CN107071876B - Wireless sensor network power control method adopting two-stage fuzzy controller - Google Patents

Wireless sensor network power control method adopting two-stage fuzzy controller Download PDF

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CN107071876B
CN107071876B CN201710234363.5A CN201710234363A CN107071876B CN 107071876 B CN107071876 B CN 107071876B CN 201710234363 A CN201710234363 A CN 201710234363A CN 107071876 B CN107071876 B CN 107071876B
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node
fuzzy controller
fuzzy
degree
energy
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CN107071876A (en
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王出航
胡黄水
沈玮娜
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Changchun Normal University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to a Power Control method of a wireless sensor network, in particular to a Power Control method of a wireless sensor network adopting a two-stage Fuzzy controller TFTPC (two Fuzzy controller based Transmission Power Control method for wireless sensor networks). The method adopts a two-stage fuzzy controller based on an input-output-feedback mechanism to control the transmitting power, wherein a master fuzzy controller is responsible for node transmitting power adjustment, a slave fuzzy controller is responsible for expected node degree adjustment, and the transmitting power is adaptively adjusted according to the node residual energy. The method solves the problem of unbalanced network energy consumption caused by neglecting node residual energy in the traditional fuzzy control method, reduces the network energy consumption and prolongs the network life cycle.

Description

Wireless sensor network power control method adopting two-stage fuzzy controller
Technical Field
The invention relates to a wireless sensor network transmission power control method, in particular to a wireless sensor network power control method TFTPC (TwoFuzzycontroller based Transmission Power control method for wireless sensor networks) adopting two-stage fuzzy controllers. The method adopts a two-stage fuzzy controller based on an input-output-feedback mechanism to control the transmitting power, wherein a master fuzzy controller is responsible for node transmitting power adjustment, a slave fuzzy controller is responsible for expected node degree adjustment, the transmitting power is adaptively adjusted according to the residual energy of the nodes, the energy consumption of the network is effectively reduced, and the life cycle of the network is prolonged.
Background
With the wide application of the wireless sensor network (WSN-wireless sensor network) in the fields of environmental monitoring, medical care, national security, space exploration and the like, the wireless sensor network continuously receives high attention in the military, the industrial and academic circles. The energy consumption is the most key factor for determining the application of the wireless sensor network, the transmitting power of each node in the network is adjusted through power control, network communication interference is reduced on the premise of meeting the network quality, the energy consumption of the network is reduced, the life cycle of the network can be effectively prolonged, and the application of the wireless sensor network is promoted.
Power control is a very complex problem and it is not realistic in theory to find an optimal solution to the power control problem. Therefore, the solutions proposed at present try to find a practical solution for power control, such as the common power algorithm COMPOW, which can achieve a good effect on a network with uniformly distributed nodes by ensuring that all nodes transmit data with the same power under network connectivity. And the distributed power control mechanism based on the utility model comprehensively considers factors such as routing, signal-to-interference ratio, bit error rate and the like, and optimizes the node transmitting power under the condition of maximizing network utility. With the superior performance of the fuzzy theory in the aspects of optimizing decision and reducing resource consumption of the wireless sensor network, the fuzzy theory is also used for power control. The transmission power of the node is adjusted by controlling the number of neighbors through a closed loop by using a fuzzy controller, so that the node degree of the node is within the error range of the node degree of the node and the expected node degree. Point-to-point fuzzy control power regulation may also be employed to regulate the transmit power of the transmitting node based on the receiving node link quality indicator LQI value, reducing network energy consumption while ensuring link quality.
The above methods can improve the performance of the network in some aspects, but none of them has certain limitations. If the network with non-uniform distribution of the nodes by the comp may cause a large number of nodes to transmit data with power much larger than the required power, resulting in energy waste; when the network scale is increased, the algorithm complexity is increased exponentially; the expected node degree of each node in the method for controlling the number of neighbors by using the fuzzy controller through the closed loop cannot change along with the dynamic state of the network, so that some nodes are easy to die early, and the life cycle of the network is reduced; the point-to-point fuzzy control power regulation method needs additional point-to-point communication protocol support, and the link quality indicator LQI is used as the input of power regulation, so that the LQI changes irregularly and frequently in the actual wireless environment, which causes the power regulation to be frequently executed, thereby reducing the network performance. Especially, all the above power regulation methods do not consider the residual energy of the nodes, which undoubtedly will lead to early death of the nodes with low residual energy, thereby reducing the life cycle of the network.
