CN113395660A - WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree - Google Patents

WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree Download PDF

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CN113395660A
CN113395660A CN202110679745.5A CN202110679745A CN113395660A CN 113395660 A CN113395660 A CN 113395660A CN 202110679745 A CN202110679745 A CN 202110679745A CN 113395660 A CN113395660 A CN 113395660A
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CN113395660B (en
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魏倩
白可
郭睿杰
李军伟
周林
金勇�
胡振涛
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Henan University
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    • H—ELECTRICITY
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
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    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
    • 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
    • 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
    • H04W40/12—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
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Abstract

The invention provides a self-adaptive position updating energy consumption optimization method of WSNs mobile sink nodes based on trees, wherein the mobile sink nodes move according to a specific motion model, and the method comprises the following steps: firstly, determining an ellipse position updating area according to the motion parameters of the mobile sink node, thereby determining a meeting point selection threshold; secondly, constructing a rendezvous point set and a non-rendezvous point set according to the rendezvous point selection threshold; then according to a LEACH algorithm basic framework, cluster head selection is carried out on the non-rendezvous point set; finally, according to the combination of the rest energy and the transmission energy consumption of the cluster heads, a father node selection target function is constructed, and a next-hop father node is selected for each cluster head, so that a node-cluster head-rendezvous point-mobile rendezvous node transmission path tree is constructed; repeating the process until all the nodes die; the method comprehensively considers the factors of the movement parameter change of the mobile sink node, the directionality of data transmission, the energy consumption of the node and the like, prolongs the service life of the network, reduces the data delay and balances the network load.

Description

WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree
Technical Field
The invention belongs to the technical field of wireless sensor network communication, and particularly relates to a self-adaptive position updating energy consumption optimization method for WSNs (wireless sensor networks) mobile convergent nodes based on a tree.
Background
Wireless Sensor Networks (WSNs) are composed of thousands of Sensor nodes. With the rapid development of wireless communication technology, wireless sensor networks have a larger application space. The sensor nodes can be deployed in many areas without human being or with bad living conditions, and the collection of the monitoring data of the sensor nodes brings great challenges. The introduction of mobile aggregation nodes provides great help for the collection of monitoring data. Therefore, aiming at the introduction of the mobile aggregation node, how to balance the network energy consumption, prolong the network service life and reduce the network data delay is also an important research topic.
In the WSNs, the energy carried by the sensor nodes is limited, and cannot be supplemented in time, so that once the energy is exhausted, the topological structure of the network nodes is changed, the monitoring performance is reduced, and the life cycle of the whole network is also reduced. WSNs can be divided into various types according to the types of nodes in sensor networks, including static WSNs (static WSNs) and mobile WSNs (mobile WSNs). The sensor nodes in the Mobile WSNs can move after being distributed, and the introduction of the Mobile Sink node (Mobile Sink) can effectively alleviate the problems. Many scholars have proposed many relevant routing algorithms for energy optimization problems in wireless sensor networks. The most classical Clustering routing algorithm is a Low power Adaptive Clustering routing protocol (LEACH) proposed by Heinzelman et al, which proposes the idea of performing process rotation and network node Clustering for the first time, divides the whole process into several turns, and each complete working process is one turn. Wherein, each round comprises two stages: a cluster establishment phase and a stable data communication phase. In the cluster establishing stage, cluster heads are selected randomly mainly through a cluster head selection threshold, and a cluster head set and a non-cluster head set are determined. Then, the cluster head nodes broadcast the information which becomes the cluster heads, and the non-cluster head nodes select the cluster head nodes with the strongest signals according to the signal intensity of each received cluster head node to establish a plurality of clusters. Secondly, the members in the cluster send the monitoring data to the corresponding cluster head nodes, the cluster head nodes perform fusion processing on the received data and send the fused data to the sink nodes, and certain effects on saving network energy consumption and prolonging network service life are achieved.
The classic LEACH clustering routing algorithm establishes a basic framework of the clustering routing algorithm and has good significance for the clustering routing algorithm aiming at energy consumption optimization in the wireless sensor network. In the existing clustering routing algorithm, the following problems exist:
1, a mobile sink node needs to frequently inform new position information of a sensor node, so that the energy consumption of the sensor node is overlarge;
2, the mobility of the mobile sink node causes the topology structure of the data transmission network to change continuously, and the energy consumption of each sensor node in the network is unbalanced and the data is delayed;
and 3, the cluster head node sends data to the mobile sink node in a single-hop mode, and part of cluster heads are far away and are easy to die prematurely.
Disclosure of Invention
The invention aims to provide a self-adaptive position updating energy consumption optimization method of WSNs mobile sink nodes based on trees, which reduces data delay, prolongs the service life of a network and reduces the energy consumption of the network by considering factors such as local position updating areas of the mobile sink nodes, the directionality of data transmission, multi-hop transmission paths, the residual energy of cluster head nodes and the like.
