CN105898764B - Multi-level energy heterogeneous wireless sensor network deployment method - Google Patents
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Abstract
The invention relates to a network deployment method based on multi-level energy heterogeneous. In the stage of establishing a network model, the invention constructs a circular hierarchical network model with randomly distributed nodes and uniform clustering, adopts a Leach algorithm to elect temporary cluster heads, then analyzes the influence of the randomly distributed network nodes on the scale difference of each cluster in the network, proposes a cluster scale balance algorithm to balance the number of nodes of each cluster, balance the energy consumption of the cluster heads and complete the construction of the temporary clusters. In the data routing stage, the problems of the temporary cluster constructed by the Leach algorithm are considered, formal cluster heads are reselected in the temporary cluster, the network routing path is further optimized, and then the optimal relay forwarding cluster head node is selected according to the residual energy of the cluster heads and the distance between the cluster heads. In the final network implementation deployment stage, the method accurately calculates the energy consumption rate of each position in the network based on the network model, the energy consumption model and the routing, finally completes the initial multi-level energy heterogeneous network deployment by utilizing the integer programming thought, and determines the network deploymentNSpecific deployment locations of individual nodes.
Description
Technical Field
The invention belongs to the technical field of energy hole avoidance strategies in wireless sensor networks, and particularly relates to a network deployment and routing method based on multi-level energy heterogeneous.
Background
In recent years, the common application of Wireless Sensor Networks (WSNs) in the fields of academic research, industrial production, military and national defense and the like brings great convenience and benefits to daily life of people, but the energy limitation and lower energy utilization efficiency of the WSNs greatly restrict the development of the WSNs. Therefore, how to effectively alleviate the Energy Hole problem (Energy Hole) has become a research hotspot. The deployment of the network is the basis of the WSNs, the overall performance of the network is determined by the deployment method in the WSNs, and the problem of energy holes can be effectively relieved by the optimized deployment mode.
At present, the existing wireless sensor network energy hole avoiding methods based on different deployment modes mainly include the following methods:
1) and (4) a node non-uniform distribution strategy. In a WSNs environment adopting multi-hop transmission, energy holes are generated due to the fact that node energy cost of an adjacent sink area is too fast, a non-uniform distribution strategy aims at the problem, the number of nodes correspondingly arranged in an area with high energy cost is increased, and the number of nodes used for relaying and forwarding data is increased. Lian J et al propose a node non-uniform distribution strategy, namely in a wireless sensor network, correspondingly adjusting the deployment density of nodes according to the distance value between the nodes and the sink. In data forwarding, energy overhead of the original nodes in the 'hot zone' is balanced. Bulut E adopts a node-in-turn sleep mode to save energy on the basis of non-uniform distribution of nodes, so that performance problems which are easy to occur in a system with non-uniform node deployment are avoided, namely signal interference, mutual conflict and information repetition caused by simultaneous working of a large number of nodes.
2) And (4) node energy heterogeneous strategy. The Yankee nations and the like propose a strategy of heterogeneous initial energy, namely, nodes with higher initial energy are deployed at positions where energy holes are easy to occur, and the occurrence of the energy holes is effectively relieved. Wanghui et al performs heterogeneous configuration of energy at an initial stage and elects cluster head nodes based on the energy distribution of each node in the network. When the energy distribution of the monitoring area is balanced, the energy consumption of the area is mainly reduced as a target key point, so that the nodes with relatively less average communication energy consumption are preferentially selected as cluster heads; when the energy difference of the monitored areas is obvious, the load balance in the monitoring range is mainly considered, so that the nodes with relatively more residual energy are preferentially selected as cluster heads.
Compared with a network deployment method with non-uniform distribution of nodes, the network deployment method based on multi-level energy isomerism can avoid the problems of large amount of redundant information and incomplete network coverage brought by the deployment method with non-uniform distribution of nodes to a certain extent. However, the method of only optimizing deployment cannot effectively improve the network energy consumption balance and greatly improve the network life cycle. Because of any deployment mode, the energy consumption imbalance between the cluster head nodes and the member nodes in the network still exists, and the energy consumption between the cluster heads is not balanced.
