CN111770512B - Wireless sensor network sector routing method based on fuzzy logic - Google Patents

Wireless sensor network sector routing method based on fuzzy logic Download PDF

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CN111770512B
CN111770512B CN202010509499.4A CN202010509499A CN111770512B CN 111770512 B CN111770512 B CN 111770512B CN 202010509499 A CN202010509499 A CN 202010509499A CN 111770512 B CN111770512 B CN 111770512B
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CN111770512A (en
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胡黄水
韩优佳
赵宏伟
王宏志
姚美琴
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Changchun University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • 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
    • 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
    • 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

Abstract

The invention relates to a multi-hop routing method of a wireless sensor network, in particular to a sector routing method of the wireless sensor network based on fuzzy logic. The network is divided into different ring sectors according to the optimal number of clusters in each ring. In addition, a fuzzy logic control system is adopted to select Cluster Heads (CH) of all ring sectors, and the hop count of transmitting forwarding data by the CH is determined, so that the hot spot problem of intra-cluster and inter-cluster communication is relieved, and the energy efficiency of intra-cluster and inter-cluster communication is improved.

Description

Wireless sensor network sector routing method based on fuzzy logic
Technical Field
The invention relates to a multi-hop routing method of a wireless sensor network, in particular to a sector routing method of the wireless sensor network based on fuzzy logic. The network is divided into different ring sectors according to the optimal number of clusters in each ring. In addition, a fuzzy controller is adopted to select Cluster Heads (CH) of all ring sectors, and the hop count of transmitting forwarding data by the CH is determined, so that the hot spot problem of intra-cluster and inter-cluster communication is relieved, and the energy efficiency of intra-cluster and inter-cluster communication is improved.
Background
Nowadays, with the rapid development of information technologies such as big data and internet of things, various wireless sensor networks are widely applied in the fields of health, environmental monitoring, battlefield monitoring, space exploration and the like, wherein the most common wireless sensor networks are ring sector networks, and all nodes send data to one Base Station (BS). In these networks, nodes are typically randomly deployed in a target area of severely changing environment, and these nodes are typically grouped into clusters to minimize energy consumption and thereby maximize the life cycle of the network. Because of the limitation of node communication, single-hop transmission is impossible, so that nodes located far from the outer ring of the BS communicate with the BS through nodes nearby the BS to improve the energy utilization rate of the nodes and balance the energy consumption of the network. Therefore, nodes near the BS cause unbalanced load data transfer and uneven energy consumption due to more network load carried by the nodes than other nodes, thereby causing a so-called hot spot problem. In order to overcome the hot spot problem, an unequal clustering scheme is adopted in the wireless sensor network to balance the load among the nodes, so that the size of clusters close to the BS is reduced, and the size of the clusters is increased along with the increase of the distance between the BS and the nodes. Typically, these schemes include two phases: cluster construction and data transmission. In the clustering construction stage, probability, weight, intelligence and other methods are adopted to select the clustering result. Clustering is performed using single-hop, multi-hop, or hybrid approaches. From the point of view of uniform energy consumption, the network is divided into concentric rings or ring sectors, however, existing schemes divide the network into ring areas with identical cells, which makes it impossible for each ring area to consume uniform energy. Furthermore, the weight-based clustering approach does not handle the different uncertainties and dynamics in the actual network well. In particular, hop-by-hop data communication places a burden on intermediate nodes, which are prone to premature death.
Disclosure of Invention
The technical problem to be solved by the invention is to divide the network into ring areas with identical cells for existing solutions, which makes it impossible for each ring area to consume uniform energy. Furthermore, the weight-based clustering approach does not handle the uncertainty and dynamics in the actual network well. In particular, hop-by-hop data communication places a burden on intermediate nodes, which are prone to premature death. In response to this problem, an annular sector cluster routing method (FASC) based on fuzzy logic is presented herein. Firstly, dividing the looped network into different looped networks, and dividing each looped network into different sectors according to the calculated optimal cluster number of each looped network. A new fuzzy controller is then designed to determine the probability and hop count of the channel using the four parameters of the remaining energy, data length, node centrality and distance to the BS. The data of the cluster is transmitted to the BS according to the hop count of each CH instead of hop by hop, which can further reduce the hop count to the BS, thereby minimizing the end-to-end delay and the average hop count.
