CN115412966A - Green energy-saving unloading method based on multi-edge node cooperation under power Internet of things - Google Patents
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Abstract
The invention discloses a green energy-saving unloading method based on multi-edge node cooperation under an electric power Internet of things, belonging to the field of electric power Internet of things; firstly, aiming at a photovoltaic power generation scene, uniformly distributing N aggregation nodes, carrying an edge node on each aggregation node, and managing a plurality of sensors by each aggregation node; respectively calculating an energy consumption model of each sink node; meanwhile, for an edge calculation task j generated by a sensor, constructing a time delay constraint model according to whether the edge calculation task is a time delay sensitive task or not; then, an energy consumption objective function for minimizing the energy consumption of all sink nodes on the premise of meeting the task time delay requirement is established by using the energy consumption model of each sink node and the time delay constraint model of the task; finally, decoupling the solving process of the objective function into two subproblems, clustering and unloading to finally obtain an optimal green energy-saving unloading result; the invention realizes better balance between reducing the energy consumption of the sink node and improving the completion rate of the edge calculation task.
Description
Technical Field
The invention belongs to the field of power Internet of things, and particularly relates to a green energy-saving unloading method based on multi-edge node cooperation under the power Internet of things.
Background
In recent years, the global energy crisis and the deterioration of the ecological environment are aggravated by the development pattern of high carbon economy in countries around the world. At present, the problem of new energy consumption seriously restricts the green low-carbon transformation of the power industry because the wind and light abandonment quantity of new energy is continuously increased [1]. The sensing capability of the new energy power generation unit and the controllable load information is insufficient, and the new energy consumption is one of the reasons. Therefore, the operation management level of the new energy power generation equipment is improved by means of artificial intelligence, 5G and other rapidly developed information technologies, and efficient consumption of new energy is realized.
With the rapid increase of access types and the number of distributed power generation equipment, the data volume of the power internet of things is continuously increased [2]. At present, millions of distributed new energy power generation units exist in the whole country. The number of new energy power generation units will reach tens of millions in the future, and accompanying various signals such as meteorological environment, operation control and the like will reach billions [3].
To address the challenges of data multiplication in the power internet of things, an effective approach is to combine multiple access edge computing (MEC) with the power internet of things to process an increasing amount of data [4]. The MEC further sinks the computing capacity to the edge of a network by deploying the server at the user side so as to provide a quick and reliable computing service at the user side, and can solve the problems of insufficient computing capacity and storage space in the power Internet of things.
The sink node in the power internet of things provides sufficient computing power and storage space for coping with the increasing data volume by carrying edge nodes with computing and storing functions. However, the sink node undertakes both data transceiving work and part of computing work of the power internet of things service, and the limited energy of the sink node becomes one of the key problems restricting the combination of edge computing and the power internet of things [5]. Therefore, a multi-edge node cooperation unloading strategy considering the energy-saving problem under the power Internet of things is designed, and the method has important significance for green low-carbon transformation in the power industry.
In the prior art, wen et al [6] designed a location-aware algorithm to determine the location of users with offloading needs. And according to factors such as idle state of the server and the like calculated at the edge around the area, the server for task unloading is distributed, and higher task completion rate and lower service delay are obtained. Cui et al [7] construct a gradient algorithm of a self-adaptive weight depth determination strategy by adopting a DDPG strategy in deep reinforcement learning, thereby greatly reducing service delay of unloading and migration of edge calculation tasks. Document [8] models the task unloading problem into a series of short-term deterministic optimization problems by utilizing Lyapunov optimization, and realizes the joint allocation and optimization of communication, energy and computing resources. Document [9] proposes a layered clustering routing protocol in which nodes are divided into different clusters, and routing is performed between the nodes through cluster heads and gateway nodes, and the routing algorithm greatly improves the scalability of the network. Document [10] has a more obvious energy-saving effect compared with the classic energy-saving protocol LEACH protocol by setting two stages of cluster heads.
Although the above research achieves the purpose of energy saving, all the researches are applied to a sensor network and are not suitable for a sink node level. Meanwhile, the existing research on the internet of things of electric power generally assumes that the energy of the sink node is infinite, which is not in accordance with the actual situation. In the existing literature, most of the unloading strategies of the edge calculation tasks are designed through reinforcement learning, but when the calculation and storage capacities of the edge nodes carried by the aggregation nodes are limited, reinforcement learning models cannot be carried.
[1] Liu Lin Hai, new energy consumption and Power grid planning [ J ]. China Ming Dynasty, 2022 (02): 48-50.
[2]F.Samie,V.Tsoutsouras,L.Bauer,S.Xydis,D.Soudris and J.Henkel,"Computation offloading and resource allocation for low-power IoT edge devices,"2016 IEEE 3rd World Forum on Internet of Things(WF-IoT),2016,pp.7-12,doi:10.1109/WF-IoT.2016.7845499.
[3] Yankun, sunlie, shuangyun, zhang Yi, a hybrid power generation system [ J ] Chinese electric power for promoting new energy consumption, 2022,55 (02): 145-151.
[4] Zhao honing, miao 22531, old base, hummincxia, zhang Yong le, liuyang, electric power Internet of things networking scheme research based on edge calculation [ J ] singlechip and embedded system application, 2021,21 (10): 7-11.
[5] The wireless sensor network data collection energy-saving algorithm [ J ] under the environment of Tyjje, liudan spectrum and mobile Sink, the university of Beijing post and telecommunications, 2013,36 (5) is 51-55.
