CN111770512A - Wireless sensor network fan-out routing protocol based on fuzzy logic - Google Patents

Wireless sensor network fan-out routing protocol based on fuzzy logic Download PDF

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CN111770512A
CN111770512A CN202010509499.4A CN202010509499A CN111770512A CN 111770512 A CN111770512 A CN 111770512A CN 202010509499 A CN202010509499 A CN 202010509499A CN 111770512 A CN111770512 A CN 111770512A
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CN111770512B (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

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Abstract

The invention relates to a wireless sensor network multi-hop routing method, in particular to a wireless sensor network sectoring routing protocol 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 controller is adopted to select a Cluster Head (CH) of each ring sector and determine the hop count of the CH for sending and forwarding data, 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 fan-out routing protocol based on fuzzy logic
Technical Field
The invention relates to a wireless sensor network multi-hop routing method, in particular to a wireless sensor network sectoring routing protocol 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 controller is adopted to select a Cluster Head (CH) of each ring sector and determine the hop count of the CH for sending and forwarding data, 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 is an annular sector network, and all nodes transmit data to a Base Station (BS). In these networks, nodes are typically deployed randomly in target areas with strongly varying environments, and the nodes are typically grouped into clusters to minimize energy consumption, thereby maximizing the lifetime of the network. Because single-hop transmission is impossible due to the limitation of node communication, a node which is far away from the outer ring of the BS communicates with the BS through a node near the BS so as to improve the energy utilization rate of the node and balance the energy consumption of the network. Therefore, compared with other nodes, nodes near the BS have a larger load of the network, which results in unbalanced load data transfer and uneven energy consumption, 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, the size of a cluster close to a BS is reduced, and the size of the cluster is increased along with the increase of the distance between the BS and the nodes. Generally, these schemes comprise two phases: cluster building and data transmission. In the clustering construction stage, the clustering result is selected by adopting methods such as probability, weight, intelligence and the like. Clustering is performed using single-hop, multi-hop, or hybrid methods. From the viewpoint of uniform energy consumption, the network is divided into concentric rings or ring sectors, however, the existing scheme divides the network into ring areas having the same cells, which makes it impossible for each ring area to consume uniform energy. In addition, the weight-based clustering method cannot handle different uncertainties and dynamics in the actual network well. In particular, hop-by-hop data communication places a burden on intermediate nodes that are prone to premature death.
Disclosure of Invention
The technical problem to be solved by the invention is to divide the network into annular zones with identical cells for existing solutions, which makes it impossible for each annular zone to consume a uniform amount of energy. In addition, the weight-based clustering method cannot handle the uncertainty and dynamics in the actual network well. In particular, hop-by-hop data communication places a burden on intermediate nodes that are prone to premature death. To address this problem, a fuzzy logic based annulus sector clustering routing protocol (FASC) is proposed herein. Firstly, dividing the ring network into different ring networks, and dividing each ring network into different sectors according to the calculated optimal cluster number of each ring network. Then a new fuzzy logic controller is designed, and the probability and the hop count of the channel are determined by using four parameters of residual energy, data length, node centrality and distance from the BS. The data of the cluster is transmitted to the BS on a hop-by-hop basis rather than on a hop-by-hop basis per CH, which further reduces the number of hops to the BS, thereby minimizing end-to-end delay and average hop count.
The invention relates to a fuzzy logic-based wireless sensor network sectored routing protocol which is composed of four parts, namely a network model, optimal cluster number determination, fuzzy logic CH selection, hop number calculation and multi-hop routing. The network model is specifically 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 nodes are not burdened with data forwarding tasks, the energy consumed by them is also different. Thus determining the optimal cluster numbers for the outermost and inner rings, respectively. The fuzzy control system is used for enabling the system to have fuzzy logic reasoning capability and meanwhile continuously improving and adjusting the fuzzy logic reasoning capability through system self-adaption, so that a better control effect is achieved. The fuzzy control system determines the 'opportunity' to become CH and the 'hop count' to transmit the forwarding data according to four parameters of residual energy, data length, node centrality and distance to the BS.
