CN108848476A - Power-efficient data assembly algorithms based on communication distance control in sensor network - Google Patents
Power-efficient data assembly algorithms based on communication distance control in sensor network Download PDFInfo
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Abstract
The present invention provides the power-efficient data assembly algorithms based on communication distance control in sensor network, are integrally divided into election of cluster head stage and cluster stage；The election of cluster head stage includes that distance threshold is arranged, selects candidate cluster head, calculate the potential energy consumption of candidate cluster head and screen formal cluster head；In the cluster stage, node is directly communicated with base-station node according to distance threshold selection；When at a distance from base station being more than distance threshold, selection is communicated with the leader cluster node nearest from it, at this point, being related to selecting the leader cluster node nearest apart from it according to distance；In the cluster stage, for command range, and control sub-clustering size is selected, distance is indirectly controlled with this.The algorithm is more than that the node of distance threshold carries out data convergence by the way of sub-clustering.The strategy for taking limitation sub-clustering size, guarantees the harmony of node energy consumption, LEACH and SEP agreement is apparently higher than in terms of energy efficiency.
Description
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to an energy-efficient data aggregation algorithm based on communication distance control in a sensor network.
Background
The characteristics of low energy consumption, wireless transmission, large-scale deployment and the like of the wireless sensor network determine that the wireless sensor network can be widely applied to various scenes which need long-time periodic monitoring and data acquisition, such as cultural relic protection, structural deformation of buildings, traffic flow monitoring and the like. In such application scenarios, how to reduce the energy consumption of the node, maintain the long-time periodic work of the wireless sensor network, and collect the target data as much as possible is a primary problem. The wireless sensor nodes are divided into cluster head nodes and in-cluster member nodes through a certain election strategy based on a clustering hierarchical routing algorithm, the cluster head nodes are responsible for collecting data of all in-cluster member nodes, performing data fusion and forwarding the data to a sensor network data Sink node Sink, excessive energy consumption caused by the fact that most of sensor nodes directly perform long-distance communication with Sink nodes of a sensor network is avoided, network energy consumption can be reduced on the whole, and therefore the service life of the network is prolonged. However, in the sensor network, due to different deployment positions of the sensor nodes, distances between the sensor nodes and the base station nodes are also obviously different, and energy consumption of the nodes on data transmission is also obviously different. For the sensor nodes, the communication distance difference is fully considered when a clustering routing algorithm is designed, and the energy efficiency of the nodes is improved, so that the network service life is optimized.
The advantages of clustering algorithms in energy efficiency have yielded numerous research results. The network types suitable for the clustering algorithm are classified, and the clustering algorithms can be roughly classified into a homogeneous clustering algorithm and a heterogeneous clustering algorithm. Considering the imbalance of node energy consumption and the dynamics and complexity of the network topology, it is very difficult to design a clustering algorithm capable of maximizing the network lifetime.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an energy-efficient data aggregation algorithm based on communication distance control in a sensor network, and the algorithm enables a node with the distance smaller than a threshold value to be directly communicated with a base station node in a mode of setting a distance threshold value; and carrying out data aggregation on the nodes exceeding the distance threshold in a clustering mode. In the clustering process, in order to avoid the excessively small average communication distance of the clusters, a strategy of limiting the cluster size is adopted, the balance of node energy consumption is ensured, and the energy efficiency is obviously higher than those of an LEACH protocol and an SEP protocol.
In order to achieve the purpose, the invention adopts the specific scheme that: the method comprises the following steps that an energy-efficient data aggregation algorithm based on communication distance control in a sensor network, wherein the sensor network comprises a plurality of nodes, the nodes are divided into a sensor node and a base station node, and the data aggregation algorithm specifically comprises the following steps:
step one, a cluster head election stage: the cluster head election stage comprises the steps of setting a distance threshold, selecting candidate cluster heads, calculating potential energy consumption of the candidate cluster heads and screening formal cluster heads, and the specific implementation steps are as follows:
s1, setting a threshold value,
wherein, P is the probability of the node becoming the cluster head, r is the number of the current running rounds of the network, and G is the node set which is not selected as the cluster head in the latest 1/P rounds;
s2, at the beginning of each round, each surviving node generates a random number, and the generated random number is compared with the threshold set in the step S1 to select candidate cluster heads;
s3, calculating potential energy consumption of the candidate cluster head, wherein the potential energy consumption of the candidate cluster head selected in the step S2 comprises the energy consumption of communication with potential cluster membersData fusion energy consumption EDAAnd energy consumption when cluster head communicates with base stationS4, residual energy E of candidate cluster headR≥EchThen, the candidate cluster heads are formal cluster heads obtained by screening;
step two, clustering stage: firstly, calculating the size of a cluster; then setting a distance threshold, comparing the distance between the non-cluster-head node and the base station node with the distance threshold, and when the distance between the non-cluster-head node and the base station node is smaller than the distance threshold, directly communicating the non-cluster-head node with the base station node; and when the distance between the non-cluster-head node and the base station node exceeds a distance threshold value, selecting the cluster-head node closest to the non-cluster-head node for communication.
