CN116193466B - Adaptive anchor point selection method for wireless sensor network - Google Patents
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Abstract
The invention discloses a self-adaptive anchor point selection method of a wireless sensor network, which comprises the steps of firstly generating wireless sensor network structure diagrams with different node numbers through a topological structure generation algorithm, and establishing a weight matrix and a connection matrix; then, an energy consumption model of the wireless sensor network is established, and the number of optimal anchor points is calculated; calculating the link packet loss rate, the buffer area residual space and the residual energy of each sensor node at different moments, so as to calculate the energy weighted average value of each sensor node; and finally, constructing a self-adaptive anchor point selection algorithm, and selecting a proper anchor point. The invention solves the problem of transmission delay caused by long data acquisition stroke of the mobile collector in mobile data acquisition in the prior art.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a self-adaptive anchor point selection method of a wireless sensor network.
Background
The wireless sensor network is a distributed network, a multi-hop self-organizing wireless network is formed by a plurality of sensors, and monitoring, acquisition and data transmission are carried out on a monitoring area in a cooperative mode. In wireless sensor networks, data acquisition is the most important one. The data acquisition means that the sensor senses the data of the monitoring area and sends the monitored data to the base station or the sink node. The data acquisition modes can be divided into static data acquisition and mobile data acquisition. Static data acquisition is the transmission of data by a sensor network to a fixed base station. The method can easily cause excessive consumption of energy of the sensor nodes which are close to the sink nodes in the wireless sensor network, and greatly reduces the efficiency of data transmission. The mobile data acquisition mode is to collect data in the marked nodes (anchor points) of the sensor network by adopting one or more mobile collectors, and divide the whole network into a plurality of sub-networks for data transmission.
In mobile data acquisition, a mobile collector performs data acquisition of the part of the sub-networks at anchor points, and the selection of the number and the positions of the anchor points is a key for influencing the transmission efficiency of the wireless sensor network. If the number of anchors is small, each anchor needs to cover more nodes, but forwarding nodes may be too far from the anchor, resulting in more energy being consumed for data transmission. If the number of anchor points is large, the data acquisition journey of the mobile collector is elongated, resulting in longer transmission delays. Therefore, how to determine the positions of the anchor points and the optimal number of anchor points are key to ensuring the optimality of mobile data acquisition.
Disclosure of Invention
The invention aims to provide a self-adaptive anchor point selection method of a wireless sensor network, which solves the problem of transmission delay caused by long data acquisition travel of a mobile collector in mobile data acquisition in the prior art.
The technical scheme adopted by the invention is that the self-adaptive anchor point selection method of the wireless sensor network is implemented according to the following steps:
step 1, generating a wireless sensor network structure diagram with different node numbers through a topological structure generation algorithm, and establishing a weight matrix and a connection matrix;
step 2, building an energy consumption model of the wireless sensor network, and calculating the optimal anchor point number;
step 3, calculating the link packet loss rate, the buffer area residual space and the residual energy of each sensor node at different moments, so as to calculate the energy weighted average value of each sensor node;
and 4, constructing a self-adaptive anchor point selection algorithm according to the result of the step 3, and selecting a proper anchor point.
The present invention is also characterized in that,
the step 1 is specifically implemented according to the following steps:
step 1.1, firstly, giving the scale of the wireless sensor network, namely the number M of topological nodes of the wireless sensor network, and generating a bottom topological structure diagram of the wireless sensor network according to the given number M of topological nodes;
step 1.2, based on the network topology structure generated in step 1.1, obtaining a link set E of the network, in the link set E, if two nodes are connected, the value of the corresponding coordinate in the link set E is 1, according to the finally obtained link set E, the number of 1 in each node row is represented as out degree, the number of 1 in each node column is represented as in degree, the total number of in-degree nodes of each node is calculated by traversing the link set E, the weight of each link can be calculated according to the total number of in-degree nodes, the weight matrix W of the bottom structure of the wireless sensor network can be obtained according to the weight of each link, and the weight matrix W meets the following conditions:
wherein W is ij (k) The weights of links (j, i) in the generated weight matrix representing the kth moment,representing the ingress neighbor node of node i at the kth time.
On the basis of a bottom topology structure diagram of the wireless sensor network, a transmission direction is set for links between nodes and is used as a data flow direction during data transmission of the wireless sensor network;
and 1.3, calculating and generating a connection matrix X of the wireless sensor network based on the network topology structure and the weight matrix generated in the step 1.1 and the step 1.2, calculating the hop count distance from each node to the node i by the node i within the determined m hop range according to the weight matrix W, and marking.