The network model is a ring network, the BS is positioned in the center, the radius is R, and the N nodes are uniformly distributed in a 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 energy consumed by data sending, data receiving 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, based on the number, each ring is divided into equally divided corresponding grid numbers, each grid is a cluster, and each cluster is a cluster head, namely the cluster head number is determined. And (4) obtaining the optimal cluster number of each ring by derivation calculation according to the outermost ring and inner ring energy consumption formulas respectively.
The fuzzy logic CH selection and the hop count calculation. The "chance" to be CH is decided according to the distance to the BS based on the remaining energy, the node centrality, and the "hop count" to the next CH is decided based on the remaining energy, the data length, and the distance to the BS. To our knowledge, this is the first time that fuzzy logic is used to determine the hop count.
The fuzzy controller comprises fuzzy controller input and output variable fuzzification and fuzzy rule definition and defuzzification. Input parameters of the fuzzy controller are 'residual energy', 'node centrality', 'distance to BS' and 'data length', and output parameters are 'opportunity' and 'hop count'.
The multi-hop routing mechanism in the FASC in the steady state stage is different from the hop-by-hop routing mechanism in the traditional protocol, and is used for forwarding data to the BS. In a cluster, member nodes transmit data to their CH during their time periods, and the CH aggregates and transmits data to the optimal intermediate CH based on its "number of hops" with the maximum remaining energy and the minimum number of member nodes.
Drawings
FIG. 1 is a model of a torus network of the present invention;
FIG. 2 is a schematic diagram of the distance relationship between cluster heads according to the present invention;
FIG. 3 is a schematic diagram illustrating the distance relationship between the members and the cluster head according to the present invention;
FIG. 4 is a fuzzy logic controller of the present invention;
FIG. 5 is a graph of membership functions for the residual energy of the input variables according to the present invention;
FIG. 6 is a membership function graph of the node centrality of the input variables of the present invention;
FIG. 7 is a graph of membership function for the distance of the input variable to BS according to the invention;
FIG. 8 is a graph of membership function for input variable data length of the present invention;
FIG. 9 is a graph of membership functions of output variable opportunities in accordance with the present invention;
FIG. 10 is a graph of membership function for output variable hops in accordance with the present invention;
FIG. 11 is a diagram illustrating 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 schematic view of the life cycle of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention relates to a fuzzy logic-based wireless sensor network sectored routing protocol which is composed of three parts, namely a network model, optimal cluster number determination, fuzzy logic CH selection, hop number calculation and multi-hop routing. The network model is specifically 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 nodes are not burdened with data forwarding tasks, the energy consumed by them is also different. Thus determining the optimal cluster numbers for the outermost and inner rings, respectively. The fuzzy control system is used for enabling the system to have fuzzy logic reasoning capability and meanwhile continuously improving and adjusting the fuzzy logic reasoning capability through system self-adaption, so that a better control effect is achieved. The fuzzy control system determines the 'opportunity' to become CH and the 'hop count' to transmit the forwarding data according to four parameters of residual energy, data length, node centrality and distance to the BS.
The network model is a ring network, as shown in fig. 1, a BS is located at the center, the radius is R, N nodes are uniformly distributed in a target area, each node has a unique ID, the position of the node does not change after the network is initialized, the BS sends its own position information to all sensor nodes, data from all member nodes (CM) are fused by a CH, and only the CH is allowed to communicate with the BS, no collision or retransmission occurs in links 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 energy consumed by data sending, data receiving and data fusion. Energy consumed by data transmitted or received between two nodes with a distance d is represented by equations (1), (2) and (3):
Figure DEST_PATH_IMAGE001
（1）
Figure 6865DEST_PATH_IMAGE002
（2）
Figure DEST_PATH_IMAGE003
（3）
wherein the content of the first and second substances,
Figure 762331DEST_PATH_IMAGE004
is the energy consumed by the node when the sensor node sends or receives 1bit data,
Figure DEST_PATH_IMAGE005
is an amplification parameter when a free space model is adopted,
Figure 682882DEST_PATH_IMAGE006
is an amplification parameter when a multipath attenuation model is adopted,
Figure DEST_PATH_IMAGE007
is a distance threshold. Fusion
Figure 555566DEST_PATH_IMAGE008
The energy consumed by the data sent by each sensor node is
Figure DEST_PATH_IMAGE009
The expression is as follows:
Figure 499252DEST_PATH_IMAGE010
（4）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
is the energy consumed to fuse 1bit data,
Figure 855147DEST_PATH_IMAGE012
is the length of the data packet. The energy consumption of the wireless sensor network comprises the energy consumption required by the CH for receiving the data sent by the CM, the energy consumption required by forwarding the data in the previous ring, the energy consumption required by fusing the received data and the energy consumption required by sending the data to the next hop CH, and the energy consumed by the CM node for communication.