[6]W.Shi,S.Liu,J.Zhang and R.Zhang,"A Location-aware Computation Offloading Policy for MEC-assisted Wireless Mesh Network,"2020 IEEE/CIC International Conference on Communications in China(ICCC Workshops),2020,pp.53-58,doi:10.1109/ICCCWorkshops49972.2020.9209947.
[7]Y.Cui,D.Zhang,J.Zhang,T.Zhang,L.Cao and L.Chen,"Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy,"2021 IEEE 46th Conference on Local Computer Networks(LCN),2021,pp.411-414,doi:10.1109/LCN52139.2021.9524987.
[8] Makewater, hongkong, renhua, maccept, xubrave. 5G Mobile edge computing task offload method for electric Internet of things [ J ] electric measurement and instrumentation, 2022,59 (02): 105-111. DOI.
[9]T.Acharjee and S.Roy,"A mobility-aware cluster based routing for large wireless mesh network,"2016 International Conference on Signal Processing,Communication,Power and Embedded System(SCOPES),2016,pp.1376-1380,doi:10.1109/SCOPES.2016.7955666.
[10]S.Nasr and M.Quwaider,"LEACH Protocol Enhancement for Increasing WSN Lifetime,"2020 11th International Conference on Information and Communication Systems(ICICS),2020,pp.102-107,doi:10.1109/ICICS49469.2020.239542.
Disclosure of Invention
Aiming at the problem that sink nodes carry edge nodes to process increasingly multiplied data in an electric power Internet of things and the energy consumption of the sink nodes is uneven, the invention provides a green energy-saving unloading method based on multi-edge node cooperation under the electric power Internet of things, and when the edge nodes carried by the sink nodes have sufficient computing and storage resources, the data of the governed sensors are locally computed; when the local edge node is busy, unloading the task to other edge nodes in the same cluster for calculation; and when all edge nodes in the same cluster are busy, unloading the task to a private cloud in a scene in a multi-hop mode for computing. The offloading strategy is divided into two parts, clustering and task offloading. Through clustering, edge nodes in a cluster cooperate with each other to balance energy consumption of all sink nodes. In the task unloading stage, the processing time delay of the edge computing task can be reduced by selecting a proper unloading node. Simulation results show that the method can reduce the energy consumption of the sink node as much as possible while meeting the time delay constraint.
The green energy-saving unloading method based on multi-edge node cooperation under the power Internet of things comprises the following specific steps:
aiming at a photovoltaic power generation scene, N sink nodes are uniformly distributed in the scene, each sink node carries an edge node, and each sink node governs a plurality of sensors;
step two, respectively calculating an energy consumption model of each sink node aiming at each sink node;
for a single sink node n i Two kinds of processing are carried out on a task set T uploaded by a sensor, an edge node carried by the sensor calculates a task set J, and the task set V is forwarded to a private cloud or other aggregation nodes; t = J + V.
Then the sink node n i The energy consumption of (c) includes: the energy consumption of calculation generated when the edge nodes carried by the self carry out calculation and the energy consumed when the aggregation nodes carry out data receiving and sending are reduced.
Sink node n i The energy consumption of the loaded edge node when the task J belongs to J is calculated, and the calculation formula is as follows:
f represents the CPU frequency of the edge node carried by the aggregation node; k 0 Is a constant related to the CPU frequency; b is j Is generated by a sensorCalculating the data volume of the task j; c j Is the number of CPU cycles required per bit for task j;
the energy consumption in data transceiving comprises the following steps:
first, a sink node n i The energy consumed by receiving the calculation task k epsilon T is as follows:
B k is the amount of data that the sensor generates or that the task k is off-loaded from other nodes; e elec Is the energy required by the transmit and receive circuits to transmit each bit of data.
Then, the sink node n i To another sink node n at a distance d from itself x Or the private cloud xi sends a calculation task V epsilon V, and the consumed energy is as follows:
b thereof v The amount of data for task v offloaded from other nodes; epsilon fs And epsilon mp Amplifier for free space and multipath transmission respectively
Characteristic constant, d 0 Is a distance critical value; when the distance d between the two convergent nodes is less than d 0 A free space propagation model is adopted; when the distance d between the two convergent nodes is more than or equal to d 0 A multipath fading model is used.
Step three, for an edge calculation task j generated by a sensor, constructing a time delay constraint model according to whether the edge calculation task is a time delay sensitive task or not;
when the task j is a delay sensitive task, the sink node should be selected as far as possible to perform calculation at the local or surrounding sink nodes.
When the task j is locally calculated at the sink node, the time delay is as follows:
when the local load of the corresponding sink node is too heavy, the task j is selected to be unloaded to the peripheral sink nodes, and the time delay is as follows:
where R' represents the data transmission rate of the sink node.
And when j is a time delay insensitive task or when the local and surrounding edge nodes of the aggregation node are overloaded, the edge computing task is transmitted to the private cloud xi for computing.
Assuming that the time for forwarding the data packet by each sink node is constant, the unloading time delay D of the task of unloading to the private cloud xi offload Expressed as:
wherein D is process Processing time delay f for the unloading task transmitted to the private cloud xi to pass through each node MEC Representing the CPU frequency of a large MEC server located in the private cloud ξ, and n representing the number of hops the packet has experienced in its transmission to the private cloud ξ.