Step 1.1 is specifically as follows:
step 1.1.1, determining the arrangement range of a wireless sensor network, namely, the length and the width of an area where the wireless sensor network is required to be constructed, setting a coordinate axis range through the length and the width of the area, and marking an x axis and a y axis;
step 1.1.2, giving the number N of nodes of a network, randomly generating N coordinate pairs to represent the placement positions of the nodes, numbering each sensor node, and marking when the anchor point is selected conveniently;
step 1.1.3, randomly selecting two nodes i and j, wherein the satisfaction degree of the node i is not 2, and simultaneously, the node i and the node j satisfyThe calculation method for calculating the connectivity probability p (i, j) of the node i and the node j is as follows:
wherein b is a constant,the method comprises the steps of representing the sum of the outbound degree and the inbound degree of nodes, wherein the sum is used for controlling the average degree of the nodes of a network, M represents the number of the nodes, tau and gamma are parameters for regulating the characteristics of the network, the value range is [0,1 ], D (i, j) represents the distance between the node i and the node j, and D represents the maximum value of the distances among all the nodes in the network;
determining whether the two nodes should be connected to form a communication link through p (i, j), and if p (i, j) meets the connection condition, directly connecting the node i with the node j;
step 1.1.4, for each node i, selecting a peer node is divided into two cases:
a. if the degree of the node i is 0, randomly selecting a node j, wherein the satisfaction degree of the node j is greater than 0, and the node i and the node j satisfy
b. If the degree of the node i is 1, randomly selecting a node j, wherein the node i and the node j meet the following conditions
After the node selection is completed, calculating the communication probability p (i, j) of the nodes i and j through the formula (1), and if the p (i, j) meets the connection condition, directly connecting the node i with the node j;
step 1.1.5, after step 1.1.4 is completed, forming a basic network structure, judging whether the topology at the moment is connected or not, if the network is not connected, repeating step 1.1.3 and step 1.1.4 again to add links, and ensuring that the degree of the nodes is equal to that of the nodesIs a value of (2).
In the step 1.3, the connection matrix X is specifically generated according to the following steps:
generating a connection matrix X in a recursion mode, setting a termination condition of recursion to be that the hop count is larger than the upper limit m, recording the hop count of a current node through a variable count, recording the value of the position as the count if the connection matrix of the position is 0, taking the minimum value among the current value and the count if the connection matrix of the position is not 0, and then continuously recursively calling until the whole matrix is traversed, and finally obtaining the connection matrix X, wherein the X meets the following conditions:
the step 2 is specifically implemented according to the following steps:
the energy consumption models of the source node, the forwarding node and the anchor point are respectively established as follows:
the power consumption of the source node includes generating/bytes of data and transmitting the data to a distance d s Node of (a), energy consumption model e t The following is shown:
wherein N is out Representing the number of external neighbor nodes of the source node, and l represents the large size of the transmitted dataSmall e trans Represents the energy consumed in transmitting 1 byte of data, μ represents the energy consumption coefficient, d s Representing the transmission distance between the source node and the neighboring node outside the source node;
the power consumption of the forwarding node comprises receiving/bytes of data and transmitting the data to a distance d f Node of (a), energy consumption model e f The following is shown:
wherein N is in Representing the number of internal neighbor nodes of the forwarding node, e rece Represents the energy consumed in receiving 1 byte of data, d f Representing the transmission distance between the forwarding node and the neighbor node in the forwarding node;
the power consumption of an anchor point includes receiving data transmitted by nodes within its subnetwork and transmitting the data to a distance d SC Is a mobile collector of (a), energy consumption model e A The following is shown:
wherein N is sub,r Representing the number of nodes of the sub-network where the anchor point is located, R t Representing the total amount of data transmitted by all nodes within the sub-network of the anchor point, d SC Representing the transmission distance between the anchor point and the mobile collector;
obtaining an overall energy consumption model e of the wireless sensor network according to the energy consumption models of the source node, the forwarding node and the anchor point established above total The following is shown:
wherein T is t Representing the number of source nodes, T f Representing the number of forwarding nodes, T A Representing the number of anchor points, M represents the side length of a square area where the wireless sensor network is located, and h represents the level of sensor nodes to the anchor pointsNumber of hops;
in order to calculate the optimal anchor point number, equation (7) is applied to T A The derivative is obtained by seeking a derivative,
let (8) be 0 to obtain the optimal anchor point number N opt Is calculated according to the formula:
if N opt And if the number is a non-integer, upwards rounding the result of the formula (9) to obtain the optimal anchor point number.
In step 3, defining the remaining energy E of the sensor node i at the time t i,t ＝E i,t-1 -U i,t-1 ，E i,t-1 Representing the remaining energy of node i at time t-1, U i,t-1 Representing the energy consumed by node i at time t-1,
defining packet loss rate of node i at t momentN in Representing the number of neighbor nodes in node i and count j,send Representing the number of data packets sent by node j to node i, count i,recv Indicating that node i accepts the number of packets from the inner neighbor node j, count j,send Sum and count of (2) i,recv The ratio is the packet loss rate;
will lose packet rate l i,t Expanding 100 times, expressing by an integer, for the test of the packet loss rate, transmitting data packets in the network every time, wherein the number of the data packets can be 50 or 100, and then each sensor node calculates the packet loss rate of the link where the sensor node is located and stores the packet loss rate so as to be used when calculating the energy weighted average value;
defining buffer remaining space Q of node i at time t i,t The residual space of the node i is the buffer zone for storing data inside the sensor node iThe remaining size.