The specific process of calculating the potential energy consumption of the candidate cluster head in step S3 is as follows:
wherein E iseRepresenting the energy consumed by the circuit in transmitting and receiving data, doDenotes a distance threshold value, εfsAnd εmpIndicating that the communication distance is less than and greater than the distance threshold d, respectivelyoDifferent powers usedThe power loss of the amplification loss model, d the communication distance, and l the packet size.
Step S4, when filtering formal cluster heads, respectively calculating communication distances d between nodes and cluster head nodesscCommunication distance d between node and base station nodessAnd judging the energy consumption of the node.
Step two, the specific process of calculating the cluster size is as follows:
t1, at the beginning of each round, calculating the total cluster head number generated in the past n roundsAnd average cluster head number
T2, number of nodes surviving in this round NaliveCalculating the average cluster size Nalive/Cavg；
And T3, in the node clustering process, controlling the number of cluster members on the condition of the average clustering size calculated in the step T2.
Has the advantages that:
(1) the invention provides an energy-efficient data aggregation algorithm based on communication distance control in a sensor network, which enables a node with the distance to a base station node smaller than a threshold value to be directly communicated with the node by setting a distance threshold value; and carrying out data aggregation on the nodes exceeding the distance threshold in a clustering mode. In the clustering process, in order to avoid the excessively small average communication distance of the clusters, a strategy of limiting the cluster size is adopted, the balance of node energy consumption is ensured, and the energy efficiency is obviously higher than those of an LEACH (low energy access and security association protocol) and an SEP (session initiation protocol);
(2) the invention provides an energy-efficient data aggregation algorithm based on communication distance control in a sensor network, which can obviously reduce the energy consumption of nodes, maintain the long-time periodic work of the wireless sensor network, collect target data as far as possible and contribute to optimizing the service life of the network.
Drawings
FIG. 1 is a graph of average communication distance versus number of surviving nodes;
FIG. 2 is a graph of average communication distance versus number of cluster heads;
FIG. 3 is a graph of average communication distance versus overall network energy consumption;
FIG. 4 is a network lifetime graph for different algorithms under the same network parameter configuration;
FIG. 5 is a communication distance diagram of different algorithms under the same network parameter configuration;
fig. 6 is a graph of energy consumption for different algorithms under the same network parameter configuration.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention is integrally divided into a cluster head election stage and a clustering stage; in the cluster head election stage, candidate cluster heads are selected according to the comparison between a threshold value T (n) and random numbers generated by nodes, and then a formal cluster head is selected from the candidate cluster heads according to the potential energy consumption condition; in the clustering stage, the nodes are selected to directly communicate with the base station nodes according to the distance threshold; when the distance between the base station and the cluster head node exceeds a distance threshold value, selecting to communicate with the cluster head node closest to the base station, wherein the cluster head node closest to the base station is selected according to the distance; in the clustering stage, in order to control the distance, the cluster size is selected to be controlled, so that the distance is indirectly controlled.
The method comprises the following steps that an energy-efficient data aggregation algorithm based on communication distance control in a sensor network, wherein the sensor network comprises a plurality of nodes, the nodes are divided into a sensor node and a base station node, and the data aggregation algorithm specifically comprises the following steps:
step one, a cluster head election stage: the cluster head election stage comprises the steps of setting a distance threshold, selecting candidate cluster heads, calculating potential energy consumption of the candidate cluster heads and screening formal cluster heads, and the specific implementation steps are as follows:
s1, setting a threshold value,
wherein, P is the probability of the node becoming the cluster head, r is the number of the current running rounds of the network, and G is the node set which is not selected as the cluster head in the latest 1/P rounds.