Fourthly, constructing an energy consumption objective function for minimizing the energy consumption of all sink nodes on the premise of meeting the task time delay requirement by using the energy consumption model of each sink node and the time delay constraint model of the task;
the objective function includes the following two:
1) Total energy consumption objective function of all sink nodes in the scene:
wherein N represents the number of sink nodes; r represents the number of data processing rounds;
representing a sink node n i Energy consumed in the r-th data processing cycle; the calculation is expressed as:
t represents the total number of all tasks in the scene;
2) Objective function of average delay of all tasks in the scene:
Local j represents the corresponding sink node n i Whether the carried edge node storage resources are enough and whether the index amount of the task j is calculated locally is calculated; if so, local j =1; otherwise, local j ＝0；
Around j Representing a sink node n i Sending unloading requests to other aggregation nodes of the cluster group, judging whether the surrounding aggregation nodes have enough computing and storing resources, and sending the unloading requests to the aggregation node n i Sending out a response and starting to receive an index quantity of an unloading task; if so, around j =1; otherwise, around j ＝0；
Offload i When the local and peripheral sink nodes of the sink node are in busy state, the sink node n i The method comprises the steps that the method serves as a router, data packets are unloaded to a private cloud xi positioned in a scene center in a multi-hop mode, and whether index quantities of sufficient computing and storage resources exist in the private cloud; if it is Offload i =1, otherwise Offload i ＝0。
The constraints are as follows:
C3:Local j +Around j +Offload j ＝1
C4:Φ i,j +Φ i,ξ ＝1
C5:D local ≤D j ，D around ≤D j ，D offload ≤D j ；
wherein C1 represents a sink node n i The energy consumed by each round cannot exceed the remaining energy of the current round
C2 represents that the total energy consumed by all the convergent nodes every day cannot be more than the energy supplemented by the solar energy in the day
C3 represents that each task has one and only one processing mode;
c4 ensures that each sink node has one and only one forwarding node in each cycle.
C5 indicates that all three ways of handling a task must be less than the maximum delay D that the task can tolerate j 。
Step five, decoupling the solving process of the objective function into two subproblems, and clustering and unloading to finally obtain an optimal green energy-saving unloading result;
(1) Two-layer clustering
Firstly, the sink nodes are divided into different clusters, cluster heads are elected in each cluster according to the weight of the sink nodes, and the sink node with the largest weight is upgraded into the cluster head node of the cluster.
The weight calculation formula of the sink node is as follows:
wherein alpha is a sink node n i A weight coefficient of the remaining energy; beta is a sink node n i Weighting coefficients of distances from other nodes in the same cluster; gamma is a sink node n i A weight coefficient of the distance between the cluster and other nodes around the cluster; e 0 Is the initial energy of each sink node.
d inside (n i ) As a sink node n i Average distance to other sink nodes in the same cluster;
as a sink node n i Average communication distance between all aggregation nodes in the cluster group; d side (n i ) As a sink node n i Average distance to each aggregation node in adjacent cluster groups;
the average distance between a convergent node in a cluster group and all convergent nodes of an adjacent cluster group is calculated;
(2) Multi-edge node cooperative offloading
Firstly, in the data transmission stage of the initial round, each cluster group selects the aggregation node with the maximum weight as the cluster head node of the group.
Updating cluster heads after a fixed transmission period and re-electing;
then, each sensor collects data and sends the task set T to the corresponding sink node.
If the edge node deployed in the currently-owned sink node has enough computing and storage resources,all tasks are offloaded to the Local sink node, which is Local at this time i ＝1。
If the current sink node is located in the coverage range of the private cloud and the corresponding edge node is overloaded, and each sink node in the cluster has no available resource, the task set T is packed and unloaded to a cluster head node of the cluster, and the cluster head node directly unloads the received data to the xi of the private cloud;
if the current sink node is located outside the coverage range of the private cloud, whether a sink node with enough computing and storage resources exists in the cluster is firstly searched, if yes, the task is unloaded to the peripheral sink nodes in the cluster, and at the moment, the Local sink node is Local i ＝0，Around i And =1. If the surrounding nodes do not have enough computing and storage resources, the currently affiliated sink node sends the task to the cluster head node of the cluster group, the cluster head node selects the cluster head node in the coverage range of the private cloud nearest to the cluster head node as a forward-transmitting node, data is packaged and transmitted to the forward-transmitting node, then the forward-transmitting node unloads the task to the private cloud xi, and at the moment, the Offload node does not have enough computing and storage resources j ＝1。
The invention has the advantages that:
1. a green energy-saving unloading method based on multi-edge node cooperation under an electric power Internet of things balances the energy consumption of each sink node in a data forwarding stage through clustering on the basis of considering time delay constraint and sink node energy consumption, and avoids the phenomenon of node shutdown caused by uneven sink node energy consumption by combining with solar energy supplement.
2. A green energy-saving unloading method based on multi-edge node cooperation in an electric power Internet of things effectively reduces data forwarding hops, reduces unloading time delay of an edge calculation task and improves the completion rate of the edge calculation task by mutually sharing calculation and storage resources among sink nodes in the same cluster.
3. A green energy-saving unloading method based on multi-edge node cooperation under an electric power Internet of things realizes better balance between reduction of sink node energy consumption and improvement of edge calculation task completion rate.