The technical scheme for solving the technical problems of the invention is as follows: the WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on the tree comprises the following steps:
s1: setting parameters:
setting wireless sensor network monitoring area SMThe mobile sink node is an L multiplied by L square area, and the mobile sink node is located in the area and moves according to a specific motion model; n sensor nodes are randomly deployed in the wireless sensor network, form a sensor node set and are recorded as S ═ S1,…,si,…,sN}; 1,2, …, N; each sensor node siAfter deployment, the position is not changed any more, and the initial energy is the same as E0(ii) a Selecting the expected probability of the cluster head as p; the maximum number of running wheels is rmax(ii) a The optimal number of the rendezvous points is n;
s2: dividing a mobile sink node position updating area:
according to the number N of the optimal meeting points, the total number N of the sensor nodes and the area S of the network monitoring area in the step S1MObtaining the area S of the ideal mobile sink node position updating aream(ii) a Back focus F using current time position of mobile sink node as ellipse1(Jx(t),Jy(t)), calculating the front focus F of the ellipse of the location update area according to the motion state information (location and speed) of the current location of the mobile sink node2(J′x(t),J′y(t)), a semi-focal length c, a major semi-axis a, a minor semi-axis b;
s3: f represents the front and rear focal coordinates of the ellipse at time t obtained in step S22(J′x(t),J′y(t)) and F1(Jx(t),Jy(t)), F, combined with moving the sink node's position coordinates at time t + Δ t1′(Jx(t+Δt),Jy(T + Δ T)), the ellipse update threshold T at the time T + Δ T is calculatedarea(t + Δ t) ═ 1; when T isareaWhen the (t + delta t) is 1, constructing a new elliptical area according to the motion state of the mobile sink node at the time of t + delta t; otherwise, the ellipse region reconstruction is not carried out;
s4: selecting a rendezvous point according to the division of the mobile convergence position updating area:
calculating a meeting point selection threshold T according to the semi-focal length c, the major semi-axis a and the minor semi-axis b of the mobile sink node elliptical position updating area obtained in the steps S2 and S3rp(si) (ii) a When T isrp(si) 1, node siAdding a rendezvous point set R; otherwise, adding a non-rendezvous point set R';
s5: selecting a cluster head:
obtaining a non-convergent point set R' according to the step S4, calculating a node S by using a basic architecture of a basic classical clustering algorithm LEACHi(siE R') cluster head selection threshold T(s)i) (ii) a Each node siGenerate an equal distribution in [0,1 ]]Random number T in betweenrand(si). If T isrand(si) Less than cluster head selection threshold T(s)i) Then node siAdding a cluster head set C when the current round is selected as a cluster head; otherwise, node siAdding a non-cluster head set C' for the non-cluster head node;
s6: formation of clusters:
according to the cluster head set C obtained in the step S6, each cluster head broadcasts a message of becoming a cluster head in the whole monitoring area, and non-cluster-head nodes S are calculatedp(p e C') to each cluster head node sqDistance set of (q ∈ C)
Figure BDA0003122417530000041
Set DpqAnd q corresponding to the minimum value of the element is recorded as a node s away from the non-cluster headpNearest cluster head, by comparing distance sets DpqElement derived off-non-cluster-head node spOf the nearest cluster headThe distance between them is recorded as dscminThen the non-cluster head node sp(p belongs to C') adding the cluster where the cluster head node q nearest to the cluster head node q is located;
s7: constructing a path tree:
when the cluster head node sends data to the mobile sink node, the data can be forwarded to the rendezvous point through other cluster head nodes in the wireless sensor network; the rendezvous point receives the position information of the mobile rendezvous point and finally forwards the data to the mobile rendezvous point, namely a routing tree taking the mobile rendezvous point as a root node is formed;
s8: the cluster internal nodes of each cluster send the data monitored by the cluster internal nodes to the cluster head nodes of the cluster, and the cluster head nodes receive the data transmitted by a plurality of cluster member nodes and perform fusion processing on the data;
s9: each cluster head node in the WSNs sends the data subjected to fusion processing to the next hop node of the WSNs;
s10: repeating the steps S2 to S10 until the preset running wheel number r is reachedmaxOr the total node residual energy is 0 joules.
The area S of the ideal region where the meeting points are distributed in the step S2mThe following method is adopted for calculation:
n/N＝Sm/SM
wherein N is the number of the optimal rendezvous points, the total number of the nodes of the N sensors, and SMMonitoring the area of the area for the network;
the method for calculating the elliptical half focal length c in step S2 includes:
c＝1/2|F1F2|＝λv
wherein lambda is a half-focal length c speed weight coefficient, the speed of the mobile sink node is v, and two focus coordinates of the ellipse are respectively F1(Jx(t),Jy(t)),F2(J′x(t),J′y(t))；
In step S2, the method for calculating the major axis a and the minor axis b includes:
Figure BDA0003122417530000051
Figure BDA0003122417530000061
wherein, lambda is a half-focal length c speed weight coefficient, and the speed of the mobile sink node is v, SmThe area of the region is updated for the mobile sink node elliptical position.
