CN107872809B - Software defined sensor network topology control method based on mobile node assistance - Google Patents

Software defined sensor network topology control method based on mobile node assistance Download PDF

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CN107872809B
CN107872809B CN201711119251.1A CN201711119251A CN107872809B CN 107872809 B CN107872809 B CN 107872809B CN 201711119251 A CN201711119251 A CN 201711119251A CN 107872809 B CN107872809 B CN 107872809B
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CN107872809A (en
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燕锋
尹浩浩
夏玮玮
丁翠
马文钰
兰卓睿
沈连丰
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Southeast University
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    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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    • H—ELECTRICITY
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    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/248—Connectivity information update
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
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    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/08—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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Abstract

The invention relates to a software defined sensing network topology control method based on mobile node assistance, which fully utilizes the centralized control characteristic of a controller and the mobile characteristic of a mobile node, comprehensively considers the node state of the existing nodes of a network and the network topology redundancy condition, realizes the topology control based on the real-time scheduling and real-time updating of the mobile node on the premise of ensuring the topology integrity and the network connectivity, can carry out real-time monitoring and quick repair updating on the network topology through the mobile node on the premise of ensuring the network connectivity of a sensor at the bottom layer, effectively reduces the average node transmitting power of the network, and prolongs the life cycle of the network to the greatest extent.

Description

Software defined sensor network topology control method based on mobile node assistance
Technical Field
The invention relates to a mobile node assistance-based software defined sensor network topology control method, and belongs to the technical field of software defined sensor network topology control.
Background
In the topology control of a traditional Wireless Sensor Network (WSN), on the premise of ensuring Network connectivity, a Network node uses the minimum transmission power as much as possible, thereby prolonging the life cycle of the Network as much as possible and increasing the service time of a Network system. Therefore, the method ensures the network connectivity, prolongs the network life cycle as far as possible, and is a core target for the topology control of the wireless sensor network. The importance of topology control is found in two areas: from the node level, the energy consumption of the nodes is accelerated and the interference between adjacent nodes is increased due to the overlarge power; from the link layer, although the transmission power is too high, the communication quality of the link can be enhanced, the redundancy is also too high, and the vulnerability of the whole network is increased by too low power, and even the network is broken down.
Under the excitation of Software Defined Networking (SDN), a data forwarding plane and a control plane of a Network are separated, and a new development direction of the Network including a traditional IP Network, a WSN and the like is provided. The state updating and monitoring capabilities of the traditional wireless sensor network are limited, and the self-organizing capability and the limited computing capability bring huge challenges to topology control and the like. Software Defined Sensor Network (SDSN) is proposed in this context and is gradually applied to problems such as topology control and coverage optimization of wireless Sensor networks. By using the centralized control of the software defined sensing network and the global control of the network state, the network fault can be quickly positioned, and the network state can be globally analyzed and globally optimized. In recent years, rapid development of technologies such as Unmanned Aerial Vehicles (UAVs) and Inertial Navigation (Inertial Navigation) also brings a new idea to topology control of wireless sensor networks: by utilizing the free, fast and controllable moving characteristics of the movable nodes, the network fault can be quickly responded and repaired. The topology control method based on mobile node assistance is to deploy a certain number of mobile nodes in a network, and perform joint scheduling on the mobile nodes through a centralized controller and other nodes, so as to achieve the purposes of optimizing network topology and reducing network average power consumption.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a mobile node-assisted software defined sensing network topology control method, which aims to reduce the average power consumption of network nodes and prolong the life cycle of the whole network, and carries out real-time optimization and restoration on the network topology by using a mobile node by monitoring the states of all nodes in the network on the premise of ensuring the network connectivity.
The invention adopts the following technical scheme for solving the technical problems: the invention designs a mobile node-assisted software defined sensor network topology control method, which introduces a preset number of mobile nodes based on a target sensor network to construct a software defined sensor network, and firstly executes the following initialization operation:
the method comprises the steps that a software definition controller obtains preset type state information of each sensor node in a software definition sensing network and preset type state information of each mobile node; then, a network topology structure is constructed by the software defined controller aiming at the software defined sensing network according to the position and state information of each node in the software defined sensing network;
then the software defined controller periodically executes the following steps to realize topology control aiming at the software defined sensing network;
a, a software defined controller acquires preset type state information of each node in a network topology structure; then, the software definition controller updates the network topology structure according to the position and state information of each node in the network topology structure, and then the step B is carried out;
b, aiming at each node in the network topological structure, calculating to obtain the weight of each node by adopting a preset node weight function according to the state information of each node, and then entering the step C;
c, scheduling the mobile node in the network topology structure by the software defined controller, moving the mobile node to the side of an adjacent sensor node of which the transmitting power is greater than a preset transmitting power threshold value and the residual energy is lower than a preset energy lower limit, and then entering the step D;
d, the software definition controller acquires preset type state information of each node in the network topology structure; and then updating the network topology structure by the software defined controller according to the position and state information of each node in the network topology structure.
