CN111885532A - Heterogeneous network energy efficiency optimization method based on Internet of things - Google Patents

Heterogeneous network energy efficiency optimization method based on Internet of things Download PDF

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CN111885532A
CN111885532A CN202010712184.XA CN202010712184A CN111885532A CN 111885532 A CN111885532 A CN 111885532A CN 202010712184 A CN202010712184 A CN 202010712184A CN 111885532 A CN111885532 A CN 111885532A
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sensor
constellation
node
value
constellation size
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杨政
尹春林
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H04L41/0833—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for reduction of network energy consumption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering 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 application relates to the technical field of power Internet of things, and provides a heterogeneous network energy efficiency optimization method based on the Internet of things
Figure DDA0002596924080000011
Sum constellation minimum
Figure DDA0002596924080000012
And determining node information and constellation size b of each sensor by using an optimal rapid enumeration methodiValue to obtain an optimized variable of the sensor with minimum transmission power of the sensor
Figure DDA0002596924080000013
And further determines the sampling period hiOptimum value of (2)
Figure DDA0002596924080000014
Sum packet error probability piOptimum value of (2)
Figure DDA0002596924080000015
Therefore, the optimization method provided by the application optimizes the energy consumption of sensor signal transmission and the performance of the monitoring system.

Description

Heterogeneous network energy efficiency optimization method based on Internet of things
Technical Field
The application relates to the technical field of power internet of things, in particular to a heterogeneous network energy efficiency optimization method based on the internet of things.
Background
The power internet of things is an application of the internet of things in a smart grid, and can effectively integrate communication infrastructure resources and power system infrastructure resources so as to improve the informatization level of a power system, improve the utilization efficiency of the existing infrastructure of the power system and provide important technical support for the links of power generation, power transmission, power transformation, power distribution and power utilization of the power grid.
The application scenarios of the internet of things are very rich, for example, narrow-band applications (wireless meter reading and the like) characterized by low speed and massive devices, and broadband applications (video and the like) characterized by high speed. In the construction process of the communication network, a proper communication technology needs to be adopted so as to simultaneously support narrow-band and broadband application and intelligent integration of various devices and terminals, and an extremely high safety guarantee system is provided so as to meet the special communication requirement of information interaction of the internet of things laboratory.
Referring to fig. 1, for an internet of things architecture and a wide-narrow heterogeneous wireless network, a wide-narrow heterogeneous wireless network monitoring system is often adopted in an electric internet of things, and the requirements of reliability, real-time performance, safety, economy and bandwidth are also required to be met while the system is suitable for multiple points and wide areas of equipment and different environments.
The heterogeneous wireless network monitoring system is a spatially distributed monitoring system in which sensors, actuators, and monitors communicate over a wireless network. The communication system design of the wireless network monitoring system requires that the performance and stability of the monitoring system are ensured under the condition that the battery resources of the sensor nodes are limited, wherein key parameters which need to be considered by the monitoring system and the communication system are the packet error probability, the time delay requirement and the sampling period of the sensor nodes in the network, the values of the packet error probability, the time delay requirement and the sampling period are reduced, and the performance of the monitoring system can be improved; however, when the packet error probability, the delay requirement, and the sampling period are reduced, the energy consumed by the sensor node in the wireless transmission may be increased. That is, the energy consumption of sensor signaling needs to be balanced against the performance of the monitoring system.
And energy consumption for sensor signal transmission and monitoring of system performance. There are two optimization methods adopted at present, one is: and according to the packet loss probability of the wireless link and/or the energy constraint of the sensor node, the performance of the monitoring system is maximized. However, this method mostly assumes that the packet loss probability on the wireless link is constant, the energy consumption of each packet transmission is fixed, and the dependence of the packet transmission on the transmission power and the transmission rate of the sensor node is not analyzed, and the scheduling of the sensor node transmission is also not analyzed, so that the gap between the theoretical optimization effect and the temporary effect is large; the other optimization mode is as follows: m-ary (multi-state) quadrature amplitude modulation is used as a modulation scheme, and earliest deadline is first used as a scheduling algorithm, but the optimization framework and the solution thereof are only limited to the aim of minimizing the total power consumption of a communication system, and cannot be widely applied to the heterogeneous wireless network of the Internet of things.
