CN111200856B - Multi-hop optimal path selection method of wireless sensor - Google Patents

Multi-hop optimal path selection method of wireless sensor Download PDF

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CN111200856B
CN111200856B CN202010105831.0A CN202010105831A CN111200856B CN 111200856 B CN111200856 B CN 111200856B CN 202010105831 A CN202010105831 A CN 202010105831A CN 111200856 B CN111200856 B CN 111200856B
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data
cluster
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CN111200856A (en
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谢昊飞
贾哲健
王平
袁兴未
徐国超
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/048—Fuzzy inferencing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • 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
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention belongs to the technical field of wireless sensor networks, in particular to a multi-hop optimal path selection method of a wireless sensor network; the method comprises the steps of establishing an energy efficiency calculation model to calculate residual energy according to initial node energy and transmission node consumption; establishing a data aggregation model according to the neighbor node information and the position information of the next hop node, and calculating the distance between a cluster head and a member node; determining m initial clustering centers according to the node density; correspondingly outputting m optimal cluster heads from m initial cluster centers by using a membership function in an improved IF-THEN fuzzy rule; sending the information to the corresponding optimal cluster heads, and transmitting data to a destination through repeated forwarding between the optimal cluster heads; discarding or combining invalid paths according to the iteration times and the inter-cluster transmission success rate to complete path search; and selecting and solving the optimal path from the searched paths by evaluating the path quality. The invention can effectively reduce and balance network energy consumption and prolong the service life of the network.

Description

Multi-hop optimal path selection method of wireless sensor
Technical Field
The invention belongs to the technical field of wireless sensor networks, and relates to a fuzzy algorithm-based wireless sensor network routing algorithm with optimal cluster head selection and high energy efficiency, in particular to a multi-hop optimal path selection method for a wireless sensor.
Background
Wireless sensor networks consist of small and miniature devices dispersed throughout a region for event monitoring. However, a common sensor node is deployed in a severe environment, and charging or battery replacement of the node is very difficult. In a multi-hop wireless sensor network, due to high packet loss rate and energy utilization rate of the multi-hop wireless sensor network, reliable links are required to realize end-to-end data transmission, and multiple retransmission may be caused by adopting multiple paths to transmit data, so that resource consumption and long time delay are caused.
The existing method is mainly based on single-path routing, the single path can calculate the complexity and the resource utilization rate of a system with the minimum power to realize the network throughput, but when the path fails to send a data packet and generates extra cost, the single-path routing cannot solve the problems. Although the multi-path can be used to meet the requirements of link quality of service, etc., the data of the node is forwarded for many times, which causes interference to the wireless channel and causes excessive energy consumption by increasing the number of paths. Therefore, a need exists for a method that can improve the reliability of node transmission in multipath routing and reduce the network energy consumption.
Disclosure of Invention
Based on the problems in the prior art, the data aggregation model is established by analyzing the data transmission characteristics of the wireless sensor network and combining the position information of the neighbor node and the next hop node, the data nodes are aggregated to form a plurality of cluster heads, and the distance between each cluster head and the member node of the cluster head is calculated; calculating transmission energy and path loss, establishing a node energy consumption estimation model and calculating node residual energy; determining a plurality of initial clustering centers in a wireless sensor network based on the density of nodes, inputting residual energy and distance, and correspondingly outputting a plurality of optimal cluster heads by utilizing a membership function in an improved IF-THEN fuzzy rule; and the member nodes send the information to the corresponding optimal cluster heads to complete the optimal path search.
And the optimal cluster head corresponds to the adjusted clustering center of the initial clustering center.