Disclosure of Invention
The invention aims to solve the technical problem that the existing power control method based on fuzzy control ignores the network energy consumption imbalance caused by node residual energy, adopts the fuzzy control power control method of using the residual energy to adjust the expected node degree, and has the basic idea that the residual energy of any node of the network determines the expected node degree, when the node degree is higher than the expected node degree, the transmitting power of the node is reduced, otherwise, the transmitting power of the node is increased. The method is realized by adopting a two-stage fuzzy controller based on an input-output-feedback mechanism, wherein a main controller is responsible for adjusting the transmitting power, and a slave controller is responsible for adjusting the degree of an expected node, so that the transmitting power is adaptively adjusted according to the residual energy of the node, the energy consumption of the network is balanced, and the life cycle of the network is prolonged.
The method comprises three parts, namely a network model, a master fuzzy controller and a slave fuzzy controller; the network model provides a model for method implementation, and specifically comprises a node communication model and a system model. The node model employs a disk model, while the system model employs two fuzzy controllers based on an "input-output-feedback" mechanism. The master and slave fuzzy controllers make the system have fuzzy logic reasoning ability, and meanwhile, the system can be continuously improved and adjusted through system self-adaptation, thereby achieving better control effect. The main controller is responsible for node transmitting power adjustment, the sub controller is responsible for expected node degree adjustment, transmitting power is adjusted according to node residual energy in a self-adaptive mode, network energy consumption is effectively reduced, and the life cycle of the network is prolonged.
The network model provides a model for node communication and system control, wherein the node communication adopts a disc model, and based on the node disc model, any node m can be conveniently determined to be at different power levels pA0、pA1、pA2The node degree can be used to simply calculate the residual energy E (m) of m. For the system control model, two fuzzy controllers based on an input-output-feedback mechanism are adopted, and a master fuzzy controller and a slave fuzzy controller are respectively responsible for regulating the transmitting power and the desired nodeAnd (4) degree. Input from the control system is the residual energy E (m) and the energy threshold value of the nodes in the networkIs output as the desired node degree deviation
Figure GDA0002278919360000022
The input of the main fuzzy controller is the adjustment quantity delta nd of the node degree and the change rate of the node degree/transmission power adjustment quantity
Figure GDA0002278919360000023
The output of which is the transmitted power regulating quantity delta Pu。
The master fuzzy controller is a double-input single-output fuzzy controller. In the wireless sensor network, the nodes have the theoretical optimal node degree, which is expressed as the expected node degree in the invention
Figure GDA0002278919360000024
Expected node degree deviation through output of fuzzy controllerAnd (4) adjusting to obtain the target node degree nd. And calculating a difference value between the target node degree and the actual node degree according to the actual node degree of the nodes in the sensor network, namely the adjustment quantity delta nd of the node degree of the first input quantity of the main fuzzy controller. The second input quantity of the main fuzzy controller is node degree/transmission power regulating quantity change rateThe two input quantities enter a main fuzzy controller for fuzzy decision after being adjusted by a quantization factor, and the transmitting power regulating quantity delta P is outputuAnd with the node initial transmission power p'uCalculating to obtain the transmitting power p of the output node of the control systemu。
The slave fuzzy controller is a single-input single-output fuzzy controller. Input from the control system is the residual energy E (m) and the energy threshold value of the nodes in the network
Figure GDA0002278919360000032
Is output as the desired node degree deviationIn order to reduce the node degree of the nodes with less residual energy and ensure the service life of the nodes, when the node residual energy E (m) and the energy threshold value in the network
Figure GDA0002278919360000034
Is positive, i.e. the residual energy E (m) is the specific energy thresholdLarge time, expected node degree deviation
Figure GDA0002278919360000036
Is positive and increases the desired node degree deviation as Δ e increases
Figure GDA0002278919360000037
When node residual energy E (m) and energy threshold value in network
Figure GDA0002278919360000038
Is negative, i.e. the residual energy E (m) is the specific energy thresholdDeviation from expected node degree of hour
Figure GDA00022789193600000310
Is negative and increases with decreasing Δ eThe node degree reduction amplitude is increased, and therefore the node service life is prolonged.