The elliptical location update area update threshold T in the step S3area(t + Δ t) was calculated as follows:
Figure BDA0003122417530000062
in the formula (I), the compound is shown in the specification,
d(t+Δt)＝|F1′F1|+|F1′F2|
wherein a is the major axis of the elliptical region at time t; mu is a region updating weight coefficient; the front and rear focal coordinates of the ellipse at time t are respectively F2(J′x(t),J′y(t)) and F1(Jx(t),Jy(t)); moving F of the position coordinates of the sink node at time t + Δ t1′(Jx(t+Δt),Jy(t + Δ t)). When T isareaWhen the (t + delta t) is 1, constructing a new elliptical area according to the motion state of the mobile sink node at the time of t + delta t; otherwise, the elliptical region reconstruction is not performed.
The meeting point selection threshold T in the step S4rp(si) The following method is adopted for calculation:
Figure BDA0003122417530000063
in the formula (I), the compound is shown in the specification,
di＝|PF1|+|PF2|
wherein, the front and back focal coordinates of the ellipse at the time t are respectively F2(J′x(t),J′y(t)) and F1(Jx(t),Jy(t))，
Figure BDA0003122417530000064
Are sensor node coordinates.
The cluster head selection threshold T (S) in the step S5i) The following method is adopted for calculation:
Figure BDA0003122417530000071
wherein p is the expected probability that the number of cluster heads required in each round accounts for the total number of all nodes in the network; r is the current running wheel number; g represents a node set which does not select a cluster head in the last 1/p round; mod denotes a modulo operation.
The distance from the non-cluster-head node to each cluster-head node in the step S6
Figure BDA0003122417530000072
The following method is adopted for calculation:
Figure BDA0003122417530000073
wherein (x)s(p),ys(p)) is a sensor node sp(x) of (C)c(q),yc(q)) is the coordinates of cluster head node q.
The step S7 specifically includes:
s7.1: sorting cluster head nodes in the wireless sensor network from near to far according to the distance between the cluster head nodes and an ellipse central point O according to the local position updating area of the mobile sink node obtained in the step S2 and the step S3 to form a cluster head node set S sorted from near to far;
s7.2: step S4 obtains the rendezvous point set R, rendezvous point Si(siE.g. R) is directly connected with the mobile sink node to form a first layer of branches, and an optional father node set Z is added;
s7.3: obtaining a cluster head node set S and an optional father node set Z which are sequenced from the steps S7.1 and S7.2, sequentially selecting a father node for each cluster head node in the cluster head node set S and connecting the father node to a tree, wherein except for a mobile sink node, the optional father nodes of other cluster head nodes in the wireless sensor network belong to the optional father node set Z which is formed by other cluster head nodes closer to a central point in an elliptical position updating area than the self and a meeting point; specifically, the next nearest cluster head node constructs an optimal efficiency objective function f (A, Z) for all selectable father nodes in a selectable father node set Z according to a next hop energy factor E (j), a next hop path energy consumption factor P (j) and a next hop path energy consumption factor PP (j), wherein A is an efficiency matrix formed by all selectable father nodes, Z is a secondary solution matrix of the selectable father nodes, j is a jth selectable father node, and j belongs to Z;
s7.4: repeating the step S7.2 and the step S7.3 until all cluster head nodes in the wireless sensor network are connected to the routing tree, namely the construction of the routing tree is completed;
in the step S7.3, the next-hop energy factor e (j), the next-hop path energy consumption factor p (j), and the next-hop path energy consumption factor pp (j) are calculated by the following method:
Figure BDA0003122417530000081
wherein E iscur(j) The current remaining energy of the jth optional parent node; eavgAverage remaining energy for all optional parent nodes;
in the step S7.3, the energy consumption factor p (j) of the next hop path is calculated by the following method:
Figure BDA0003122417530000082
wherein d isi,jIs a cluster head CHiWith the jth optional parent node CHi jThe transmission distance of (a); e (d)i,j) Is a cluster head CHiWith the jth optional parent node CHi jEnergy consumption of data transmission; djmaxIs a cluster head CHiAnd optional fatherNode CHi jThe maximum transmission distance of; e (d)jmax) Is a cluster head CHiAnd an optional parent node CHi jMaximum energy consumption for data transmission;
in the step S7.3, the path energy consumption factor pp (j) of the next hop is calculated by the following method:
Figure BDA0003122417530000083
wherein, dj,pThe transmission distance between the optional father node and the self father node; e (d)j,p) The energy consumption for data transmission between the selectable father node and the next-hop father node;
in the step S7.3, the optimal performance objective function f (a, Z) is constructed by all the selectable father nodes in the selectable father node set Z by using the following method:
objective function
Figure BDA0003122417530000091
Constraint 1Tz＝1
1≤j≤k
0<α,β,δ＜1
Ecur(j)≤E0
di,j≤djmax
dj,p≤djpmax
In the formula (I), the compound is shown in the specification,
A＝[a1 a2 … aj]
aj＝αE(j)+βP(j)+δPP(j)
wherein, ajComposed of next-hop energy factor E (j), next-hop path energy consumption factor P (j) and next-hop path energy consumption factor PP (j), where alpha, beta, and delta are weight coefficients, E0Is the node initial energy.