Specifically, the technical solution adopted to solve the above technical problems of the present invention includes:
a multi-hop optimal path selection method for a wireless sensor, the method comprising the steps of:
s1, establishing an energy efficiency calculation model of the wireless sensor node according to the initial node energy and the transmission node consumption, and calculating the residual energy RE of each node;
s2, establishing a data aggregation model according to the neighbor node information and the position information of the next hop node, and calculating the distance D between each cluster head and the member nodes thereoftoC；
S3, determining m initial clustering centers in the wireless sensor network according to the node density;
s4, inputting residual energy RE and distance DtoC, correspondingly outputting m optimal cluster heads by using the m initial cluster centers by using a membership function in the improved IF-THEN fuzzy rule;
s5, the member nodes send the information to the corresponding optimal cluster heads in a centralized manner, and the optimal cluster heads transmit the data to the destination through multiple times of forwarding; according to the iteration times and the inter-cluster transmission success rate, discarding or combining an invalid path to complete path search;
and S6, solving the multi-hop optimal path from the paths searched in the step S5 by evaluating the path quality and adopting a competition comparing and selecting mode.
The invention has the beneficial effects that:
1) aiming at the optimization of a multi-hop network data transmission path, the invention obtains the optimal cluster head by utilizing fuzzy reasoning, reduces mutual data forwarding and repeated retransmission among nodes and improves the transmission efficiency of energy.
2) The invention can utilize the data transmission times of the intermediate node overhearing the neighbor node, recreate the path to process abnormal data, reduce the invalid path and effectively improve the reliability of the end-to-end node.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purpose of making the objects, aspects and advantages of the present invention more apparent, there is described in detail preferred embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a multi-hop path selection method for a wireless sensor according to the present invention;
FIG. 2 is a flow chart of an optimal path selection method based on a fuzzy algorithm according to the present invention;
FIG. 3 is a schematic diagram of the optimal path selection based on the fuzzy algorithm of the present invention;
FIG. 4 is a diagram of a fuzzy inference model of the present invention;
FIG. 5 is a flow chart of fuzzy algorithm based cluster head selection according to the present invention;
FIG. 6 is a flow chart of the fault adaptive process selection of the present invention;
fig. 7 is a schematic diagram of the optimal path search of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration, and not limitation, the invention; some parts of the drawings may be omitted, enlarged or reduced for better illustrating the embodiments of the present invention, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and it will be understood that specific meanings of the terms described above will be understood by those skilled in the art according to specific situations.
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention are described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1, the present invention provides a multi-hop optimal path selection method for a wireless sensor, the method comprising the following steps:
s1, establishing an energy efficiency calculation model of the wireless sensor node according to the initial node energy and the transmission node consumption, and calculating the residual energy RE of each node;
s2, establishing a data aggregation model according to the neighbor node information and the position information of the next hop node, and calculating the distance D between each cluster head and the member nodes thereoftoC；
S3, determining m initial clustering centers in the wireless sensor network according to the node density;
s4, inputting residual energy RE and distance DtoC, correspondingly outputting m optimal cluster heads by using the m initial cluster centers by using a membership function in the improved IF-THEN fuzzy rule;
s5, the member nodes send the information to the corresponding optimal cluster heads in a centralized manner, and the optimal cluster heads transmit the data to the destination through multiple times of forwarding; according to the iteration times and the inter-cluster transmission success rate, discarding or combining an invalid path to complete path search;
and S6, solving the multi-hop optimal path from the paths searched in the step S5 by evaluating the path quality and adopting a competition comparing and selecting mode.
In one embodiment, fig. 2 shows an optimal path selection method based on a fuzzy algorithm, which includes:
sending node active information, receiving feedback information from a neighbor node, setting a random active node, and sending an active data packet to the neighbor node; judging whether a destination is accessed, if the destination is accessed really, selecting an evaluation standard, detecting the applicability of a path, and meeting the requirement of the applicability, adopting optimal path search based on a distributed path search and a forward forwarding node set, searching the optimal path from the node to a cluster head node, and continuously checking whether data is sent, if the data is continuously sent, successfully sending a data packet to the destination, otherwise, resetting a random active node to carry out a flow, and when the requirement of the applicability is not met, adopting an abnormal data processing mode, including discarding the path or monitoring intermediate nodes, and judging whether the path needs to be reestablished.