The invention discloses a wireless sensor network sector routing method based on fuzzy logic, which consists of four parts, namely a network model, optimal cluster number determination, fuzzy logic CH selection, hop count calculation and multi-hop routing. The network model is a ring network, the BS is positioned at the center of a circle, the target monitoring area is divided into a plurality of concentric rings, and the nodes are uniformly distributed in each ring. The optimal cluster number determination is based on the minimum energy consumption of each ring as a target, and the optimal cluster number of each ring is calculated and obtained. Since the outermost ring node does not take on the task of data forwarding, its consumed energy is also different. The optimal cluster numbers of the outermost ring and the inner ring are thus determined, respectively. The fuzzy controller system is used for enabling the system to have fuzzy logic reasoning capability and can be continuously improved and adjusted through system self-adaption, so that a better control effect is achieved. The fuzzy controller system determines the "opportunity" to become CH and the "hop count" to send the forwarding data based on four parameters, namely, the remaining energy, the data length, the node centrality and the distance to the BS.
The network model is a ring network, the BS is positioned at the center, the radius is R, and N nodes are uniformly distributed in the target area. Each node has a unique ID. The energy consumption of the nodes is calculated by adopting a free space model, and specifically comprises the energy consumed by data transmission, data reception and data fusion.
The optimal cluster number determination is to calculate the optimal cluster number with the minimum energy consumption of each ring as a target, and based on the optimal cluster number, each ring is divided into equal divided corresponding grid numbers, each grid is one cluster, and each cluster is provided with one cluster head, namely the cluster head number is determined. And respectively calculating the optimal cluster number of each ring according to the energy consumption formulas of the outermost ring and the inner ring.
And the fuzzy logic CH selects and counts the hops. The probability that a node becomes a cluster head is decided based on the remaining energy, the node centrality, and the distance to the BS, and the "hop count" to the next CH is decided based on the remaining energy, the data length, and the distance to the BS.
The fuzzy controller system comprises fuzzy controller system input and output variable fuzzification and fuzzy rule definition and fuzzy resolution. The input parameters of the fuzzy controller system are "residual energy", "node centrality", "distance to BS", "data length", and the output parameters are "opportunity" and "hop count".
The multi-hop routing mechanism in the FASC in the steady state phase is different from the hop-by-hop routing mechanism in the conventional protocol for forwarding data to the BS. In a cluster, a member node sends data to its CH for its period of time, the CH aggregates with the maximum remaining energy and the minimum number of member nodes based on its "hops" and sends data to the optimal intermediate CH.
Drawings
FIG. 1 is a ring network model of the present invention;
FIG. 2 is a schematic diagram of a distance relationship between cluster heads according to the present invention;
FIG. 3 is a schematic diagram of the distance relationship between a member and a cluster head according to the present invention;
FIG. 4 is a fuzzy controller of the present invention;
FIG. 5 is a membership function graph of the residual energy of the input variables of the present invention;
FIG. 6 is a membership function graph of the centrality of input variable nodes of the present invention;
FIG. 7 is a membership function graph of the distance of an input variable to a BS according to the present invention;
FIG. 8 is a membership function graph of input variable data length of the present invention;
FIG. 9 is a membership function graph of the output variable opportunity of the present invention;
FIG. 10 is a membership function graph of the hop count of the output variable of the present invention;
FIG. 11 is a graph showing the number of surviving nodes according to the present invention;
FIG. 12 is a schematic diagram of the energy consumption of the present invention;