The above description shows that the wireless sensor network power control method adopting the two-stage fuzzy controller comprises a network model, a master fuzzy controller and a slave fuzzy controller, and based on a simple communication disc model, the two-stage fuzzy control is adopted to realize the self-adaptive regulation of node transmitting power, balance network energy consumption and prolong the network life cycle.
Drawings
FIG. 1 is an overall framework of the invention
FIG. 2 is a relationship between node degree and transmission power of the present invention
FIG. 3 is a system model of the present invention
FIG. 4 is a membership function of node degree adjustment Δ nd of the master fuzzy controller according to the present invention
FIG. 5 is a graph of the node degree/transmit power adjustment rate of the master fuzzy controller according to the present invention
Figure GDA00022789193600000312
Function of degree of membership
FIG. 6 shows the transmission power adjustment Δ P of the master fuzzy controller of the present inventionuFunction of degree of membership
FIG. 7 is a table of master fuzzy controller rules in accordance with the present invention
FIG. 8 is a membership function of energy difference Δ e of slave fuzzy controller according to the present invention
FIG. 9 is a graph of the desired node degree deviation from the fuzzy controller of the present invention
Figure GDA00022789193600000313
Function of degree of membership
FIG. 10 is a slave fuzzy controller rule table of the present invention
FIG. 11 is a comparison of convergence time at different initial transmit powers in accordance with the present invention
FIG. 12 is a comparison of average energy consumption at different network sizes in accordance with the present invention
FIG. 13 is a comparison of life cycles for different network sizes in accordance with the present invention
Detailed Description
The invention is further described in detail with reference to the accompanying drawings, and as shown in fig. 1, the method for controlling the power of a wireless sensor network using a two-stage fuzzy controller according to the invention includes a network model, a master fuzzy controller and a slave fuzzy controller, and based on a simple communication disc model, the method uses two-stage fuzzy control to realize adaptive adjustment of the transmitting power of a node. The method is specifically realized by adopting a two-stage fuzzy controller based on an input-output-feedback mechanism to control the transmitting power, wherein a main controller of the fuzzy controller is responsible for node transmitting power adjustment, a slave controller of the fuzzy controller is responsible for expected node degree adjustment, the transmitting power is adjusted according to node residual energy in a self-adaptive manner, network energy consumption is effectively balanced, and the life cycle of the network is prolonged.
The network model provides a model for node communication and system control. The disk model is adopted for node communication, and although the actual communication range of the nodes may be irregular or asymmetric, the disk model is widely adopted for simplifying the model. The input required by the fuzzy controller is node degree and energy, and the design of the control system is not directly and negatively influenced by adopting a disc model. Thus, the relationship between node degree and transmission power using the disk model is shown in fig. 2. As can be seen from the figure, node m is at different power levels pA0、pA1、pA2The node degrees are respectively 3, 5 and 9. In the figure, in order to calculate the residual energy of any node m, it is assumed that the data transmission rates of all nodes in the network are the same, and have k discrete power levels, and the maximum and minimum transmission powers are p respectivelymax,pminAnd with equal initial energy EINIThe neighbor node is marked as N (m), and the distance between the neighbor node and the neighbor node v is d (m, v), v belongs to N (m), LMAXThe maximum amount of data packets that a node can transmit, the time taken for the node to send and receive data is proportional to the packet size, and when the data transmission rate is constant,
Figure GDA0002278919360000041
indicating a node transmission size of LmThe time taken for the data packet. EeFor power consumption on the transmitting \ receiving circuits of the nodes, ErFor power amplifier consumption, EidThe power consumption of the node in idle state is the residual energy E of the node uuAs shown in formula (1)
Figure GDA0002278919360000042