As a preferred technical scheme of the invention: the preset type state information comprises node transmitting power, node residual energy and a node value.
As a preferred technical scheme of the invention: in the step B, for each node in the network topology, according to the state information of each node, a preset node weight function is adopted as follows:
Figure GDA0003008687010000021
calculating to obtain the weight of each node
Figure GDA0003008687010000022
Represents the weight of the nth node and the nth time in the network topology structure,
Figure GDA0003008687010000023
respectively representing the residual energy, the transmitting power and the node degree of the node v; alpha, beta, gamma and eta are non-negative scheduling parameter factors, PmaxIs the maximum transmit power of the node, E0Is the residual energy, dg, at the initial state of the nodemaxIs the maximum node degree in the network topology;
Figure GDA0003008687010000024
is the farthest reachable neighbor set for mobile node k.
As a preferred technical scheme of the invention: in the step C, the software-defined controller executes scheduling operations for each mobile node in the network topology structure, and moves the mobile node to a position near an adjacent sensor node whose transmission power is greater than a preset transmission power threshold and whose remaining energy is lower than a preset energy lower limit.
As a preferred technical scheme of the invention: in the step C, according to the weight of each node in the network topology structure, each mobile node whose weight is greater than the preset weight threshold is selected, and the software-defined controller executes scheduling operation for each mobile node, and moves the mobile node to a position near an adjacent sensor node whose transmission power is greater than the preset transmission power threshold and whose remaining energy is lower than the preset energy lower limit.
As a preferred technical scheme of the invention: the scheduling operation aiming at the mobile node comprises the steps of firstly obtaining a relocation vector of the mobile node according to the position and the weight of each node in a network topological structure, and establishing a Markov chain transfer probability matrix of the mobile node; and then moving the mobile node to the side of an adjacent sensor node of which the transmitting power is greater than a preset transmitting power threshold value and the residual energy is lower than a preset energy lower limit according to the relocation vector of the mobile node and the Markov chain transfer probability matrix.
As a preferred technical solution of the present invention, the scheduling operation for the mobile node includes the following steps:
step 001, acquiring the farthest reachable neighbor node set of the mobile node:
first, the farthest reachable neighbor node set of mobile node k is obtained:
Figure GDA0003008687010000031
wherein N isκ＝{v|dkv≤rmax，v∈Vdn∪Vsn，k∈VmnDenotes a set of nodes adjacent to the mobile node, where dkvRepresents the distance, r, between node k and node vmmaxIs the maximum transmission range of the node, VdnRepresenting sensor nodes, VmnDenotes a mobile node, VsnRepresenting software-defined nodes, Lκ：Nκ→(R+，R+) Is the set of coordinates for all nodes; centering on a mobile node k and taking the maximum transmission range r of the node as the centermaxFor a circular area of radius, the vector for the maximum reachable neighbor node set is represented as:
Figure GDA0003008687010000032
Figure GDA0003008687010000033
represents a vector from node k to node v;
step 002, obtaining a scheduling parameter matrix of the node v at the moment n, and calculating the node weight:
at the nth moment in the network topology updating process, acquiring the state of the network nodes, and establishing a scheduling parameter matrix of each node:
Figure GDA0003008687010000034
wherein,
Figure GDA0003008687010000035
respectively representing the residual energy, the transmitting power and the node degree of the node v; alpha, beta, gamma and eta are non-negative scheduling parameter factors, PmaxIs the maximum transmit power of the node, E0Is the residual energy, dg, at the initial state of the nodemaxIs the maximum node degree in the network topology;
then, the following node weight functions are preset:
Figure GDA0003008687010000036
calculating to obtain the node calculation weight
Figure GDA0003008687010000037
Step 003, according to the node weight function, calculating a relocation vector of the mobile node in the farthest reachable neighbor node set:
firstly, calculating the weight of each node in the range of the farthest reachable neighbor node set, dividing the maximum reachable range of the mobile node into four target areas, namely four quadrants, and summing the node weights in each target area to obtain the sum of the weights in the four areas:
Figure GDA0003008687010000041
wherein
Figure GDA0003008687010000042
Representing the ith quadrant, the total weight of the nodes in all the target areas is:
Figure GDA0003008687010000043
the mobile node relocation vector is then established to schedule the mobile node, assuming
Figure GDA0003008687010000044