Disclosure of Invention
The application provides a heterogeneous network energy efficiency optimization method based on the Internet of things, and aims to optimize energy consumption of sensor signal transmission and performance of a monitoring system.
The application provides a heterogeneous network energy efficiency optimization method based on the Internet of things, which comprises the following steps:
periodically acquiring output signals of each node sensor of the power grid, and determining the maximum value of the constellation of the output signals of each node sensor
Figure BDA0002596924060000021
Sum constellation minimum
Figure BDA0002596924060000022
Determining node information i and constellation size b of each sensor by using an optimal rapid enumeration methodiA value;
according to the constellation size b of each sensoriValue, obtaining optimized variables for each sensor
Figure BDA0002596924060000023
According to the optimization variable
Figure BDA0002596924060000024
Obtaining a sampling period hiSum packet error probability piThe optimum value of (c).
Optionally, the maximum allowable power level W of the sensor is not exceeded according to the transmitting power of the sensort，maxDetermining the maximum value of the constellation of the sensor output signal
Figure BDA0002596924060000025
Sum constellation minimum
Figure BDA0002596924060000026
Figure BDA0002596924060000027
Wherein:
Figure BDA0002596924060000028
satisfies the following formula:
Figure BDA0002596924060000029
di≤Δ；
wherein: wherein b isiThe number of bits used or the constellation size for each symbol; for a predetermined modulation scheme, diThe transmission delay for the ith node sensor, which can be denoted as biA function of (a); Δ is the maximum allowable delay of the stability control system;
Figure BDA00025969240600000210
a transmission power for a given modulation;
Figure BDA00025969240600000211
power consumption of the circuit in the active mode of the transmitter.
Optionally, to determine the maximum value of the constellation of the sensor output signal
Figure BDA00025969240600000212
Sum constellation minimum
Figure BDA00025969240600000213
Further comprising: determining a constellation maximum with minimized power consumption of a sensor node
Figure BDA00025969240600000214
Sum constellation minimum
Figure BDA00025969240600000215
Figure BDA00025969240600000216
Figure BDA00025969240600000217
Figure BDA00025969240600000218
Wherein Ω is a random maximum allowable transmission interval of the sensor;
Figure BDA00025969240600000219
is kiSatisfy the formula
Figure BDA00025969240600000220
Can be given a constellation size biFunction of (2)
Figure BDA00025969240600000221
Represents; sfeasibleIs a sensor schedulability constraint.
Optionally, the sensor schedulability constraint is a set of transmission delay and sampling period of each node sensor, which can be expressed as:
{(d1(b1)h1)，(d2(b2)h3)，...，...，(dN(bN)hN)}Sfeasible，i∈[1，N]。
optionally, the node information i and the constellation size b of each sensor are determined by using an optimal fast enumeration methodiThe step of value further comprises:
increasing the power consumption of each node sensor, and rearranging the constellation size set of the output signals of each node sensor;
under the condition of minimum power consumption of each node sensor, estimating schedulability and a target function of a constellation size vector;
pruning vectors with the schedulable constellation size and vectors with the poorer objective function values;
the constellation size vector is correlated with the number of vectors into which it branches, and the constellation size vector used for evaluation is regenerated without repeatedly covering all constellation size vectors.
Optionally, in terms of constellation size b of each sensoriValue, obtaining optimized variables for each sensor
Figure BDA0002596924060000031
In the step (2), the optimized variables of the sensor are obtained by the following inequality
Figure BDA0002596924060000032
Figure BDA0002596924060000033
Optionally, according to the optimization variables
Figure BDA0002596924060000034
Obtaining a sampling period hiSum packet error probability piIn the step of the optimal value of (b), the sampling period h is determined by the following equationiOptimum value of (2)
Figure BDA0002596924060000035
Sum packet error probability piOptimum value of (2)
Figure BDA0002596924060000036
Figure BDA0002596924060000037
And omega is the random maximum allowable transmission interval of the sensor, and is the minimum probability required by realizing the random maximum allowable transmission interval of the sensor.