The adaptability requirement comprises that each wireless sensor is equipped with FIS to search the applicability of the wireless sensor, and mainly comprises whether the node can normally transmit and receive data and has lower time delay and packet loss rate; the main criteria for its suitability are those skilled in the art which can carry out the treatment by conventional means.
In an embodiment, fig. 3 is a schematic diagram of an optimal path selection method based on a fuzzy C-means-MPB-means algorithm, where the embodiment employs the fuzzy C-means-MPB-means algorithm; the method specifically comprises the steps of constructing an event model from event occurrence, and calculating the distance from a node to a cluster center through the position of a next hop node and neighbor node information; calculating the residual energy of the node according to the initial energy of the node and the path loss of the node; in the cluster head selection process, a node cluster center and member nodes are created by using fuzzy C-node density, nodes with higher energy are randomly selected to become cluster heads and broadcast energy, the cluster heads and the members synchronously transmit data, and the nodes with higher adaptability are selected to become new cluster heads; in the optimal path selection process, performing optimal path evaluation by using iteration times, data transmission rate and minimum delay, counting the number of monitored neighbor nodes, judging whether the current path is the optimal path, and selecting a reconstructed path or discarding the path; and finally, completing the searching process of the optimal path and preparing data for sending.
In one embodiment, fig. 4 shows a flow chart of a fuzzy inference system, which calculates node residual energy by constructing a node energy consumption model, and selects a clustering center by using node density and domain node density; and calculating the distance from the node to the cluster center by constructing a data aggregation model, wherein the fuzzy processing comprises utilizing an iF-THEN fuzzy rule, carrying out fuzzy reasoning by using a fuzzifier, and solving the optimal cluster head by solving the fuzzy.
In some implementable embodiments, for the energy efficiency calculation model and the data aggregation model, the following are included:
the wireless sensor network model is formed by N randomly deployed nodes V ═ V0,V1,V2,...,VNIs composed of0As receiving nodes, RsAs node perception range, RCFor the node communication range, the network model assumes that the MAC layer provides a packet reception ratio PRR by which link reliability is obtained for QoS, and assumes that the network graph G ═ V, E and the link quality QoS ═ Wk(u, v) for any K ═ 1,2,3.. n, resulting in a path model P; wherein the content of the first and second substances,
Wk(p)＝∑(u,v)∈p Wk(u,v)≤c(u,v)for K＝1,2,3...n
Wk(p) is a linear representation of the link quality; wk(u, v) represents a path formed with the nodes (u, v); c (u, v) represents feasible path minimization;
aiming at the problem of energy consumption in a network model, the invention adopts the network energy consumption model:
Figure BDA0002387584730000061
Figure BDA0002387584730000062
wherein E isT(l) Representing the energy consumption in the transmission of data, ER(l) Representing the energy consumption of data reception, EelecRepresenting individual node energy consumption, d0Is the transmission distance threshold, d is the actual transmission distance, εfsAnd εampAll represent the necessary energy consumption parameters, l represents the data length;
Figure BDA0002387584730000069
representing the total energy consumption; eviIndicating that there is energy consumption by the data-receiving node. Therefore, the remaining energy RE ═ E of data can be calculated from the energy consumption of data transmission and receptionR(l)-d×Eelec。
According to the neighbor node information and the next hop position, adopting the data aggregation model node information to aggregate, and defining deltaiRepresents a node SiUnaggregated data including the location of nodes in the network, the distance D between nodes;
Figure BDA0002387584730000063
represents a node SiAnd SjAs the node SiReceiving node SjData of thetajThen, the self data is compared with thetajAnd (4) polymerizing. If the aggregated data are all original data, i.e. node SiData of is deltai，θj＝δjThen the formula of polymerization is
Figure BDA0002387584730000064
If the original data aggregation is directly adopted, the method comprises the following steps
Figure BDA0002387584730000065
Wherein the content of the first and second substances,
Figure BDA0002387584730000066
represents a node SiAnd node SjAn intermediate polymerization of (a);
Figure BDA0002387584730000067
represents a node SiCurrent aggregation results; thetajRepresents a node SjThe data of (a); τ represents a data aggregation parameter; c represents the aggregation constant.