Is the position of mobile node k at time n, the relocation position space is defined as:
Figure GDA0003008687010000045
wherein,
Figure GDA0003008687010000046
the scalar representation of the mobile node scheduling space is obtained, and the vector form can be obtained according to the node position and the weight in each target area as follows:
Figure GDA0003008687010000047
Figure GDA0003008687010000048
Figure GDA0003008687010000049
Figure GDA00030086870100000410
wherein N isI，NII，NIII，NIVIs a four quadrant region
Figure GDA00030086870100000411
The number of common nodes in the node;
step 004, acquiring a relocation probability matrix of the mobile node, and scheduling the mobile node:
the repositioning probability matrices within the four target quadrant regions are represented as:
Figure GDA00030086870100000412
establishing a first-order Markov chain transition probability matrix, wherein the relocation probability of the mobile nodes in different target areas is as follows:
Figure GDA00030086870100000413
since the mobile node relocates the position at the next moment
Figure GDA00030086870100000414
Relying only on current position
Figure GDA00030086870100000415
And current node weight
Figure GDA00030086870100000416
Regardless of the previous state, the scheduling of the mobile node is therefore based on first order markov chain transition probabilities;
and 005, moving the mobile node to the side of the adjacent sensor node with the transmitting power larger than the preset transmitting power threshold value and the residual energy lower than the preset energy lower limit by the software defined controller according to the relocation vector of the mobile node and the Markov chain transition probability matrix.
Compared with the prior art, the application system of the software defined sensor network topology control method based on mobile node assistance has the following technical effects by adopting the technical scheme: the invention designs a software defined sensing network topology control method based on mobile node assistance, which fully utilizes the centralized control characteristic of a controller and the mobile characteristic of a mobile node, comprehensively considers the node state of the existing nodes of the network and the network topology redundancy condition, realizes the topology control based on real-time scheduling and real-time updating of the mobile node on the premise of ensuring the topology integrity and the network connectivity, can carry out real-time monitoring and quick repair updating on the network topology through the mobile node on the premise of ensuring the network connectivity of a sensor at the bottom layer, effectively reduces the average node transmitting power of the network, and prolongs the network life cycle to the greatest extent.
Drawings
FIG. 1 is a schematic diagram of a mobile node assisted software defined network layered architecture;
FIG. 2 is a flow chart of a mobile node assisted software defined sensor network topology control based method of the present invention;
fig. 3 is a process of scheduling a mobile node in the topology control method of the present invention.
Detailed Description
The following description will explain embodiments of the present invention in further detail with reference to the accompanying drawings.
The invention is based on a novel mobile node-assisted software defined sensing network, optimizes the network topology by using a mobile node, fully utilizes the factors of real-time transmitting power, residual energy and the like of the network node to evaluate the state of the network node, calculates to generate a relocation vector of the mobile node, establishes a first-order Markov chain transition probability matrix and schedules the mobile node in real time.
As shown in fig. 1, the architecture of the software-defined sensor network based on mobile node assistance includes three layers: an infrastructure layer, a control layer, and an application layer. There are three kinds of nodes in the infrastructure layer, i.e. the bottom layer sensor network, including common nodes, software defined nodes, mobile nodes, etc. The function of the software defined node can be reconfigured by the controller, and the software defined node supports over-the-air downloading, and has a local control function, for example, a topology discovery process can be periodically initiated, and information such as the state of an active node in a network is collected, so that the node is also a sink node of sensor data, and all data are converged to the node and uploaded to the centralized controller. The mobile node can be controlled and scheduled by the controller, and can be used for topology optimization, hole repair and the like. The infrastructure layer and the control layer are connected through a southbound interface, and the control layer can monitor the network state for a long time except for having the global information of the whole network, so that the functions of topology control, flow table generation, packet processing, mobile node management and the like can be performed. The controller and the three nodes carry out joint scheduling of the mobile node together, so that the optimization target of the whole network topology is realized.