Disclosure of Invention
Aiming at the defects of the existing wireless sensor network energy hole avoiding method, the invention provides a wireless sensor network deployment method based on multilevel energy isomerism. The method accurately calculates the energy consumption rate of each position in the network based on a network model, an energy consumption model and routing, and finally completes a multi-level energy heterogeneous network deployment scheme by combining an integer planning idea. Effectively prolongs the service life of the network and relieves the problem of energy holes. The method comprises two stages of establishing a cluster topological structure and deploying a network, and comprises the following specific steps:
1) and establishing a cluster topological structure. The establishment of the cluster topological structure comprises three parts of temporary cluster construction, cluster scale optimization realization and formal cluster head election. Firstly, after the temporary cluster is constructed, the formula is used
And calculating a threshold value T, generating a random number Drand by the nodes which have not elected the cluster head in the 1/P round, wherein the random number Drand satisfies 0 < Drand < 1, and when Drand < T, electing the nodes as temporary cluster heads. Calculating the mean value of the number of the member nodes of the neighbor cluster according to a formula; if N is present0＞NaverIf so, triggering a feedback mechanism by the temporary cluster head, starting a cluster scale balancing algorithm, and aiming at each neighbor cluster CiFind out the distance CiMost recent Nac(i) A node compensating it to CiAnd updates the temporary cluster head ID of the compensating node. The cluster scale equilibrium method is shown in figure 4. According to the formula
The probability of a node being elected to be a cluster head is calculated. And finally, finishing the construction of the cluster topological structure.
2) And (5) network deployment. The network deployment phase of the present invention includes two parts, data transmission routing and energy consumption rate calculation. In the data transmission routing part, after the common member nodes collect data, the common member nodes firstly send the data to the cluster head nodes to which the common member nodes belong, and the cluster head nodes perform fusion processing and then forward the data to the next hop relay cluster head nodes, wherein the formula is used for
Obtaining the selection probability of the next-hop relay cluster head, selecting a cluster head node which can make the sum of the transmission distances relatively shorter and has higher current residual energy as the next-hop relay cluster head, and the data transmission process is shown in fig. 2; the energy consumption rate analysis part divides the nodes into common member nodes, outmost cluster head nodes and inner-layer cluster head nodes, and respectively calculates the energy consumed in a unit life cycle, namely the energy consumption rate. And (4) finishing network optimization deployment by combining an integer programming idea.
The invention has the following beneficial effects: 1) in the stage of constructing the cluster topological structure, the invention provides a cluster scale balance algorithm to generate an optimized network topology, so that the energy consumption of each cluster head in the network is more balanced. 2) In the data forwarding stage, the invention optimizes the route path selection and considers two factors at the same time: the distance relation ratio among the sending end cluster head, the receiving end cluster head and the sink and the current remaining energy ratio of each cluster head are used for reducing network communication energy consumption and improving network energy utilization efficiency. 3) In the network deployment stage, the energy consumption rate of each position in the network is accurately calculated based on the network model, the energy consumption model and the routing. And combining an integer programming idea to complete the multilevel energy heterogeneous deployment. The energy consumption level of each part of the network is more balanced, and the problem of energy holes is effectively relieved.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of data transmission according to the present invention.
Fig. 3 is a schematic diagram of a network model according to the present invention.
FIG. 4 is a schematic diagram of a cluster scale balancing process of the present invention.
Fig. 5 is a graph comparing network performance.
Detailed Description
1) Network initialization
FIG. 3 is a schematic diagram of a network model, wherein the network is divided into m layers of rings, C respectively, from inside to outside1C2…CmEach layer of rings has the same width and the radius is r1r2…rmAnd r is2-r1＝…＝rm-rm-1 ═ d, d is the width of the ring, satisfying d ═ R/m, i.e. R1＝d，r2＝2d，rmMd. The total number of nodes in the network is N, Ni、SiRespectively represent distribution in the i-th layerThe number of nodes and the size of the torus area. Uniform clustering in each ring, NciRepresenting the total number of clusters contained in the i-layer circle. Due to the large number of nodes and the random deployment mode, the nodes in the network can be assumed to be uniformly distributed theoretically, and the density is rho.
The concrete model is as follows:
① assume that N nodes are randomly scattered at N locations within a circular area of radius R, remain stationary after deployment, and sink is located in a central zone.
② the node energy in the network is heterogeneous, can select a plurality of nodes with different electric quantities, and it also has the current residual energy E for obtaining itselfrThe ability of the cell to perform.