The optimal cluster number determination is to calculate the optimal cluster number by taking the minimum energy consumption of each ring as a target, each ring is averagely divided into corresponding grid numbers based on the optimal cluster number, each grid is a cluster, and each cluster is a cluster head, namely the cluster head number is determined. And (4) obtaining the optimal cluster number of each ring by derivation calculation according to the outermost ring and inner ring energy consumption formulas respectively. In a ring network, the energy consumption of the last ring is different from the energy consumption of other rings without data forwarding, and can be expressed as
Figure DEST_PATH_IMAGE013
（5）
Wherein the content of the first and second substances,
Figure 567888DEST_PATH_IMAGE012
is the length of the data that is,
Figure 491981DEST_PATH_IMAGE014
is the number of clusters and is,
Figure DEST_PATH_IMAGE015
is the distance to the next hop CH, which follows the free space model as shown in fig. 1. A. the
Figure 719700DEST_PATH_IMAGE016
, B
Figure DEST_PATH_IMAGE017
And C is CH in the last ring, then
Figure 816969DEST_PATH_IMAGE018
. In addition, there are
Figure DEST_PATH_IMAGE019
(cluster radius) is used for correct data transmission. When the BC-line is perpendicular to the tangent line z,
Figure 885682DEST_PATH_IMAGE015
is that
Figure 42994DEST_PATH_IMAGE020
Minimum value
Figure DEST_PATH_IMAGE021
（6）
At the same time, the energy consumption of the network can be expressed as
Figure 961271DEST_PATH_IMAGE022
（7）
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
is the distance between the member node and the CH, and can be represented by the expected value of its square
Figure 596652DEST_PATH_IMAGE024
（8）
Then, the method is calculated according to the cosine theorem
Figure DEST_PATH_IMAGE025
Figure 815144DEST_PATH_IMAGE026
（9）
In the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE027
is the corresponding center angle of the light beam,
Figure 143357DEST_PATH_IMAGE028
indicating the optimal cluster number. Thus, the total energy consumption of the ring can be expressed as
Figure 486613DEST_PATH_IMAGE029
（10）
Take formula (9) pair
Figure 456843DEST_PATH_IMAGE030
Derivative of (1), optimal number of clusters being
Figure 497218DEST_PATH_IMAGE031
（11）
Wherein
Figure 199595DEST_PATH_IMAGE032
,
Figure 92465DEST_PATH_IMAGE033
And
Figure 538490DEST_PATH_IMAGE034
Figure 934836DEST_PATH_IMAGE035
likewise, the best cluster number can be found in other rings. In the general case, the total energy consumption is, for the ith ring
Figure 870431DEST_PATH_IMAGE036
Then, each ring is averagely divided into a plurality of sectors according to the optimal cluster number, each sector is a cluster, and a fuzzy logic controller is adopted to perform CH selection on the cluster.
The fuzzy controller not only has fuzzy logic reasoning capability, but also can continuously improve and adjust through system self-adaptation, thereby achieving better control effect. The fuzzy controller system decides the "chance" to be CH based on the residual energy, node centrality and distance to BS and decides the "hop count" to the next CH based on the residual energy, data length and distance to BS. To our knowledge, this is the first time that fuzzy logic is used to determine the hop count.