In specific practical application, the network model is represented as a directed fully connected graph:
G＝(Vdn∪Vmn∪Vsn，E)
wherein VdnDenotes a common node, VmnDenotes a mobile node, VsnRepresenting software defined nodes. If used duvRepresents the distance, r, between node u and node vmaxRepresenting the maximum transmission range of the nodes, the original topology when each node operates at the maximum transmission power can be represented as:
Go＝(Vdn∪Vmn∪Vsn，Ein)
wherein EinA set of edges representing the original topology is shown,
Ein＝{(u，v)：duv≤rmax，u，v∈Vdn∪Vmn∪Vsn}
the original topology of the network contains large redundancy, and each node works with the maximum transmitting power, so that the interference among the nodes is increased, the energy consumption of the nodes is accelerated, and the survival time of the network is reduced. In an initial state, the controller can adjust the node transmitting power according to the node distribution of the network, so that the network redundancy is reduced, and the connectivity of the network can be ensured. As shown in fig. 2, after the network topology is established, the controller calculates the forwarding flow table of each node and issues the forwarding flow table, and the network is in a normal working state. The different forwarding roles played by each node in the network and the difference of the transmitting power can cause that the energy consumption of some nodes is too fast, thereby affecting the survival time of the nodes and further affecting the life cycle of the network. At the moment, the controller actively optimizes the network topology by using the mobile node, and schedules the mobile node to the position near the node with high transmitting power and low residual energy, so that the states and forwarding roles of the nodes are changed, the link communication quality is improved, and the survival time of the nodes is prolonged.
Based on the graph shown in fig. 2, the invention designs a mobile node-assisted software defined sensor network topology control method, which introduces a preset number of mobile nodes based on a target sensor network to construct a software defined sensor network, and in the actual application process, the following initialization operations are firstly executed:
the method comprises the steps that a software definition controller obtains preset type state information of each sensor node in a software definition sensing network and preset type state information of each mobile node; and then, the software-defined controller constructs a network topology structure aiming at the software-defined sensing network according to the position and state information of each node in the software-defined sensing network. In practical applications, the preset type state information includes node transmission power, node residual energy and node degree value.
And then periodically executing the following steps by the software-defined controller, and realizing topology control for the software-defined sensing network.
A, a software defined controller acquires preset type state information of each node in a network topology structure; and then the software defined controller updates the network topology structure according to the position and state information of each node in the network topology structure, and then the step B is carried out.
And B, aiming at each node in the network topological structure, adopting a preset node weight function according to the state information of each node as follows:
Figure GDA0003008687010000071
calculating to obtain the weight of each node
Figure GDA0003008687010000072
Then step C is entered. Wherein,
Figure GDA0003008687010000073
represents the weight of the nth node and the nth time in the network topology structure,
Figure GDA0003008687010000074
respectively representing the residual energy, the transmitting power and the node degree of the node v; alpha, beta, gamma and eta are non-negative scheduling parameter factors, PmaxIs the maximum transmit power of the node, E0Is the residual energy, dg, at the initial state of the nodemaxIs the maximum node degree in the network topology;
Figure GDA0003008687010000075
is the farthest reachable neighbor set for mobile node k.
And C, scheduling the mobile node in the network topology structure by the software defined controller, moving the mobile node to the side of the adjacent sensor node of which the transmitting power is greater than a preset transmitting power threshold value and the residual energy is lower than a preset energy lower limit, and then entering the step D.
For the step C, two schemes are designed, wherein in one scheme, the software-defined controller performs scheduling operations on each mobile node in the network topology structure, and moves the mobile node to a position near an adjacent sensor node whose transmission power is greater than a preset transmission power threshold and whose remaining energy is lower than a preset energy lower limit.