The step 3 is specifically implemented according to the following steps:
step 3.1 each sensor node is based on the remaining energy E i,t Link packet loss rate l ij,t And buffer remaining space Q i,t Calculating the weight coefficient of the self, and respectively carrying out normalization operation on the three elements to obtainAnd->The normalization formula is shown as formula (10):
in the method, in the process of the invention,representing the normalized value, y min Representing normalized minimum, y max Representing a normalized maximum; will y min Is set to a number greater than 0, x represents the source data, x min Representing the minimum value, x, of the source data max Representing a maximum value of the source data;
step 3.2, weighting calculation is carried out on the residual energy, the link packet loss rate and the residual space of the buffer zone of the sensor node i to obtain a weight coefficient v selected by the anchor point of the final node i i The weight distribution calculation of the three elements is obtained by setting a weight calculation function, and the weight alpha of the residual energy is obtained by the weight calculation function i Weight beta of link packet loss rate i And the weight gamma of the remaining space i ；
Calculating through (11) to obtain a weighted value w selected by the anchor point of the sensor node i i,t The following is shown:
step 3.3, when each candidate anchor point a calculates the energy weighted average value of the candidate anchor points a, the weight coefficient of the candidate anchor point a isI for one-hop intra-neighbor set of candidate anchor point a at t moment 1,t Representing neighbor set I within one hop of candidate anchor a 1,t The farther the transmission distance from the candidate anchor point a is, the smaller the weight coefficient is, i.e. for any i, j e S 1,t When d i,a ＜d j,a When the distance from the node i to the candidate anchor point a is smaller than the distance from the node j to the candidate anchor point a, v is shown i,t ＞v j,t The method comprises the steps of carrying out a first treatment on the surface of the Order theRepresenting set I 1,t The maximum value of the medium weight coefficient is +.>Similarly, candidate anchor point a is I for neighbor set in two hops at time t 2,t Maximum value of candidate anchor point a weight coefficient +.>The larger the number of hops is, the smaller the weight coefficient corresponding to the node i is, and the +.>n represents the hop count range at anchor point selection;
the calculation formula of the energy weighted average of the candidate anchor point a is as follows:
wherein I is k,t Node set representing kth hop of candidate anchor point a at t moment, |I k,t I represents I k,t Number of nodes, w i,t Representing the weighted value of the node i,the weight coefficient at the kth hop representing node i.
Step 4 is specifically implemented according to the following steps:
each node calculates its own energy weighted average EW a And weighting the energy to average EW a The method comprises the steps of sending the energy weighted average value to a mobile collector, obtaining an energy weighted average value List of nodes by the mobile collector, ordering the energy weighted average values of all sensor nodes in a descending order, storing the energy weighted average values in the List, enabling the nodes with larger energy weighted average values to be more likely to be selected as anchor points, and updating the anchor points by the mobile collector through an adaptive anchor point selection algorithm at the time t. The self-adaptive anchor point selection method comprises the following specific steps:
(1) Firstly, list is ordered in descending order to obtain List s List at this time s The first node i of (a) is the maximum energy weighted average value and is selected as a candidate anchor point to be put into an anchor point set AList t In (a) and (b);
(2) Deleting the candidate anchor point i and the nodes within k hops of the candidate anchor point i from the List, and forming a sub-network by the anchor point i and the nodes within k hops of the anchor point i at the moment;
(3) Judging the anchor point set AList at the moment t Whether the length is equal to the number N of the optimal anchor points opt Three situations are known:
a. if anchor point set AList t Length of (2) and number of optimal anchor points N opt Equal and List is empty, then the anchor set AList is output t ；
b. If anchor point set AList t Length of (2) and number of optimal anchor points N opt Not equal and List is not empty, repeating the operation of step (2);
c. if anchor point set AList t Length of (2) and number of optimal anchor points N opt Unequal and List empty, empty List s And (3) updating the hop count range k by all elements in the set, and repeating the operation of the step (1).
When the above condition a is satisfied, i.e. anchor point set AList t Length and length of (2)Number of optimal anchor points N opt Equal and List is empty, and the algorithm meets the termination condition to obtain an anchor point set AList under the current wireless sensor network t 。
The self-adaptive anchor point selection method of the wireless sensor network has the advantages that an energy consumption model of the wireless sensor network is established, and the optimal anchor point number of the whole network is calculated. Meanwhile, by combining the number of optimal anchor points, a self-adaptive anchor point selection method is designed, and the position of each anchor point is determined, so that the energy consumption of mobile data acquisition in the whole network is lower. According to the method, the anchor point can be automatically updated according to the residual energy of the sensor node at each moment, and the condition that the network is paralyzed due to excessive consumption of the energy of the node is avoided. Meanwhile, the method is not limited by the scale of the network, and can be applied to a large-scale wireless sensor network.
Drawings
FIG. 1 is a schematic diagram of the overall structure of an algorithm;
FIG. 2 is a schematic diagram of a sensor network;
fig. 3 is a graph of transmission delays for different hops for different numbers of nodes;
FIG. 4 is a graph of network energy consumption for different numbers of anchor points for different numbers of nodes;
FIG. 5 is a schematic diagram of a topology network with 50 nodes (black squares represent anchor points);
FIG. 6 is a schematic diagram of a topology network with 100 nodes (black squares represent anchor points);
FIG. 7 is a schematic diagram of a topology network with 150 nodes (black squares represent anchor points);
fig. 8 is a schematic diagram of a topology network with 200 nodes (black squares represent anchor points).