According to the data aggregation model, the distance D between the aggregated nodes and the cluster head can be calculatedtoC；
Figure BDA0002387584730000068
And simultaneously, the optimal path search is converted into three constraint conditions, the link reliability is used as a first constraint condition, the end-to-end delay is used as a second constraint condition, and the network energy consumption is used as a third constraint condition.
To calculate the data transmission delay, the patent ignores the signal transmission delay, and models the end-to-end delay for path P as D(p)To minimize the overall path delay, D(p)Should be minimum and D(p)≥0；
Figure BDA0002387584730000071
Figure BDA0002387584730000072
subject·to Dp＞0；
In one embodiment, fig. 5 shows a fuzzy algorithm-based cluster head selection flowchart, a non-supervised learning clustering algorithm-fuzzy C-means algorithm (FCM) is used to create a cluster center and related member nodes, the nodes and the cluster center randomly select an initial cluster head, the selected cluster head broadcasts the highest power, the nodes select the nearest cluster head to become a cluster node, and the cluster head and the members synchronously transmit data; and the nodes use fuzzy reasoning to calculate and adaptively search the next hop cluster head until all the selected cluster heads of one route are selected, and the process is finished.
In another practical embodiment, a cluster center and related member nodes are created through K-means, the nodes and the cluster center randomly select an initial cluster head, select a cluster head to broadcast the highest power, the nodes select the nearest cluster head to become a cluster node, and the cluster head and the members synchronously transmit data; and the nodes use a K-means algorithm to search the cluster head of the next hop until all the cluster heads in the route are selected.
In one implementable embodiment, the cluster head selection consists essentially of:
step1, defining node density parameter, including two parameters of DensCluster and phi. And are respectively represented as:
DensCluster＝N/c
Figure BDA0002387584730000073
wherein N represents the total number of nodes; c denotes the total number of clusters, SΦThe area of the network is represented, the density of the wireless sensor node distribution is reflected by the parameter DensCluster, and phi is used for representing the size of a field range.
step2 finding Domain node Density, i.e. processing for a location, the number of nodes in the unknown Domain is the Domain node Density pφIf p isφAnd the position point is confirmed to be a high-density area data point if the position point is more than or equal to DensCluster, so that a first data point set L can be obtainedaSimultaneously at LaIn (1), a first initial center p is selected1For the k-th initial cluster center pkMust satisfy
Figure BDA0002387584730000074
Thus, m initial cluster centers p can be found from the cluster C-node density1,m2...pm。
step3, calculating the residual energy RE and the distance D between the node and the cluster head according to the energy consumption estimation model and the data aggregation model obtained in the step S1toC, establishing a fuzzy rule based on the IF-THEN rule, and allocating each sensor by using a membership function in the IF-THEN ruleFIS is prepared to search the applicability of the fuzzy inference method, and a triangular membership function is used for fuzzy inference:
Figure BDA0002387584730000081
a, b and c represent membership function parameters, input sets (low (VL), low, medium and high) are sequentially represented as relative priorities (very low, medium and high) of the input sets, and output levels correspond to (VL, L, LM, HM and VH). In turn, as output set priorities (very low, medium high, very high). Adopting IF-THEN fuzzy rule connection, carrying out fuzzy input on an input set, selecting a higher priority in an output set as an output, and using a fuzzy rule Ri: suppose Ai＝RE，Bi＝DtoC，CiZ, i 1, 2. The fuzzy optimal output set can be expressed as:
Figure BDA0002387584730000082
c ' represents a fuzzy optimal fuzzy output set, A ' and B ' represent fuzzy input sets, after fuzzy reasoning is completed, linear de-fuzzy processing is carried out on an output object to obtain an optimal cluster head, namely the optimal cluster head is represented as:
Figure BDA0002387584730000083
wherein u isc(z) fuzzy aggregation membership functions representing node priorities; z represents a specific value of the node priority; z represents a fuzzy inference system, and the equation converts fuzzy output into a cluster head with higher priority by using linearization, so that the cluster head is selected as an optimal cluster head.
