CN110381161B - Game theory-based resource scheduling method in power Internet of things system - Google Patents

Game theory-based resource scheduling method in power Internet of things system Download PDF

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CN110381161B
CN110381161B CN201910686327.1A CN201910686327A CN110381161B CN 110381161 B CN110381161 B CN 110381161B CN 201910686327 A CN201910686327 A CN 201910686327A CN 110381161 B CN110381161 B CN 110381161B
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夏玮玮
高航
燕锋
成华清
张雅雯
胡静
宋铁成
沈连丰
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Abstract

The invention discloses a resource scheduling method based on a game theory in an electric power Internet of things system, which comprises the following steps: (1) and the intelligent ammeter and the access point AP in the same area form a network. The smart meter communicates information to the AP in a single-hop or multi-hop manner. Establishing a transmission energy consumption function; (2) the intelligent electric meter has a calculation task, if the local calculation resource is insufficient, the task is unloaded to other electric meters or APs on the path, and a task unloading energy consumption function is established; (3) the intelligent electric meter selects the next accessible intelligent electric meter or AP; all intelligent electric meters and APs in the network establish the network to form a game; (4) the intelligent electric meter calculates the cost function of each strategy of the intelligent electric meter, and the strategy with the minimum cost function is selected by comparison; (5) updating the connection state of the network once when the strategy of one intelligent electric meter is changed; (6) and the game reaches Nash equilibrium, all the intelligent electric meters do not change the strategy any more, and each intelligent electric meter performs multi-hop transmission and task unloading according to the path formed by the selected strategy.

Description

Game theory-based resource scheduling method in power Internet of things system
Technical Field
The invention belongs to the field of power Internet of things, and particularly relates to a resource scheduling method based on a game theory in a power Internet of things system.
Background
In recent years, the demand for electric power from human beings has increased greatly, but the conventional power distribution network is inefficient. The power internet of things is a next generation electric energy distribution network composed of a power control center, a power transmission line, a transformer substation, a transformer, users and the like. It can integrate green power resources such as solar energy, wind energy and the like into an energy distribution system, control power usage and balance energy loads. It uses bi-directional flow of power and information to create an automated and distributed advanced energy delivery network, providing a fully observable energy distribution process, and service providers and consumers can implement monitoring and control of their pricing, production and consumption, thereby allowing service providers to achieve efficient load balancing and reliable energy delivery, and consumers to reduce their power costs. The smart meters are household electric meters using a two-way communication technology, and they are generally considered as a first step of establishing an electric power internet of things. The main function of the smart meter is to collect and upload power information of a user to a control center. Meanwhile, the smart meter can also provide real-time information about the electricity consumption of the consumer, and help the consumer control the electricity use.
In the power internet of things, data collected from smart meters are generally stored in cloud data centers. However, the cloud data center is usually far away from the user, which causes that the energy consumed by the smart electric meter to upload data to the cloud is long in time delay, and actual use requirements cannot be met. Fog calculation has proven to be an effective solution to many large-scale problems. Fog computing may help solve the above-described problems in the power internet of things by moving cloud data and computing tasks to the edge of the network. However, the existing literature related to the aspect only considers the data of the smart meter to be directly transmitted to the access point, and does not consider the energy consumption for further optimizing the transmission process. Meanwhile, the computing resources of the intelligent electric meter are limited, and when the intelligent electric meter needs to deal with the computation-intensive tasks, huge time delay and energy consumption overhead are brought by the computing resources of the intelligent electric meter. If idle computing resources on other intelligent electric meters or access points AP can be fully utilized, local computing tasks are unloaded to the intelligent electric meters or the access points AP and are handed to the intelligent electric meters or the access points AP for processing, and the performance of system processing tasks is effectively improved.
Therefore, cooperation among a plurality of intelligent electric meters is considered, each intelligent electric meter can take other intelligent electric meters as relays, and all the intelligent electric meters cooperate with each other through the relays to transmit information to the AP, so that the node benefits from space diversity gain. Meanwhile, the unloading of the computing tasks of the intelligent electric meters is considered, and under the condition that the local computing resources of the intelligent electric meters are insufficient, the computing resources of other intelligent electric meters and the AP are fully utilized, and the tasks are unloaded to the intelligent electric meters or the AP. The strategy selection of the intelligent electric meter directly influences the allocation of wireless resources and computing resources, so that the cost function of the intelligent electric meter is influenced significantly. Therefore, how to realize the optimal path selection of each intelligent electric meter in the electric power internet of things system, realize the effective allocation of resources and meet the service requirements of each intelligent electric meter is a problem to be solved urgently.