According to the technical scheme, the application provides a heterogeneous network energy efficiency optimization method based on the internet of things, and the method comprises the following steps: periodically acquiring output signals of each node sensor of the power grid, and determining the maximum value of the constellation of the output signals of each node sensor
Figure BDA0002596924060000038
Sum constellation minimum
Figure BDA0002596924060000039
Determining node information i and constellation size b of each sensor by using an optimal rapid enumeration methodiA value; according to the constellation size b of each sensoriValue, obtaining optimized variables for each sensor
Figure BDA00025969240600000310
According to the optimization variable
Figure BDA00025969240600000311
Obtaining a sampling period hiSum packet error probability piThe optimum value of (c).
The maximum value of the constellation of the output signal of each node sensor is determined by collecting the output signal of each node sensor
Figure BDA00025969240600000312
Sum constellation minimum
Figure BDA00025969240600000313
And determining node information and constellation size b of each sensor by using an optimal rapid enumeration methodiValue to obtain an optimized variable of the sensor with minimum transmission power of the sensor
Figure BDA00025969240600000314
And further determines the sampling period hiOptimum value of (2)
Figure BDA00025969240600000315
Sum packet error probability piOptimum value of (2)
Figure BDA00025969240600000316
Therefore, the optimization method provided by the application optimizes the energy consumption of sensor signal transmission and the performance of a monitoring system
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 illustrates an internet of things architecture and a wide-narrow heterogeneous wireless network;
FIG. 2 is a diagram of a wireless network monitoring system;
FIG. 3 is a timing diagram of wireless communication of a sensor and a monitor;
fig. 4 is a flowchart of a heterogeneous network energy efficiency optimization method based on the internet of things according to the embodiment of the present application;
fig. 5 is a flowchart for determining node information and constellation size of a sensor by using an optimal fast enumeration method according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.
Referring to fig. 2, a diagram of a wireless network monitoring system is shown.
Referring to fig. 3, a timing diagram of wireless communication of the sensor and monitor is shown.
In order to better illustrate the energy efficiency optimization method for the heterogeneous network based on the internet of things provided by the embodiment of the present application, a hardware structure and functions based on the technical scheme of the present application are described, referring to fig. 2, in a wireless network monitoring system, a plurality of power plant devices are controlled through a wireless communication network, and the power plant devices are physical components of the wireless network monitoring system, are connected to sensor nodes on the power plant devices, periodically sample sensor outputs, and transmit data to a monitor monitoring the power plant devices through a wireless channel. The wireless network monitoring system consists of a plurality of monitors, each of which monitors a certain physical domain of the system. One of the monitors is assigned as a coordinator. The coordination monitor is responsible for time synchronization in the network and resource allocation of network elements; i.e. running the resource allocation algorithm in a centralized framework and informing the nodes about the decision and monitoring the network topology and channel conditions.
Referring to fig. 4, a flowchart of an energy efficiency optimization method for a heterogeneous network based on the internet of things is provided in the embodiment of the present application.
The heterogeneous network energy efficiency optimization method based on the Internet of things comprises the following steps:
s101, output signals of each node sensor of the power grid are periodically acquired, and the maximum value of the constellation of the output signals of each node sensor is determined
Figure BDA0002596924060000041
Sum constellation minimum
Figure BDA0002596924060000042
The periodic transfer of information between a sensor attached to a certain power plant equipment and a monitor controlling the plant is illustrated in fig. 3. The sensor sampling period, transmission delay and packet error probability of the node i are respectively hi、diAnd piAnd (4) showing. Wherein, if di≧hiIt means that the packet is out of date and is replaced by new sampled data. Outdated or missing data packets are retransmitted due to large transmission delays and packet errors, respectively, so the sampling period is set to a value greater than the transmission delay.
Acquiring the maximum value of the constellation of the output signal of each node sensor through the periodically acquired output signal of the sensor
Figure BDA0002596924060000043
Sum constellation minimum
Figure BDA0002596924060000044
Due to the limited weight and size of the sensor nodes, the sensor transmit power cannot exceed the maximum allowable power level Wt，maxThe maximum transmit power constraint is:
Figure BDA0002596924060000051
wherein:
Figure BDA0002596924060000052
satisfies the following formula:
Figure BDA0002596924060000053
di≤Δ。
wherein: wherein b isiThe number of bits used or the constellation size for each symbol; for a predetermined modulation scheme, diFor transmission of the ith node sensorDelay, which can be expressed as biA function of (a); Δ is the maximum allowable delay of the stability control system;
Figure BDA0002596924060000054
a transmission power for a given modulation;
Figure BDA0002596924060000055
power consumption of the circuit in the active mode of the transmitter.