Drawings
FIG. 1 is a flow chart of a green energy-saving unloading method based on multi-edge node cooperation in an electric power Internet of things;
FIG. 2 is a schematic diagram of a photovoltaic power generation scene including N uniformly distributed sink nodes constructed by the present invention;
FIG. 3 is a schematic diagram of a simple solar energy replenishment model constructed according to the invention;
FIG. 4 is a schematic diagram of the present invention dividing a scene into inner and outer regions while dividing sink nodes into different clusters;
fig. 5 is a schematic diagram illustrating comparison of the number of surviving offload policy nodes according to the present invention and the existing two methods.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
In order to solve the energy defect of sink nodes in the power Internet of things, the invention discloses a green energy-saving unloading method based on multi-edge node cooperation in the power Internet of things. Meanwhile, the energy balance of the sink nodes in the scene and the uninterrupted work aim are achieved through reasonable clustering and energy supplement of the solar charging panel.
As shown in fig. 1, the green energy-saving unloading method based on multi-edge node cooperation under the power internet of things specifically includes the following steps:
aiming at a photovoltaic power generation scene, N sink nodes are uniformly distributed in the scene, each sink node carries an edge node, and each sink node simultaneously governs a plurality of sensors;
step two, respectively calculating an energy consumption model of each sink node aiming at each sink node;
for a single sink node n i Two kinds of processing are carried out on the task set T uploaded by the sensor, and the edge node carried by the sensor carries out processing on the task set JLine calculation, forwarding the task set V to a private cloud or other aggregation nodes; t = J + V.
Then the sink node n i The energy consumption of (c) includes: the energy consumption of calculation generated when the edge node carried by the self carries out calculation and the energy consumed when the sink node carries out data receiving and sending.
Sink node n i The energy consumption of the carried edge node when the calculation task J belongs to J is calculated by the following formula:
f represents the CPU frequency of the edge node carried by the aggregation node; k 0 Is a constant related to the CPU frequency; b j Is the amount of data for the computing task j generated by the sensor; c j Is the number of CPU cycles required per bit for task j;
the energy consumption in data transceiving comprises the following steps:
first, a sink node n i The energy consumed by receiving the calculation task K belongs to K is as follows:
B k is the amount of data that the sensor generates or that the task k is off-loaded from other nodes; e elec Is the energy required by the transmit circuitry and receive circuitry to transmit each bit of data.
Then, the sink node n i To another sink node n with a distance d from itself x Or the private cloud xi sends a calculation task V epsilon V, and the consumed energy is as follows:
wherein, B v The amount of data for task v offloaded from other nodes; epsilon fs And ε mp Characteristic constants of amplifier, d, corresponding to free space and multipath transmission, respectively 0 Is the distance critical value, and is the distance critical value,
when the distance d between the two sink nodes is less than d 0 A free space propagation model is adopted; when the distance d between two aggregation nodes is more than or equal to d 0 Then, a multipath fading model is adopted; suppose that the coordinates of two nodes are respectively (x) 1 ，y 1 ) And (x) 2 ,y 2 )，
Step three, for an edge calculation task j generated by a sensor, constructing a time delay constraint model according to whether the edge calculation task is a time delay sensitive task or not;
when the task j is a delay sensitive task, the sink node should be selected as far as possible to perform calculation at the local or surrounding sink nodes.
When the task j is locally calculated at the sink node, the time delay is as follows:
when the local load of the corresponding sink node is too heavy, the task j is selected to be unloaded to the surrounding sink nodes, and the time delay is as follows:
where R' represents the data transmission rate of the sink node.
And when j is a task with insensitive time delay, or when the local and surrounding edge nodes of the aggregation node are overloaded, the edge computing task is transmitted to the private cloud xi for computing.
Assuming that the time for forwarding the data packet by each sink node is constant, the unloading time delay D of the task of unloading to the private cloud xi offload Expressed as:
wherein D is process Processing time delay f for the unloading task transmitted to the private cloud xi to pass through each node MEC Representing the CPU frequency of a large MEC server located in the private cloud ξ, and n representing the number of hops the packet has experienced in transmitting to the private cloud ξ.
Fourthly, constructing an energy consumption objective function for minimizing the energy consumption of all sink nodes on the premise of meeting the task time delay requirement by using the energy consumption model of each sink node and the time delay constraint model of the task;
the objective function includes the following two:
1) Total energy consumption objective function of all sink nodes in the scene:
wherein N represents the number of sink nodes; r represents the number of rounds of data processing;
representing a sink node n i Energy consumed in the r-th data processing cycle; the calculation is expressed as:
t represents the total number of all tasks in the scene;
2) Objective function of average delay of all tasks in the scene:
Local j represents the corresponding sink node n i Whether the carried edge node storage resources are enough and whether the index amount of the task j is calculated locally is calculated; if so, local j =1; otherwise, local j ＝0；
Around j Representing a sink node n i Sending unloading requests to other aggregation nodes of the cluster group, judging whether the peripheral aggregation nodes have enough computing and storing resources, and sending the unloading requests to the aggregation node n i Sending out a response and starting to receive the index quantity of the unloading task; if so, around j =1; otherwise, around j ＝0；
Offload i When the sink node is in a busy state, the sink node n i The method comprises the steps that the method serves as a router, data packets are unloaded to a private cloud xi positioned in a scene center in a multi-hop mode, and whether index quantities of sufficient computing and storage resources exist in the private cloud; if it is Offload i =1, otherwise Offload i ＝0。
The constraints are as follows:
C3:Local j +Around j +Offload j ＝1
C4:Φ i,j +Φ i,ξ ＝1
C5:D local ≤D j ，D around ≤D j ，D offload ≤D j ；
wherein C1 represents a sink node n i The energy consumed by each round cannot exceed the remaining energy of the current round
C2 represents that the total energy consumed by all the convergent nodes every day cannot be more than the energy supplemented by the solar energy in the day
C3 represents that each task has one and only one processing mode;
c4 ensures that each sink node has one and only one forwarding node in each cycle.