The invention has the beneficial effects that: by the technical scheme, the invention provides the WSNs improved clustering energy consumption optimization method based on the mobile sink nodes aiming at the problems of unbalanced energy consumption, overlarge energy consumption and the like of the existing routing algorithm with the mobile sink nodes.
Firstly, the invention comprehensively considers the motion parameters (speed and distance) of the mobile sink node, constructs the elliptical area where the rendezvous point is located, so that the node close to the mobile sink node becomes the rendezvous point, replaces the mobile sink node to receive data, relieves the hot spot problem of data transmission, and prolongs the service life of the network;
secondly, an adaptive position updating threshold is constructed, the design of the threshold fully considers the motion change parameters (speed and distance) of the mobile sink node and the continuity of data transmission, the stability of data transmission is improved, and the service life of the network is effectively prolonged;
and finally, based on an LEACH framework, cluster head selection is carried out, meanwhile, residual energy of the cluster heads and energy consumption of transmission paths are introduced into a path tree construction mechanism, an inter-cluster transmission optimal father node selection objective function is designed, the number of dead network nodes is effectively reduced, and network loads are balanced.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram of a multi-hop transmission path according to the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention comprises the steps of:
s1: setting parameters:
setting wireless sensor network monitoring area SMThe mobile sink node is an L multiplied by L square area, and the mobile sink node is located in the area and moves according to a specific motion model; n sensor nodes are randomly deployed in the wireless sensor network, form a sensor node set and are recorded as S ═ S1,…,si,…,sN}; 1,2, …, N; each sensor node siDeployingAfter that, the position is not changed any more, and the initial energy is the same as E0(ii) a Selecting the expected probability of the cluster head as p; the maximum number of running wheels is rmax(ii) a The optimal number of the rendezvous points is n;
s2: dividing a mobile sink node position updating area:
according to the number N of the optimal meeting points, the total number N of the sensor nodes and the area S of the network monitoring area in the step S1MObtaining the area S of the ideal mobile sink node position updating aream(ii) a Back focus F using current time position of mobile sink node as ellipse1(Jx(t),Jy(t)), calculating the front focus F of the ellipse of the location update area according to the motion state information (location and speed) of the current location of the mobile sink node2(J′x(t),J′y(t)), a semi-focal length c, a major semi-axis a, a minor semi-axis b;
s3: f represents the front and rear focal coordinates of the ellipse at time t obtained in step S22(J′x(t),J′y(t)) and F1(Jx(t),Jy(t)), F, combined with moving the sink node's position coordinates at time t + Δ t1′(Jx(t+Δt),Jy(T + Δ T)), the ellipse update threshold T at the time T + Δ T is calculatedarea(t + Δ t) ═ 1; when T isareaWhen the (t + delta t) is 1, constructing a new elliptical area according to the motion state of the mobile sink node at the time of t + delta t; otherwise, the ellipse region reconstruction is not carried out;
s4: selecting a rendezvous point according to the division of the mobile convergence position updating area:
calculating a meeting point selection threshold T according to the semi-focal length c, the major semi-axis a and the minor semi-axis b of the mobile sink node elliptical position updating area obtained in the steps S2 and S3rp(si) (ii) a When T isrp(si) 1, node siAdding a rendezvous point set R; otherwise, adding a non-rendezvous point set R';
s5: selecting a cluster head:
obtaining a non-convergent point set R' according to the step S4, calculating a node S by using a basic architecture of a basic classical clustering algorithm LEACHi(siE.g. R') ofCluster head selection threshold T(s)i) (ii) a Each node siGenerate an equal distribution in [0,1 ]]Random number T in betweenrand(si). If T isrand(si) Less than cluster head selection threshold T(s)i) Then node siAdding a cluster head set C when the current round is selected as a cluster head; otherwise, node siAdding a non-cluster head set C' for the non-cluster head node;
s6: formation of clusters:
according to the cluster head set C obtained in the step S6, each cluster head broadcasts a message of becoming a cluster head in the whole monitoring area, and non-cluster-head nodes S are calculatedp(p e C') to each cluster head node sqDistance set of (q ∈ C)
Figure BDA0003122417530000121
Set DpqAnd q corresponding to the minimum value of the element is recorded as a node s away from the non-cluster headpNearest cluster head, by comparing distance sets DpqElement derived off-non-cluster-head node spThe distance between nearest cluster heads is denoted dscminThen the non-cluster head node sp(p belongs to C') adding the cluster where the cluster head node q nearest to the cluster head node q is located;
s7: constructing a path tree:
when the cluster head node sends data to the mobile sink node, the data can be forwarded to the rendezvous point through other cluster head nodes in the wireless sensor network; the rendezvous point receives the position information of the mobile rendezvous point and finally forwards the data to the mobile rendezvous point, namely a routing tree taking the mobile rendezvous point as a root node is formed;
s8: the cluster internal nodes of each cluster send the data monitored by the cluster internal nodes to the cluster head nodes of the cluster, and the cluster head nodes receive the data transmitted by a plurality of cluster member nodes and perform fusion processing on the data;
s9: each cluster head node in the WSNs sends the data subjected to fusion processing to the next hop node of the WSNs;
s10: repeating the steps S2 to S10 until the preset running wheel number r is reachedmaxOr total node residual energyIs 0 joules.