S2, at the beginning of each round, each surviving node generates a random number, and the generated random number is compared with the threshold set in the step S1 to select candidate cluster heads; and adopting a node energy consumption prediction-based method to avoid selecting nodes with too low residual energy as cluster head nodes.
S3, calculating the potential energy consumption of the candidate cluster head, wherein the specific process of calculating the potential energy consumption of the candidate cluster head is as follows: first, the sensor nodes employ an energy consumption model of the LEACH protocol. The energy consumption of the node is mainly divided into three parts: energy consumption in transmitting data ETxEnergy consumption when receiving data ERxAnd energy consumption E in fusing dataDA. Combining the communication distance d and the packet size l, equation (1) can be obtained:
wherein E iseRepresenting the energy consumed by the circuit in transmitting and receiving data, doDenotes a distance threshold value, εfsAnd εmpIndicating that the communication distance is less than and greater than the distance threshold d, respectivelyoThe power loss of different power amplification loss models is adopted, d represents the communication distance, and l represents the size of the data packet;
secondly, when a node becomes a cluster head, the potential energy consumption can be divided into three parts: energy consumption for communicating with potential cluster membersData fusion energy consumption EDAAnd energy consumption when cluster head communicates with base station
Wherein,representing the energy consumption of the cluster head in receiving data when a potential cluster member i communicates with the cluster head,indicating the energy consumption of the cluster head in transmitting data to the base station. Both are calculated using equation (1).
S4, residual energy E of candidate cluster headR≥EchThen, the candidate cluster heads are formal cluster heads obtained by screening; in order to realize the control of the communication distance, when the formal cluster heads are screened, the communication distance d between the nodes and the cluster head nodes is respectively calculatedscCommunication distance d between node and base station nodessAnd judging the energy consumption of the node. Selecting the node meeting the distance control condition and directly communicating with the base station,thereby keeping the average communication distance of the whole network to be maintained at the position with optimal energy;
for each node, data transmission can be completed in two ways: (1) directly forwarding to a base station in a long distance; (2) and communicating with the cluster head node, and forwarding the cluster head node to the base station after performing data fusion. In both modes, the energy consumption of the nodes is as follows, (the invention only considers the short-distance communication case, namely, the communication distance between the related nodes is assumed to be smaller than do)：
The first method is as follows: the energy consumption of a node is mainly the energy consumed by sending data (the energy consumption of a base station is not considered):
the second method comprises the following steps: the energy consumption comprises data sending, data receiving of the cluster head nodes and data fusion. Considering that the cluster head node only sends one data packet to the base station after data fusion, for each cluster member node, the invention does not consider the energy consumption of the communication between the cluster head node and the base station. Therefore, the temperature of the molten metal is controlled,
based on the two energy consumption calculation methods, the problem of optimizing node energy consumption is converted intoThe formula (2) can be obtained:
and if and only if the formula (2) is less than 0, the energy optimal transmission mode selected by the node is to directly communicate with the base station. Based on equation (2) we can obtain:final dssAnd dscThe relationship of (c) translates into:considering dsc＜doAnd the length of the data packet is 4000, there isIt can be seen that, for a node, when the distance between the node and the base station is less than doWhen the communication is performed, the base station is selected to directly communicate with the base station so as to reduce the energy consumption of the whole network. Experiments prove that when the distance between the node and the base station is less than 0.8doWhen the communication is selected to be directly communicated with the base station, the energy saving effect is best.
Step two, clustering stage: firstly, calculating the size of a cluster; then setting a distance threshold, comparing the distance between the non-cluster-head node and the base station node with the distance threshold, and when the distance between the non-cluster-head node and the base station node is smaller than the distance threshold, directly communicating the non-cluster-head node with the base station node; and when the distance between the non-cluster-head node and the base station node exceeds a distance threshold value, selecting the cluster-head node closest to the non-cluster-head node for communication.
In the second step, the specific process of calculating the cluster size is as follows:
t1, at the beginning of each round, calculating the total cluster head number generated in the past n roundsAnd average cluster head number
T2, number of nodes surviving in this round NaliveCalculating the average cluster size Nalive/Cavg；
And T3, in the node clustering process, controlling the number of cluster members on the condition of the average clustering size calculated in the step T2.
