ,TnTotal delay for wireless body area network processing tasks, EnTotal energy consumption for wireless body area network processing tasks, unIndicating an offloading decision for the nth task within the wireless body area network,
respectively representing the latency and energy consumption of the local processing task,
respectively representing the latency and energy consumption of offloading the processing task.
And 4, step 4: respectively quantifying the total time delay and the total energy consumption of a task processed by the wireless body area network into time cost and energy consumption cost through unit time cost and unit energy consumption cost, introducing a time delay factor and an energy consumption factor to represent the ratio of the time cost to the energy consumption cost, and determining the total cost for processing the task, wherein the total cost for processing the task is represented by the following formula in the step 4:
in the above formula, the cost per unit time
And unit energy cost
The total time delay and total energy consumption of a certain task processed by the wireless body area network are quantized into time cost and energy consumption cost, and meanwhile, the time delay factor is set
And factor of energy consumption
Represents the ratio of the total cost of the two, and further determines the total cost C for processing the taskn；
At the same time, the total cost of a certain task can be determined by unloading the decision unAnd the cost of processing the task locally
And off-loading the cost of processing the task
To show that:
in which the local costs
The latency and energy consumption corresponding to local processing can be expressed as:
cost of unloading
The time delay and energy consumption corresponding to the unloading process can be expressed as:
and 5: the task renting the edge server resource unloaded by the wireless body area network needs to pay certain fee, the two transaction parties determine the amount and the fee of the rented resource through a full-information bilateral price auction game to respectively obtain the income of the wireless body area network and the edge server, and the step 5 specifically comprises the following steps:
step 5.1: the cost of the edge server for processing the task is obtained by calculating the time delay and the energy consumption of the edge server for processing the unloading task:
wherein ,
representing the cost consumed by the edge server to handle the task of wireless body area network offloading to the server,
indicating the time at which the server is processing the task,
representing the energy consumption for processing the task,
respectively representing the corresponding time cost and energy consumption cost,
corresponding time factor and energy consumption factor;
and (3) determining the value evaluation of the goods of the buyer and the seller through the profit and loss of the transaction:
wherein ,
for value assessment of wireless body area networks,
for the value assessment of the edge server,
the cost consumed for the wireless body area network to handle the task of offloading to the server.
Step 5.2: and (3) unloading the benefits of the task through the wireless body area network and the cost of the unloading task processed by the edge server to obtain the valuations of the two transaction parties, and calling by the two parties:
wherein ,
for pricing of wireless body area networks, deltaWBANA jitter parameter for pricing a wireless body area network,
for pricing of edge servers, deltaMECA jitter parameter for the edge server bid;
conclusively determining the pricing p of a task for an off-load processnThe method comprises the following steps:
step 5.3: respectively obtaining the benefits of the wireless body area network and the edge server:
wherein ,UWBANFor wireless body area network revenue, UMECFor the edge serverIt is beneficial to.
Step 6: respectively obtaining the total income of the wireless body area network and the edge server according to the value of the task, the cost of processing the task and the cost of unloading the task, and further converting the total income into the problem of solving the system income maximization, wherein the step 6 specifically comprises the following steps:
integrating the two profits by a linear weighting method and introducing a parameter w0、w1，w0Representing a revenue weight, w, of a wireless body area network1Representing the profit weight of the edge server, and converting the original problem of resource allocation and task unloading decision of the wireless body area network into the problem of solving the system profit maximization
Wherein u represents an unloading strategy set of the task; f. oflocRepresenting local resource allocation sets
FWBANRepresenting locally allocatable computing resources; b represents a network communication resource allocation set, and B represents allocable network communication resources; p denotes the cost set for offloading traffic
fserRepresenting server resource allocation collections
FMECRepresenting assignable server computing resources; tau isnIndicating the maximum completion of the task by time.
And 7: the method comprises the steps that a double-layer optimization idea is adopted to decompose an original system profit maximization problem into two embedded problems, wherein the two embedded problems comprise the distribution problem of network communication resources in the process of computing resources and unloading tasks of a wireless body area network and an edge server, and the unloading decision problem of an optimal task;
and 8: solving the distribution problem of the computing resources and the communication resources of the edge server and the wireless body area network by adopting a Lagrange multiplier algorithm, wherein the Lagrange multiplier is subjected to iterative solution by a sub-gradient iteration method; the step 8 specifically comprises the following steps:
step 8.1: firstly, fixing an unloading decision u, and then constructing a Lagrangian function by adopting a Lagrangian multiplier algorithm
Wherein α, β, γ and
lagrange multipliers are represented separately:
for lagrange multipliers α, β, γ sum in resource allocation results
Solving by adopting a sub-gradient iteration method:
wherein ,sα,sβ,sγ，sθThe iteration step size is indicated.