The area S of the ideal region where the meeting points are distributed in the step S2mThe following method is adopted for calculation:
n/N＝Sm/SM
wherein N is the number of the optimal rendezvous points, the total number of the nodes of the N sensors, and SMMonitoring the area of the area for the network;
the method for calculating the elliptical half focal length c in step S2 includes:
c＝1/2|F1F2|＝λv
wherein lambda is a half-focal length c speed weight coefficient, the speed of the mobile sink node is v, and two focus coordinates of the ellipse are respectively F1(Jx(t),Jy(t)),F2(J|x(t),J|y(t))；
In step S2, the method for calculating the major axis a and the minor axis b includes:
Figure BDA0003122417530000131
Figure BDA0003122417530000132
wherein, lambda is a half-focal length c speed weight coefficient, and the speed of the mobile sink node is v, SmThe area of the region is updated for the mobile sink node elliptical position.
The elliptical location update area update threshold T in the step S3area(t + Δ t) was calculated as follows:
Figure BDA0003122417530000133
in the formula (I), the compound is shown in the specification,
d(t+Δt)＝|F1′F1|+|F1′F2|
wherein a is the major axis of the elliptical region at time t; mu is a region updating weight coefficient;the front and rear focal coordinates of the ellipse at time t are respectively F2(J′x(t),J′y(t)) and F1(Jx(t),Jy(t)); moving F of the position coordinates of the sink node at time t + Δ t1′(Jx(t+Δt),Jy(t + Δ t)). When T isareaWhen the (t + delta t) is 1, constructing a new elliptical area according to the motion state of the mobile sink node at the time of t + delta t; otherwise, the elliptical region reconstruction is not performed.
The meeting point selection threshold T in the step S4rp(si) The following method is adopted for calculation:
Figure BDA0003122417530000134
in the formula (I), the compound is shown in the specification,
di＝|PF1|+|PF2|
wherein, the front and back focal coordinates of the ellipse at the time t are respectively F2(J′x(t),J′y(t)) and F1(Jx(t),Jy(t))，
Figure BDA0003122417530000141
Are sensor node coordinates.
The cluster head selection threshold T (S) in the step S5i) The following method is adopted for calculation:
Figure BDA0003122417530000142
wherein p is the expected probability that the number of cluster heads required in each round accounts for the total number of all nodes in the network; r is the current running wheel number; g represents a node set which does not select a cluster head in the last 1/p round; mod denotes a modulo operation.
The distance from the non-cluster-head node to each cluster-head node in the step S6
Figure BDA0003122417530000143
The following method is adopted for calculation:
Figure BDA0003122417530000144
wherein (x)s(p),ys(p)) is a sensor node sp(x) of (C)c(q),yc(q)) is the coordinates of cluster head node q.