③ the wireless transmission power of the node in the wireless sensor network is adjustable, that is, the node can adjust its own message transmission power according to the distance between the node and the receiving end.
④ the operation of each node is independent, i.e. each node remains non-interfering throughout the operation of the network.
⑤ the working time of the network is calculated by cycle, each node in the network will complete the data collection task of the monitoring object in charge of itself in one working cycle, and send the collected data packet to the sink.
⑥ sink has more powerful computing, data storage and other capabilities than common nodes, and has abundant energy.
⑦ the sensor nodes are organized into clusters, and the cluster head of each cluster transmits data to the sink in a multi-hop communication mode among clusters after completing the data collection task in the clusters.
2) Calculating the value range of the cluster radius
In order to ensure the full coverage of the network, the invention has been proved by the research on the premise of uniform clustering, the quantity of the cluster heads is equal to the quantity of the cluster heads
Number of cluster heads up equal to
Wherein A is the area of the monitoring region, rcIs the cluster radius.
Cluster radius rcThe maximum value of (d) is:
cluster radius rcThe minimum value of (d) is: r isc＝d/3。
3) And constructing a temporary cluster. A schematic of the cluster scale equilibration process is shown in figure 4.
① after the temporary cluster is constructed, according to the formula
And calculating a threshold value T, generating a random number Drand by the nodes which have not elected cluster heads in 1/P round, wherein the random number Drand meets the condition that 0 is more than Drand less than 1, when Drand is less than T, electing the nodes as temporary cluster heads, P is the ratio of the total number of cluster heads in the network to the total number of nodes, and r is the current round number.
② according to the formula
Calculating the mean value of the number of member nodes of the neighbor cluster, wherein n is the number of the neighbor cluster,
number of nodes of ith neighbor cluster, N0The number of nodes of the local cluster;
③ if N0＞NaverIf so, triggering a feedback mechanism by the temporary cluster head, starting a cluster scale balance algorithm, and calculating the difference value between the number of member nodes in the local cluster and the average value: n is a radical ofredundant(0)＝N0-Naver；
④ performing traversal operation if the number of nodes in the neighbor cluster
Adding the node into a compensation set CC, and calculating the number of the missing nodes as follows:
⑤ elements in the compensation set CC are in accordance with Nlack(i) The values of the cluster are arranged in a descending order, the order of the values is used as a compensation order, and the compensation of the local cluster is waited;
⑥ according to the formula
Calculating the number of nodes of each neighbor cluster in the CC set which can be distributed by the local cluster;
⑦ traverse all neighbor clusters in the CC set in turn, for Nlack(i) Neighbor cluster with larger value is compensated preferentially, and for each neighbor cluster CiFind out the distance CiMost recent Nac(i) A node compensating it to CiAnd updates the temporary cluster head ID of the compensating node.
4) Formal cluster construction
① selecting a set of candidate cluster heads
Defining a lower energy limit E for electing cluster headsfloorWhen the temporary cluster head elects the formal cluster head, only the current energy value ratio E of the membersfloorAnd when the height is high, the user is qualified to join the candidate set to participate in the election.
② election formal cluster head
Definition PCHOpportunity factor, P, for a node in a set of candidate cluster heads to become a cluster headCHThe value of (A) is closely related to the following three elements: the current energy left by the node, the average distance between the node and other members in the cluster, and the distance between the node and the sink. PCHThe calculation formula of (2) is as follows:
Eresidual(i) representing the amount of energy currently left by the node,
representing the mean of the current energies of all nodes in the set, daverAs candidate cluster heads and others within the clusterMean value of nodal distances, rcIs the cluster radius size, dtoSinkAnd the length of the interval between the candidate cluster head and the sink is shown, and R represents the network radius. Omega1,ω2,ω3As a weight value, the value range is [0,1 ]]Has omega1+ω2+ω3＝1。
5) Data routing-data forwarding
① first, cluster heads with a distance from the sink less than the distance between the current cluster and the sink are added to the candidate relay set j.c.
② secondly, selecting relay cluster heads from j.c set by taking the two factors of transmission distance between cluster heads and their current energy ratio into consideration at the same time, namely:
c(icluster,jCluster) Represents the data from iClusterTransmitting to next hop relay cluster head jClusterThe size of the probability of (c). d (i)Cluster,jCluster) Is a cluster head iCluster、jClusterThe same holds true for d (i)ClusterSink) is a cluster head iClusterDistance to sink. Ej clusterAs a relay cluster head jClusterThe current remaining energy of the energy storage device,
the mean of the remaining energy of all candidate relay cluster heads in the j.c set is α, and 0 < α < 1.