fig. 13 is a life cycle schematic of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention discloses a wireless sensor network sector routing protocol based on fuzzy logic, which consists of three parts, namely a network model, optimal cluster number determination, fuzzy logic CH selection, hop count calculation and multi-hop routing. The network model is a ring network, the BS is positioned at the center of a circle, the target monitoring area is divided into a plurality of concentric rings, and the nodes are uniformly distributed in each ring. The optimal cluster number determination is based on the minimum energy consumption of each ring as a target, and the optimal cluster number of each ring is calculated and obtained. Since the outermost ring node does not take on the task of data forwarding, its consumed energy is also different. The optimal cluster numbers of the outermost ring and the inner ring are thus determined, respectively. The fuzzy controller system is used for enabling the system to have fuzzy logic reasoning capability and can be continuously improved and adjusted through system self-adaption, so that a better control effect is achieved. The fuzzy controller system determines the probability of being a node selected as a cluster head and the "hop count" of the transmitted forwarding data based on four parameters, namely, the remaining energy, the data length, the node centrality and the distance to the BS.
The network model is a ring network, as shown in fig. 1, the BS is located in the center, the radius is R, N nodes are uniformly distributed in the target area, each node has a unique ID, the position of the node is not changed after the network is initialized, the BS sends its own position information to all sensor nodes, the data from all member nodes (CM) are fused by CH, and only the CH is allowed to communicate with the BS, there is no collision and retransmission in the network, and the network has good connectivity. The energy consumption of the nodes is calculated by adopting a free space model, and specifically comprises the energy consumed by data transmission, data reception and data fusion.
The optimal cluster number determination is to calculate the optimal cluster number by taking the minimum energy consumption of each ring as a target, and based on the optimal cluster number, each ring is divided into corresponding grid numbers, each grid is one cluster, and each cluster is provided with one cluster head, namely the cluster head number is determined. And calculating the optimal cluster number of each ring by derivation according to the energy consumption formulas of the outermost ring and the inner ring. In a ring network, the energy consumption of the last ring is different from that of other annular rings without data forwarding, and can be expressed as
E ch ＝l×E elec ×(N n -1)+l×E da ×(N n -1)+(l×E elec +l×ε fs ×d 2 ch )N n (1)
Where l is the length of the data, E elec Is the energy, epsilon, consumed by the sensor node when sending or receiving 1bit data fs Is the amplification parameter when adopting a free space model, E da Is the energy consumed by fusing the data sent by the sensor nodes, N n Is the number of nodes per cluster of the outermost ring, d as shown in FIG. 1 ch Is the distance to the next hop CH, and in addition the FACS method follows a free space model, so d needs to be satisfied ch ＜d 0 ，d 0 A threshold value for a transmission distance of the free space model; at the same time, the CH is guaranteed to transmit data to the next hop receiving point in the communication range, so d is also required to be satisfied ch ＜r c (r c Cluster radius) for correct data transmission, furthermore, d ch And also needs to be greater than the width of each ring, thus d ch Needs to be within the range of formula (2);
Figure GDA0003904767900000041
meanwhile, the energy consumption of the network is expressed as
E cm ＝(N n -1)(l×E elec +l×ε fs ×d 2 cc ) (3)
Wherein d cc Is the distance between the member node and CH, which can be expressed in terms of the expected value of its square, where r is the radius in the polar fixed integral equation and ρ is the value of r in the interval [0,2 pi d ] c ]On the non-negative succession of the two,
Figure GDA0003904767900000042
then according to the cosine lawd c
Figure GDA0003904767900000043
In the method, in the process of the invention,
Figure GDA0003904767900000044
is the corresponding central angle, m n Indicating the optimal cluster number. Thus, the total energy consumption of the loop can be expressed as
E total ＝m n ×(E ch +E cm ) (6)
Taking the pair m of (6) n The derivative of (a) and the optimal cluster number is
Figure GDA0003904767900000045
Wherein the method comprises the steps of
Figure GDA0003904767900000046
Figure GDA0003904767900000047
Wherein the method comprises the steps of
A＝m n ×l[N n (3E elec +E da +ε fs ×d 2 ch +ε fs ×d 2 cc )-ε fs ×d 2 cc -2E elec -E da ]
Figure GDA0003904767900000051
Likewise, the optimal number of clusters can be found in other rings. In a general case, for the ith ring, its optimal cluster head is denoted as m i .
Figure GDA0003904767900000052
Then, each ring is divided into a plurality of sectors according to the optimal cluster number, each sector is a cluster, and a fuzzy controller is adopted to select CH of the cluster.
The fuzzy controller system is not only provided with fuzzy logic reasoning capability, but also can be continuously improved and adjusted through system self-adaption, so that a better control effect is achieved. The fuzzy controller system decides "opportunities" to become CH based on the remaining energy, node centrality and distance to BS, and decides "hops" to next CH based on the remaining energy, data length and distance to BS.
