CN107995657B - Method and system for estimating number of low-power-consumption concurrent transmission nodes of power internet of things - Google Patents
Method and system for estimating number of low-power-consumption concurrent transmission nodes of power internet of things Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
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- H—ELECTRICITY
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- H04B17/318—Received signal strength
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
- H04W28/065—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information using assembly or disassembly of packets
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- 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
- H04W52/0212—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave
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Abstract
The invention discloses a low-power-consumption concurrent transmission node number estimation method and system for an electric power internet of things. In the process of concurrent transmission, if the sending end can accurately predict the number of concurrent transmission nodes before transmitting the data frame, the size of the data frame can be effectively adjusted to optimize the efficiency of concurrent transmission. The invention discloses a low-power-consumption concurrent transmission node number estimation method for an electric power Internet of things, which comprises the following steps: 1) segmenting the received signal strength sequence to form a plurality of RSSI segments; 2) clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes; 3) in each class, the preliminarily estimated number of concurrent transmission nodes is corrected according to the time characteristics. The method and the device realize the estimation of the number of concurrent transmission nodes of the distributed power Internet of things, and can be used for improving the channel use efficiency of the wireless network concurrent transmission.
Description
Technical Field
The invention belongs to the field of power Internet of things, and particularly relates to a low-power-consumption concurrent transmission node number estimation method and system for power Internet of things control.
Background
In a case where any wireless device among a plurality of wireless devices is within the interference range of other devices, a case where a plurality of transmitting devices (transmitting ends) simultaneously transmit data to one or more receiving devices (receiving ends) is called concurrent transmission. Under the condition that various heterogeneous networks coexist, the number of concurrent transmission nodes is effectively estimated when a plurality of wireless devices concurrently transmit, and the channel utilization efficiency can be effectively optimized.
In the power internet of things, a WiFi network, a mobile network and a wireless Ad-Hoc network (a sensor network and a Mesh network) are widely applied, and with the rapid increase of the number of wireless devices and the increase of the types of data services, the situation that a plurality of wireless devices need to transmit data simultaneously is almost ubiquitous, and due to the limited frequency spectrum resources, the reliable concurrent transmission technology can effectively improve the channel use efficiency. In the process of concurrent transmission, if the sending end can accurately predict the number of concurrent transmission nodes before transmitting the data frame, the size of the data frame can be effectively adjusted to optimize the efficiency of concurrent transmission.
Disclosure of Invention
In view of the problems in the prior art, the invention provides a method for estimating the number of concurrent transmission nodes of a distributed power internet of things, which judges the number of concurrent transmission nodes according to the time and space characteristics of a received signal strength sequence in a short time, and can effectively estimate the number of concurrent transmission nodes without centralized control and negotiation of a plurality of wireless devices, so as to optimize the channel utilization efficiency.
Therefore, the invention adopts the following technical scheme: the method for estimating the number of low-power-consumption concurrent transmission nodes of the power internet of things comprises the following steps:
1) segmenting a received signal strength sequence (RSSI) to form a plurality of RSSI segments;
2) clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes;
3) in each class, the preliminarily estimated number of concurrent transmission nodes is corrected according to the time characteristics.
The invention judges the number of concurrent transmission nodes by the time and space characteristics of the Received Signal Strength (RSSI) sequence in a short time. Spatial signatures refer to received signal strength, which is expected to vary from transmitter to transmitter. The time characteristic means that the maximum length of data is limited, which may be caused by multiple transmitters if the received continuous signal time exceeds the maximum length.
The low power consumption means that the algorithm complexity of the concurrent transmission node number estimation method is low, the method is suitable for running on a low-power consumption MCU, only signal strength information needs to be acquired, and the method is suitable for most low-power consumption radios.
Distributed means that in a distributed network, no information needs to be exchanged between nodes, and all the information is completed through local information.