(1) In the formula, the first part in the square brackets represents energy consumption of a node u in a sending state, the second part represents energy consumption of the node u in receiving data of a neighbor node, and the third part represents energy consumption of the node u in an idle state. Furthermore, for the control system model, two fuzzy control systems based on an "input-output-feedback" mechanism are employed, the master and slave fuzzy controllers being responsible for adjusting the transmit power and the desired node degree, respectively. The model is shown in fig. 3. Input of the residual energy E (m) and the energy threshold value of the nodes in the network from the fuzzy controller
Figure GDA0002278919360000051
From the fuzzy controller, adjusting the deviation of the desired node degree according to the value of delta e
Figure GDA0002278919360000052
The main fuzzy controller is a double-input single-output fuzzy controller. Expected node degree of node in network
Figure GDA0002278919360000053
Plus output from the control system
Figure GDA0002278919360000054
And calculating to obtain the target node degree nd. And subtracting the actual node degree ND from the target node degree ND to obtain the adjustment quantity delta ND of the node degree, which is used as an input quantity of the main fuzzy controller. Another input quantity of the controller is the node degree/transmission power regulating quantity change rate
Figure GDA0002278919360000055
The main fuzzy controller performs fuzzy control according to the two input quantities and outputs a transmitting power regulating quantity delta PuP 'in the drawing'uIs the initial transmit power, p 'of the node'uAnd Δ PuThe difference is the transmission power pu. K in FIG. 3E、kN、knd、knp、kUAre all quantization factors for discourse domain variation, U andand N is the fuzzy output of the master fuzzy controller and the slave fuzzy controller, and delta p is the power regulating quantity. The numerical relationship of each variable in the system model is as follows (2) to (10):
Δnd＝nd-ND (3)
pu＝p'u±ΔPu(4)
Figure GDA0002278919360000057
e1＝kE×Δe (6)
e2＝knd×Δnd(7)
Figure GDA0002278919360000058
ΔPu＝kU×U (9)
Figure GDA0002278919360000059
the master fuzzy controller is a double-input single-output fuzzy controller. In the wireless sensor network, the nodes have the theoretical optimal node degree, which is expressed as the expected node degree in the invention
Figure GDA00022789193600000510
Expected node degree deviation through output of fuzzy controllerAnd (4) adjusting to obtain the target node degree nd. And calculating a difference value between the target node degree and the actual node degree according to the actual node degree of the nodes in the sensor network, namely the adjustment quantity delta nd of the node degree of the first input quantity of the main fuzzy controller.The second input quantity of the main fuzzy controller is node degree/transmission power regulating quantity change rate
Figure GDA00022789193600000512
Namely the ratio of the node degree deviation to the transmitting power deviation, the two input quantities enter a main fuzzy controller for fuzzy decision after being adjusted by a quantization factor, and the transmitting power regulating quantity delta P is outputuAnd with the node initial transmission power p'uCalculating to obtain the transmitting power p of the output node of the control systemu。
The method comprises the following specific steps:
the first step is as follows: fuzzification of input and output variables of main fuzzy controller
(1) Node degree adjustment amount Δ nd: and selecting linguistic variable values of the node degree adjustment quantity delta nd as d2s, d1s, hold, u1s and u2s, wherein linguistic variable domain elements are-2, -1, 0, +1 and + 2. Trapezoidal membership functions are adopted for d2s and u2s, triangular membership functions are adopted for d1s, hold and u1s, and the node degree regulating quantity delta nd membership function is shown in figure 4.
(2) Node degree/transmit power adjustment rate of change
Figure GDA0002278919360000061
Selecting node degree/transmission power adjustment quantity change rate
Figure GDA0002278919360000062
The linguistic variable values of (1) are nb, ns, 0, ps and pb, and the linguistic variable domain elements of (1) and (2) are-1, 0, 1 and 2. The nb and pb adopt trapezoidal membership function, the ns, 0 and ps adopt triangular membership function, and the node degree/transmitting power regulating variable change rate
Figure GDA0002278919360000063
The membership function is shown in figure 5.
(3) Transmission power adjustment quantity delta Pu: selecting a transmit power adjustment Δ PuThe linguistic variable values of (1) are nb, nm, ns, hold, ps, pm and pb, and the linguistic variable domain elements of (1) are-3, -2, -1, 0, +1, +2 and + 3. nb and pb adopt trapezoidal membership function,the nm, ns, hold, ps and pm adopt a triangular membership function, and the transmitting power regulating quantity delta PuThe membership function is shown in fig. 6.