6) Node energy consumption rate analysis
① the invention firstly classifies the energy consumption type in the network, including five types, energy consumption for sending data in a cluster, energy consumption for receiving data in the cluster, energy consumption for fusing data in the cluster, energy consumption for receiving data between clusters, and energy consumption for sending data between clusters.
② the node types are divided into common member nodes, outmost cluster head nodes and inner-layer cluster head nodes, and the energy consumption conditions are analyzed respectively.
Common member nodes: the energy consumption calculation result is as follows, because the energy consumption calculation result is only responsible for sending the sensed information to the cluster head to which the energy consumption calculation result belongs:
sender node iNode (C)Energy consumption of ETx(iNode (C)) Wherein E iselecWhich represents the energy consumption of the transmitting circuit (receiving circuit) when transmitting (receiving) every bit of data. When the distance d is less than the threshold d0, the power amplifier selects to use a free space model, and the energy consumed by the power amplifier is l epsilon every time lbit data is transmittedfsd2(ii) a When the distance d is larger than or equal to a threshold value d0, the power amplifier selects to use a multipath attenuation model, and the energy consumed by the power amplifier is epsilon every time lbit data is transmittedmpd4And CH is a cluster head.
Outmost layer cluster head node: the system is responsible for receiving data transmitted by member nodes in a cluster, performing data fusion on the data, and transmitting the processed data to a next hop cluster head. Therefore, the energy consumption calculation results are as follows:
wherein l is data volume, unit bit, g is data fusion rate, EdfEnergy consumed by the nodes for fusing data of 1 unit of bits is required; epsilonfsRepresents the energy overhead, ε, generated by the amplifier when selecting the free space model, when the information amount of 1bit is transmitted through 1 unit lengthmpWhich represents the energy overhead incurred when the amplifier chooses to use the multipath fading model, every 1bit of information is transmitted over 1 unit of length.
Inner-layer cluster head node: it is responsible for collecting intra-cluster data and fusion, collecting inter-cluster data, and transmitting the processed data to the next hop cluster head. Therefore, the energy consumption calculation results are as follows:
③ calculate the node energy consumption rate.
7) And completing network deployment. The ultimate goal is to maximize the life cycle of the network within a given construction cost, namely: maxl. The solution is whether node K is deployed at location j. Namely:
where K represents node K, where N represents the nth node for K and the nth position for j.
And is constrained by the following conditions:
c denotes the total cost of the network node, CKRepresenting the cost price, v, of node KKRepresenting the rate of energy consumption at the node, the life cycle of the nodeIs 1KAnd l represents the minimum life cycle required by the network.
And finally, obtaining the energy consumption rate arrangement of each position in the network and the sequencing of the charge quantity of each node. Mapping the two sets one by one to finally obtain xKjI.e. determining the network deployment.
The invention is compared and analyzed with three algorithms of LEACH, NDS and LBCA, the performance comparison result of the network is shown in figure 5, and figure 5 shows that the life cycle and the energy consumption balance of the network are better than those of other three methods.