In addition to the above technical solution, it is assumed that there are n sampling points { s ] in the received signal strength sequence1,s2,s3,…,snSegmenting a received signal strength sequence;
for an RSSI segment, the initial sampling point index G and the final sampling point index E are used for representing the RSSI segment; for a starting point, the signal intensity of the starting point is different from that of the previous sampling point by more than a threshold value; similarly, for an end point, the signal strength of the end point is different from that of the next sampling point by more than a threshold value; there are w RSSI segments, and the set of start and end points is obtained by the following formula:
wherein G isjIs an initial sampling point sjThe index is a function of the number of times,jto terminate the sampling point sjThe index is a function of the number of times,
is an initial sampling point sjThe strength of the signal of (a) is,
to terminate the sampling point sjTheta is a predetermined threshold, and the same applies to the sampling point s1The index is set to G1Sampling point snIndex is set to Ew。
As a supplement to the above technical solution, the clustering steps are as follows:
1) for each RSSI segment, using its median RSSI value as its spatial signature;
2) according to the spatial characteristics of w RSSI segments, clustering the w RSSI segments into N' classes by using a k-means algorithm, wherein the distance between the signal intensity of any RSSI segment in each class and the class center point is not more than theta;
3) for each of the N ' classes, if the number of the sampling points contained in the N ' class is less than a preset threshold value delta, deleting the N ' class due to too few sampling points; if the signal intensity of the class center point is less than the preset background noise signal intensity, the class is background noise, and the class is also deleted;
and finally, obtaining the number N of the classes according to the spatial characteristics as the estimated number of the concurrent transmission nodes.
As a supplement to the above technical solution, the correction process of the number of the initially estimated concurrent transmission nodes is as follows:
a) the time characteristic of the jth RSSI segment is: t isj＝Ej-Gj；
b) For each of the N classes, setting the RSSI segment with the longest time characteristic as the time characteristic;
c) for each class, if its time characteristic is greater than the time length of the maximum data frame, adding one to the number of concurrent transmission nodes;
and finally, taking the number of the concurrent transmission nodes corrected according to the time characteristics as the estimated number of the concurrent transmission nodes.
Another object of the present invention is to provide a low power consumption concurrent transmission node number estimation system for an electric power internet of things, which includes:
a segmentation unit: segmenting the received signal strength sequence to form a plurality of RSSI segments;
a concurrent transmission node number estimation unit: clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes;
a correction unit: in each class, the preliminarily estimated number of concurrent transmission nodes is corrected according to the time characteristics.
The invention realizes the estimation of the number of the distributed concurrent transmission nodes and can be used for improving the channel use efficiency of the wireless network concurrent transmission.
Drawings
Fig. 1 is an RSSI sequence diagram of 2 concurrent transmission nodes in embodiment 1 of the present invention;
fig. 2 is an RSSI sequence diagram of 5 concurrent transmission nodes in embodiment 1 of the present invention.
Detailed Description
The invention is further described with reference to the drawings and the detailed description.
Example 1
The embodiment provides a method for estimating the number of low-power-consumption concurrent transmission nodes of an electric power internet of things, which comprises the following steps:
1) segmenting a received signal strength sequence (RSSI) to form a plurality of RSSI segments;
2) clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes;
3) in each class, the preliminarily estimated number of concurrent transmission nodes is corrected according to the time characteristics.
Let n sample points s in the received signal strength sequence1,s2,s3,…,snSegmenting a received signal strength sequence;
for an RSSI segment, the initial sampling point index G and the final sampling point index E are used for representing the RSSI segment; for a starting point, the signal intensity of the starting point is different from that of the previous sampling point by more than a threshold value; similarly, for an end point, the signal strength of the end point is different from that of the next sampling point by more than a threshold value; there are w RSSI segments, and the set of start and end points is obtained by the following formula:
wherein G isjIs an initial sampling point sjThe index is a function of the number of times,jto terminate the sampling point sjThe index is a function of the number of times,
is an initial sampling point sjThe strength of the signal of (a) is,
to terminate the sampling point sjTheta is a predetermined threshold, and the same applies to the sampling point s1The index is set to G1Sampling point snIndex is set to Ew。
The clustering steps are as follows:
1) for each RSSI segment, using its median RSSI value as its spatial signature;
2) according to the spatial characteristics of w RSSI segments, clustering the w RSSI segments into N' classes by using a k-means algorithm, wherein the distance between the signal intensity of any RSSI segment in each class and the class center point is not more than theta;
3) for each of the N ' classes, if the number of the sampling points contained in the N ' class is less than a preset threshold value delta, deleting the N ' class due to too few sampling points; if the signal intensity of the class center point is less than the preset background noise signal intensity, the class is background noise, and the class is also deleted;
and finally, obtaining the number N of the classes according to the spatial characteristics as the estimated number of the concurrent transmission nodes.