The fuzzy controller system comprises fuzzy controller system input and output variable fuzzification and fuzzy rule definition and fuzzy resolution. The input parameters of the fuzzy controller system are "residual energy", "node centrality", "distance to BS", "data length", and the output parameters are "opportunity" and "hop count". The following is a specific description of fuzzy controller system input and output variable fuzzification and fuzzy rule definition and defuzzification.
(1) The language variables of the residual energy are 'low', 'medium', 'high', wherein 'low' and 'high' adopt trapezoid membership functions, and 'medium' adopts triangle membership functions.
(2) The linguistic variables of the node centrality are "close", "adequate", "far", wherein the "close" and "far" adopt trapezoid membership functions, and the "adequate" adopts triangle membership functions.
(3) The linguistic variables of the "distance to BS" are "distance", "reach", and "nearest", wherein "distance" and "nearest" use a trapezoidal membership function, and "reach" uses a triangular membership function.
(4) The linguistic variables of the "data length" are "big", "medium", and "lite", wherein "big" and "lite" use a trapezoidal membership function, and "medium" uses a triangular membership function.
(5) The language variables of the "cluster head probability" are "ver low", "rate low", "low medium", "high medium", "rather high", "ver high". "veryLow" and "veryhigh" use trapezoidal membership functions, the rest use triangular membership functions.
(6) The language variables of the hop are "quality title", "rather title", "low medium", "medium", "high medium", "rather more", "more", "quality more", wherein "quality title" and "quality more" adopt trapezoid membership functions, and the rest adopt triangle membership functions.
(7) The fuzzy inference uses 27 if-then rules, based on the language variables, and finally, the fuzzy inference output is defuzzified by using a centroid method to obtain a clear value of the hop count. The fuzzy rule table is as follows:
Figure GDA0003904767900000061
after determining the hop count of the route by the fuzzy controller, the member node of each cluster transmits data to the CH thereof in the steady state of the route in the time period, and then each cluster head gathers and transmits data to the optimal middle CH with the maximum remaining energy and the least member node count based on the hop count based on the CH.
First, the number of network surviving nodes is compared and analyzed, and the result is shown in fig. 11. It can be seen from the figure that the number of surviving nodes in the network is unchanged during the initial working phase of the network, but the number of surviving nodes in the network starts to decrease at different times of rotation of the CH node due to different workloads of nodes in the LEACH algorithm, the TSTCS algorithm and the AEBDC algorithm. TSTCS has more surviving nodes than LEACH. In addition, AEBDC also takes into account the remaining energy and cluster center distance, resulting in CHs that are more efficient than TSTCS. Thus, AEBDC has more surviving nodes than TSTCS. In the FASC, data forwarding with different hops is performed according to the data length, so that energy consumption of CHs can be balanced, and when CHs are selected, factors such as residual energy, node centrality, data length, distance from a BS and the like are also considered, so that an optimal node becomes CHs. Thus, the number of surviving nodes of FASC is the greatest.
Second, the network energy consumption is tested to evaluate the energy efficiency of the network and to take into account all energy consumption for cluster formation, intra-cluster and inter-cluster communication. The results are shown in FIG. 12. As can be seen from fig. 12, the FASC, AEBDC and TSTCS have lower network energy consumption than LEACH due to their evenly distributed clusters and multi-hop communication patterns in the ring sector. In addition, AEBDC selects the node with the largest remaining energy as the next-hop channel and selects the distance to the cluster center as the next-hop channel, thus being superior to TSTCS. The FASC selects the optimal hop count using fuzzy logic and calculates the appropriate hop count to perform optimally in terms of network power consumption.
Finally, the FND, HND and LND are tested to evaluate the network lifetime, wherein the FND represents the death number of the first node, the HND represents the death number of the general node, and the LND represents the death number of the last node. The results are shown in FIG. 13. The FND of LEACH occurs at wheel 239, HND occurs at wheel 445, and LND occurs at wheel 1164. Meanwhile, the FND of TSTCS, AEBDC and FASC are 488, 677 and 833, HND are 701, 899 and 1140, respectively, and LND are 1699, 1732 and 2511, respectively. The FASC has the longest network lifetime because it divides the network into ring sectors according to the calculated optimal cluster and uses a fuzzy controller with four descriptors to select the optimal CHs while forwarding data with the appropriate number of hops, which can alleviate the hot spot problem and balance the energy consumption, thereby greatly extending the lifetime of the network.