The Time Division Multiple Access (TDMA) of the monitoring system is a Medium Access Control (MAC) protocol, and explicit scheduling of node transmission in the TDMA can meet strict delay and reliability requirements of the monitoring system, and meanwhile, when no data packet is sent or received, the radio of the sensor node is adjusted to a sleep mode, thereby minimizing energy consumption of the sensor node.
It should be noted that there are multiple modes of operation of the sensor node, which are a sleep mode (no data packet is scheduled to be sent or received), an active mode (data packet is scheduled to be sent or received), and a transient mode (switching from the active mode to the sleep mode), and since the power consumption in the active mode is much larger than that in the sleep mode and the transient mode, in the optimization process, only the power consumption of the sensor for transmitting the data packet is considered in the embodiment of the present application.
Determining a constellation maximum with minimized power consumption of a sensor node
Figure BDA0002596924060000056
Sum constellation minimum
Figure BDA0002596924060000057
Figure BDA0002596924060000058
Figure BDA0002596924060000059
Figure BDA00025969240600000510
Wherein Ω is a random maximum allowable transmission interval of the sensor;
Figure BDA00025969240600000511
is kiSatisfy the formula
Figure BDA00025969240600000512
Can be given a constellation size biFunction of (2)
Figure BDA00025969240600000513
Represents; sfeasibleIs a sensor schedulability constraint.
For a predetermined scheduling algorithm, the schedulability constraint represents the feasibility of allocating corresponding time slots according to a given network node constellation size and sampling period without allowing concurrent transmission by the sensor nodes. In other words, it indicates whether a schedule can be constructed given the transmission duration and period of each node in the network under a predetermined scheduling algorithm. The schedulability constraint is expressed as:
{(d1(b1)h1)，(d2(b2)h2)，...，...，(dN(bN)hN)}∈Sfeasible。
in the formula, SfeasibleRepresents the set of transmission delays and sampling periods for each nodal sensor, i.e., { (d)1(b1)h1)，(d2(b2)h2)，...，...，(dN(bN)hN) From which feasible schedules can be constructed.
S102, determining node information i and constellation size b of each sensor by using an optimal rapid enumeration methodiThe value is obtained.
Referring to fig. 5, a flowchart for determining node information and constellation size of a sensor by using an optimal fast enumeration method is provided in an embodiment of the present application.
Each vector is generated only once in an OFE (Optimal Fast Enumeration algorithm), all possible constellation size vectors are generated under the condition of not pruning, and when schedulable constellation size vectors exist, the OFE algorithm can find the Optimal constellation size vector within a limited time. The method comprises the following steps:
s201 increases the power consumption of each node sensor, and rearranges the constellation size set of the output signal of each node sensor.
S202, under the condition of minimum power consumption of each node sensor, the schedulability and the objective function of the constellation size vector are evaluated.
S203 prunes the schedulable constellation size vector and the vector with the worse objective function value.
S204 associates the constellation size vector with the number of vectors into which it branches, and regenerates the constellation size vector for evaluation without repeatedly covering all constellation size vectors.