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
The invention discloses a self-adaptive anchor point selection method of a wireless sensor network, which is implemented by the following steps, wherein the whole flow chart is shown in figure 1:
step 1, generating a wireless sensor network structure diagram with different node numbers through a topological structure generation algorithm, and establishing a weight matrix and a connection matrix;
referring to fig. 2, step 1 is specifically implemented as follows:
step 1.1, firstly, giving the scale of the wireless sensor network, namely the number M of topological nodes of the wireless sensor network, and generating a bottom topological structure diagram of the wireless sensor network according to the given number M of topological nodes;
step 1.1 is specifically as follows:
step 1.1.1, determining the arrangement range of a wireless sensor network, namely, the length and the width of an area where the wireless sensor network is required to be constructed, setting a coordinate axis range through the length and the width of the area, and marking an x axis and a y axis;
step 1.1.2, giving the number N of the nodes of the network, randomly (if the pseudo random number is generated, a random number seed is also required to be set) generating N coordinate pairs, representing the placement positions of the nodes, numbering each sensor node, and marking when the anchor point is convenient to select;
step 1.1.3, randomly selecting two nodes i and j, wherein the satisfaction degree of the node i is not 2, and simultaneously, the node i and the node j satisfyThe calculation method for calculating the connectivity probability p (i, j) of the node i and the node j is as follows:
wherein b is a constant,the method comprises the steps of representing the sum of the outbound degree and the inbound degree of nodes, wherein the sum is used for controlling the average degree of the nodes of a network, M represents the number of the nodes, tau and gamma are parameters for regulating the characteristics of the network, the value range is [0,1 ], D (i, j) represents the distance between the node i and the node j, and D represents the maximum value of the distances among all the nodes in the network;
determining whether the two nodes should be connected to form a communication link through p (i, j), and if p (i, j) meets the connection condition, directly connecting the node i with the node j;
step 1.1.4, for each node i, selecting a peer node is divided into two cases:
a. if the degree of the node i is 0, randomly selecting a node j, wherein the satisfaction degree of the node j is greater than 0, and the node i and the node j satisfy
b. If the degree of the node i is 1, randomly selecting a node j, wherein the node i and the node j meet the following conditions
After the node selection is completed, calculating the communication probability p (i, j) of the nodes i and j through the formula (1), and if the p (i, j) meets the connection condition, directly connecting the node i with the node j;
and step 1.1.5, after the step 1.1.4 is completed, forming a basic network structure, judging whether the topology at the moment is connected or not, if the network is not connected, repeating the step 1.1.3 and the step 1.1.4 again to add links, and ensuring that the degree of the node is equal to the value of e.
Step 1.2, based on the network topology structure generated in step 1.1, obtaining a link set E of the network, in the link set E, if two nodes are connected, the value of the corresponding coordinate in the link set E is 1, according to the finally obtained link set E, the number of 1 in each node row is represented as out degree, the number of 1 in each node column is represented as in degree, the total number of in-degree nodes of each node is calculated by traversing the link set E, the weight of each link can be calculated according to the total number of in-degree nodes, the weight matrix W of the bottom structure of the wireless sensor network can be obtained according to the weight of each link, and the weight matrix W meets the following conditions:
wherein W is ij (k) The weights of links (j, i) in the generated weight matrix representing the kth moment,representing the ingress neighbor node of node i at the kth time.
Meanwhile, the network topology structure generated in the step 1.1 is an undirected graph, and the wireless sensor network is directed when data transmission is performed. Therefore, on the basis of the bottom topological structure diagram of the wireless sensor network, a transmission direction is set for the links between the nodes and is used as the data flow direction in the data transmission of the wireless sensor network: in order to avoid the occurrence of hot nodes, the selection of the transmission direction of each link should flow from the node with the larger number of ingress nodes to the node with the smaller number of ingress nodes.
And 1.3, calculating and generating a connection matrix X of the wireless sensor network based on the network topology structure and the weight matrix generated in the step 1.1 and the step 1.2, calculating the hop count distance from each node to the node i by the node i within the determined m hop range according to the weight matrix W, and marking.
In the step 1.3, the connection matrix X is specifically generated according to the following steps:
generating a connection matrix X in a recursion mode, setting a termination condition of recursion to be that the hop count is larger than the upper limit m, recording the hop count of a current node through a variable count, recording the value of the position as the count if the connection matrix of the position is 0, taking the minimum value among the current value and the count if the connection matrix of the position is not 0, and then continuously recursively calling until the whole matrix is traversed, and finally obtaining the connection matrix X, wherein the X meets the following conditions:
step 2, building an energy consumption model of the wireless sensor network, and calculating the optimal anchor point number;
the step 2 is specifically implemented according to the following steps:
the energy consumption models of the source node, the forwarding node and the anchor point are respectively established as follows:
the power consumption of the source node includes generating/bytes of data and transmitting the data to a distance d s Node of (a), energy consumption model e t The following is shown:
wherein N is out Representing the number of external neighbor nodes of the source node, i represents the size of the transmitted data, e trans Represents the energy consumed in transmitting 1 byte of data, μ represents the energy consumption coefficient, d s Representing the transmission distance between the source node and the neighboring node outside the source node;
the power consumption of the forwarding node comprises receiving/bytes of data and transmitting the data to a distance d f Node of (a), energy consumption model e f The following is shown:
wherein N is in Representing the number of internal neighbor nodes of the forwarding node, e rece Represents the energy consumed in receiving 1 byte of data, d f Representing the transmission distance between the forwarding node and the neighbor node in the forwarding node;
the power consumption of an anchor point includes receiving data transmitted by nodes within its subnetwork and transmitting the data to a distance d SC Is a mobile collector of (a), energy consumption model e A The following is shown:
wherein N is sub,r Representing the number of nodes of the sub-network where the anchor point is located, R t Data aggregate transmitted by all nodes in a sub-network representing an anchor pointQuantity d SC Representing the transmission distance between the anchor point and the mobile collector;
obtaining an overall energy consumption model e of the wireless sensor network according to the energy consumption models of the source node, the forwarding node and the anchor point established above total The following is shown:
wherein T is t Representing the number of source nodes, T f Representing the number of forwarding nodes, T A The number of anchor points is represented, M represents the side length of a square area where the wireless sensor network is located, and h represents the average hop count from the sensor node to the anchor points;
in order to calculate the optimal anchor point number, equation (7) is applied to T A The derivative is obtained by seeking a derivative,
let (8) be 0 to obtain the optimal anchor point number N opt Is calculated according to the formula:
if N opt And if the number is a non-integer, upwards rounding the result of the formula (9) to obtain the optimal anchor point number.