Step 8.2: based on the Lagrangian condition:
the optimal local computing resource allocation strategy (f) can be obtained by solving the Lagrange functionloc)*(u) server computing resource allocation policy (f)ser)*(u), and communication resource allocation policy b*(u)。
And step 9: for the unloading decision problem, substituting the optimal resource allocation result about the unloading decision variable obtained in the resource allocation problem into the original system profit maximization problem formula to obtain a 0-1 shaping planning problem only about the task unloading decision, and then solving the problem by means of a genetic algorithm to obtain the optimal unloading decision of the task, wherein the step 9 specifically comprises the following steps:
step 9.1: substituting the resource allocation result of the first problem and obtaining the task unloading decision problem only about the unloading decision variable u
wherein 
Feasible fields representing offload decisions u:
step 9.2: the problem can be known by analysis
Is a 0-1 integer programming problem, and solves the problem by means of genetic algorithm to obtain the optimal unloading decision u*：
The invention takes the edge server providing computing resources as a resource seller in the model, and the wireless body area network distributed in the service range of the edge server as a resource buyer, and the two parties are directed at CPU resources and wireless channel resources, and bargain is offered by taking the maximum benefit as a target according to a selfish behavior mode, and finally the optimal resource allocation scheme and task unloading scheme of the whole system are obtained.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, which is defined by the appended claims.
Claims (10)
1. The wireless body area network resource allocation and task unloading algorithm with maximized system benefits is characterized by comprising the following steps:
step 1: the wireless body area network generates a plurality of tasks with different priorities and data volumes according to the priority specification of the wireless body area network of IEEE802.15.6 at a certain moment, and each task calculates the value of the task according to the priority and the data volume of the task;
step 2: the task can be selected to be processed locally or unloaded to an edge server for processing;
and step 3: calculating the total time delay and the total energy consumption of the wireless body area network processing task according to the time delay and the energy consumption of the wireless body area network local processing task and the time delay and the energy consumption of the unloading processing task;
and 4, step 4: respectively quantifying the total time delay and the total energy consumption of a certain task processed by the wireless body area network into time cost and energy consumption cost through unit time cost and unit energy consumption cost, introducing a time delay factor and an energy consumption factor to express the ratio of the time cost to the energy consumption cost, and determining the total cost for processing the task;
and 5: tasks unloaded by the wireless body area network rent the edge server resources and need to pay certain fee, and the two transaction parties determine the amount and the fee of the leased resources through a full-information bilateral price auction game to respectively obtain the profits of the wireless body area network and the edge server;
step 6: respectively obtaining the total income of the wireless body area network and the edge server according to the value of the task, the cost of processing the task and the cost of unloading the task, and further converting the total income into the problem of solving the system income maximization;
and 7: the method comprises the steps that a double-layer optimization idea is adopted to decompose an original system profit maximization problem into two embedded problems, wherein the two embedded problems comprise the distribution problem of network communication resources in the process of computing resources and unloading tasks of a wireless body area network and an edge server, and the unloading decision problem of an optimal task;
and 8: solving the distribution problem of the computing resources and the communication resources of the edge server and the wireless body area network by adopting a Lagrange multiplier algorithm, wherein the Lagrange multiplier is subjected to iterative solution by a sub-gradient iteration method;
and step 9: for the unloading decision problem, substituting the optimal resource allocation result about the unloading decision variable obtained in the resource allocation problem into the original system profit maximization problem formula to obtain a 0-1 shaping planning problem only about the task unloading decision, and then solving the problem by means of a genetic algorithm to obtain the optimal unloading decision of the task.
2. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 1, wherein the step 1 is specifically:
generating N tasks with different priorities and data volumes at a certain time by a wireless body area network
The value of the task, i.e., the revenue payment, may be expressed as follows:
Rn＝r×(1+Kn)2×log2(1+Dn)
wherein ,RnRepresenting the value of the task, r representing the unit task value constant, DnData volume, K, representing the tasknIndicating its priority and complying with the ieee802.15.6 wireless body area network priority specification.
3. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 1, wherein the step 2 is specifically:
according to the resource limitation condition and task attribute in the current scene, the wireless body area network can select local processing or unloading to an edge server for processing for a certain task, and introduces unE {0,1} represents the offloading decision of the nth task in the wireless body area network when unWhen 0 indicates that the task is executed locally, unAnd 1 indicates that the task is unloaded to the edge server for execution.
4. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 3, wherein step 3 is calculated according to the following equation:
wherein ,TnTotal delay for wireless body area network processing tasks, EnTotal energy consumption for wireless body area network processing tasks, unIndicating an offloading decision for the nth task within the wireless body area network,
respectively representing the latency and energy consumption of the local processing task,
respectively representing the latency and energy consumption of offloading the processing task.
5. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 4 wherein the total cost of processing the task in step 4 is represented by:
in the above formula, the cost per unit time
And unit energy cost
The total time delay and total energy consumption of a certain task processed by the wireless body area network are quantized into time cost and energy consumption cost, and meanwhile, the time delay factor is set
And factor of energy consumption
Represents the ratio of the total cost of the two, and further determines the total cost C for processing the taskn；
At the same time, the total cost of a certain task can be determined by unloading the decision unAnd the cost of processing the task locally
And off-loading the cost of processing the task
To show that:
in which the local costs
The latency and energy consumption corresponding to local processing can be expressed as:
cost of unloading
The time delay and energy consumption corresponding to the unloading process can be expressed as:
6. the system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 5, wherein the step 5 is specifically:
step 5.1: the cost of the edge server for processing the task is obtained by calculating the time delay and the energy consumption of the edge server for processing the unloading task:
wherein ,
representing the cost consumed by the edge server to handle the task of wireless body area network offloading to the server,
indicating the time at which the server is processing the task,
representing the energy consumption for processing the task,
respectively representing the corresponding time cost and energy consumption cost,
corresponding time factor and energy consumption factor;
and (3) determining the value evaluation of the goods of the buyer and the seller through the profit and loss of the transaction:
wherein ,
for value assessment of wireless body area networks,
for the value assessment of the edge server,
the cost consumed for the wireless body area network to handle the task of offloading to the server.
Step 5.2: and (3) unloading the benefits of the task through the wireless body area network and the cost of the unloading task processed by the edge server to obtain the valuations of the two transaction parties, and calling by the two parties:
wherein ,
for pricing of wireless body area networks, deltaWBANA jitter parameter for pricing a wireless body area network,
for pricing of edge servers, deltaMECA jitter parameter for the edge server bid;
conclusively determining the pricing p of a task for an off-load processnThe method comprises the following steps:
step 5.3: respectively obtaining the benefits of the wireless body area network and the edge server:
wherein ,UWBANFor wireless body area network revenue, UMECIs the revenue of the edge server.
7. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 1, wherein the step 6 is specifically:
integrating the two profits by a linear weighting method and introducing a parameter w0、w1，w0Representing a revenue weight, w, of a wireless body area network1Representing the profit weight of the edge server, and converting the original problem of resource allocation and task unloading decision of the wireless body area network into the problem of solving the system profit maximization
Wherein u represents an unloading strategy set of the task; f. oflocRepresenting local resource allocation sets
FWBANRepresenting locally allocatable computing resources; b represents a network communication resource allocation set, and B represents allocable network communication resources; p denotes the cost set p for offloading trafficn∈p,
fserRepresenting server resource allocation collections
FMECRepresenting assignable server computing resources; tau isnIndicating the maximum completion of the task by time.
8. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 7, wherein the step 8 is specifically:
step 8.1: firstly, fixing an unloading decision u, and then constructing a Lagrangian function by adopting a Lagrangian multiplier algorithm
Wherein α, β, γ and
lagrange multipliers are represented separately:
step 8.2: based on the Lagrangian condition:
the optimal local computing resource allocation strategy (f) can be obtained by solving the Lagrange functionloc)*(u) server computing resource allocation policy (f)ser)*(u), and communication resource allocation policy b*(u)。
9. The system revenue maximizing wireless of claim 8Body area network resource allocation and task offloading algorithm, wherein the lagrangian multipliers α, β, γ sum in the resource allocation result in the step 8.1
Solving by adopting a sub-gradient iteration method:
wherein ,sα,sβ,sγ，sθThe iteration step size is indicated.
10. The system revenue maximizing wireless body area network resource allocation and task offloading algorithm of claim 7, wherein the step 9 is specifically:
step 9.1: substituting the resource allocation result of the first problem and obtaining the task unloading decision problem only about the unloading decision variable u
Step 9.2: the problem can be known by analysis
Is a 0-1 integer programming problem, and solves the problem by means of genetic algorithm to obtain the optimal unloading decision u*：
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