The second step is that: fuzzy rule definition and disambiguation
According to the adjustment purpose of the main fuzzy controller, the fuzzy rule is defined in the form of an if-then conditional statement, and the specific rule is shown in fig. 7. The main fuzzy controller adopts a centroid method to solve the fuzzy, and the output is the transmitting power regulating quantity delta Pu。
The slave fuzzy controller is a single-input single-output fuzzy controller. In order to reduce the node degree of the nodes with less residual energy and ensure the service life of the nodes, when the node residual energy E (m) and the energy threshold value in the network
Figure GDA0002278919360000064
Is positive, i.e. the residual energy E (m) is the specific energy threshold
Figure GDA0002278919360000065
Large time, expected node degree deviationIs positive and increases the desired node degree deviation as Δ e increases
Figure GDA0002278919360000067
When node residual energy E (m) and energy threshold value in network
Figure GDA0002278919360000068
Is negative, i.e. the residual energy E (m) is the specific energy threshold
Figure GDA0002278919360000069
Deviation from expected node degree of hour
Figure GDA00022789193600000610
Is negative and increases with decreasing Δ e
Figure GDA00022789193600000611
The node degree reduction amplitude is increased, and therefore the node service life is prolonged. The method comprises the following specific steps:
the first step is as follows: fuzzification of input and output variables from a fuzzy controller
(1) Energy difference Δ e: selecting the linguistic variable values of the energy difference value delta e as NB, NM, NS, ZO, PS, PM and PB, wherein the linguistic variable domain elements are-3, -2, -1, 0, +1, +2 and + 3. NB and PB adopt trapezoidal membership functions, NM, NS, ZO, PS and PM adopt triangular membership functions, and energy difference value delta e membership functions are shown in figure 8.
(2) Expected node degree deviation
Figure GDA00022789193600000612
Selecting the deviation of the expected node degree
Figure GDA00022789193600000613
The language variable values of (a) are D2S, D1S, HOLD, U1S, U2S, wherein: D2S represents two node degrees decrease, D1S represents one node degree decrease, HOLD represents node degree remain unchanged, U1S represents one node degree increase, U2S represents two node degrees increase, and its linguistic variables domain elements are-2, -1, 0, +1, + 2. D2S and U2S adopt trapezoidal membership functions, D1S, HOLD and U1S adopt triangular membership functions, and deviation of expected node degreeThe membership function is shown in fig. 9.
The second step is that: fuzzy rule definition and disambiguation
The fuzzy rules are also defined in the form of "if-then" conditional statements, according to the adjustment purpose from the fuzzy controller, and the specific rules are as shown in fig. 10. The slave fuzzy controller adopts a centroid method to solve the fuzzy, and the output is the deviation of the expected node degree
Figure GDA0002278919360000072
In order to verify the invention, the invention relates to a wireless sensor network power control adopting a two-stage fuzzy controllerThe performance of the method is simulated by adopting MATLAB algorithm, compared with FCTP algorithm, and the characteristics of the transmission power control method in the aspects of network convergence time, average energy consumption and life cycle are analyzed. Setting nodes to be randomly deployed at 600 x 600m2Within the square region of (1), the initial energy of the node is 1J, the energy threshold
Figure GDA0002278919360000073
The energy consumption of idle, receiving and transmitting states is the same as that of FCTP, and the maximum data packet quantity L which can be transmitted by the nodeMAX5000B power consumption E of power amplifierr＝0.01nj/bit/m2And the transmission rate of data is 19.2 Kbps. The period T of data release of the node is 1 second, and the initial expected node degree of the node
Figure GDA0002278919360000074
Firstly, 100 nodes are uniformly deployed in the square area, the network convergence time of the initial transmitting power in the range of-20 dBm to 5dBm is measured, and the average value is obtained after 50 times of operation. As shown in FIG. 11, the SAFPC considers the node residual energy and the actual node degree of the node, so that the convergence time of the method is slightly slower than that of FCTP when the transmitting power is between-20 dBm and-15 dBm, and the convergence time of the method is totally faster than that of FCTP when the transmitting power is between-15 dBm and 5 dBm.