Step 3, calculating the link packet loss rate, the buffer area residual space and the residual energy of each sensor node at different moments, so as to calculate the energy weighted average value of each sensor node;
in step 3, defining the remaining energy E of the sensor node i at the time t i,t ＝E i,t-1 -U i,t-1 ，E i,t-1 Representing the remaining energy of node i at time t-1, U i,t-1 Representing the energy consumed by node i at time t-1,
defining packet loss rate of node i at t momentN in Representing the number of neighbor nodes in node i and count j,send Representing the number of data packets sent by node j to node i, count i,recv Indicating that node i accepts the number of packets from the inner neighbor node j, count j,send Sum and count of (2) i,recv The ratio is the packet loss rate;
to better introduce the packet loss rate in the following energy weighted average, the packet loss rate l is calculated i,t Expanding 100 times, expressing by an integer, for the test of the packet loss rate, transmitting data packets in the network every time, wherein the number of the data packets can be 50 or 100, and then each sensor node calculates the packet loss rate of the link where the sensor node is located and stores the packet loss rate so as to be used when calculating the energy weighted average value;
defining buffer remaining space Q of node i at time t i,t The remaining space of the node i is the remaining size of the buffer area for storing data inside the sensor node i.
The step 3 is specifically implemented according to the following steps:
step 3.1 each sensor node is based on the remaining energy E i,t Link packet loss rate l ij,t And buffer remaining space Q i,t Calculating the weight coefficient of the self, and respectively carrying out normalization operation on the three elements to obtainAnd->The normalization formula is shown as formula (10):
in the method, in the process of the invention,representing the normalized value, y min Representing normalized minimum, y max Representing a normalized maximum; to avoid meaningless zero values after normalization, y can be chosen to be min Is set to a number greater than 0, x represents the source data, x min Representing the minimum value, x, of the source data max Representing a maximum value of the source data;
step 3.2, weighting calculation is carried out on the residual energy, the link packet loss rate and the residual space of the buffer zone of the sensor node i to obtain a weight coefficient v selected by the anchor point of the final node i i The weight distribution calculation of the three elements is obtained by setting a weight calculation function, such as f 1 (E i,t )＝0.019E i.t +0.8，f 2 (l i,t )＝20-0.36l i,t ，f 3 (Q i,t )＝0.015Q i,t +4.6. Obtaining a weight alpha of the residual energy through a weight calculation function i Weight beta of link packet loss rate i And the weight gamma of the remaining space i ；
Calculating through (11) to obtain a weighted value w selected by the anchor point of the sensor node i i,t The following is shown:
step 3.3, when each candidate anchor point a calculates the energy weighted average value of the candidate anchor points a, the weight coefficient of the candidate anchor point a isI for one-hop intra-neighbor set of candidate anchor point a at t moment 1,t Representing neighbor set I within one hop of candidate anchor a 1,t The farther the transmission distance from the candidate anchor point a is, the smaller the weight coefficient is, i.e. for any i, j e S 1,t When d i,a ＜d j,a When the distance from the node i to the candidate anchor point a is smaller than the distance from the node j to the candidate anchor point a, v is shown i,t ＞v j,t The method comprises the steps of carrying out a first treatment on the surface of the Order theRepresenting set I 1,t The maximum value of the medium weight coefficient is +.>Similarly, candidate anchor point a is I for neighbor set in two hops at time t 2,t Maximum value of candidate anchor point a weight coefficient +.>The larger the number of hops is, the smaller the weight coefficient corresponding to the node i is, and the +.>n represents the hop count range at anchor point selection;
the calculation formula of the energy weighted average of the candidate anchor point a is as follows:
wherein I is k,t Node set representing kth hop of candidate anchor point a at t moment, |I k,t I represents I k,t Number of nodes, w i,t Representing the weighted value of the node i,the weight coefficient at the kth hop representing node i.
And 4, constructing a self-adaptive anchor point selection algorithm according to the result of the step 3, and selecting a proper anchor point.