Claims (1)

1. A wireless sensor network sector routing method based on fuzzy logic is called FACS, and is characterized by comprising four aspects of network model, optimal cluster number determination, fuzzy logic CH selection, hop count calculation and multi-hop routing, wherein the four aspects are as follows:
the network model is a ring network, the radius of the network is R, the base station is positioned at the center of a circle, the target monitoring area is divided into N concentric rings, and the N nodes are uniformly distributedA personal ring; calculating the optimal cluster number by taking the minimum energy consumption of each ring as a target, and calculating the optimal cluster number m of each ring by derivation according to the energy consumption formulas of the outermost ring and the inner ring respectively n The method comprises the steps of carrying out a first treatment on the surface of the In a ring network, the energy consumption of the last ring is different from the energy consumption of the other rings without data forwarding, expressed as
E ch ＝l×E elec ×(N n -1)+l×E da ×(N n -1)+(l×E elec +l×ε fs ×d 2 ch )N n (1)
Where l is the length of the data, E elec Is the energy, epsilon, consumed by the sensor node when sending or receiving 1bit data fs Is the amplification parameter when adopting a free space model, E da Is a fusion N l The energy consumed by the data sent by each sensor node is expressed as E da ＝l*E pDb Wherein E is pDb Is the energy consumed by fusing 1bit data, l is the length of the data packet, N n Is the number of clusters, d ch The FACS method follows the free space model, which is the distance to the next hop CH, and thus needs to satisfy d ch ＜d 0 ，d 0 A threshold value for a transmission distance of the free space model; at the same time, the CH is guaranteed to transmit data to the next hop receiving point in the communication range, so d is also required to be satisfied ch ＜r c ，r c For the cluster radius, for correct data transmission, furthermore, d ch And also greater than the width of each ring, i.e
Figure FDA0004140634620000011
n is the number of rings in the network, thus d ch Within the range of formula (2);
Figure FDA0004140634620000012
meanwhile, the energy consumption of the network is expressed as
E cm ＝(N n -1)(l×E elec +l×ε fs ×d 2 cc ) (3)
Wherein d cc Is the distance between the member node and CH, expressed in terms of the expected value of its square,
Figure FDA0004140634620000013
wherein d is c Is the maximum d cc D is obtained according to the cosine law c
Figure FDA0004140634620000021
In the method, in the process of the invention,
Figure FDA0004140634620000022
is the corresponding central angle, m n Represents the optimal cluster number, and thus the total energy consumption of the loop can be expressed as
E total ＝m n ×(E ch +E cm ) (6)
Taking the pair m of (6) n The derivative of (a) and the optimal cluster number is
Figure FDA0004140634620000023
Wherein the method comprises the steps of
Figure FDA0004140634620000024
Wherein the method comprises the steps of
A＝m n ×l[N n (3E elec +E da +ε fs ×d 2 ch +ε fs ×d 2 cc )-ε fs ×d 2 cc -2E elec -E da ]
Figure FDA0004140634620000025
For the ith ring, its optimal cluster head number is denoted as m i .
Figure FDA0004140634620000026
Then dividing each ring into a plurality of sectors according to the optimal cluster number, wherein each sector is a cluster, and selecting a cluster head for the cluster by adopting a fuzzy controller, wherein the fuzzy controller comprises input and output variable fuzzification of the fuzzy controller, definition of a fuzzy rule and defuzzification; the input parameters of the fuzzy controller are 'residual energy', 'node centrality', 'distance to BS', 'data length', and the output parameters are 'cluster head probability' and 'hop count'; determining the probability that each node is selected as a cluster head by the remaining energy, the node centrality and the distance to the BS, and determining the 'hop count' to the next CH by the remaining energy, the data length and the distance to the BS; the following is a specific description of fuzzy controller input and output variable fuzzification and fuzzy rule definition and fuzzy resolution;
(1) The language variables of the residual energy are 'low', 'medium', 'high', wherein 'low' and 'high' adopt trapezoid membership functions, and 'medium' adopts triangle membership functions;
(2) The language variables of the node centrality are "close", "adequate" and "far", wherein the "close" and the "far" adopt trapezoid membership functions, and the "adequate" adopts triangle membership functions; (3) The linguistic variables of the distance to the BS are distance and reach and nearest, wherein the distance and nearest adopt trapezoid membership functions, and the nearest adopts triangle membership functions;
(4) The language variables of the data length are "big", "medium" and "lite", wherein the "big" and "lite" adopt trapezoid membership functions, and the "medium" adopts triangle membership functions;
(5) The language variables of the cluster head probability are "verylow", "low", "rate low", "low medium", "high medium", "rather high", "veryhigh", "veryLow" and "veryhigh", wherein trapezoidal membership functions are adopted, and the rest triangular membership functions are adopted;
(6) The language variables of the hop are "quality title", "rather title", "low medium", "medium", "high medium", "rather more", "more", "ver more", wherein "quality title" and "quality more" adopt trapezoid membership functions, and the rest adopt triangle membership functions;
(7) Designing 27 if-then rules based on the language variables, generating two outputs from 4 inputs according to a rule table in an if-then rule, and finally, performing disambiguation on fuzzy reasoning output by using a centroid method to obtain a clear value of the hop count;
after the hop count of the route is determined by the fuzzy controller, the member node of each cluster transmits data to the CH thereof in the time period of the member node in the steady state of the route, and then each cluster head gathers and transmits data to the optimal middle CH with the maximum remaining energy and the least member node count based on the 'hop count' of the member node based on the CH, so that the hop count required from the cluster head to the base station is ensured to be not more than the hop count generated by the fuzzy controller.
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102026331A (en) * 2010-12-23 2011-04-20 重庆邮电大学 Distributed multi-jump energy-saving communication method in wireless sensor network
CN102281608A (en) * 2011-07-04 2011-12-14 南京邮电大学 Wireless sensor network clustering routing method based on fuzzy control
CN102572996A (en) * 2012-02-24 2012-07-11 重庆大学 Annulus-based node energy consumption balancing method in heterogeneous sensor network
CN103052147A (en) * 2013-01-06 2013-04-17 浙江大学 Energy effectiveness multistage ring-type networking method based on wireless sensor network
CN106023655A (en) * 2016-06-30 2016-10-12 南京航空航天大学 Sector air traffic congestion state monitoring method
CN106304235A (en) * 2016-08-22 2017-01-04 广东工业大学 A kind of collaborative clustering routing communication means divided based on hierarchical region in WSN
CN107071811A (en) * 2017-04-18 2017-08-18 长春师范大学 A kind of fault-tolerant Uneven Cluster algorithms of WSN based on fuzzy control
CN110062432A (en) * 2019-04-26 2019-07-26 长春师范大学 A kind of Wireless sensor network clustering routing algorithm based on least energy consumption
CN110536372A (en) * 2019-07-17 2019-12-03 长春工业大学 A kind of annular wireless sensor network Uneven Cluster algorithm based on fuzzy control
CN111194065A (en) * 2020-02-13 2020-05-22 吉林建筑科技学院 High-energy-efficiency multi-hop clustering routing method for ring-shaped wireless sensor network