The correction process of the number of the initially estimated concurrent transmission nodes is as follows:
a) the time characteristic of the jth RSSI segment is: t isj＝Ej-Gj；
b) For each of the N classes, setting the RSSI segment with the longest time characteristic as the time characteristic;
c) for each class, if its time characteristic is greater than the time length of the maximum data frame, adding one to the number of concurrent transmission nodes;
and finally, taking the number of the concurrent transmission nodes corrected according to the time characteristics as the estimated number of the concurrent transmission nodes.
Fig. 1 and fig. 2 show RSSI sequences of 2 and 5 concurrent transmission nodes, respectively, where the bottommost sample represents background noise, and the higher RSSI line segment represents signals of different transmitting ends, and it can be seen that the number of concurrent transmission nodes can be effectively estimated according to spatial characteristics and temporal characteristics.
Example 2
The embodiment provides a low-power consumption concurrent transmission node number estimation system for an electric power internet of things, which includes:
a segmentation unit: segmenting the received signal strength sequence to form a plurality of RSSI segments;
a concurrent transmission node number estimation unit: clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes;
a correction unit: in each class, the preliminarily estimated number of concurrent transmission nodes is corrected according to the time characteristics.
Let n sample points s in the received signal strength sequence1,s2,s3,…,snSegmenting a received signal strength sequence;
for an RSSI segment, the initial sampling point index G and the final sampling point index E are used for representing the RSSI segment; for a starting point, the signal intensity of the starting point is different from that of the previous sampling point by more than a threshold value; similarly, for an end point, the signal strength of the end point is different from that of the next sampling point by more than a threshold value; there are w RSSI segments, and the set of start and end points is obtained by the following formula:
wherein G isjIs an initial sampling point sjThe index is a function of the number of times,jto terminate the sampling point sjThe index is a function of the number of times,
is an initial sampling point sjThe strength of the signal of (a) is,
to terminate the sampling point sjTheta is a predetermined threshold, and the same applies to the sampling point s1The index is set to G1Sampling point snIndex is set to Ew。
The clustering steps are as follows:
1) for each RSSI segment, using its median RSSI value as its spatial signature;
2) according to the spatial characteristics of w RSSI segments, clustering the w RSSI segments into N' classes by using a k-means algorithm, wherein the distance between the signal intensity of any RSSI segment in each class and the class center point is not more than theta;
3) for each of the N ' classes, if the number of the sampling points contained in the N ' class is less than a preset threshold value delta, deleting the N ' class due to too few sampling points; if the signal intensity of the class center point is less than the preset background noise signal intensity, the class is background noise, and the class is also deleted;
and finally, obtaining the number N of the classes according to the spatial characteristics as the estimated number of the concurrent transmission nodes.
The correction process of the number of the initially estimated concurrent transmission nodes is as follows:
a) the time characteristic of the jth RSSI segment is: t isj＝Ej-Gj；
b) For each of the N classes, setting the RSSI segment with the longest time characteristic as the time characteristic;
c) for each class, if its time characteristic is greater than the time length of the maximum data frame, adding one to the number of concurrent transmission nodes;
and finally, taking the number of the concurrent transmission nodes corrected according to the time characteristics as the estimated number of the concurrent transmission nodes.