Step 4 is specifically implemented according to the following steps:
each node calculates its own energy weighted average EW a And weighting the energy to average EW a The energy weighted average value List of the nodes is obtained by the mobile collector, the energy weighted average values of all the sensor nodes are ordered in a descending order and stored in the List, the nodes with larger energy weighted average values are more likely to be selected as anchor points, and the mobile collector passes through an adaptive anchor point selection algorithm at the moment tAnd updating the anchor point. The self-adaptive anchor point selection method comprises the following specific steps:
(1) Firstly, list is ordered in descending order to obtain List s List at this time s The first node i of (a) is the maximum energy weighted average value and is selected as a candidate anchor point to be put into an anchor point set AList t In (a) and (b);
(2) Deleting the candidate anchor point i and the nodes within k hops of the candidate anchor point i from the List, and forming a sub-network by the anchor point i and the nodes within k hops of the anchor point i at the moment;
(3) Judging the anchor point set AList at the moment t Whether the length is equal to the number N of the optimal anchor points opt Three situations are known:
a. if anchor point set AList t Length of (2) and number of optimal anchor points N opt Equal and List is empty, then the anchor set AList is output t ；
b. If anchor point set AList t Length of (2) and number of optimal anchor points N opt Not equal and List is not empty, repeating the operation of step (2);
c. if anchor point set AList t Length of (2) and number of optimal anchor points N opt Unequal and List empty, empty List s And (3) updating the hop count range k by all elements in the set, and repeating the operation of the step (1).
When the above condition a is satisfied, i.e. anchor point set AList t Length of (2) and number of optimal anchor points N opt Equal and List is empty, and the algorithm meets the termination condition to obtain an anchor point set AList under the current wireless sensor network t 。
Fig. 3 is a graph of transmission delays for different hop counts for different numbers of nodes, and it can be seen from the graph that the data delay is minimal when the hop count m is equal to 3 at a scale of 50 for the wireless sensor network. When the hop count m is smaller than 3, the number of sub-network nodes where each anchor point is located is large, and large data delay is generated when data transmission is carried out between the nodes. When the hop count m is greater than 3, the excessive anchor points can cause the travel of the mobile collector for data acquisition to be too long, so that the delay of the whole data transmission process is increased.
Fig. 4 is a graph of network energy consumption for different numbers of anchors for different numbers of nodes, and it can be seen from the graph that the energy consumption is the lowest when the number of anchors is 3 at a scale of 50 for the wireless sensor network. From step 2, the total energy consumption e of the wireless sensor network can be obtained total The calculation formula is as follows:
it can be seen that the number of anchor points T A At the total energy consumption e total Is present in the numerator and denominator in the calculation formula of (c). As can be taken from fig. 4, when the number of anchor points is smaller than 3, the number of anchor points T A And total energy consumption e total In inverse proportion, the fewer the number of anchor points, the greater the energy consumption. This is because, when the number of anchor points is small, the number of sub-network nodes is large, retransmission caused by congestion may be encountered in the process of data transmission, and this part consumes a part of energy. When the number of anchor points is more than 3, the number of anchor points T A And total energy consumption e total In proportion, the more anchor points, the greater the energy consumption. The number of anchor points is large, and the data acquisition travel of the mobile collector is increased. Each anchor point and the node of the sub-network in which it resides, due to the long travel delay, causes an increase in the time to wait for data to be uploaded to the mobile collector, which increases a portion of the power consumption.
Fig. 5 is a schematic diagram of a topology structure of a wireless sensor network with 50 nodes, and the number of optimal anchor points is calculated to be 3 according to the formula (9) in the step 2. The coordinate position of each anchor point is calculated and marked (represented by black squares) by the adaptive anchor point selection method of step 4.
Fig. 6 is a schematic diagram of a topology structure of a wireless sensor network with 100 nodes, and the number of optimal anchor points is calculated to be 5 according to the formula (9) in the step 2. The coordinate position of each anchor point is calculated and marked (represented by black squares) by the adaptive anchor point selection method of step 4.
Fig. 7 is a schematic diagram of a topology structure of a wireless sensor network with 150 nodes, and the number of optimal anchor points is calculated to be 7 according to the formula (9) in the step 2. The coordinate position of each anchor point is calculated and marked (represented by black squares) by the adaptive anchor point selection method of step 4.
Fig. 8 is a schematic diagram of a topology structure of a wireless sensor network with 50 nodes, and the number of optimal anchor points is calculated to be 8 according to the formula (9) in the step 2. The coordinate position of each anchor point is calculated and marked (represented by black squares) by the adaptive anchor point selection method of step 4.