The step S7 specifically includes:
s7.1: sorting cluster head nodes in the wireless sensor network from near to far according to the distance between the cluster head nodes and an ellipse central point O according to the local position updating area of the mobile sink node obtained in the step S2 and the step S3 to form a cluster head node set S sorted from near to far;
s7.2: step S4 obtains the rendezvous point set R, rendezvous point Si(siE.g. R) is directly connected with the mobile sink node to form a first layer of branches, and an optional father node set Z is added;
s7.3: obtaining a cluster head node set S and an optional father node set Z which are sequenced from the steps S7.1 and S7.2, sequentially selecting a father node for each cluster head node in the cluster head node set S and connecting the father node to a tree, wherein except for a mobile sink node, the optional father nodes of other cluster head nodes in the wireless sensor network belong to the optional father node set Z which is formed by other cluster head nodes closer to a central point in an elliptical position updating area than the self and a meeting point; specifically, the next nearest cluster head node constructs an optimal efficiency objective function f (A, Z) for all selectable father nodes in a selectable father node set Z according to a next hop energy factor E (j), a next hop path energy consumption factor P (j) and a next hop path energy consumption factor PP (j), wherein A is an efficiency matrix formed by all selectable father nodes, Z is a secondary solution matrix of the selectable father nodes, j is a jth selectable father node, and j belongs to Z;
s7.4: repeating the step S7.2 and the step S7.3 until all cluster head nodes in the wireless sensor network are connected to the routing tree, namely the construction of the routing tree is completed;
in the step S7.3, the next-hop energy factor e (j), the next-hop path energy consumption factor p (j), and the next-hop path energy consumption factor pp (j) are calculated by the following method:
Figure BDA0003122417530000151
wherein E iscur(j) The current remaining energy of the jth optional parent node; eavgAverage remaining energy for all optional parent nodes;
in the step S7.3, the energy consumption factor p (j) of the next hop path is calculated by the following method:
Figure BDA0003122417530000152
wherein d isi,jIs a cluster head CHiWith the jth optional parent node CHi jThe transmission distance of (a); e (d)i,j) Is a cluster head CHiWith the jth optional parent node CHi jEnergy consumption of data transmission; djmaxIs a cluster head CHiAnd an optional parent node CHi jThe maximum transmission distance of; e (d)jmax) Is a cluster head CHiAnd an optional parent node CHi jMaximum energy consumption for data transmission;
in the step S7.3, the path energy consumption factor pp (j) of the next hop is calculated by the following method:
Figure BDA0003122417530000161
wherein, dj,pThe transmission distance between the optional father node and the self father node; e (d)j,p) The energy consumption for data transmission between the selectable father node and the next-hop father node;
in the step S7.3, the optimal performance objective function f (a, Z) is constructed by all the selectable father nodes in the selectable father node set Z by using the following method:
objective function
Figure BDA0003122417530000162
Constraint 1Tz＝1
1≤j≤k
0<α,β,δ＜1
Ecur(j)≤E0
di,j≤djmax
dj,p≤djpmax
In the formula (I), the compound is shown in the specification,
A＝[a1 a2 … aj]
aj＝αE(j)+βP(j)+δPP(j)
wherein, ajComposed of next-hop energy factor E (j), next-hop path energy consumption factor P (j) and next-hop path energy consumption factor PP (j), where alpha, beta, and delta are weight coefficients, E0Is the node initial energy.
The invention has the beneficial effects that: by the technical scheme, the invention provides the WSNs improved clustering energy consumption optimization method based on the mobile sink nodes aiming at the problems of unbalanced energy consumption, overlarge energy consumption and the like of the existing routing algorithm with the mobile sink nodes.
Firstly, the invention comprehensively considers the motion parameters (speed and distance) of the mobile sink node, constructs the elliptical area where the rendezvous point is located, so that the node close to the mobile sink node becomes the rendezvous point, replaces the mobile sink node to receive data, relieves the hot spot problem of data transmission, and prolongs the service life of the network;
secondly, an adaptive position updating threshold is constructed, the design of the threshold fully considers the motion change parameters (speed and distance) of the mobile sink node and the continuity of data transmission, the stability of data transmission is improved, and the service life of the network is effectively prolonged;
and finally, based on an LEACH framework, cluster head selection is carried out, meanwhile, residual energy of the cluster heads and energy consumption of transmission paths are introduced into a path tree construction mechanism, an inter-cluster transmission optimal father node selection objective function is designed, the number of dead network nodes is effectively reduced, and network loads are balanced.