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11218907B2 (en) * 2018-08-08 2022-01-04 Intel Corporation Publisher control in an information centric network

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102026331A (en) * 2010-12-23 2011-04-20 重庆邮电大学 Distributed multi-jump energy-saving communication method in wireless sensor network
CN102281608A (en) * 2011-07-04 2011-12-14 南京邮电大学 Wireless sensor network clustering routing method based on fuzzy control
CN102572996A (en) * 2012-02-24 2012-07-11 重庆大学 Annulus-based node energy consumption balancing method in heterogeneous sensor network
CN103052147A (en) * 2013-01-06 2013-04-17 浙江大学 Energy effectiveness multistage ring-type networking method based on wireless sensor network
CN106023655A (en) * 2016-06-30 2016-10-12 南京航空航天大学 Sector air traffic congestion state monitoring method
CN106304235A (en) * 2016-08-22 2017-01-04 广东工业大学 A kind of collaborative clustering routing communication means divided based on hierarchical region in WSN
CN107071811A (en) * 2017-04-18 2017-08-18 长春师范大学 A kind of fault-tolerant Uneven Cluster algorithms of WSN based on fuzzy control
CN110062432A (en) * 2019-04-26 2019-07-26 长春师范大学 A kind of Wireless sensor network clustering routing algorithm based on least energy consumption
CN110536372A (en) * 2019-07-17 2019-12-03 长春工业大学 A kind of annular wireless sensor network Uneven Cluster algorithm based on fuzzy control
CN111194065A (en) * 2020-02-13 2020-05-22 吉林建筑科技学院 High-energy-efficiency multi-hop clustering routing method for ring-shaped wireless sensor network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A Fuzzy Based Energy Aware Unequal Clustering for Wireless Sensor Networks;Sabrine Khriji 等;《International Conference on Ad-Hoc Networks and Wireless》;20180830;全文 *
基于环的模糊控制无线传感器网络非均匀分簇算法;姚美琴 等;《长春工业大学学报》;20200228;正文第1-2节 *

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