Disclosure of Invention
In order to solve the problems, the invention discloses a resource scheduling method based on a game theory in an electric power Internet of things system, which fully utilizes computing resources and wireless resources of each intelligent electric meter and an Access Point (AP), and meets the data transmission and task unloading requirements of each intelligent electric meter while aiming at minimizing the average transmission energy consumption and the unloading energy consumption of the network.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the resource scheduling method based on the game theory in the power Internet of things system comprises the following steps:
(1) all the intelligent electric meters in the same area communicate the collected information to an access point, AP, in a single-hop or multi-hop mode. Meanwhile, establishing a transmission energy consumption function of each intelligent electric meter on a path; the wireless resources required by all the intelligent electric meters during data transmission are the same; the wireless resources of each intelligent electric meter are different;
(2) and the intelligent electric meter in the electric power Internet of things system initiates a calculation task unloading request and establishes a task unloading cost function of the intelligent electric meter. The task unloading cost function is an energy consumption function during unloading, and comprises transmission energy consumption on a path during unloading and calculation energy consumption on an intelligent electric meter for assisting calculation; when all the intelligent electric meters perform task unloading once, the intelligent electric meters are required to unload the same task amount; the computing resources of each intelligent electric meter are different;
(3) sequentially selecting strategies, namely nodes connected by the next hop, from all the intelligent electric meters in the network, and calculating a cost function (the sum of a transmission energy consumption function and an unloading energy consumption function); all intelligent electric meters and APs in the network establish the network to form a game;
(4) each intelligent electric meter selects a strategy for minimizing the cost function through comparison; after all the intelligent electric meters select the strategies, calculating an average cost function of the network;
(5) all the intelligent electric meters sequentially execute a strategy which is one iteration; the wireless resources and the computing resources required by all the intelligent electric meters for data transmission and task unloading are the same, and the system performs wireless resource and computing resource allocation according to path selection; after the system carries out one iteration, strategies of part of the intelligent electric meters in the network are changed, the number of the intelligent electric meters accessed to each intelligent electric meter is increased or reduced, and the distributed computing resources and wireless resources are correspondingly reduced or increased, so that the connection state of each intelligent electric meter in the network needs to be updated;
(6) the game reaches Nash equilibrium, all the intelligent electric meters in the network do not change strategies any more, each intelligent electric meter obtains the intelligent electric meters on the selected path and the computing resources and wireless resources distributed by the AP, and the intelligent electric meters can perform data transmission and task unloading.
The invention has the beneficial effects that:
the method and the system enable each intelligent electric meter in the network to sequentially realize strategy selection based on the game theory, thereby fully utilizing the limited computing resources and wireless resources in the intelligent electric meters and the AP, and meeting the data transmission and task unloading requirements of each intelligent electric meter while aiming at minimizing the average cost function of all the intelligent electric meters. The method comprehensively considers the wireless resource demand of the intelligent electric meter, the task unloading demand, the wireless resource and the calculation resource capacity limit of the intelligent electric meter, jointly allocates the wireless resource and the calculation resource under the condition of ensuring the real-time performance of the task, and minimizes the average cost function of all the intelligent electric meters in the network.
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FIG. 1 is a flow chart of a resource scheduling method based on game theory according to the present invention;
FIG. 2 is a basic architecture diagram of the electric power Internet of things based on fog calculation;
FIG. 3 is a flow chart of the game theory Nash equilibrium solving process of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
Based on the game theory, the method fully utilizes limited wireless resources and computing resources in the intelligent electric meters and the AP, guarantees the real-time performance of tasks of the intelligent electric meters while aiming at minimizing the average cost function of all the intelligent electric meters, and meets the requirements of data transmission and task unloading of the intelligent electric meters. The following describes the method of the present invention with reference to the accompanying drawings.
As shown in fig. 2, in consideration of the power internet of things scene based on fog computing, a power grid center is responsible for data storage and processing, and a fog computing node (AP) is a bridge for users to communicate with the center. In this architecture, the smart meter needs to communicate different information, such as meter readings, load reports, price inquiries, event detection data, or network repair information, to the AP. The AP is a repeater installed by the grid operator connecting the area to the rest of the grid. The AP uploads the data to the control center through a wired connection, and the center can be expanded to a plurality of distributed centers with the same structure so as to control the system more accurately.