In one embodiment, FIG. 6 presents a selection flow diagram of a fault adaptation process, comprising:
monitoring the abnormal conditions of the transmission path specifically includes three conditions:
firstly, when all paths are successfully established, data are directly forwarded;
and secondly, constructing a part of paths, checking the transmission rate of the paths, if the transmission rate of the paths reaches a threshold value, reconstructing the paths by the nodes, forwarding data, and otherwise, terminating.
And thirdly, judging whether the monitoring quantity is greater than the calculation path or not when the path which is not constructed exists, if so, calculating the quantity of the paths meeting the transmission path request (Preq), merging invalid paths and then forwarding data, otherwise, discarding the paths.
In an embodiment, the specific process of the path search may include:
step1, according to the obtained optimal cluster head, the node sends the information to the optimal cluster head, the optimal cluster head transmits the data to the destination by multiple times of forwarding, wherein the next hop route selection is based on the iteration times and the transmission success rate, the transmission success rate is set P (u, v) is the packet receiving ratio between the node u and the node v, and the successful transmission rate of the packet is expressed as follows:
Figure BDA0002387584730000091
Nsuccess＝Nsent×PDR；
Figure BDA0002387584730000092
subject·to PDR≥TPDR；
where PDR is the packet transfer rate, NsentIndicating the number of data packet transmissions, NsuccessThe indicator represents the success ratio of the data transmitted by the node to the destination, and is mainly used for determining the reliability of the data to the destination. The packet transmission rate needs to be maximized to maximize the number of successful node transmissions. Wherein T is the total time, TPDRIs the threshold for PDR. Number of iterations TKMainly, the times of forwarding data to a destination by a cluster head are used as indexes, the initial iteration times are assumed to be 1, and the probability product of the action selected by the last iteration is obtained if the product is obtained<1, proving that the iteration times are larger, simultaneously comparing the iteration times of the path A and the path B, and selecting the minimum iterationThe number of times.
step2, selecting the optimal path, namely, evaluating the path quality P (n) through the performance index function, comprehensively considering the iteration times of the cluster head and the successful transmission rate of the data packet, transforming the data by adopting Max-Min standardization, and selecting the path with smaller iteration times and higher transmission success rate of the data packet as the optimal path by adopting a competition comparison and selection mode.
