CN109005113B - Low-delay routing method of intertidal zone sensor network based on machine learning - Google Patents
Low-delay routing method of intertidal zone sensor network based on machine learning Download PDFInfo
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Abstract
The invention discloses a low-delay routing method of a intertidal zone sensor network based on machine learning, which comprises the following steps of 1) modeling the delay of the intertidal zone wireless sensor network, 2) calculating the delay caused by data packet blocking by each node, 3) calculating the link delay from each node to a neighbor node, and 4) calculating a delay optimal path by using a shortest path algorithm aiming at delay optimization. The method comprises the steps of modeling each component of the time delay by analyzing the main reason of the time delay in the intertidal zone sensor network, quantizing each time delay component, and finally selecting the route with the optimal time delay as a data transmission path with the aim of time delay optimization. The invention takes time delay as guidance, and greatly relieves the time delay problem of the intertidal zone sensor network.
Description
Technical Field
The invention belongs to the field of wireless sensor networks, and particularly relates to a low-delay routing method of an intertidal zone sensor network based on machine learning.
Background
The intertidal zone is an area along the coastline, between the high tide line and the low tide line. The intertidal zone is a habitat for many marine organisms to live on, however, the zone has complicated physical environment changes, such as water level, temperature, oxygen content and the like. Therefore, the deployment of the wireless sensor network in the intertidal zone has important significance for environmental monitoring and ecological protection.
Deploying wireless sensor networks in the intertidal zone faces a number of challenges. The first is the choice of communication medium. Usually for land-based sensor networks, it is common to use radio frequency signals as a carrier for data transmission. However, the penetration capacity of the radio frequency signal to water, especially seawater, is poor, and is only about 12 centimeters, so that data cannot be uploaded to the base station in real time during the period that the sensor node is submerged by seawater. On the other hand, even in a low tide period, normal communication is seriously affected by tidal water due to a harsh physical environment in the intertidal zone, and the efficiency of data transmission is very low. The intuitive manifestation of these problems is that the transmission delay of data is much higher than that of other terrestrial sensor networks of the same scale.
According to experimental statistics, the time delay of the intertidal zone wireless sensor network is far higher than that of an indoor wireless sensor network under the same scale. Taking the delay caused by the link quality as an example, the link delay of the indoor wireless sensor network is usually within one system period, while in the intertidal wireless sensor network, the value is 15 system periods. In addition, the accumulation of data packets due to link congestion also becomes a major cause of data transmission delay.
In order to solve the problems, the invention provides a low-delay routing method of an intertidal zone sensor network based on machine learning. The method comprises the steps of firstly modeling the delay of the intertidal zone wireless sensor network, quantizing the model by using a machine learning method, and finally designing a routing method taking low delay as guidance.
Disclosure of Invention
The invention provides a low-delay routing method of a intertidal zone sensor network based on machine learning.
The technical scheme of the invention is as follows:
a low-delay routing method of a intertidal zone sensor network based on machine learning comprises the following steps:
1) modeling intertidal zone wireless sensor network delay;
2) each node calculates the delay caused by the data packet blockage;
3) each node calculates the link delay to the neighbor node;
4) and calculating the optimal delay path by using a shortest path algorithm aiming at delay optimization.
In the above technical solution, the step 1) is specifically as follows:
the latency of the intertidal zone wireless sensor network is divided into link latency and congestion latency, and the total latency of a selected route is:
where n is the number of nodes passing through a route, ldi,i+1For the link delay, nd, of nodes i to i +1j+1The congestion delay for node j + 1.
The step 2) is specifically as follows: the congestion delay caused by the data packet congestion of the node is obtained according to the following formula:
wherein, Bi(t) is the number of data packets cached by the node i in the t-th cycle, which is directly obtained by reading the register of the cache region, Ci(t) is the number of data packets that can be processed by the node i in one cycle, and is obtained from the average number of data packets processed by the node i in the previous 10 cycles, and is calculated as follows:
Ci(t)＝Avg.{Ci(t-λ)},λ＝1,2,…,10
Si' (t + τ) is the future state of node i, i.e., whether the node is on water or submerged in seawater at the t + τ th cycle, estimated using a machine learning algorithm.
Future state S of the node ii' (t + τ) was obtained by the following method: so as to: 1) time D when node has been on waterawAnd 2) the number N of the current neighbor nodes of the nodenbAnd 3) the number N of currently available links of the nodelinkAnd selecting a logistic regression model as a machine learning classification algorithm to estimate the state of the node in the future period of tau.