Each AP accesses a fog calculation server with computing power. The smartmeters transmit their data to the AP via multi-hop links, thus requiring the formation of a tree structure. In this network, the smart meter may transmit information to the AP through other smart meters, or may directly transmit information to the AP. And each smart meter can utilize other smart meters and idle computing resources on the AP. When the computing resources of the intelligent electric meter are insufficient, the computing tasks can be unloaded to other intelligent electric meters or APs.
Figure BDA0002146419190000031
Representing a collection of networked smart meters. All smart meters use orthogonal sub-channels, so the method does not consider interference between sub-channels. By using hi,jRepresenting the channel gain between the smart meters i, j,
Figure BDA0002146419190000032
representing the transmission power between them, the arrival rate is expressed as:
Figure BDA0002146419190000036
wherein B isi,jAnd N0The bandwidth between i, j and the gaussian white noise channel variance represent the base station, respectively.
The data volume transmitted between the intelligent electric meters i and j is li,jThe data volume to be unloaded when the task is unloaded is xi,j. The wireless resource and the computing resource of each intelligent electric meter are respectively Bi，Xi. The transmission energy consumption of the smart meters i to j is expressed as:
Figure BDA0002146419190000033
wherein
Figure BDA0002146419190000034
The method is the basic energy consumption of the intelligent ammeter for transmitting single-bit data, gamma is a path loss factor and satisfies that gamma is more than or equal to 2 and less than or equal to 4, and d isi,jIs the distance, zeta, between the smart meters i, jiThe energy loss factor of the intelligent electric meter meets the following requirements:
Figure BDA0002146419190000035
Figure BDA0002146419190000041
is the signal-to-noise ratio, V, on the smart meter jjIs the receive noise factor at j, and λ is the wavelength.
The transmission energy consumption required for the intelligent electric meter i to unload the task to the task j is as follows:
Figure BDA0002146419190000042
suppose that the intelligent electric meter j processes the unloading data with the size of
Figure BDA0002146419190000043
Then the computation time on j is;
Figure BDA0002146419190000044
cjnumber of CPU cycles required to calculate 1bit data, fjIs the CPU rate of smart meter j. The calculated energy consumption is thus:
Figure BDA0002146419190000045
as shown in fig. 1, the resource scheduling method based on the game theory in the power internet of things system of the present invention specifically includes:
(1) all the intelligent electric meters in the same area communicate the collected information to an Access Point (AP) in a single-hop or multi-hop mode. Meanwhile, establishing a transmission energy consumption function of each intelligent electric meter on the path, wherein the transmission energy consumption function is expressed as
Figure BDA0002146419190000046
The method comprises the steps that it is assumed that wireless resources required by all the intelligent electric meters during data transmission are the same; the wireless resources of each intelligent electric meter are different;
(2) initiating a calculation task unloading request by an intelligent electric meter in the electric power Internet of things system, and establishing a task unloading cost function of the intelligent electric meter, wherein the task unloading cost function is expressed as
Figure BDA0002146419190000047
The task unloading cost function is an energy consumption function during unloading, and comprises transmission energy consumption on a path during unloading and calculation energy consumption on an intelligent electric meter for assisting calculation; when all the intelligent electric meters perform task unloading once, the intelligent electric meters are required to unload the same task amount; the computing resources of each intelligent electric meter are different;
(3) strategy s is selected in sequence by all intelligent electric meters in networkiI.e. the node connected by the next hop, calculating a cost function (the sum of the transmission energy consumption function and the unloading energy consumption function); all intelligent electric meters and APs in the network establish the network to form a game;
(4) each intelligent electric meter selects a strategy for minimizing the cost function through comparison; after all the intelligent electric meters select the strategies, calculating the average cost function of the network
Figure BDA0002146419190000048
(5) All the intelligent electric meters sequentially execute a strategy which is one iteration; the wireless resources and the computing resources required by all the intelligent electric meters for data transmission and task unloading are the same, and the system performs wireless resource and computing resource allocation according to path selection; after the system carries out one iteration, strategies of part of the intelligent electric meters in the network are changed, the number of the intelligent electric meters accessed to each intelligent electric meter is increased or reduced, and the distributed computing resources and wireless resources are correspondingly reduced or increased, so that the connection state of each intelligent electric meter in the network needs to be updated;
(6) the game reaches Nash equilibrium, all the intelligent electric meters in the network do not change strategies any more, each intelligent electric meter obtains the intelligent electric meters on the selected path and the computing resources and wireless resources distributed by the AP, and the intelligent electric meters can perform data transmission and task unloading.