C5 indicates that all three ways of handling a task must be less than the maximum delay D that the task can tolerate j 。
Step five, decoupling the solving process of the objective function into two subproblems, clustering and unloading to finally obtain an optimal green energy-saving unloading result;
the method is summarized as the following steps:
1) Firstly, determining the current weight of each sink node according to the current residual energy of each sink node, the communication distance between each sink node and other sink nodes and the like, and selecting the cluster head of each cluster according to the weight.
2) After the sensor node generates an edge calculation task, three unloading schemes are provided according to the current state of convergence: if the sink node has sufficient computing and storage resources, the computing task is unloaded to the sink node to which the sensor belongs for computing; if the current load of the corresponding sink node is too heavy, the calculation task is unloaded to the idle sink nodes in the same cluster for calculation; and if no idle node exists under the current cluster, unloading the computing task into an edge cloud in the scene for computing.
The method comprises the following specific steps:
(1) Two-layer clustering
Firstly, dividing the sink nodes into different clusters, electing cluster heads in each cluster according to the weight of the sink nodes, and upgrading the sink node with the largest weight to the cluster head node of the cluster.
The weight calculation formula of the sink node is as follows:
wherein alpha is a sink node n i A weight coefficient of the remaining energy; beta is a sink node n i Weighting coefficients of distances from other nodes in the same cluster; gamma is a sink node n i Weighting coefficients of distances from other nodes of surrounding clusters; e 0 Is the initial energy of each sink node.
d inside (n i ) As a sink node n i Average distance to other sink nodes in the same cluster;
as a sink node n i Average communication distance between all aggregation nodes in the cluster group; d is a radical of side (n i ) As a sink node n i Average distance to each aggregation node in adjacent cluster groups;
the average distance between a convergent node in a cluster group and all convergent nodes of adjacent cluster groups is calculated;
(2) Multi-edge node cooperative offloading
Firstly, in the data transmission stage of the initial round, each cluster selects the aggregation node with the largest weight as the cluster head node of the cluster.
Updating cluster heads after a fixed transmission period and re-electing;
then, each sensor collects data and sends the task set T to the corresponding sink node.
If the edge node deployed in the currently-owned sink node has enough computing and storage resources, all tasks are unloaded to the Local sink node, and Local is at the moment i ＝1。
If the current sink node is located in the private cloud coverage range and the corresponding edge node is overloaded, and each sink node in the cluster has no available resource, the task set T is packed and unloaded to the cluster head node of the cluster, and the cluster head node directly unloads the received data to the private cloud xi;
if the current sink node is located outside the coverage range of the private cloud, whether a sink node with enough computing and storage resources exists in the cluster is firstly searched, if yes, the task is unloaded to the peripheral sink nodes in the cluster, and at the moment, the Local sink node is Local i ＝0，Around i =1. If the surrounding nodes do not have enough computing and storage resources, the currently affiliated sink node sends the task to the cluster head node of the cluster group, the cluster head node selects the cluster head node in the coverage range of the private cloud nearest to the cluster head node as a forward-transmitting node, data is packaged and transmitted to the forward-transmitting node, then the forward-transmitting node unloads the task to the private cloud xi, and at the moment, the Offload node does not have enough computing and storage resources j ＝1。
Example (b):
as shown in fig. 2, a photovoltaic power generation scene is provided, and in order to realize efficient consumption of photovoltaic energy, a plurality of sensors are disposed in the scene for sensing environmental changes and monitoring the operation state of the device. The N aggregation nodes are uniformly distributed in a scene, each aggregation node carries an edge node, and the N aggregation nodes have certain calculation and storage capacity.
Meanwhile, each sink node governs a plurality of sensors, when the sensors collect data and generate an edge calculation task j, the sensors pack and transmit the data to the sink node to which the sensors belong, and if the sink nodes n to which the sensors belong i The calculation and storage resources of the carried edge nodes are enough, the calculation task is calculated locally, and Local is recorded j ＝1；
Otherwise, local j When =0, the sink node n i Sending unloading requests to other aggregation nodes of the cluster group, and if the peripheral aggregation nodes have enough computing and storing resources, sending the unloading requests to the aggregation node n i Sending out a response and starting to receive the unloading task, and recording the process as Around j ＝1；
Otherwise, when Local j =0 and Around j =0, indicating that sink node is busy at local and surrounding sink nodes, sink node n i Unloading the data packet into a private cloud xi positioned in a scene center in a multi-hop mode, and recording Offlood on the assumption that sufficient computing and storage resources exist in the private cloud xi i ＝1；
In this scenario, an 802.11ah protocol is used for data transmission.
Assuming that the initial energy of each sink node is the same as E 0 Sink node n i The current remaining energy is
The computational task j produced by the sensor is described as { B } j ,C j ,D j J ∈ T, T being the set of computing tasks generated by the sensors in the scene. B is j Is the data amount of task j, C j Is the number of CPU cycles required per bit, D j Is the maximum delay that task j can tolerate. For a node n at a sink node i An edge calculation task j of performing the calculation, which consumes the energy of
The energy dissipated during data transmission between the sensor and the sink node, between the sink node and between the sink node and the private cloud is counted by adopting a basic radio model, and the sink node n i The energy consumed by receiving a computation task k (k ∈ T) is
Suppose a sink node n i To another sink node n with a distance d from itself x Or the private cloud xi sends a calculation task v (v is equal to T), and the consumed energy is
According to the difference of the size, the real-time requirement and the reliability requirement of the power Internet of things service, the processing of the power Internet of things service is divided into three modes of local calculation at a sink node, unloading to the surrounding sink nodes for calculation and transmitting to a private cloud xi for calculation.