The fuzzy controller comprises fuzzy controller input and output variable fuzzification and fuzzy rule definition and defuzzification. Input parameters of the fuzzy controller are 'residual energy', 'node centrality', 'distance to BS' and 'data length', and output parameters are 'opportunity' and 'hop count'. The following is a detailed description of the fuzzy controller input and output variable fuzzification and fuzzy rule definition and disambiguation.
(1) The linguistic variables of the residual energy are 'low', 'medium' and 'high', wherein the 'low' and the 'high' adopt trapezoidal membership functions, and the 'medium' adopts triangular membership functions.
(2) The linguistic variables of the node centrality are 'close', 'adequate' and 'far', wherein the 'close' and the 'far' adopt trapezoidal membership functions, and the 'adequate' adopts triangular membership functions.
(3) Linguistic variables of the distance from the BS are distance, reaccable and nerby, wherein the distance and the nerby adopt trapezoidal membership functions, and the reaccable adopts triangular membership functions.
(4) Linguistic variables of the data length are big, medium and little, wherein the big and the little adopt a trapezoidal membership function, and the medium adopts a triangular membership function.
(4) The linguistic variables of "opportunity" are "very low", "rate low", "low medium", "high medium", "rater high", "very high". "very low" and "very high" adopt the function of trapezoidal membership, the others adopt the function of triangular membership.
(5) The linguistic variables of the "hop count" are "very little", "little", "rate little", "low", "medium", "high medium", "rate more", "more", "top more", wherein the "very little" and the "top more" adopt a trapezoidal membership function, and the rest adopt a triangular membership function.
(6) 27 if-then rules are used in the fuzzy inference, and finally, the output of the fuzzy inference is deblurred by using a centroid method based on the linguistic variables to obtain a clear value of the hop count. The fuzzy rule table is as follows:
Figure 188280DEST_PATH_IMAGE037
in the multi-hop routing stage, the multi-hop routing mechanism of the FASC is different from the hop-by-hop routing mechanism in the conventional protocol, and is used for forwarding data to the BS. The member nodes of the FASC transmit data to their CH during their time periods, which aggregates and transmits data to the optimal intermediate CH based on their "hop count" with the largest remaining energy and the least number of member nodes. The multihop routing is explained in detail below.
Figure 969154DEST_PATH_IMAGE038
The number of nodes of the network is first 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 in the initial working phase of the network, but the number of surviving nodes in the network starts to decrease at different rotation times of the CH nodes due to the different workloads of the nodes in the LEACH algorithm, the algorithm of the TSTCS and the AEBDC algorithm. TSTCS has more surviving nodes than LEACH. Furthermore, AEBDC also takes into account the remaining energy and cluster center distance, resulting in more efficient CHs than TSTCS. Thus, AEBDC has more surviving nodes than TSTCS. In the FASC, data forwarding of different hop counts is performed according to the data length, so that the energy consumption of CHs can be balanced, and factors such as residual energy, node centrality, data length, distance to a BS and the like are considered when CHs are selected, so that the optimal node becomes CHs. Therefore, FASC has the highest number of surviving nodes.
Second, the network energy consumption is tested to evaluate the energy efficiency of the network and to consider 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, due to the cluster and multi-hop communication manner in which the fastc, AEBDC and TSTCS are uniformly distributed in the ring sector, their network energy consumption is lower than LEACH. Furthermore, 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, and thus is superior to TSTCS. FASC utilizes fuzzy logic to select the optimal hop count and calculate the appropriate hop count, which is best in terms of network energy consumption.