The step 3) is specifically as follows:
first, whether communication can be established between any two nodes is judged
When there is an available communication link between two nodes, i.e. /)ijWhen (t) is 1, node i broadcasts a beacon estimate with CTP routing algorithm and the link quality ETX between node jij(t)；
Node i andlink delay between nodes j, ldij(t) is calculated by the following formula:
the step 4) is specifically as follows: after the nodes calculate the corresponding congestion delay and the link delay between the nodes and other neighbor nodes, the current shortest path of the node and the congestion delay of the node are broadcasted to all the neighbor nodes in a beacon form, and finally all the nodes in the network use a shortest path algorithm to calculate iteration to obtain the shortest delay route from each node to a base station, wherein the shortest path algorithm adopts a dijkstra method.
The invention has the following beneficial effects: the method firstly models the time delay of the intertidal zone wireless sensor network, divides the time delay in the wireless sensor network into node congestion delay and link delay, and respectively quantizes the node congestion delay and the link delay. In the link delay quantification process, the method uses a machine learning algorithm. The method finally takes time delay as guidance, designs a data forwarding route, and finally effectively shortens the time delay of the intertidal zone sensor.
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FIG. 1 is a schematic diagram of routing implemented by the present invention.
FIG. 2 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Fig. 1 shows the routing method of the present invention. The invention selects a path based on a shortest path algorithm, and mainly contributes to providing a routing matrix which aims at the intertidal zone sensor network and takes time delay as guidance, and each time delay component in the intertidal zone sensor network needs to be modeled and quantized in the process. As shown in fig. 1, the present invention mainly divides the delay components in the intertidal sensor network into link delay (numbers on the solid line) and congestion delay of the node (numbers in the circle), and the larger the number is, the larger the delay for selecting the link or the node as the route is. The circles in fig. 1 represent a node, the dotted lines represent the available links, and the solid lines represent the final selected path.
The low-delay routing method of the intertidal zone sensor network based on machine learning comprises the following steps:
the method comprises the following steps: modeling the intertidal zone wireless sensor network delay. Through data analysis of a wireless sensor network deployed in an intertidal zone, the main reasons for causing large delay of the intertidal zone wireless sensor network are found to be as follows: 1) poor link quality due to the environment and link delay due to tidal fluctuations; and 2) congestion delay of the buffer area, which is caused by the network congestion and the data packets are accumulated continuously. For convenience of explanation, the symbols used in the following description are set forth in table 1.
TABLE 1 symbolic meanings description used in summary of the invention
The latency of the intertidal wireless sensor network is divided into link latency and congestion latency, and the total latency of a selected route is defined by the following equation:
where n is the number of nodes traversed in a route, i ∈ {1,2, …, n }, j ∈ {1,2, …, n-1.
Step two: and each node calculates congestion delay of each node. Because data packets are continuously accumulated in the buffer area of the node, according to the FIFO (first in first out) principle, a newly generated data packet needs to wait for the existing data in the buffer area to be transmitted before being transmitted. The time from when a packet is pushed into the buffer until it is sent is called the congestion delay. For a general sensor network, network connectivity and link quality are better, so the congestion delay is usually shorter. But for the wireless sensor network in the intertidal zone, the congestion delay is very serious. According to experimental data, the number of data packets in the cache region of the intertidal wireless sensor network node is often hundreds. The congestion delay of a node may be calculated by:
wherein B isi(t) can be obtained directly by reading the register of the buffer, Ci(t) is derived from the average number of packets processed by node i in the first 10 cycles, as follows:
Ci(t)＝Avg.{Ci(t-λ)},λ＝1,2,…,10
in the calculation process of congestion delay, the future state of a node, namely S, is usedi' (t + τ). We have found that the future state of the node needs to be taken into account when estimating congestion delay. If a node will be flooded with seawater after a period of τ in the future, the congestion delay of the node should be set to a higher value to avoid that the node is selected as parent by other nodes. To accurately estimate the future state of a node, we look at: 1) time D when node has been on waterawAnd 2) the number N of the current neighbor nodes of the nodenbAnd 3) the number N of currently available links of the nodelinkCharacteristically, a machine learning algorithm is used to estimate the state of the node for the future τ cycles. The node future state can be predicted because the node state is directly related to the tidal water cycle, and the tidal water has obvious periodicity, so that a machine learning algorithm can be used for predicting the possible future state of the node. The performance of three common machine learning algorithms including logistic regression (logistic regression), Adaboost classifier, Gaussian naive Bayes algorithm and the like is compared on actual experimental data, the overhead of each algorithm is considered comprehensively, and a logistic regression model is selected as the classification algorithm of machine learning finally.