Based on the first design scheme, in order to reduce the operation data amount in practical application and improve the operation efficiency, the second scheme is further designed, each mobile node with the weight larger than a preset weight threshold value is selected according to the weight of each node in the network topology structure, a software defined controller respectively executes scheduling operation aiming at each mobile node, and the mobile node is moved to the side of an adjacent sensor node with the transmitting power larger than the preset transmitting power threshold value and the residual energy lower than a preset energy lower limit; aiming at the scheduling operation of the mobile node, firstly, obtaining a relocation vector of the mobile node according to the position and the weight of each node in a network topological structure, and establishing a Markov chain transfer probability matrix of the mobile node; then, according to the relocation vector of the mobile node and the markov chain transition probability matrix, moving the mobile node to a position near an adjacent sensor node whose transmission power is greater than a preset transmission power threshold and whose remaining energy is lower than a preset energy lower limit, wherein in practical application, as shown in fig. 3, the method specifically includes the following steps:
step 001, acquiring the farthest reachable neighbor node set of the mobile node:
first, the farthest reachable neighbor node set of mobile node k is obtained:
Figure GDA0003008687010000076
wherein N isκ＝{v|dkv≤rmax，v∈Vdn∪Vsn，k∈VmnDenotes a set of nodes adjacent to the mobile node, where dkvRepresents the distance, r, between node k and node vmaxIs the maximum transmission range of the node, VdnRepresenting sensor nodes, VmnDenotes a mobile node, VsnRepresenting software-defined nodes, Lκ：Nκ→(R+，R+) Is the set of coordinates for all nodes; centering on a mobile node k and taking the maximum transmission range r of the node as the centermaxFor a circular area of radius, the vector for the maximum reachable neighbor node set is represented as:
Figure GDA0003008687010000077
Figure GDA0003008687010000081
representing a vector from node k to node v.
For each sensor network node, node parameters such as node transmitting power, node residual energy, node degree and the like influence the network topology, so that the network life cycle is influenced, the transmitting power of certain nodes in the network topology can be reduced by utilizing the mobile node, and the network life cycle is prolonged.
And 002, acquiring a scheduling parameter matrix of the node v at the moment n, and calculating the node weight:
at the nth moment in the network topology updating process, acquiring the state of the network nodes, and establishing a scheduling parameter matrix of each node:
Figure GDA0003008687010000082
wherein,
Figure GDA0003008687010000083
respectively representing the residual energy, the transmitting power and the node degree of the node v; alpha, beta, gamma and eta are non-negative scheduling parameter factors, PmaxIs the maximum transmit power of the node, E0Is the residual energy, dg, at the initial state of the nodemaxIs the maximum node degree in the network topology;
in order to objectively reflect the weight of a network node in mobile node scheduling, a node weight function is established
Figure GDA0003008687010000084
The node weight function objectively reflects the node state by comprehensively considering factors in the scheduling parameter matrix, including the factors of node transmitting power, node residual energy, node degree and the like. Since the purpose of topology control is to reduce the average power of the nodes, the node power pvWill be the first factor to be considered. The node residual energy reflects the survival time of the node, less residual energy means shorter survival time, and similarly, the node degree reflects the redundancy of the network and the throughput of the node.
Then, the following node weight functions are preset:
Figure GDA0003008687010000085
calculating to obtain the node calculation weight
Figure GDA0003008687010000086
Still consider the mobile node k as the center, with the node's maximum transmission range rmaxA circular area of radius, assuming that the area is the maximum range that a mobile node can react quickly and move within a topology update period, weights are calculated for each node within the range, and different nodes may have different weights.
Step 003, according to the node weight function, calculating a relocation vector of the mobile node in the farthest reachable neighbor node set:
firstly, calculating the weight of each node in the range of the farthest reachable neighbor node set, dividing the maximum reachable range of the mobile node into four target areas, namely four quadrants, and summing the node weights in each target area to obtain the sum of the weights in the four areas:
Figure GDA0003008687010000087
wherein
Figure GDA0003008687010000088
Representing the ith quadrant, the total weight of the nodes in all the target areas is:
Figure GDA0003008687010000089
the mobile node relocation vector is then established to schedule the mobile node, assuming
Figure GDA0003008687010000091
Is the position of mobile node k at time n, the relocation position space is defined as:
Figure GDA0003008687010000092
wherein,
Figure GDA0003008687010000093
the scalar representation of the mobile node scheduling space is obtained, and the vector form can be obtained according to the node position and the weight in each target area as follows:
Figure GDA0003008687010000094
Figure GDA0003008687010000095
Figure GDA0003008687010000096
Figure GDA0003008687010000097
wherein N isI，NII，NIII，NIVIs a four quadrant region
Figure GDA0003008687010000098
The number of common nodes in.