And finally, testing the FND, the HND and the LND, and evaluating the life cycle of the network, wherein the FND represents the death round number of the first node, the HND represents the death round number of the general node, and the LND represents the death round number of the last node. The results are shown in FIG. 13. LEACH' FND occurred at round 239, HND at round 445, and LND at round 1164. Meanwhile, FNDs of TSTCS, AEBDC and FASC are 488, 677 and 833 rounds, HNDs are 701, 899 and 1140 rounds, respectively, and LNDs are 1699, 1732 and 2511 rounds, respectively. FASC has the longest network life because it divides the network into annular sectors according to the calculated optimal cluster, and uses the fuzzy logic controller with four descriptors to select the optimal CHs, and forwards the data with proper hop count, thus not only alleviating the hot spot problem, but also balancing the energy consumption, and greatly prolonging the lifetime of the network.

Claims (5)

1. A wireless sensor network fan-out routing protocol based on fuzzy logic is characterized by comprising four parts, namely a network model, optimal cluster number determination, fuzzy logic CH selection, hop number calculation and multi-hop routing; the network model is specifically an annular network, the base station 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; because the outermost ring node does not undertake the data forwarding task and consumes different energy, the optimal cluster numbers of the outermost ring and the inner ring are respectively determined; the fuzzy control system determines the chance of becoming CH and the hop count of transmitting and forwarding data according to four parameters of residual energy, data length, node centrality and distance from a base station; multi-hop routing is used to forward data to the BS, with member nodes sending data to their CH for their time periods, and the CH sending data based on its "number of hops" to the optimal intermediate CH for the largest remaining energy and the fewest member nodes.
2. The fuzzy logic-based wireless sensor network fan-out routing protocol of claim 1, wherein the network model is a ring network, the base station BS is located at the center, the radius is R, N nodes are uniformly distributed in a target area, each node has a unique ID, the position of the node does not change after the network initialization, the BS sends the position information of the node to all the sensor nodes, the data of all the CMs are fused by CH, and only the CH is allowed to communicate with the BS; calculating the energy consumption of the nodes by adopting a free space model, wherein the energy consumption comprises energy consumed by data sending, data receiving and data fusion; energy consumed by data transmitted or received between two nodes with a distance d is represented by equations (1), (2) and (3):
Figure FDA0002525414560000011
Erx＝Eelec*l (2)
Figure FDA0002525414560000012
wherein E iselecIs the energy consumed by the sensor node when transmitting or receiving 1bit data,fsis an amplification parameter when a free space model is adopted,mpis an amplification parameter when a multipath attenuation model is used, d0Is a distance threshold, fuse NiThe energy consumed by the data sent by each sensor node is EDAThe expression is as follows:
EDA＝l*EpDb(4)
wherein E ispDbThe energy consumption of the wireless sensor network comprises the energy consumption required by the CH for receiving data sent by the CM, the energy consumption required by forwarding the data in the previous ring, the energy consumption required by fusing received data, the energy consumption required by sending the data to the next hop CH and the energy consumed by the CM for communication.
3. The fuzzy logic-based wireless sensor network sectored routing protocol of claim 1, wherein: the optimal cluster number determination is to calculate the optimal cluster number by taking the minimum energy consumption of each ring as a target, based on the number, each ring is averagely divided into corresponding grid numbers, each grid is a cluster, and each cluster is a cluster head, namely the cluster head number is determined; obtaining the optimal cluster number of each ring by derivation calculation according to the outermost ring and inner ring energy consumption formulas respectively; in a ring network, the energy consumption of the last ring is different from the energy consumption of the other rings without data forwarding, and can be expressed as
Ech＝l×Eelec×(Nn-1)+l×Eda×(Nn-1)+(l×Eelec+l×fs×d2 ch)Nn(5)
Where l is the length of the data, NnIs the number of clusters, dchIs the distance to the next hop CH, which follows the free space model, A (x)n,yn),B(xn-1,yn-1) And C is CH in the last ring, then dch＜d0(ii) a In addition, there are dch＜rc(Cluster radius) for proper data transfer, d when BC line is perpendicular to tangent line zchIs that
Figure FDA0002525414560000021
Minimum value
Figure FDA0002525414560000022
At the same time, the energy consumption of the network can be expressed as
Ecm＝(Nn-1)(l×Eelec+l×fs×d2 cc) (7)
Wherein d isccIs the distance between the member node and the CH, and can be represented by the expected value of its square
Figure FDA0002525414560000023
Wherein d iscIs the maximum dccD is obtained from the cosine theoremc
Figure FDA0002525414560000024
In the formula (I), the compound is shown in the specification,
Figure FDA0002525414560000025
is the corresponding central angle, mnIndicates the optimal cluster number, and therefore, the total energy consumption of the ring can be expressed as
Etotal＝mn×(Ech+Ecm) (10)
Taking formula (9) to mnDerivative of, optimal number of clusters
Figure FDA0002525414560000026
Wherein
Figure FDA0002525414560000031
And
A＝mn×l[Nn(3Eelec+Eda+fs×d2 ch+fs×d2 cc)-fs×d2 cc-2Eelec-Eda]
Figure FDA0002525414560000032
similarly, the optimal cluster number can be found in other rings, and in general, the total energy consumption is
Figure FDA0002525414560000033
Then, each ring is averagely divided into a plurality of sectors according to the optimal cluster number, each sector is a cluster, and a fuzzy logic controller is adopted to perform CH selection on the cluster.