And then testing the average energy consumption and the network life cycle in different network scales, wherein the number of the nodes is increased from 50 to 400, and the average value is obtained by respectively running for 50 times. The results are shown in fig. 12 and fig. 13, respectively. As can be seen from fig. 12, as the network scale increases, the average energy consumption of the FCTP and the method generally increases, but the increasing trend of the method is generally gentler than that of the FCTP algorithm, and as the network scale increases, the advantage of the method over the FCTP algorithm is more obvious. Fig. 13 shows that, under different network scales, the network life cycle of the method is longer than that of the FCTP algorithm, mainly because the method takes the node residual energy into account when adjusting the node degrees, and for the nodes with low residual energy in the network, the energy consumption is reduced by adjusting the expected node degrees, so as to avoid the nodes with low energy consuming more energy due to large node degrees, resulting in premature death, and finally prolong the life cycle of the network.

Claims (4)

1. A wireless sensor network power control method adopting a two-stage fuzzy controller is characterized in that: the method comprises a network model, a master fuzzy controller and a slave fuzzy controller, and is based on a simplified disc communication network, and two-stage fuzzy control is adopted to realize node transmitting power control; specifically, a two-stage fuzzy controller based on an input-output-feedback mechanism is adopted to control the transmitting power, a master fuzzy controller is responsible for node transmitting power adjustment, a slave fuzzy controller is responsible for expected node degree adjustment, and the transmitting power is adaptively adjusted according to node residual energy; the slave fuzzy controller is a single-input single-output fuzzy controller, and the inputs are the residual energy E (m) and the energy threshold value of the nodes in the networkIs output as the deviation of the expected node degree
Figure FDA0002278919350000012
The main fuzzy controller is a double-input single-output fuzzy controller, and the expected node degree of the nodes in the network
Figure FDA0002278919350000013
Plus output from the control system
Figure FDA0002278919350000014
Calculating to obtain a target node degree ND, subtracting the actual node degree ND from the target node degree ND to obtain an adjustment quantity delta ND of the node degree, taking the adjustment quantity delta ND as an input quantity of the master fuzzy controller, and taking the other input quantity of the controller as the change rate of the node degree/the adjustment quantity of the transmitting power
Figure FDA0002278919350000015
The main fuzzy controller is based on the two input variablesLine fuzzy reasoning control, output transmitting power regulating quantity delta PuAnd node initial transmit power p'uCalculating to obtain the adjusted target transmitting power puThe numerical relationships of the variables are shown in the following formulas (1) to (9):
Figure FDA0002278919350000016
Δnd＝nd-ND (2)
pu＝p'u±ΔPu(3)
Figure FDA0002278919350000017
e1＝kE×Δe (5)
e2＝knd×Δnd (6)
Figure FDA0002278919350000018
ΔPu＝kU×U (8)
Figure FDA0002278919350000019
wherein k isE、kN、knd、knp、kUFor the quantization factor for the discourse domain variation, U and N are divided into fuzzy outputs of the master and slave fuzzy controllers, and Δ p is the power adjustment.
2. The method of claim 1 for power control in a wireless sensor network using a two-stage fuzzy controller, wherein: the network model provides a model for a node communication and control system, wherein the node communication adopts a disk model, and the residual energy of any node m in the disk model can be calculated by the following formula:
Figure FDA0002278919350000021
wherein the first part in the square brackets represents the energy consumption of the node m in the sending state, the second part represents the energy consumption of the node m in the receiving state of the neighbor node data, the third part represents the energy consumption of the node m in the idle state, and EINIFor initial energy, N (m) set of neighbor nodes, d (m, v), v ∈ N (m) is its distance from neighbor node v, LMAXThe maximum amount of data packets that a node can transmit, the time taken for the node to send and receive data is proportional to the packet size, and when the data transmission rate is constant,
Figure FDA0002278919350000022
indicating a node transmission size of LmTime taken for the data packet of (E)eFor power consumption on node transmit/receive circuits, ErFor power amplifier consumption, EidPower consumption when the node is in an idle state.