In one embodiment, fig. 7 shows a structure diagram of the optimal path searching method, where the starting node a, the destination node B, and the distance a are1、a2、a3Three optimal cluster heads are closest, with a2As a center of circle, r0Is a radius, wherein a1The next hop b closest to itself can be selected2、b3For preparing cluster head nodes, like a2B nearest to itself can be selected1、b2Preparing cluster head nodes for next hop, a3B nearest to itself can be selected4、b5Preparing cluster heads for the next hop, by means of a contention-based selection scheme, a2And b2In between, the number of iterations is minimal and the inter-cluster transmission rate is highest, so a is selected2-b2And sequentially searching the optimal path downwards for the current optimal path.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A multi-hop optimal path selection method of a wireless sensor is characterized by comprising the following steps:
s1, establishing an energy efficiency calculation model of the wireless sensor node according to the initial node energy and the transmission node consumption, and calculating the residual energy RE of each node;
s2, according to the neighbor node information and the next hop nodePosition information, establishing a data aggregation model, and calculating the distance D between each cluster head and the member nodes thereoftoC；
S3, determining m initial clustering centers in the wireless sensor network according to the node density;
s4, inputting residual energy RE and distance DtoC, correspondingly outputting m optimal cluster heads by using the m initial cluster centers by using a membership function in the IF-THEN fuzzy rule;
s5, the member nodes send the information to the corresponding optimal cluster heads in a centralized manner, and the optimal cluster heads transmit the data to the destination through multiple times of forwarding; according to the iteration times and the inter-cluster data packet transmission success rate, discarding or combining invalid paths to complete path search;
s6, selecting and solving a multi-hop optimal path from the paths searched in the step S5 by evaluating the path quality;
wherein, step S5 specifically includes:
assuming that P (u, v) is the packet reception ratio between node u and node v, the packet transmission success rate is expressed as follows:
Figure FDA0003348764510000011
subject·to PDR≥TPDR；
wherein, PDR is the data packet transmission success rate,
Figure FDA0003348764510000012
t is the total time, TPDRIs the threshold of PDR;
number of iterations TKAccording to the number of times that the optimal cluster head forwards data to a destination as an index, assuming that the number of initial iterations is 1, according to the probability product of the action selected by the last iteration, if the product is the product<1, the larger the iteration number is;
the step S6 specifically includes evaluating the path quality through a performance index function, comprehensively considering the cluster head iteration number and the data packet transmission success rate, transforming data by Max-Min standardization, and selecting a path with a smaller iteration number and a higher data packet transmission success rate as an optimal path by a competition comparison selection method.
2. The method as claimed in claim 1, wherein the calculation of the remaining energy of each node includes RE-ER(l)-d×EelecThe method specifically comprises the following steps:
Figure FDA0003348764510000021
wherein RE represents the remaining energy of all nodes; eR(l) Representing the energy consumption of data reception, ET(l) Representing the energy consumption in the transmission of data, EelecRepresenting individual node energy consumption, d0Is a transmission distance threshold, epsilonfsAnd εampAll represent the necessary energy consumption parameters, l represents the data length; d is the actual transmission distance.
3. The method of claim 1, wherein the formula for calculating the distance between each cluster head and its member nodes comprises:
Figure FDA0003348764510000022
wherein the content of the first and second substances,
in the case of raw data aggregation, i.e. thetaj＝δj，
Then the aggregation formula is:
Figure FDA0003348764510000023
if the aggregated data is not the original data,
then the aggregation formula is:
Figure FDA0003348764510000024
Figure FDA0003348764510000025
represents a node SiAnd node SjAn intermediate polymerization of (a);
Figure FDA0003348764510000026
represents a node SiCurrent aggregation results; thetajRepresents a node SjThe data of (a); deltaiRepresents a node SiThe original data of (2); deltajRepresents a node SjThe original data of (2); τ represents a data aggregation parameter; c represents the aggregation constant; d represents the distance between the nodes.
4. The method according to claim 1, wherein the formula for determining the m initial cluster centers comprises defining a node density of the wireless sensor, searching a node density in a certain field, and if the node density in the field is greater than the defined node density of the wireless sensor, combining data points in a first high-density area as a first initial cluster center, and then using a data point farthest from the first cluster center as a second cluster center, wherein for the m cluster centers, the requirement for the m initial cluster centers is satisfied
Figure FDA0003348764510000031
Selecting m clustering centers according to the formula; wherein p is1Representing a first cluster center; p is a radical ofmRepresenting the mth cluster center.
5. The method for selecting the multi-hop optimal path of the wireless sensor according to claim 1, wherein the determining manner of the m optimal cluster heads comprises establishing a fuzzy rule by adopting an IF-THEN rule, and performing fuzzy inference by using a triangular membership function by using a membership function in the IF-THEN rule; and outputting the corresponding m optimal cluster heads.
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