Step 004, acquiring a relocation probability matrix of the mobile node, and scheduling the mobile node:
the repositioning probability matrices within the four target quadrant regions are represented as:
Figure GDA0003008687010000099
establishing a first-order Markov chain transition probability matrix, wherein the relocation probability of the mobile nodes in different target areas is as follows:
Figure GDA00030086870100000910
since the mobile node relocates the position at the next moment
Figure GDA00030086870100000911
Relying only on current position
Figure GDA00030086870100000912
And current node weight
Figure GDA00030086870100000913
Regardless of the previous state, the scheduling of the mobile node is therefore a first order markov chain transition probability based scheduling.
And 005, moving the mobile node to the side of the adjacent sensor node with the transmitting power larger than the preset transmitting power threshold value and the residual energy lower than the preset energy lower limit by the software defined controller according to the relocation vector of the mobile node and the Markov chain transition probability matrix.
D, the software definition controller acquires preset type state information of each node in the network topology structure; and then updating the network topology structure by the software defined controller according to the position and state information of each node in the network topology structure.
The technical scheme designs a software defined sensing network topology control method based on mobile node assistance, fully utilizes the centralized control characteristic of a controller and the mobile characteristic of a mobile node, comprehensively considers the node state of the existing nodes of the network and the network topology redundancy condition, realizes the topology control based on real-time scheduling and real-time updating of the mobile node on the premise of ensuring the topology integrity and the network connectivity, can carry out real-time monitoring and quick repair updating on the network topology through the mobile node on the premise of ensuring the network connectivity of a sensor at the bottom layer, effectively reduces the average node transmitting power of the network, and prolongs the life cycle of the network to the greatest extent.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (6)

1. A software defined sensor network topology control method based on mobile node assistance is characterized in that: based on a target sensing network, introducing a preset number of mobile nodes, constructing a software defined sensing network, and firstly executing the following initialization operation: the method comprises the steps that a software definition controller obtains preset type state information of each sensor node in a software definition sensing network and preset type state information of each mobile node; then, a network topology structure is constructed by the software defined controller aiming at the software defined sensing network according to the position and state information of each node in the software defined sensing network; the preset type state information comprises node transmitting power, node residual energy and node values;
then the software defined controller periodically executes the following steps to realize topology control aiming at the software defined sensing network;
a, a software defined controller acquires preset type state information of each node in a network topology structure; then, the software definition controller updates the network topology structure according to the position and state information of each node in the network topology structure, and then the step B is carried out;
b, aiming at each node in the network topological structure, calculating to obtain the weight of each node by adopting a preset node weight function according to the state information of each node, and then entering the step C;
c, scheduling the mobile node in the network topology structure by the software defined controller, moving the mobile node to the side of an adjacent sensor node of which the transmitting power is greater than a preset transmitting power threshold value and the residual energy is lower than a preset energy lower limit, and then entering the step D;
d, the software definition controller acquires preset type state information of each node in the network topology structure; and then updating the network topology structure by the software defined controller according to the position and state information of each node in the network topology structure.
2. The method for controlling the topology of the software defined sensor network based on the assistance of the mobile node according to claim 1, wherein: in the step B, for each node in the network topology, according to the state information of each node, a preset node weight function is adopted as follows:
Figure FDA0003008686000000011
calculating to obtain the weight of each node
Figure FDA0003008686000000012
Figure FDA0003008686000000013
Represents the weight of the nth node and the nth time in the network topology structure,
Figure FDA0003008686000000014
respectively representing the residual energy, the transmitting power and the node degree of the node v; alpha, beta, gamma and eta are non-negative scheduling parameter factors, PmaxIs the maximum transmit power of the node, E0Is the residual energy, dg, at the initial state of the nodemaxIs the maximum node degree in the network topology;
Figure FDA0003008686000000015
is the farthest reachable neighbor set for mobile node k.
3. The method for controlling the topology of the software defined sensor network based on the assistance of the mobile node according to claim 1, wherein: in the step C, the software-defined controller executes scheduling operations for each mobile node in the network topology structure, and moves the mobile node to a position near an adjacent sensor node whose transmission power is greater than a preset transmission power threshold and whose remaining energy is lower than a preset energy lower limit.
4. The method for controlling the topology of the software defined sensor network based on the assistance of the mobile node according to claim 1, wherein: in the step C, according to the weight of each node in the network topology structure, each mobile node whose weight is greater than the preset weight threshold is selected, and the software-defined controller executes scheduling operation for each mobile node, and moves the mobile node to a position near an adjacent sensor node whose transmission power is greater than the preset transmission power threshold and whose remaining energy is lower than the preset energy lower limit.