For the edge calculation task j generated by the sensor, the maximum tolerableTime delay of D j Then the calculation delay D calculated locally at the sink node local Time delay D calculated by unloading to surrounding sink nodes around Or the transmission time delay D is sent to the private cloud xi for calculation offload Should be less than the maximum delay D that the task can tolerate j 。
Assuming that the task j is a delay-sensitive service, the sink node should be selected as far as possible to perform calculation at the local sink node or the peripheral sink nodes. When task j is calculated locally at the sink node, local j 1, time delay D local 。
When the local load of the corresponding sink node is too heavy, the task j is selected to be unloaded to the surrounding sink nodes, and then Around j 1, time delay D around 。
And assuming that the task j is a time delay insensitive task, or when the local and surrounding edge nodes of the aggregation node are overloaded, selecting to transmit the edge computing task to the private cloud xi for computing. Assuming that the time for forwarding the data packet by each sink node is constant, the unloading time delay D of the task of unloading to the private cloud ξ is accordingly set offload 。
The convergent node is carried with the solar power generation panel, and can supplement consumed electric quantity under the condition of sufficient illumination. The application draws a simple solar energy supply model schematic diagram according to the illumination condition in one day, as shown in fig. 3. Suppose that the sink node has a data processing period duration of T for each round, and has R data processing periods in total in one day. Carrying out extremization processing on the solar energy supply value in the graph, and then carrying out extremization processing on the convergent node n i It can be concluded that within the data processing round r, the sun it can supplement is:
in this scenario, the aggregation node not only undertakes part of the MEC calculation task, but also undertakes the routing forwarding task of the MEC data. If the power shortage of the sink node causes the shutdown condition, the sink node will be governed by the sink nodeThe sensors can not work normally, so that the normal work of the sink node is very important for the network. The energy consumption of the sink node mainly comes from calculation consumption and routing consumption, and the sink node n can be connected i The energy consumed in the r-th data processing cycle is represented as:
the total energy consumption of the sink nodes in the network can thus be expressed as:
the computing tasks generated in the network scenario are divided into three modes, namely local computing at the corresponding aggregation node, computation by being unloaded to the surrounding aggregation nodes and private cloud computing, so that the average time delay of the computing tasks can be expressed as:
the optimization goal is to minimize the energy consumption of the sink node on the premise of meeting the task delay requirement, so the problem is expressed as follows:
p1:min E total
C3:Local j +Around j +Offload j ＝1
C4:Φ i,j +Φ i,ξ ＝1
C5:D local ≤D j ，D around ≤D j ，D offload ≤D j ；
two objective functions in the P1 and the P2 are both non-convex functions and have non-convex constraint inequalities C1, C2 and C5, so that the problem is difficult to solve by a traditional method. Therefore, by mutual cooperation between edge nodes and between the edge nodes and the private cloud, the energy consumption of the sink nodes is minimized on the premise of ensuring the task completion rate, and the whole solving process is decoupled into two sub-problems so as to reduce the algorithm complexity; the method specifically comprises the following steps:
(1) Two-layer clustering algorithm
In the present scenario, the distance between the aggregation nodes is usually over 200 meters, and the communication range is usually 400-500 meters. If a clustering algorithm in a conventional sensor network is adopted to select cluster heads among the sink nodes, the distance between the selected cluster heads may be too far and exceed the communication range of each other.
Therefore, in the embodiment, the distance from the sink node to the private cloud ξ is taken as a reference, and the scene is divided into an inner region and an outer region, so that the requirement of the maximum three hops of the service route in the scene is met, and the purpose of energy conservation is achieved. And taking the private cloud xi as a circle center, taking an area within 600 m of radius as an inner area, and taking the rest area as an outer area. Meanwhile, the sink nodes are divided into different clusters, denoted as { ZA1-ZA4}, { ZB1-ZB12}, according to the positions and areas of the sink nodes, as shown in FIG. 4.
And (4) electing a cluster head in each cluster according to the weight of the sink node, and upgrading the sink node with the largest weight to the cluster head node of the cluster. The weight of the sink node mainly considers three factors, namely the residual energy of the current sink node, the communication cost of the sink node and other sink nodes in the cluster, and the communication cost of the sink node and nodes of adjacent clusters. In order to save signaling overhead, the two-layer clustering algorithm performs cluster head re-election every 10 rounds, which is different from the conventional clustering algorithm in that the cluster head is replaced in each round.
Sink node n i And clusterThe average distance of other aggregation nodes in the cluster is defined as:
wherein d is im As a sink node n i With the sink node n in the same cluster m Euclidean distance of. N is a radical of inside The number of sink nodes within the cluster.
Sink node n i The average communication distance between the convergent nodes in the cluster is defined as:
sink node n i The average distance to a sink node in a neighboring cluster is defined as:
wherein d is il As a sink node n i With sink node n in a neighbouring area l Euclidean distance of N side Indicating the number of sink nodes of the neighboring cluster. If the sink node n i Located in an inner area, Z side Is a sink node n i The outer area adjacent to the area; if the sink node n i Located in an outer region, Z side Is a sink node n i The inner region nearest to the located region.
The average distance between a sink node in a cluster and an adjacent cluster sink node is defined as:
sink node n i The weight of (b) is defined as:
wherein alpha, beta and gamma are respectively the weight coefficients of the residual energy of the sink node, the distance between the sink node and other nodes in the same cluster and the distance between the sink node and other nodes in the surrounding clusters.