3. The method of claim 1 for power control in a wireless sensor network using a two-stage fuzzy controller, wherein: the specific steps of the design of the master fuzzy controller are as follows:
the first step is as follows: fuzzification of input and output variables of main fuzzy controller
(1) Node degree adjustment amount Δ nd: selecting linguistic variable values of the node degree regulating quantity delta nd as d2s, d1s, hold, u1s and u2s, wherein linguistic variable domain elements of the node degree regulating quantity delta nd are-2, -1, 0, +1 and +2, d2s and u2s adopt a trapezoidal membership function, and d1s, hold and u1s adopt a triangular membership function;
(2) node degree/transmit power adjustment rate of change
Figure FDA0002278919350000023
Selecting node degree/transmission power adjustment quantity change rate
Figure FDA0002278919350000024
The linguistic variable values of (1) are nb, ns, 0, ps and pb, the linguistic variable domain elements of (2), -1, 0, +1 and + 2), nb and pb adopt trapezoidal membership functions, and ns, 0 and ps adopt triangular membership functions;
(3) transmission power adjustment quantity delta Pu: selecting linguistic variable values of the emission power regulating quantity u as nb, nm, ns, hold, ps, pm and pb, wherein linguistic variable domain elements are-3, -2, -1, 0, +1, +2 and +3, nb and pb adopt trapezoidal membership functions, and nm, ns, hold, ps and pm adopt triangular membership functions;
the second step is that: fuzzy rule definition and disambiguation
Defining the fuzzy rule in the form of an if-then conditional statement according to the adjustment purpose of the master fuzzy controller, which is as follows:
the main fuzzy controller adopts a centroid method to solve the fuzzy, and the output is the transmitting power regulating quantity u.
4. The method of claim 1 for power control in a wireless sensor network using a two-stage fuzzy controller, wherein: the slave fuzzy controller is a single-input single-output fuzzy controller, and the input is node residual energy E (m) and an energy threshold value in the network
Figure FDA0002278919350000032
Is output as the deviation of the expected node degree
Figure FDA0002278919350000033
When node residual energy E (m) and energy threshold value in network
Figure FDA0002278919350000034
Is positive, i.e. the residual energy E (m) is the specific energy thresholdLarge time, expected node degree deviation
Figure FDA0002278919350000036
Is positive and increases the desired node degree deviation as Δ e increases
Figure FDA0002278919350000037
When node residual energy E (m) and energy threshold value in network
Figure FDA0002278919350000038
Is negative, i.e. the residual energy E (m) is the specific energy threshold
Figure FDA0002278919350000039
Deviation from expected node degree of hour
Figure FDA00022789193500000310
Is negative and increases with decreasing Δ e
Figure FDA00022789193500000311
Increasing the reduction amplitude of the node degree, and specifically comprising the following steps:
the first step is as follows: fuzzification of input and output variables from a fuzzy controller
(1) Energy difference Δ e: selecting language variable values of the energy difference value delta e as NB, NM, NS, ZO, PS, PM and PB, wherein language variable domain elements of the language variable values are-3, -2, -1, 0, +1, +2 and + 3; NB and PB adopt trapezoidal membership functions, and NM, NS, ZO, PS and PM adopt triangular membership functions;
(2) expected node degree deviationSelecting the deviation of the expected node degree
Figure FDA00022789193500000313
The language variable values of (a) are D2S, D1S, HOLD, U1S, U2S, wherein: D2S represents that two node degrees are reduced, D1S represents that one node degree is reduced, HOLD represents that the node degrees are kept unchanged, U1S represents that one node degree is increased, U2S represents that two node degrees are increased, the linguistic variable domain elements are-2, -1, 0, +1, +2, D2S and U2S adopt a trapezoidal membership function, and D1S, HOLD and U1S adopt a triangular membership function;
the second step is that: fuzzy rule definition and disambiguation
The fuzzy rule is defined in the form of an "if-then" conditional statement, according to the adjustment purpose from the fuzzy controller, as follows:
if (energy difference is NB) then (expected nodal degree deviation is D2S) If (energy difference is NM) then (expected nodal degree deviation is D1S) If (energy difference is NS) then (expected nodal degree deviation is D1S) If (energy difference is ZO) then (expected node degree deviation is HOLD) If (energy difference is PS) then (expected nodal degree deviation is U1S) If (energy difference is PM) then (expected nodal degree deviation is U1S) If (energy difference is PB) then (expected nodal degree deviation is U2S)
From fuzzy controllersResolving the ambiguity by using a centroid method, and outputting the result as the deviation of the expected node degree
Figure FDA0002278919350000041
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