4. The fuzzy logic-based wireless sensor network sectored routing protocol of claim 1, wherein: the fuzzy controller not only has fuzzy logic reasoning capability, but also can be adaptively improved and adjusted through the system, thereby achieving better control effect; the fuzzy controller system decides "chance" to be CH based on remaining energy, node centrality and distance to BS, and decides "hop count" to next CH based on remaining energy, data length and distance to BS; to our knowledge, this is the first time that fuzzy logic is used to determine the hop count;
the fuzzy controller comprises fuzzy controller input and output variable fuzzification, fuzzy rule definition and fuzzy solution; input parameters of the fuzzy controller are 'residual energy', 'node centrality', 'distance to BS' and 'data length', and output parameters are 'opportunity' and 'hop count'; the following is a specific introduction to the fuzzy controller input and output variable fuzzification and fuzzy rule definition and defuzzification;
(1) the linguistic variables of the residual energy are 'low', 'medium' and 'high', wherein the 'low' and the 'high' adopt trapezoidal membership functions, and the 'medium' adopts triangular membership functions;
(2) the linguistic variables of the node centrality are 'close', 'adequate' and 'far', wherein the 'close' and the 'far' adopt trapezoidal membership functions, and the 'adequate' adopts triangular membership functions;
(3) the linguistic variables of the distance from the BS are distance, reaccable and nerby, wherein the distance and the nerby adopt trapezoidal membership functions, and the reaccable adopts triangular membership functions;
(4) the linguistic variables of the data length are big, medium and little, wherein the big and the little adopt a trapezoidal membership function, and the medium adopts a triangular membership function;
(5) language variables of the chance are 'try low', 'ratelow', 'low medium', 'high medium', 'rater high', 'try low' and 'try high' adopting a trapezoidal membership function, and the rest adopting a triangular membership function;
(6) the linguistic variables of the 'hop count' are 'very little', 'rate little', 'low medium', 'high medium', 'rate more', 'more' and 'top more', wherein the 'very little' and the 'top more' adopt trapezoidal membership functions, and the rest adopt triangular membership functions;
(6) 27 if-then rules are used in the fuzzy inference, and finally, the output of the fuzzy inference is deblurred by using a centroid method based on the linguistic variables to obtain a clear value of the hop count, wherein a fuzzy rule table is as follows:
Figure FDA0002525414560000041
5. the fuzzy logic-based wireless sensor network sectored routing protocol of claim 1, wherein: in the multi-hop routing, in the routing steady-state stage, a multi-hop routing mechanism in the FASC is different from a hop-by-hop routing mechanism in the traditional protocol and is used for forwarding data to the BS; the member nodes of the FASC transmit data to the CH thereof in the time period, and the CH transmits data to the optimal intermediate CH with the maximum residual energy and the minimum number of the member nodes based on the hop count thereof; the multihop routing is described in detail below:
Figure FDA0002525414560000042
Figure FDA0002525414560000051
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