And each cluster selects a cluster head in each data transmission stage, and the sensor collects data and sends the tasks to the sink nodes to which the sensor belongs. If the edge node deployed in the current sink node has enough computing and storing resources, the task is unloaded to the local sink node; if the load of the edge node in the current sink node is too heavy and the sink node is in an inner area, the unloading task is packaged and transmitted to the cluster head node of the cluster, and the cluster head node directly unloads the received data to the private cloud xi; when the sink node is in an outer area, whether the sink node with enough computing and storing resources exists in the cluster is searched, and if yes, the task is unloaded to the peripheral sink nodes. If the peripheral nodes do not have enough computing and storing resources, the current affiliated sink node sends the task to the cluster head node of the cluster group, the cluster head node selects the cluster head node of the inner area closest to the current affiliated sink node as a forwarding node, data is packaged and transmitted to the forwarding node of the inner area, then the forwarding node of the inner area unloads the task to the private cloud xi, and through mutual cooperation among the sink nodes carrying the edge nodes, higher task completion rate and lower energy consumption are obtained compared with a traditional unloading algorithm.
Simulation and performance analysis
The performance of the proposed green offload method based on multi-edge node cooperation was verified by using MATLAB. 64 aggregation nodes are uniformly distributed in an area of 1600 × 1600, and the distance between the aggregation nodes is 200-250 meters. Each aggregation node carries an edge node with the CPU frequency of 1.8GHZ and the storage space of 1 MB. At the scene center, a private cloud is set, and an edge server with enough storage space and 3.6GHz CPU frequency is placed in the private cloud. Other parameters are shown in table 1:
TABLE 1
In order to compare the performance of the proposed cooperative offloading of the multi-edge node, two comparison offloading policies are set in this embodiment:
1) The aggregation nodes carry edge nodes which are the same as the provided algorithm, clustering is not carried out among the aggregation nodes, and when the load of the edge nodes is too heavy, the distance vector routing algorithm is used for unloading edge computing tasks to the private cloud for computing in a multi-hop mode.
2) The sink node carries the edge nodes which are the same as the algorithm, and all edge calculation tasks generated by the sensor are calculated only in the local place of the sink node.
As shown in fig. 5, the node survival rates of the three offloading policies and the remaining energy of each node of the three policies are respectively plotted after 576 rounds of data transmission cycles have passed.
The scheme of local computation of the edge node realizes 0 death of the node at the cost of high time delay and low completion rate of the task. In the traditional edge cloud unloading, the more the sink nodes closer to the private cloud have to receive and send more data, which causes serious imbalance of energy among the nodes. Even with solar energy supplementation, a large number of aggregation nodes located in the area within the scene still die. The proposed multi-edge node cooperative unloading strategy balances the energy consumption of the sink node through a two-layer clustering algorithm. Under the supplement of solar energy, the daily access balance of the node energy is realized, and the 0-death rate of the node is also realized.
Claims (5)
1. The green energy-saving unloading method based on multi-edge node cooperation under the power Internet of things is characterized by comprising the following specific steps:
firstly, aiming at a photovoltaic power generation scene, uniformly distributing N aggregation nodes, carrying an edge node by each aggregation node, and simultaneously, managing a plurality of sensors by each aggregation node;
respectively calculating respective energy consumption for each sink node; meanwhile, for each edge calculation task generated by the sensor, constructing respective time delay constraint according to whether the edge calculation task is a time delay sensitive task or not;
secondly, establishing an energy consumption objective function for minimizing the energy consumption of all sink nodes on the premise of meeting the task time delay requirement by using the energy consumption of each sink node and the time delay constraint of the task;
the objective function includes the following two:
1) Total energy consumption objective function of all sink nodes in the scene:
wherein N represents the number of sink nodes; r represents the number of rounds of data processing;
representing a sink node n i Energy consumed in the r-th data processing cycle;
2) Objective function of average delay of all tasks in the scene:
Local j represents the corresponding sink node n i Whether the carried edge node storage resources are enough and whether the index amount of the task j is calculated locally is calculated; if so, local j =1; otherwise, local j ＝0；
Around j Representing a sink node n i Sending unloading requests to other aggregation nodes of the cluster group, judging whether the surrounding aggregation nodes have enough computing and storing resources, and sending the unloading requests to the aggregation node n i Sending out a response and starting to receive the index quantity of the unloading task; if so, around j =1; otherwise, around j ＝0；
Offload i When the sink node is in a busy state, the sink node n i The method comprises the steps that the method serves as a router, data packets are unloaded to a private cloud xi positioned in a scene center in a multi-hop mode, and whether index quantities of sufficient computing and storage resources exist in the private cloud; if it is Offload i =1, otherwise Offload i ＝0；
The constraints are as follows:
s.t.
C2:
C3:Local j +Around j +Offload j ＝1
C4:Φ i,j +Φ i,ξ ＝1
C5:D local ≤D j ，D around ≤D j ，D offload ≤D j ；
wherein C1 represents a sink node n i The energy consumed by each round cannot exceed the remaining energy of the current round
C2 represents that the total energy consumed by all the convergent nodes every day cannot be more than the energy supplemented by the solar energy in the day
C3 represents that each task has one and only one processing mode;
c4, ensuring that each sink node has one and only one forwarding node in each period;
c5 indicates that all three ways of handling a task must be less than the maximum delay D that the task can tolerate j ；
And finally, decoupling the solving process of the objective function into two subproblems, and clustering and unloading to obtain the optimal green energy-saving unloading result.