It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (2)
1. The method for estimating the number of low-power-consumption concurrent transmission nodes of the power internet of things is characterized by comprising the following steps of:
1) segmenting the received signal strength sequence to form a plurality of RSSI segments;
2) clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes;
3) in each type, correcting the number of the preliminarily estimated concurrent transmission nodes according to the time characteristics;
let n sample points s in the received signal strength sequence1,s2,s3,…,snSegmenting a received signal strength sequence;
for an RSSI segment, the initial sampling point index G and the final sampling point index E are used for representing the RSSI segment; for a starting point, the signal intensity of the starting point is different from that of the previous sampling point by more than a threshold value; similarly, for an end point, the signal strength of the end point is different from that of the next sampling point by more than a threshold value; there are w RSSI segments, and the set of start and end points is obtained by the following formula:
wherein G isjIs an initial sampling point sjIndex, EjTo terminate the sampling point sjThe index is a function of the number of times,
is an initial sampling point sjSignal strength of，
To terminate the sampling point sjTheta is a predetermined threshold, and the same applies to the sampling point s1The index is set to G1Sampling point snIndex is set to Ew；
The clustering steps are as follows:
1) for each RSSI segment, using its median RSSI value as its spatial signature;
2) according to the spatial characteristics of w RSSI segments, clustering the w RSSI segments into N' classes by using a k-means algorithm, wherein the distance between the signal intensity of any RSSI segment in each class and the class center point is not more than theta;
3) for each of the N ' classes, if the number of the sampling points contained in the N ' class is less than a preset threshold value delta, deleting the N ' class due to too few sampling points; if the signal intensity of the class center point is less than the preset background noise signal intensity, the class is background noise, and the class is also deleted;
finally, the number N of the classes obtained according to the spatial characteristics is the estimated number of the concurrent transmission nodes;
the correction process of the number of the initially estimated concurrent transmission nodes is as follows:
a) the time characteristic of the jth RSSI segment is: t isj＝Ej-Gj；
b) For each of the N classes, setting the RSSI segment with the longest time characteristic as the time characteristic;
c) for each class, if its time characteristic is greater than the time length of the maximum data frame, adding one to the number of concurrent transmission nodes;
and finally, taking the number of the concurrent transmission nodes corrected according to the time characteristics as the estimated number of the concurrent transmission nodes.
2. Electric power thing networking low-power consumption concurrent transmission node number estimation system, its characterized in that includes:
a segmentation unit: segmenting the received signal strength sequence to form a plurality of RSSI segments;
a concurrent transmission node number estimation unit: clustering RSSI segments according to the spatial characteristics, wherein the number of classes is used as the initial estimation of the number of concurrent transmission nodes;
a correction unit: in each type, correcting the number of the preliminarily estimated concurrent transmission nodes according to the time characteristics;
let n sample points s in the received signal strength sequence1,s2,s3,…,snSegmenting a received signal strength sequence;
for an RSSI segment, the initial sampling point index G and the final sampling point index E are used for representing the RSSI segment; for a starting point, the signal intensity of the starting point is different from that of the previous sampling point by more than a threshold value; similarly, for an end point, the signal strength of the end point is different from that of the next sampling point by more than a threshold value; there are w RSSI segments, and the set of start and end points is obtained by the following formula:
wherein G isjIs an initial sampling point sjIndex, EjTo terminate the sampling point sjThe index is a function of the number of times,
is an initial sampling point sjThe strength of the signal of (a) is,
to terminate the sampling point sjTheta is a predetermined threshold, and the same applies to the sampling point s1The index is set to G1Sampling point snIndex is set to Ew；
The clustering steps are as follows:
1) for each RSSI segment, using its median RSSI value as its spatial signature;
2) according to the spatial characteristics of w RSSI segments, clustering the w RSSI segments into N' classes by using a k-means algorithm, wherein the distance between the signal intensity of any RSSI segment in each class and the class center point is not more than theta;
3) for each of the N ' classes, if the number of the sampling points contained in the N ' class is less than a preset threshold value delta, deleting the N ' class due to too few sampling points; if the signal intensity of the class center point is less than the preset background noise signal intensity, the class is background noise, and the class is also deleted;
finally, the number N of the classes obtained according to the spatial characteristics is the estimated number of the concurrent transmission nodes;
the correction process of the number of the initially estimated concurrent transmission nodes is as follows:
a) the time characteristic of the jth RSSI segment is: t isj＝Ej-Gj；
b) For each of the N classes, setting the RSSI segment with the longest time characteristic as the time characteristic;
c) for each class, if its time characteristic is greater than the time length of the maximum data frame, adding one to the number of concurrent transmission nodes;
and finally, taking the number of the concurrent transmission nodes corrected according to the time characteristics as the estimated number of the concurrent transmission nodes.
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