Step three: each node estimates the link delay of its available links. It is first determined whether communication can be established for any two nodes according to the following equation.
When there is an available communication link between two nodes, i.e. /)ijWhen (t) is 1, node i broadcasts a beacon estimate with CTP routing algorithm and the link quality ETX between node jij(t) of (d). At this time, we define the link delay between node i and node j to be calculated by:
step four: and calculating the optimal delay path by using a shortest path algorithm aiming at delay optimization. After the node calculates the congestion delay of the node and the link delay between the node and other neighbor nodes, the current shortest path and the congestion delay of the node are broadcasted to all the neighbor nodes in a beacon form. Finally, all nodes in the network use a shortest path algorithm to calculate and iterate to obtain the delay shortest route from each node to the base station, the process is shown in fig. 2, and the shortest path algorithm can directly adopt a dijkstra algorithm.
Claims (2)
1. A low-delay routing method of an intertidal zone sensor network based on machine learning is characterized by comprising the following steps:
1) modeling intertidal zone wireless sensor network delay;
2) each node calculates the delay caused by the data packet blockage;
3) each node calculates the link delay to the neighbor node;
4) calculating a delay optimal path by using a shortest path algorithm with the aim of delay optimization;
the step 1) is as follows:
the latency of the intertidal zone wireless sensor network is divided into link latency and congestion latency, and the total latency of a selected route is:
where n is the number of nodes passing through a route, ldi,i+1For the link delay, nd, of nodes i to i +1j+1Congestion delay for node j + 1;
the step 2) is specifically as follows: the congestion delay caused by the data packet congestion of the node is obtained according to the following formula:
wherein, B1() The number of the data packets cached for the node i in the t-th cycle is directly obtained by reading a register of a cache region Ci() The number of data packets that can be processed by the node i in one cycle is obtained from the average number of data packets processed by the node i in the previous 10 cycles, and is calculated as follows:
Ci(t)＝Avg.{Ci(t-λ)},λ＝1,2,…,10
S′i(t + τ) is the future state of the node i, namely whether the node is positioned on water or submerged by seawater in the t + τ period, and is estimated by using a machine learning algorithm;
the step 3) is specifically as follows:
first, whether communication can be established between any two nodes is judged
When there is an available communication link between two nodes, i.e. /)ijWhen (t) is 1, node i broadcasts a beacon estimate with CTP routing algorithm and the link quality ETX between node jij(t)；
Link delay ld between node i and node jij(t) is calculated by the following formula:
the step 4) is specifically as follows: after the nodes calculate the corresponding congestion delay and the link delay between the nodes and other neighbor nodes, the current shortest path of the node and the congestion delay of the node are broadcasted to all the neighbor nodes in a beacon form, and finally all the nodes in the network use a shortest path algorithm to calculate iteration to obtain the shortest delay route from each node to a base station, wherein the shortest path algorithm adopts a dijkstra method.
2. The method of claim 1, wherein the future state S 'of node i is a low latency routing method for machine learning based intertidal sensor networks'i(t + τ) was obtained by the following method:
so as to: 1) time D when node has been on waterawAnd 2) the number N of the current neighbor nodes of the nodenbAnd 3) the number N of currently available links of the nodelinkAnd selecting a logistic regression model as a machine learning classification algorithm to estimate the state of the node in the future period of tau.
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| US9531658B2 (en) * | 2014-07-16 | 2016-12-27 | International Business Machines Corporation | Routing messages based on geolocation information associated with both the messages and with subscribers |
| US10477359B2 (en) * | 2014-12-08 | 2019-11-12 | International Business Machines Corporation | Publishing messages based on geographic area |
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| CN101895952A (en) * | 2010-07-16 | 2010-11-24 | 山东省计算中心 | Multi-route establishment method and parallel data transmission method of wireless sensor network |
| CN102196527A (en) * | 2011-05-28 | 2011-09-21 | 东华大学 | Route recovery method and recovery protocol for mobile Sink wireless sensor network |
| CN102497440A (en) * | 2011-12-20 | 2012-06-13 | 山东大学 | Low-latency data aggregation algorithm |
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