The method of the invention reduces data delay, prolongs network service life and reduces network energy consumption by considering factors such as local position updating area of the mobile sink node, multi-hop transmission path, residual energy of cluster head node and the like.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. The WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on the tree is characterized by comprising the following steps: the method comprises the following steps:
s1: setting parameters:
setting wireless sensor network monitoring area SMThe mobile sink node is an L multiplied by L square area, and the mobile sink node is located in the area and moves according to a specific motion model; n sensor nodes are randomly deployed in the wireless sensor network, form a sensor node set and are recorded as S ═ S1,…,si,…,sN}; 1,2, …, N; each sensor node siAfter deployment, the position is not changed any more, and the initial energy is the same as E0(ii) a Selecting the expected probability of the cluster head as p; the maximum number of running wheels is rmax(ii) a The optimal number of the rendezvous points is n;
s2: dividing a mobile sink node position updating area:
according to the number N of the optimal meeting points, the total number N of the sensor nodes and the area S of the network monitoring area in the step S1MObtaining the area S of the ideal mobile sink node position updating aream(ii) a Back focus F using current time position of mobile sink node as ellipse1(Jx(t),Jy(t)), calculating the front focus F of the ellipse of the location update area according to the motion state information (location and speed) of the current location of the mobile sink node2(J′x(t),J′y(t)), a semi-focal length c, a major semi-axis a, a minor semi-axis b;
s3: f represents the front and rear focal coordinates of the ellipse at time t obtained in step S22(J′x(t),J′y(t)) and F1(Jx(t),Jy(t)), F, combined with moving the sink node's position coordinates at time t + Δ t1′(Jx(t+Δt),Jy(T + Δ T)), the ellipse update threshold T at the time T + Δ T is calculatedarea(t + Δ t) ═ 1; when T isareaWhen the (t + delta t) is 1, constructing a new elliptical area according to the motion state of the mobile sink node at the time of t + delta t; otherwise, the ellipse region reconstruction is not carried out;
s4: selecting a rendezvous point according to the division of the mobile convergence position updating area:
calculating a meeting point selection threshold T according to the semi-focal length c, the major semi-axis a and the minor semi-axis b of the mobile sink node elliptical position updating area obtained in the steps S2 and S3rp(si) (ii) a When T isrp(si) 1, node siAdding a rendezvous point set R; otherwise, adding a non-rendezvous point set R';
s5: selecting a cluster head:
obtaining a non-convergent point set R' according to the step S4, calculating a node S by using a basic architecture of a basic classical clustering algorithm LEACHi(siE R') cluster head selection threshold T(s)i) (ii) a Each node siGenerate an equal distribution in [0,1 ]]Random number T in betweenrand(si). If T isrand(si) Less than cluster head selection threshold T(s)i) Then node siAdding a cluster head set C when the current round is selected as a cluster head; otherwise, node siAdding a non-cluster head set C' for the non-cluster head node;
s6: formation of clusters:
according to the cluster head set C obtained in the step S6, each cluster head broadcasts a message of becoming a cluster head in the whole monitoring area, and non-cluster-head nodes S are calculatedp(p e C') to each cluster head node sqDistance set of (q ∈ C)
Figure FDA0003122417520000021
Set DpqAnd q corresponding to the minimum value of the element is recorded as a node s away from the non-cluster headpNearest cluster head, by comparing distance sets DpqElement derived off-non-cluster-head node spThe distance between nearest cluster heads is denoted dscminThen the non-cluster head node sp(p belongs to C') adding the cluster where the cluster head node q nearest to the cluster head node q is located;
s7: constructing a path tree:
when the cluster head node sends data to the mobile sink node, the data can be forwarded to the rendezvous point through other cluster head nodes in the wireless sensor network; the rendezvous point receives the position information of the mobile rendezvous point and finally forwards the data to the mobile rendezvous point, namely a routing tree taking the mobile rendezvous point as a root node is formed;
s8: the cluster internal nodes of each cluster send the data monitored by the cluster internal nodes to the cluster head nodes of the cluster, and the cluster head nodes receive the data transmitted by a plurality of cluster member nodes and perform fusion processing on the data;
s9: each cluster head node in the WSNs sends the data subjected to fusion processing to the next hop node of the WSNs;
s10: repeating the steps S2 to S10 until the preset running wheel number r is reachedmaxOr the total node residual energy is 0 joules.
2. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 1, wherein:
the area S of the ideal region where the meeting points are distributed in the step S2mThe following method is adopted for calculation:
n/N＝Sm/SM
wherein N is the number of the optimal rendezvous points, the total number of the nodes of the N sensors, and SMMonitoring the area of the area for the network;
the method for calculating the elliptical half focal length c in step S2 includes:
c＝1/2|F1F2|＝λv
wherein lambda is a half-focal length c speed weight coefficient, the speed of the mobile sink node is v, and two focus coordinates of the ellipse are respectively F1(Jx(t),Jy(t)),F2(J′x(t),J′y(t))；
In step S2, the method for calculating the major axis a and the minor axis b includes:
Figure FDA0003122417520000031
Figure FDA0003122417520000032
wherein, lambda is a half-focal length c speed weight coefficient, and the speed of the mobile sink node is v, SmThe area of the region is updated for the mobile sink node elliptical position.
3. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 1, wherein: the elliptical location update area update threshold T in the step S3area(t + Δ t) was calculated as follows:
Figure FDA0003122417520000033
in the formula (I), the compound is shown in the specification,
d(t+Δt)＝|F1′F1|+|F1′F2|
wherein a is the major axis of the elliptical region at time t; mu is a region updating weight coefficient; the front and rear focal coordinates of the ellipse at time t are respectively F2(J′x(t),J′y(t)) and F1(Jx(t),Jy(t)); moving F of the position coordinates of the sink node at time t + Δ t1′(Jx(t+Δt),Jy(t + Δ t)). When T isareaWhen (t + Δ t) '1', the sink is moved according to the time t + Δ tConstructing a new elliptical area by the motion state of the aggregation node; otherwise, the elliptical region reconstruction is not performed.
4. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 1, wherein: the meeting point selection threshold T in the step S4rp(si) The following method is adopted for calculation:
Figure FDA0003122417520000041
in the formula (I), the compound is shown in the specification,
di＝|PF1|+|PF2|
wherein, the front and back focal coordinates of the ellipse at the time t are respectively F2(J′x(t),J′y(t)) and F1(Jx(t),Jy(t))，
Figure FDA0003122417520000042
Are sensor node coordinates.
5. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 1, wherein: the cluster head selection threshold T (S) in the step S5i) The following method is adopted for calculation:
Figure FDA0003122417520000043
wherein p is the expected probability that the number of cluster heads required in each round accounts for the total number of all nodes in the network; r is the current running wheel number; g represents a node set which does not select a cluster head in the last 1/p round; mod denotes a modulo operation.
6. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 1, wherein: the above-mentionedDistance from non-cluster head node to each cluster head node in step S6
Figure FDA0003122417520000045
p ∈ C', q ∈ C is calculated by the following method:
Figure FDA0003122417520000044
wherein (x)s(p),ys(p)) is a sensor node sp(x) of (C)c(q),yc(q)) is the coordinates of cluster head node q.
7. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 1, wherein: the step S7 specifically includes:
s7.1: sorting cluster head nodes in the wireless sensor network from near to far according to the distance between the cluster head nodes and an ellipse central point O according to the local position updating area of the mobile sink node obtained in the step S2 and the step S3 to form a cluster head node set S sorted from near to far;
s7.2: step S4 obtains the rendezvous point set R, rendezvous point Si(siE.g. R) is directly connected with the mobile sink node to form a first layer of branches, and an optional father node set Z is added;
s7.3: obtaining a cluster head node set S and an optional father node set Z which are sequenced from the steps S7.1 and S7.2, sequentially selecting a father node for each cluster head node in the cluster head node set S and connecting the father node to a tree, wherein except for a mobile sink node, the optional father nodes of other cluster head nodes in the wireless sensor network belong to the optional father node set Z which is formed by other cluster head nodes closer to a central point in an elliptical position updating area than the self and a meeting point; specifically, the next nearest cluster head node constructs an optimal efficiency objective function f (A, Z) for all selectable father nodes in a selectable father node set Z according to a next hop energy factor E (j), a next hop path energy consumption factor P (j) and a next hop path energy consumption factor PP (j), wherein A is an efficiency matrix formed by all selectable father nodes, Z is a secondary solution matrix of the selectable father nodes, j is a jth selectable father node, and j belongs to Z;
s7.4: and repeating the step S7.2 and the step S7.3 until all cluster head nodes in the wireless sensor network are connected to the routing tree, namely the routing tree construction is completed.
8. The tree-based WSNs mobile sink node adaptive location update energy consumption optimization method of claim 7, wherein:
in the step S7.3, the next-hop energy factor e (j), the next-hop path energy consumption factor p (j), and the next-hop path energy consumption factor pp (j) are calculated by the following method:
Figure FDA0003122417520000051
wherein E iscur(j) The current remaining energy of the jth optional parent node; eavgAverage remaining energy for all optional parent nodes;
in the step S7.3, the energy consumption factor p (j) of the next hop path is calculated by the following method:
Figure FDA0003122417520000052
wherein d isi,jIs a cluster head CHiWith the jth optional parent node CHi jThe transmission distance of (a); e (d)i,j) Is a cluster head CHiWith the jth optional parent node CHi jEnergy consumption of data transmission; djmaxIs a cluster head CHiAnd an optional parent node CHi jThe maximum transmission distance of; e (d)jmax) Is a cluster head CHiAnd an optional parent node CHi jMaximum energy consumption for data transmission; in the step S7.3, the path energy consumption factor pp (j) of the next hop is calculated by the following method:
Figure FDA0003122417520000061
wherein, dj,pThe transmission distance between the optional father node and the self father node; e (d)j,p) The energy consumption for data transmission between the selectable father node and the next-hop father node;
in the step S7.3, the optimal performance objective function f (a, Z) is constructed by all the selectable father nodes in the selectable father node set Z by using the following method:
objective function
Figure FDA0003122417520000062
Constraint 1Tz＝1
1≤j≤k
0<α,β,δ＜1
Ecur(j)≤E0
di,j≤djmax
dj,p≤djpmax
In the formula (I), the compound is shown in the specification,
A＝[a1 a2…aj]
aj＝αE(j)+βP(j)+δPP(j)
wherein, ajComposed of next-hop energy factor E (j), next-hop path energy consumption factor P (j) and next-hop path energy consumption factor PP (j), where alpha, beta, and delta are weight coefficients, E0Is the node initial energy.
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