Claims (1)
1. The self-adaptive anchor point selection method of the wireless sensor network is characterized by comprising the following steps of:
step 1, generating a wireless sensor network structure diagram with different node numbers through a topological structure generation algorithm, and establishing a weight matrix and a connection matrix;
the step 1 is specifically implemented according to the following steps:
step 1.1, firstly, giving the scale of the wireless sensor network, namely the number M of topological nodes of the wireless sensor network, and generating a bottom topological structure diagram of the wireless sensor network according to the given number M of topological nodes;
the step 1.1 is specifically as follows:
step 1.1.1, determining the arrangement range of a wireless sensor network, namely, the length and the width of an area where the wireless sensor network is required to be constructed, setting a coordinate axis range through the length and the width of the area, and marking an x axis and a y axis;
step 1.1.2, giving the number N of nodes of a network, randomly generating N coordinate pairs to represent the placement positions of the nodes, numbering each sensor node, and marking when the anchor point is selected conveniently;
step 1.1.3, randomly selecting two nodes i and j, wherein the satisfaction degree of the node i is not 2, and simultaneously, the node i and the node j satisfyThe calculation method for calculating the connectivity probability p (i, j) of the node i and the node j is as follows:
wherein b is a constant,the method comprises the steps of representing the sum of the outbound degree and the inbound degree of nodes, wherein the sum is used for controlling the average degree of the nodes of a network, M represents the number of the nodes, tau and gamma are parameters for regulating the characteristics of the network, the value range is [0,1 ], D (i, j) represents the distance between the node i and the node j, and D represents the maximum value of the distances among all the nodes in the network;
determining whether the two nodes should be connected to form a communication link through p (i, j), and if p (i, j) meets the connection condition, directly connecting the node i with the node j;
step 1.1.4, for each node i, selecting a peer node is divided into two cases:
a. if the degree of the node i is 0, randomly selecting a node j, wherein the satisfaction degree of the node j is greater than 0, and the node i and the node j satisfy
b. If the degree of the node i is 1, randomly selecting a node j, wherein the node i and the node j meet the following conditions
After the node selection is completed, calculating the communication probability p (i, j) of the nodes i and j through the formula (1), and if the p (i, j) meets the connection condition, directly connecting the node i with the node j;
step 1.1.5, after step 1.1.4 is completed, forming a basic network structure, judging whether the topology at the moment is connected or not, if the network is not connected, repeating step 1.1.3 and step 1.1.4 again to add links, and ensuring the nodesThe degree of the dot is equal toIs a value of (2);
step 1.2, based on the network topology structure generated in step 1.1, obtaining a link set E of the network, in the link set E, if two nodes are connected, the value of the corresponding coordinate in the link set E is 1, according to the finally obtained link set E, the number of 1 in each node row is represented as out degree, the number of 1 in each node column is represented as in degree, the total number of in-degree nodes of each node is calculated by traversing the link set E, the weight of each link can be calculated according to the total number of in-degree nodes, the weight matrix W of the bottom structure of the wireless sensor network can be obtained according to the weight of each link, and the weight matrix W meets the following conditions:
wherein W is ij (k) The weights of links (j, i) in the generated weight matrix representing the kth moment,an ingress neighbor node representing a kth moment node i;
on the basis of a bottom topology structure diagram of the wireless sensor network, a transmission direction is set for links between nodes and is used as a data flow direction during data transmission of the wireless sensor network;
step 1.3, calculating and generating a connection matrix X of the wireless sensor network based on the network topology structure and the weight matrix generated in the step 1.1 and the step 1.2, calculating the hop count distance from each node to the node i by the node i within the determined m hop range according to the weight matrix W, and marking;
the connection matrix X in the step 1.3 is specifically generated according to the following steps:
generating a connection matrix X in a recursion mode, setting a termination condition of recursion to be that the hop count is larger than the upper limit m, recording the hop count of a current node through a variable count, recording the value of the position as the count if the connection matrix of the position is 0, taking the minimum value among the current value and the count if the connection matrix of the position is not 0, and then continuously recursively calling until the whole matrix is traversed, and finally obtaining the connection matrix X, wherein the X meets the following conditions:
step 2, building an energy consumption model of the wireless sensor network, and calculating the optimal anchor point number;
the step 2 is specifically implemented according to the following steps:
the energy consumption models of the source node, the forwarding node and the anchor point are respectively established as follows:
the power consumption of the source node includes generating/bytes of data and transmitting the data to a distance d s Node of (a), energy consumption model e t The following is shown:
wherein N is out Representing the number of external neighbor nodes of the source node, i represents the size of the transmitted data, e trans Represents the energy consumed in transmitting 1 byte of data, μ represents the energy consumption coefficient, d s Representing the transmission distance between the source node and the neighboring node outside the source node;
the power consumption of the forwarding node comprises receiving/bytes of data and transmitting the data to a distance d f Node of (a), energy consumption model e f The following is shown:
wherein N is in Representing the number of internal neighbor nodes of the forwarding node, e rece Energy representing consumption of receiving 1 byte of dataQuantity d f Representing the transmission distance between the forwarding node and the neighbor node in the forwarding node;
the power consumption of an anchor point includes receiving data transmitted by nodes within its subnetwork and transmitting the data to a distance d SC Is a mobile collector of (a), energy consumption model e A The following is shown:
wherein N is sub,r Representing the number of nodes of the sub-network where the anchor point is located, R t Representing the total amount of data transmitted by all nodes within the sub-network of the anchor point, d SC Representing the transmission distance between the anchor point and the mobile collector;
obtaining an overall energy consumption model e of the wireless sensor network according to the energy consumption models of the source node, the forwarding node and the anchor point established above total The following is shown:
wherein T is t Representing the number of source nodes, T f Representing the number of forwarding nodes, T A The number of anchor points is represented, M represents the side length of a square area where the wireless sensor network is located, and h represents the average hop count from the sensor node to the anchor points;
in order to calculate the optimal anchor point number, equation (7) is applied to T A The derivative is obtained by seeking a derivative,
let (8) be 0 to obtain the optimal anchor point number N opt Is calculated according to the formula:
if N opt If the number is a non-integer, the result of the formula (9) is rounded upwards to be used as the optimal anchor point number;
step 3, calculating the link packet loss rate, the buffer area residual space and the residual energy of each sensor node at different moments, so as to calculate the energy weighted average value of each sensor node;
in the step 3, the remaining energy E of the sensor node i at the time t is defined i,t ＝E i,t-1 -U i,t-1 ，E i,t-1 Representing the remaining energy of node i at time t-1, U i,t-1 Representing the energy consumed by node i at time t-1,
defining packet loss rate of node i at t momentN in Representing the number of neighbor nodes in node i and count j,send Representing the number of data packets sent by node j to node i, count i,recv Indicating that node i accepts the number of packets from the inner neighbor node j, count j,send Sum and count of (2) i,recv The ratio is the packet loss rate;
will lose packet rate l i,t Expanding 100 times, expressing by an integer, for the test of the packet loss rate, transmitting data packets in the network every time, wherein the number of the data packets can be 50 or 100, and then each sensor node calculates the packet loss rate of the link where the sensor node is located and stores the packet loss rate so as to be used when calculating the energy weighted average value;
defining buffer remaining space Q of node i at time t i,t The residual space of the node i is the residual size of a buffer area for storing data inside the sensor node i;
the step 3 is specifically implemented according to the following steps:
step 3.1 each sensor node is based on the remaining energy E i,t Link packet loss rate l ij,t And buffer remaining space Q i,t Calculating the weight coefficient of the self, and firstly performing normalization operation to obtainAnd->The normalization formula is shown as formula (10):
in the method, in the process of the invention,representing the normalized value, y min Representing normalized minimum, y max Representing a normalized maximum; will y min Is set to a number greater than 0, x represents the source data, x min Representing the minimum value, x, of the source data max Representing a maximum value of the source data;
step 3.2, weighting calculation is carried out on the residual energy, the link packet loss rate and the residual space of the buffer zone of the sensor node i to obtain a weight coefficient v selected by the anchor point of the final node i i The weight distribution calculation of the three elements is obtained by setting a weight calculation function, and the weight alpha of the residual energy is obtained by the weight calculation function i Weight beta of link packet loss rate i And the weight gamma of the remaining space i ；
Calculating through (11) to obtain a weighted value w selected by the anchor point of the sensor node i i,t The following is shown:
step 3.3, when each candidate anchor point a calculates the energy weighted average value of the candidate anchor points a, the weight coefficient of the candidate anchor point a isI for one-hop intra-neighbor set of candidate anchor point a at t moment 1,t The representation is made of a combination of a first and a second color,neighbor set I within one hop of candidate anchor point a 1,t The farther the transmission distance from the candidate anchor point a is, the smaller the weight coefficient is, i.e. for any i, j e S 1,t When d i,a ＜d j,a When the distance from the node i to the candidate anchor point a is smaller than the distance from the node j to the candidate anchor point a, v is shown i,t ＞v j,t The method comprises the steps of carrying out a first treatment on the surface of the Order theRepresenting set I 1,t The maximum value of the medium weight coefficient is +.>Similarly, candidate anchor point a is I for neighbor set in two hops at time t 2,t Maximum value of candidate anchor point a weight coefficient +.>The larger the number of hops is, the smaller the weight coefficient corresponding to the node i is, and the +.>n represents the hop count range at anchor point selection;
the calculation formula of the energy weighted average of the candidate anchor point a is as follows:
wherein I is k,t Node set representing kth hop of candidate anchor point a at t moment, |I k,t I represents I k,t Number of nodes, w i,t Representing the weighted value of the node i,a weight coefficient representing a node i located at a kth hop;
step 4, constructing a self-adaptive anchor point selection algorithm according to the result of the step 3, and selecting a proper anchor point;
the step 4 is specifically implemented according to the following steps:
each node calculates its own energy weighted average EW a And weighting the energy to average EW a The method comprises the steps that the method comprises the steps of sending the method to a mobile collector, the mobile collector obtains an energy weighted average value List of nodes, descending order of energy weighted averages of all sensor nodes is carried out, the nodes with larger energy weighted averages are stored in the List, the nodes with larger energy weighted averages are more likely to be selected as anchors, the mobile collector updates the anchors at the time t through an adaptive anchor selection algorithm, and the specific steps of the adaptive anchor selection method are as follows:
(1) Firstly, list is ordered in descending order to obtain List s List at this time s The first node i of (a) is the maximum energy weighted average value and is selected as a candidate anchor point to be put into an anchor point set AList t In (a) and (b);
(2) Deleting the candidate anchor point i and the nodes within k hops of the candidate anchor point i from the List, and forming a sub-network by the anchor point i and the nodes within k hops of the anchor point i at the moment;
(3) Judging the anchor point set AList at the moment t Whether the length is equal to the number N of the optimal anchor points opt Three situations are known:
a. if anchor point set AList t Length of (2) and number of optimal anchor points N opt Equal and List is empty, then the anchor set AList is output t ；
b. If anchor point set AList t Length of (2) and number of optimal anchor points N opt Not equal and List is not empty, repeating the operation of step (2);
c. if anchor point set AList t Length of (2) and number of optimal anchor points N opt Unequal and List empty, empty List s Updating the hop count range k by all elements in the set, and repeating the operation of the step (1);
when the above condition a is satisfied, i.e. anchor point set AList t Length of (2) and number of optimal anchor points N opt Equal and List is empty, and the algorithm meets the termination condition to obtain an anchor point set AList under the current wireless sensor network t 。
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