Claims (3)
1. A multi-level energy heterogeneous wireless sensor network deployment method is characterized by comprising two steps of cluster topological structure establishment and network deployment, wherein the cluster topological structure establishment comprises three steps of temporary cluster construction, cluster scale optimization realization and formal cluster head selection, and the network deployment comprises two steps of data transmission routing and energy consumption rate calculation to obtain a node energy consumption rate; optimizing the node deployment of the network by combining an integer programming idea according to the node energy consumption rate;
the method comprises the following steps:
1) carrying out network initialization, dividing the network into m layers of circular rings, wherein the width d of each layer of circular rings is the same, and uniformly clustering the circular rings;
2) calculating to obtain the radius of the cluster re；
3) And (3) performing temporary cluster construction: calculating a threshold value T, calculating the mean value of the number of member nodes of the neighbor cluster, and if the number of the nodes of the local cluster is NOMean value N of member node numbers of neighbor clustersaverIf the number of the nodes in the neighbor cluster is N, the temporary cluster head triggers a feedback mechanism, starts a cluster scale balance algorithm, calculates the difference value between the number of member nodes in the local cluster and the mean value of the number of the member nodes in the neighbor cluster, executes traversal operation, and if the number of the nodes in the neighbor cluster is Ni Adjacent to< mean value of number of member nodes of neighbor cluster NaverAdding the node number into a compensation set CC, and calculating the number N of the missing nodeslack(i) The number of the missing nodes N is the number of the elements in the compensation set CClack(i) The values of (a) are arranged in descending order, the order of which isWaiting for the compensation of the local cluster for the compensation sequence, calculating the number of nodes of each neighbor cluster in the compensation set CC to which the local cluster can be allocated, traversing all the neighbor clusters in the compensation set CC in sequence, and counting the number N of the missing nodeslack(i) Preferential compensation of larger neighbor clusters, for each neighbor cluster CiFind out the distance CiMost recent Nac(i) A node compensating it to the neighbor cluster CiUpdating the temporary cluster head ID of the compensation node;
4) formal cluster construction: firstly, selecting a candidate cluster head set, and then electing a formal cluster head;
5) data routing-data forwarding: adding cluster heads with the distance from the adjacent sink region smaller than the distance between the current cluster and the adjacent sink region into the candidate relay set j.c, and taking the transmission distance between the cluster heads and the current energy ratio of the cluster heads into consideration, and selecting relay cluster heads from the j.c set, namely:
c(icluster,jCluster) Represents the data from iClusterTransmitting to next hop relay cluster head jClusterThe size of the probability of (c);
d(icluster,jCluster) Is a cluster head iCluster、jClusterThe same holds true for d (i)ClusterSink) is a cluster head iClusterDistance to sink;
α is weight coefficient, and 0 < α < 1;
6) analyzing the node energy consumption rate: firstly, classifying energy consumption types in a network, then classifying node types into common member nodes, outmost cluster head nodes and inner-layer cluster head nodes, respectively analyzing the energy consumption conditions of the common member nodes, and finally calculating the energy consumption rate of the nodes;
7) and (3) completing network deployment: the final objective is to maximize the life cycle of the network within the given construction cost, the solved node K is deployed at the position j, the energy consumption rate arrangement of each position in the network and the ordering of the charge quantity of each node are finally obtained, the two sets are mapped one by one, and the network deployment is finally completed.
2. The deployment method of the multilevel energy heterogeneous wireless sensor network according to claim 1, wherein the establishment of the network model comprises: based on an energy consumption model and routing selection, after energy consumption rates of nodes in each position of the network are calculated, node deployment of the network is optimized by combining an integer programming idea.
3. The deployment method of the multilevel energy heterogeneous wireless sensor network according to claim 1, characterized in that in the stage of constructing the cluster structure, a cluster scale balance algorithm is adopted to adjust the size of each cluster scale, and the cluster structure is optimized; the cluster scale balance algorithm specifically comprises the following steps:
the traversal operation is performed and the traversal operation is performed,
adding the node into a compensation set CC, and calculating the number of the missing nodes as follows:
the elements in the compensation set CC are counted according to the number N of the missing nodeslack(i) The values of the cluster are arranged in a descending order, the order of the values is used as a compensation order, and the compensation of the local cluster is waited;
according to the formula
Calculating the number of nodes of which the local cluster can be distributed to each neighbor cluster in a compensation set CC, wherein NONumber of local cluster nodes, NaverIs the mean value of the number of member nodes of the neighbor cluster,
the number of nodes of the neighbor cluster;
sequentially traversing all neighbor clusters in the compensation set CC for Nlack(i) Neighbor cluster with larger value is compensated preferentially, and for each neighbor cluster CiFind out the distance CiMost recent Nac(i) A node compensating it to CiAnd updates the temporary cluster head ID of the compensating node.
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| CN108184239B (en) * | 2016-12-08 | 2021-03-26 | 中国科学院沈阳自动化研究所 | Relay node deployment method in time delay limited wireless sensor network |
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| CN106887038A (en) * | 2017-02-20 | 2017-06-23 | 张光辉 | A kind of orthopaedics inside-fixture formation system and method |
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| CN109618368B (en) * | 2018-12-28 | 2021-02-09 | 国网江苏省电力有限公司电力科学研究院 | Wireless sensor network data fusion scheduling algorithm for time delay and power consumption joint optimization |
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