Experimental study data
Correlation analysis of main features of LEACH algorithm
The correlation between the main features of the classical clustering algorithm LEACH is shown in FIGS. 1-3 when a typical many-to-one transmission mode is adopted by a wireless sensor network. LEACH organizes time in units of rounds and randomly selects nodes as cluster heads in a periodic equal probability manner. The selected cluster head nodes are responsible for organizing into clusters, collecting and fusing data of all cluster members, and then transmitting the fused data to the base station nodes. Because the sensor nodes are deployed randomly, the nodes distributed in a monitoring area are not uniform, and the cluster head nodes are selected randomly, the correlation analysis of the main characteristics of LEACH is the first step for improving the performance of the clustering algorithm.
In the experiment, 100 wireless sensor nodes are randomly deployed in an area of 200m x 200m as an experiment environment, the correlation among four factors of the average communication distance of a network, the overall energy consumption of the network, the number of cluster heads and the number of surviving nodes is researched, key factors influencing the LEACH performance are extracted from the four factors, and the performance of a clustering algorithm is optimized on the basis. During the experiment, 10 experimental network topologies were randomly generated, and the predetermined running time of each experimental topology was 2000 rounds.
As shown in fig. 1-3, wherein fig. 1 is a graph comparing the average communication distance with the number of surviving nodes, the correlation coefficient of the two factors is 0.88, in the graph, a smooth line represents the number of surviving nodes in each round, and a dotted zigzag line represents the average communication distance in each round; FIG. 2 is a graph comparing the average communication distance with the number of cluster heads, wherein the correlation coefficient of the two factors is 0.86, in the graph, a smooth line represents the number of cluster heads in each round, and a dotted zigzag line represents the average communication distance in each round; fig. 3 is a graph comparing the average communication distance with the overall energy consumption of the network, and the correlation coefficient of the two factors is 0.88, wherein, the smooth line represents the overall energy consumption of each round, and the dotted zigzag line represents the average communication distance of each round. From the correlation analysis of fig. 1-3, it can be seen that the average communication distance of the network has a high correlation with the number of nodes surviving the network, the number of cluster heads, and the overall energy consumption of the network, and the correlation is greater than 0.8. As can be seen from fig. 2 and 3, the overall energy consumption of the network also has a high correlation with the number of cluster heads, and it can be seen that the average communication distance is a key factor affecting the performance of the LEACH and is also a key point for designing an energy-efficient clustering algorithm for the sensor-oriented network. The invention provides an energy-efficient data aggregation algorithm based on communication distance control in a sensor network on the basis of the experimental research.
Second, simulation and analysis
The method takes Matlab as an experimental platform, 100 nodes are randomly deployed in an experimental area of 200m x 200m, and the coordinates of the base station nodes are (100 ). All experimental results were based on 10 random network topologies, each running an average of 2000 rounds. The algorithm assumes that an ideal MAC protocol is adopted, and no packet loss and transmission conflict exist in the data transmission process. The experimental parameter settings are shown in table 1. The comparison algorithms selected by the invention are LEACH and SEP.
TABLE 1 Experimental parameters
The network life, communication distance and energy consumption of the algorithm are respectively compared with the LEACH algorithm and the SEP algorithm, and the comparison result is as follows:
(1) network lifetime
In the present invention, the network lifetime is defined as the time that lasts from the start of the network operation to the death of all nodes in the entire network. The network lifetime of the algorithm of the present invention is compared to the LEACH and SEP algorithms. As shown in fig. 4, after the network runs for 2000 rounds, the number of nodes still survived in the network using the algorithm of the present invention is about 40, and there are no nodes that survive in the network using LEACH. Due to the energy heterogeneous nature of the SEP algorithm, in the 2000 round, there are only 1 node alive in the network. Because the invention adopts a node energy consumption prediction mechanism, the low-energy node is prevented from being selected as a cluster head node, and the communication distance control strategy effectively avoids the energy waste of the sensor node close to the base station node. By controlling the cluster size, the energy consumption of the cluster head and the cluster members is effectively limited, and the energy consumption balance of the nodes is kept. Therefore, the algorithm of the invention can obviously prolong the service life of the network.