2. The method for green energy-saving unloading based on multi-edge node cooperation under the power internet of things according to claim 1, wherein the process of calculating the energy consumption of the single aggregation node is as follows:
due to a single sink node n i Two kinds of processing are carried out on the task set T uploaded by the sensor: the edge node carried by the self calculates the task set J, and forwards the task set V to a private cloud or other aggregation nodes; t = J + V;
therefore, the sink node n i The energy consumption of (c) includes: the method comprises the following steps that calculation energy consumption generated when edge nodes carried by the aggregation nodes carry out calculation and energy consumed when the aggregation nodes carry out data receiving and sending;
sink node n i The energy consumption of the loaded edge node when the task J belongs to J is calculated, and the calculation formula is as follows:
f represents the CPU frequency of the edge node carried by the sink node; k is 0 Is a constant related to the CPU frequency; b is j Is the amount of data for computational task j generated by the sensor; c j Is the number of CPU cycles required per bit for task j;
the energy consumption in data transceiving comprises the following steps:
first, a sink node n i The energy consumed by receiving the calculation task k ∈ T is as follows:
B k is the amount of data that the sensor generates or that the task k is off-loaded from other nodes; e elec Is the energy required by the transmitting circuit and the receiving circuit to transmit each bit of data;
then, the sink node n i To another sink node n with a distance d from itself x Or the private cloud xi sends a calculation task V epsilon V, and the consumed energy is as follows:
b thereof v The amount of data for task v offloaded from other nodes; epsilon fs And ε mp Characteristic constants of amplifier, d, corresponding to free space and multipath transmission, respectively 0 Is a critical value of the distance, and is,
when the distance d between the two convergent nodes is less than d 0 A free space propagation model is adopted; when the distance d between two aggregation nodes is more than or equal to d 0 A multipath fading model is used.
3. The green energy-saving unloading method based on multi-edge node cooperation under the power internet of things according to claim 1, wherein the time delay constraint is constructed for each task, specifically:
aiming at the task j, when the task is a delay sensitive task, the local or peripheral sink nodes of the sink nodes are selected as much as possible to calculate;
the locally calculated delay is:
the time delay for unloading the task j to the surrounding sink nodes is as follows:
wherein R' represents the data transmission rate of the sink node;
when the task is a time delay insensitive task or when the local and peripheral edge nodes of the aggregation node are overloaded, the edge computing task is transmitted to the private cloud xi for computing;
assuming that the time for forwarding the data packet by each sink node is constant, the unloading time delay D of the task of unloading to the private cloud xi offload Expressed as:
wherein D is process Processing time delay f for the unloading task transmitted to the private cloud xi to pass through each node MEC Representing the CPU frequency of a large MEC server located in the private cloud ξ, and n representing the number of hops the packet has experienced in its transmission to the private cloud ξ.
4. The method for green energy-saving unloading based on multi-edge node cooperation under power Internet of things according to claim 1, wherein the energy is discharged
The calculation formula of (2) is as follows:
t represents the total number of all tasks in the scene.
5. The green energy-saving unloading method based on multi-edge node cooperation under the power internet of things according to claim 1, wherein the solving process of the objective function is clustering and unloading, and specifically comprises the following steps:
(1) Two-layer clustering
Firstly, dividing sink nodes into different clusters, electing cluster heads in each cluster according to the weight of the sink nodes, and upgrading the sink node with the largest weight to the cluster head node of the cluster;
the weight calculation formula of the sink node is as follows:
wherein alpha is a sink node n i A weight coefficient of the remaining energy; beta is a sink node n i Weighting coefficients of distances from other nodes in the same cluster; gamma is a sink node n i Weighting coefficients of distances from other nodes of surrounding clusters; e 0 Is the initial energy of each sink node;
d inside (n i ) As a sink node n i Average distance to other sink nodes in the same cluster;
for a sink node n i Average communication distance between all aggregation nodes in the cluster group; d side (n i ) For a sink node n i Average distance to each aggregation node in adjacent cluster groups;
the average distance between a convergent node in a cluster group and all convergent nodes of an adjacent cluster group is calculated;
(2) Multi-edge node cooperative offloading
Firstly, selecting a sink node with the largest weight as a cluster head node of each cluster group in a data transmission stage of an initial round;
then, each sensor collects data and sends the task set T to the corresponding sink node;
if the edge node deployed in the currently-owned sink node has enough computing and storage resources, all tasks are unloaded to the Local sink node, and Local is at the moment i ＝1；
If the current sink node is located in the private cloud coverage range and the corresponding edge node is overloaded, and each sink node in the cluster has no available resource, the task set T is packed and unloaded to the cluster head node of the cluster, and the cluster head node directly unloads the received data to the private cloud xi;
if the current sink node is located outside the coverage range of the private cloud, whether a sink node with enough computing and storage resources exists in the cluster is firstly searched, if yes, the task is unloaded to the peripheral sink nodes in the cluster, and at the moment, the Local i ＝0，Around i =1; if the surrounding nodes do not have enough computing and storage resources, the currently affiliated sink node sends the task to the cluster head node of the cluster group, the cluster head node selects the cluster head node in the coverage range of the private cloud nearest to the cluster head node as a forward-transmitting node, data is packaged and transmitted to the forward-transmitting node, then the forward-transmitting node unloads the task to the private cloud xi, and at the moment, the Offload node does not have enough computing and storage resources j ＝1。
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