Wherein, the calculation formula of the cost function of the intelligent ammeter in the step (3) is as follows:
Figure BDA0002146419190000051
in the formula (I), the compound is shown in the specification,
Figure BDA0002146419190000052
represents the transmission energy consumption of the smart meter on the path for transmitting data to the AP,
Figure BDA0002146419190000053
and
Figure BDA0002146419190000054
and respectively representing the unloading transmission energy consumption and the calculation energy consumption of the intelligent electric meter for unloading the calculation task to other electric meters or APs.
Figure BDA0002146419190000055
Figure BDA0002146419190000056
The average cost function of all the intelligent electric meters in the network is
Figure BDA0002146419190000057
The following section describes the network forming gaming process of the resource scheduling method.
In the network forming game of the method, participants are all intelligent electric meters in the electric power Internet of things system. The intelligent electric meter and the AP are provided with wireless resources and computing resources, and the intelligent electric meter performs data transmission and task unloading through the acquired wireless resources and computing resources, so that energy for data transmission and local operation is saved. The cost function of the intelligent electric meter is the sum of transmission energy consumption during data transmission and unloading energy consumption during task unloading. The network forming game is described as follows:
the participants: all the intelligent electric meters in the network are integrated into
Figure BDA0002146419190000058
Strategy: the strategy of each intelligent electric meter i is referred to in a strategy set SiTo select a particular policy siThe strategy set of the intelligent electric meter i is expressed as
Figure BDA0002146419190000059
Wherein
Figure BDA00021464191900000510
Indicates a smart meter that has been connected to the smart meter i,
Figure BDA00021464191900000511
therefore, the strategy of the intelligent electric meter is to select the next hop link;
the cost function is: the cost function of each participant is the sum of the transfer energy consumption function and the offload energy consumption function.
The average cost function of step (4) can be expressed as:
Figure BDA00021464191900000512
the nash equilibrium solving process of the network formed game in the step (6) is as follows:
firstly, initially, each intelligent electric meter is directly connected to the AP, the intelligent electric meters directly use wireless resources and computing resources of the AP, and an average cost function of a network during initial is computed;
and secondly, sequentially selecting strategies from the strategy set by using the intelligent electric meter, and calculating a cost function. Finding a strategy for minimizing the cost function, selecting the strategy and keeping the strategy unchanged, and then selecting the optimal strategy by other intelligent electric meters;
thirdly, calculating the average cost function U of all the intelligent electric meters in the network for one iteration after all the intelligent electric meters select the optimal strategy;
after the system performs one iteration, strategies of part of the intelligent electric meters in the network are changed, the number of the intelligent electric meters accessed to each intelligent electric meter is increased or reduced, and the distributed computing resources and wireless resources are correspondingly reduced or increased, so that the connection state of each intelligent electric meter in the network needs to be updated;
after one iteration, the network connection state is comprehensively updated, and each intelligent electric meter reselects the strategy for minimizing the cost function to perform a new iteration;
forming game in the network to reach Nash equilibrium, and not changing strategy for all intelligent electric meters in the network. And each intelligent electric meter obtains the calculation resources and the wireless resources distributed by the intelligent electric meters and the AP on the selected path, and each intelligent electric meter performs data transmission and task unloading by using the obtained path.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.

Claims (4)

1. A resource scheduling method based on a game theory in an electric power Internet of things system is characterized by comprising the following steps:
(1) all the intelligent electric meters in the same area communicate the collected information to the access point AP in a single-hop or multi-hop mode; meanwhile, establishing a transmission energy consumption function of each intelligent electric meter on a path; the wireless resources required by all the intelligent electric meters during data transmission are the same; the wireless resources of each intelligent electric meter are different;
(2) an intelligent electric meter in the electric power Internet of things system initiates a calculation task unloading request and establishes a task unloading cost function of the intelligent electric meter; the task unloading cost function is an energy consumption function during unloading, and comprises transmission energy consumption on a path during unloading and calculation energy consumption on an intelligent electric meter for assisting calculation; when all the intelligent electric meters perform task unloading once, the intelligent electric meters are required to unload the same task amount; the computing resources of each intelligent electric meter are different;
(3) all intelligent electric meters in the network sequentially select a strategy, namely a node connected by the next hop, and calculate a cost function; all intelligent electric meters and APs in the network establish the network to form a game; the method comprises the following specific steps:
participant Player: all the intelligent electric meters in the network are integrated into
Figure FDA0003339238490000011
Strategy Strategy: strategy of each intelligent electric meter iIs in the policy set SiTo select a particular policy siThe strategy set of the intelligent electric meter i is expressed as