(2) Communication distance
The communication distance refers to an average communication distance of the network, and includes the sum of the intra-cluster communication distance and the cluster head and sink node communication distances. The communication distance is closely related to the node energy consumption and the network overall energy consumption. As shown in fig. 5, it can be seen that after the network runs for 2000 rounds, the average communication distance of the present invention is reduced, but the present invention can still be stabilized at about 50m, so that the energy consumption of each node can be balanced, and the network life can be prolonged. And as the number of dead nodes increases, after 1200 rounds, the average communication distance between LEACH and SEP algorithm is obviously reduced, which shows that although the two algorithms can balance the energy consumption of the nodes to a certain extent, after a plurality of rounds, most of the nodes are dead rapidly in a short time due to the equivalent residual energy of the nodes.
(3) Energy consumption
As shown in fig. 6, it can be seen from the figure that the communication distance is relatively long, although the energy consumption of the network in the initial operation stage is higher than that of LEACH and SEP, because the size of each cluster is controlled, on one hand, the stability of the communication distance is ensured, and on the other hand, the energy consumption of the cluster head nodes can be controlled, so that the energy consumption is more balanced than that of the LEACH and SEP algorithms, and the service life of the network is effectively prolonged.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited to the embodiments, and various changes and modifications can be made by one skilled in the art without departing from the scope of the invention.
Claims (4)
1. The high-energy-efficiency data aggregation algorithm based on communication distance control in the sensor network comprises a plurality of nodes, wherein the nodes are divided into sensor nodes and base station nodes, and the high-energy-efficiency data aggregation algorithm is characterized in that: the data aggregation algorithm specifically comprises the following steps: step one, a cluster head election stage: the cluster head election stage comprises the steps of setting a distance threshold, selecting candidate cluster heads, calculating potential energy consumption of the candidate cluster heads and screening formal cluster heads, and the specific implementation steps are as follows:
s1, setting a threshold value,
wherein, P is the probability of the node becoming the cluster head, r is the number of the current running rounds of the network, and G is the node set which is not selected as the cluster head in the latest 1/P rounds;
s2, at the beginning of each round, each surviving node generates a random number, and the generated random number is compared with the threshold set in the step S1 to select candidate cluster heads;
s3, calculating potential energy consumption of the candidate cluster head, wherein the potential energy consumption of the candidate cluster head selected in the step S2 comprises the energy consumption of communication with potential cluster membersData fusion energy consumption EDAAnd energy consumption when cluster head communicates with base station
S4, residual energy E of candidate cluster headR≥EchThen, the candidate cluster heads are formal cluster heads obtained by screening;
step two, clustering stage: firstly, calculating the size of a cluster; then setting a distance threshold, comparing the distance between the non-cluster-head node and the base station node with the distance threshold, and when the distance between the non-cluster-head node and the base station node is smaller than the distance threshold, directly communicating the non-cluster-head node with the base station node; and when the distance between the non-cluster-head node and the base station node exceeds a distance threshold value, selecting the cluster-head node closest to the non-cluster-head node for communication.
2. The energy-efficient data convergence algorithm based on communication distance control in a sensor network according to claim 1, wherein: the specific process of calculating the potential energy consumption of the candidate cluster head in step S3 is as follows:
wherein E iseRepresenting the energy consumed by the circuit in transmitting and receiving data, doDenotes a distance threshold value, εfsAnd εmpIndicating that the communication distance is less than and greater than the distance threshold d, respectivelyoThe power loss of different power amplification loss models is adopted, d represents the communication distance, and l represents the size of the data packet.
3. The energy-efficient data convergence algorithm based on communication distance control in a sensor network according to claim 1, wherein: step S4, when filtering formal cluster heads, respectively calculating communication distances d between nodes and cluster head nodesscAnd the communication distance d between the node and the Sink nodessAnd judging the energy consumption of the node.
4. The energy-efficient data convergence algorithm based on communication distance control in a sensor network according to claim 1, wherein: step two, the specific process of calculating the cluster size is as follows:
t1, at the beginning of each round, calculating the total cluster head number generated in the past n roundsAnd average cluster head number
T2, number of nodes surviving in this round NaliveCalculating the average cluster size Nalive/Cavg；
And T3, in the node clustering process, controlling the number of cluster members on the condition of the average clustering size calculated in the step T2.
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