5. The method for controlling the topology of the software defined sensor network based on the assistance of the mobile node according to claim 3 or 4, wherein: the scheduling operation aiming at the mobile node comprises the steps of firstly obtaining a relocation vector of the mobile node according to the position and the weight of each node in a network topological structure, and establishing a Markov chain transfer probability matrix of the mobile node; and then moving the mobile node to the side of an adjacent sensor node of which the transmitting power is greater than a preset transmitting power threshold value and the residual energy is lower than a preset energy lower limit according to the relocation vector of the mobile node and the Markov chain transfer probability matrix.
6. The method for controlling the topology of the software defined sensing network based on the assistance of the mobile node as claimed in claim 5, wherein the scheduling operation for the mobile node comprises the following steps:
step 001, acquiring the farthest reachable neighbor node set of the mobile node:
first, the farthest reachable neighbor node set of mobile node k is obtained:
Figure FDA0003008686000000021
wherein N isκ＝{v|dkv≤rmax,v∈Vdn∪Vsn,k∈VmnDenotes a set of nodes adjacent to the mobile node,wherein d iskvRepresents the distance, r, between node k and node vmaxIs the maximum transmission range of the node, VdnRepresenting sensor nodes, VmnDenotes a mobile node, VsnRepresenting software-defined nodes, Lκ：Nκ→(R+,R+) Is the set of coordinates for all nodes; centering on a mobile node k and taking the maximum transmission range r of the node as the centermaxFor a circular area of radius, the vector for the maximum reachable neighbor node set is represented as:
Figure FDA0003008686000000022
Figure FDA0003008686000000023
represents a vector from node k to node v;
and 002, acquiring a scheduling parameter matrix of the node v at the moment n, and calculating the node weight:
at the nth moment in the network topology updating process, acquiring the state of the network nodes, and establishing a scheduling parameter matrix of each node:
Figure FDA0003008686000000024
wherein,
Figure FDA0003008686000000025
respectively representing the residual energy, the transmitting power and the node degree of the node v; alpha, beta, gamma and eta are non-negative scheduling parameter factors, PmaxIs the maximum transmit power of the node, E0Is the residual energy, dg, at the initial state of the nodemaxIs the maximum node degree in the network topology;
then, the following node weight functions are preset:
Figure FDA0003008686000000031
calculating to obtain the node calculation weight
Figure FDA0003008686000000032
Step 003, according to the node weight function, calculating a relocation vector of the mobile node in the farthest reachable neighbor node set:
firstly, calculating the weight of each node in the range of the farthest reachable neighbor node set, dividing the maximum reachable range of the mobile node into four target areas, namely four quadrants, and summing the node weights in each target area to obtain the sum of the weights in the four areas:
Figure FDA0003008686000000033
wherein
Figure FDA0003008686000000034
Representing the ith quadrant, the total weight of the nodes in all the target areas is:
Figure FDA0003008686000000035
the mobile node relocation vector is then established to schedule the mobile node, assuming
Figure FDA0003008686000000036
Is the position of mobile node k at time n, the relocation position space is defined as:
Figure FDA0003008686000000037
wherein,
Figure FDA0003008686000000038
is movedThe scalar representation of the node scheduling space, according to the node position and the weight in each target area, can obtain a vector form as follows:
Figure FDA0003008686000000039
Figure FDA00030086860000000310
Figure FDA00030086860000000311
Figure FDA00030086860000000312
wherein N isI,NII,NIII,NIVIs a four quadrant region
Figure FDA00030086860000000313
The number of common nodes in the node;
step 004, acquiring a relocation probability matrix of the mobile node, and scheduling the mobile node:
the repositioning probability matrices within the four target quadrant regions are represented as:
Figure FDA00030086860000000314
establishing a first-order Markov chain transition probability matrix, wherein the relocation probability of the mobile nodes in different target areas is as follows:
Figure FDA0003008686000000041
since the mobile node relocates the position at the next moment
Figure FDA0003008686000000042
Relying only on current position
Figure FDA0003008686000000043
And current node weight
Figure FDA0003008686000000044
Regardless of the previous state, the scheduling of the mobile node is therefore based on first order markov chain transition probabilities;
and 005, moving the mobile node to the side of the adjacent sensor node with the transmitting power larger than the preset transmitting power threshold value and the residual energy lower than the preset energy lower limit by the software defined controller according to the relocation vector of the mobile node and the Markov chain transition probability matrix.
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