Figure FDA0003339238490000012
Wherein
Figure FDA0003339238490000013
Indicates a smart meter that has been connected to the smart meter i,
Figure FDA0003339238490000014
therefore, the strategy of the intelligent electric meter is to select the next hop link;
cost function: cost function per participant
Figure FDA0003339238490000015
Is the sum of the transmission energy consumption function and the unloading energy consumption function;
Figure FDA0003339238490000016
representing smart meter i execution strategy siMeanwhile, all other intelligent electric meters keep the current strategy s-i＝{s1,..,si-1,si+1,...,sMObtaining a network graph unchanged;
path: in directed graphs
Figure FDA0003339238490000017
In (1), the path between the intelligent electric meter i and the intelligent electric meter j is a series of nodes qi＝(i1,i2,...,iW)，i1＝i，iW＝j；
Wherein
Figure FDA0003339238490000018
Represents a collection of all nodes; ε represents the set of all edges connecting different nodes; w represents the arrival of node i at node j over the w-1 hop,
(4) each intelligent electric meter selects a strategy for minimizing the cost function through comparison; after all the intelligent electric meters select the strategies, calculating an average cost function of the network;
(5) all the intelligent electric meters sequentially execute a strategy which is one iteration; the wireless resources and the computing resources required by all the intelligent electric meters for data transmission and task unloading are the same, and the system performs wireless resource and computing resource allocation according to path selection; after the system carries out one iteration, strategies of part of the intelligent electric meters in the network are changed, the number of the intelligent electric meters accessed to each intelligent electric meter is increased or reduced, and the distributed computing resources and wireless resources are correspondingly reduced or increased, so that the connection state of each intelligent electric meter in the network needs to be updated;
(6) the game reaches Nash equilibrium, all the intelligent electric meters in the network do not change strategies any more, each intelligent electric meter obtains the intelligent electric meters on the selected path and the computing resources and wireless resources distributed by the AP, and the intelligent electric meters can perform data transmission and task unloading.
2. The resource scheduling method based on the game theory in the power internet of things system according to claim 1, characterized in that: and (3) under the condition that the transmission energy consumption function in the step (1) and the unloading energy consumption function in the step (2) select the optimal path of each intelligent electric meter, the sum of the average transmission energy consumption and the unloading energy consumption of the whole network can be minimized.
3. The resource scheduling method based on the game theory in the power internet of things system according to claim 1, characterized in that: the specific processes of the steps (4) and (5) are as follows:
the cost function for each smart meter may be expressed as:
Figure FDA0003339238490000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003339238490000022
intelligent ammeterThe transmission of data to the AP over the path consumes energy,
Figure FDA0003339238490000023
and
Figure FDA0003339238490000024
the method comprises the following steps of respectively representing unloading transmission energy consumption and calculation energy consumption of the intelligent electric meter for unloading calculation tasks to other electric meters or APs, calculating a cost function of each strategy executed by the intelligent electric meter, and selecting the strategy with the lowest cost function in comparison, wherein the average cost function of the network is as follows:
Figure FDA0003339238490000025
4. the resource scheduling method based on the game theory in the power internet of things system according to claim 1, characterized in that: in the step (6), the network formed game achieves Nash equilibrium, and the solving process is as follows:
firstly, initially, each intelligent electric meter is directly connected to the AP, the intelligent electric meters directly use wireless resources and computing resources of the AP, and an average cost function of a network during initial is computed;
secondly, selecting strategies from the strategy set in sequence by using the intelligent electric meters, calculating a cost function, finding out the strategy which enables the cost function to be minimum, selecting the strategy and keeping the strategy unchanged, and selecting the optimal strategy by using other intelligent electric meters;
thirdly, calculating the average cost function U of all the intelligent electric meters in the network for one iteration after all the intelligent electric meters select the optimal strategy;
after the system performs one iteration, strategies of part of the intelligent electric meters in the network are changed, the number of the intelligent electric meters accessed to each intelligent electric meter is increased or reduced, and the distributed computing resources and wireless resources are correspondingly reduced or increased, so that the connection state of each intelligent electric meter in the network needs to be updated;
after one iteration, the network connection state is comprehensively updated, and each intelligent electric meter reselects the strategy for minimizing the cost function to perform a new iteration;
forming a game in the network to achieve Nash balance, not changing strategies of all the intelligent electric meters in the network, obtaining the computing resources and wireless resources distributed by the intelligent electric meters and the APs on the selected path by each intelligent electric meter, and carrying out data transmission and task unloading by each intelligent electric meter by using the obtained path.
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