The specific description is as follows: for each sensor node i ∈ [1, N)]Input of
Figure BDA0002596924060000061
A possible constellation size value bij，i∈[1，N]，j∈[1，A]Is provided with bijAnd the constellation size corresponding to the nodes from i to jth minimum power consumption. Let deg (b) denote b ═ b11，b21，...，...，bN1) The degree of (c) is defined as the number of vectors into which the vector b branches. The degree distribution of each constellation size vector ensures that the algorithm generates only one specific vector b once and generates all possible vectors without pruning. The algorithm starts from the constellation size of the minimum power consumption of each node, and obtains a root vector b ═ b11，b21，...，...，bN1) The number of degrees of (c) is N. The vector set B is defined as the set of constellation size vectors evaluated in the current iteration of the algorithm. For each vector B in B, the algorithm first determines whether it can improve the optimal solution so far with a smaller value of the objective function, and if so, the algorithm checks the schedulability of the vector B. If vector b is also schedulableRespectively updating the optimal constellation size vector b by using the objective function values corresponding to the vector b and the vector b*And an optimal solution f*，f*Represents the minimum of the objective function corresponding to the feasible constellation size vector and is initialized to ∞. If vector b is not schedulable, the algorithm breaks vector b into deg (b) vectors. For [1, deg (b)]Setting the degree of each j value in the interval to be j to generate the vector b by setting the constellation size of the N-j +1 bit value in the vector b to be the next constellation size+. Each newly generated vector b+Are all contained in the set B+In (1), set B+Will equal set B at the end for calculation in the next iteration of the algorithm. The algorithm terminates when all vectors in set B are schedulable or the target value is greater than or equal to the best solution so far.
S103, according to the constellation size b of each sensoriValue, obtaining optimized variables for each sensor
Figure BDA0002596924060000062
The optimized variables of the sensor are obtained from the following inequality
Figure BDA0002596924060000063
Figure BDA0002596924060000064
I.e. optimizing variables
Figure BDA0002596924060000065
K to satisfy the inequality of the above equationiAn optimal value.
S104 according to the optimization variables
Figure BDA0002596924060000066
Obtaining a sampling period hiSum packet error probability piThe optimum value of (c).
The sampling period h is determined by the following equationiOptimum value of (2)
Figure BDA0002596924060000071
Sum packet error probability piOptimum value of (2)
Figure BDA0002596924060000072
Figure BDA0002596924060000073
The performance and stability condition of the wireless network monitoring system is expressed in the form of a random Maximum Allowed Transmission Interval (MATI), and is defined as that the time interval between the subsequent state vector reports from the sensor node to the monitor is kept lower than the MATI value with a preset probability; and a Maximum Allowed Delay (MAD) defined as the maximum packet delay allowed for transmission from the sensor node to the monitor.
Wherein the random MATI constraint is expressed as:
Pr[ui(hi，di，pi)≤Ω]≥。
in the formula uiAs hi，diAnd piIs the time interval between subsequent status vector reports at node i, Ω is the random Maximum Allowed Transmission Interval (MATI) value for the sensor, is the minimum probability of achieving the MATI requirement, and the sum of Ω is determined by the actual control application.
The number of receiver opportunities reported by the state vector is equal to the number of receiver opportunities in each time interval of length Ω
Figure BDA0002596924060000074
Therefore, can be used as
Figure BDA0002596924060000075
Is rewritten as
Figure BDA0002596924060000076
According to the technical scheme, the embodiment of the application provides a heterogeneous network energy efficiency optimization method based on the internet of things, which comprises the following steps: periodically obtaining electricityOutput signals of each node sensor of the network, and determining the maximum value of the constellation of the output signals of each node sensor
Figure BDA0002596924060000077
Sum constellation minimum
Figure BDA0002596924060000078
Determining node information i and constellation size b of each sensor by using an optimal rapid enumeration methodiA value; according to the constellation size b of each sensoriValue, obtaining optimized variables for each sensor
Figure BDA0002596924060000079
According to the optimization variable
Figure BDA00025969240600000710
Obtaining a sampling period hiSum packet error probability piThe optimum value of (c).
The maximum value of the constellation of the output signal of each node sensor is determined by collecting the output signal of each node sensor
Figure BDA00025969240600000711
Sum constellation minimum
Figure BDA00025969240600000712
And determining node information and constellation size b of each sensor by using an optimal rapid enumeration methodiValue to obtain an optimized variable of the sensor with minimum transmission power of the sensor
Figure BDA00025969240600000713
And further determines the sampling period hiOptimum value of (2)
Figure BDA00025969240600000714
Sum packet error probability piOptimum value of (2)
Figure BDA00025969240600000715
Thereby passing through this applicationThe proposed optimization method optimizes the energy consumption of sensor signal transmission and the performance of the monitoring system.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.

Claims (7)

1. A heterogeneous network energy efficiency optimization method based on the Internet of things is characterized by comprising the following steps:
periodically acquiring output signals of each node sensor of the power grid, and determining the maximum value of the constellation of the output signals of each node sensor
Figure FDA0002596924050000011
Sum constellation minimum
Figure FDA0002596924050000012
Determining node information i and constellation size b of each sensor by using an optimal rapid enumeration methodiA value;
according to the constellation size b of each sensoriValue, obtaining optimized variables for each sensor
Figure FDA0002596924050000013
According to the optimization variable
Figure FDA0002596924050000014
Obtaining a sampling period hiSum packet error probability piThe optimum value of (c).
2. The energy efficiency optimization method for the heterogeneous network based on the Internet of things according to claim 1, wherein the maximum transmitting power of the sensor is not exceededAllowable power level Wt，maxDetermining the maximum value of the constellation of the sensor output signal
Figure FDA0002596924050000015
Sum constellation minimum
Figure FDA0002596924050000016
Figure FDA0002596924050000017
Wherein:
Figure FDA0002596924050000018
satisfies the following formula:
Figure FDA0002596924050000019
di≤Δ；
wherein: wherein b isiThe number of bits used or the constellation size for each symbol; for a predetermined modulation scheme, diThe transmission delay for the ith node sensor, which can be denoted as biA function of (a); Δ is the maximum allowable delay of the stability control system;
Figure FDA00025969240500000110
a transmission power for a given modulation;
Figure FDA00025969240500000111
power consumption of the circuit in the active mode of the transmitter.
3. The energy efficiency optimization method for the heterogeneous network based on the Internet of things of claim 2, wherein the maximum value of the constellation of the output signal of the sensor is determined
Figure FDA00025969240500000112
Sum constellation minimum
Figure FDA00025969240500000113
Further comprising: determining a constellation maximum with minimized power consumption of a sensor node
Figure FDA00025969240500000114
Sum constellation minimum
Figure FDA00025969240500000115
Figure FDA00025969240500000116
Figure FDA00025969240500000117
Figure FDA00025969240500000118
Wherein Ω is a random maximum allowable transmission interval of the sensor;
Figure FDA00025969240500000119
is kiSatisfy the formula
Figure FDA00025969240500000120
Can be given a constellation size biFunction of (2)
Figure FDA00025969240500000121
Represents; sfeasibleIs a sensor schedulability constraint.
4. The energy efficiency optimization method for the heterogeneous network based on the internet of things according to claim 3, wherein the sensor schedulability constraint is a set of transmission delay and sampling period of each node sensor, and can be expressed as:
{(d1(b1)h1)，(d2(b2)h3)，...，...，(dN(bN)hN)}Sfeasible，i∈[l，N]。
5. the energy efficiency optimization method for the heterogeneous network based on the Internet of things of claim 1, wherein node information i and constellation size b of each sensor are determined by using an optimal rapid enumeration methodiThe step of value further comprises:
increasing the power consumption of each node sensor, and rearranging the constellation size set of the output signals of each node sensor;
under the condition of minimum power consumption of each node sensor, estimating schedulability and a target function of a constellation size vector;
pruning vectors with the schedulable constellation size and vectors with the poorer objective function values;
the constellation size vector is correlated with the number of vectors into which it branches, and the constellation size vector used for evaluation is regenerated without repeatedly covering all constellation size vectors.
6. The energy efficiency optimization method for the heterogeneous network based on the Internet of things according to claim 1, wherein the constellation size b according to each sensor isiValue, obtaining optimized variables for each sensor
Figure FDA0002596924050000021
In the step (2), the optimized variables of the sensor are obtained by the following inequality
Figure FDA0002596924050000022
Figure FDA0002596924050000023
7. The foundation of claim 1The energy efficiency optimization method of the networked heterogeneous network is characterized in that the optimization variables are optimized according to
Figure FDA0002596924050000024
Obtaining a sampling period hiSum packet error probability piIn the step of the optimal value of (b), the sampling period h is determined by the following equationiOptimum value of (2)
Figure FDA0002596924050000025
Sum packet error probability piOptimum value of (2)
Figure FDA0002596924050000026
Figure FDA0002596924050000027
And omega is the random maximum allowable transmission interval of the sensor, and is the minimum probability required by realizing the random maximum allowable transmission interval of the sensor.
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