CN111490898A - Data aggregation method, system, storage medium and wireless sensor network - Google Patents

Data aggregation method, system, storage medium and wireless sensor network Download PDF

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CN111490898A
CN111490898A CN202010197692.9A CN202010197692A CN111490898A CN 111490898 A CN111490898 A CN 111490898A CN 202010197692 A CN202010197692 A CN 202010197692A CN 111490898 A CN111490898 A CN 111490898A
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data
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张朝辉
李靖
刘倩
刘三阳
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/04—Network management architectures or arrangements
    • H04L41/044—Network management architectures or arrangements comprising hierarchical management structures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/248—Connectivity information update
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention belongs to the technical field of wireless communication, and discloses a data aggregation method, a system, a storage medium and a wireless sensor network.A node is divided into different layers according to the hop count of the node in the network, and relay nodes with a certain proportion are selected from the nodes of the different layers; different initial energies are set for different layers of nodes, and because the data packets of different nodes have different sizes, the different layers of nodes adopt corresponding data aggregation coefficients according to the actual data requirements of the network during data transmission; and dynamically updating the topological structure of the tree in real time in the network operation process so as to prolong the service life of the nodes. The invention applies the data aggregation technology, ensures the authenticity and accuracy of data, removes the redundancy of the data, reduces the load of nodes, ensures the low-energy consumption operation of the nodes in the network, prolongs the service life of the network, and provides a new idea for the cross research of mathematics and engineering problems by establishing a model and solving the model to obtain results.

Description

Data aggregation method, system, storage medium and wireless sensor network
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a data aggregation method, a data aggregation system, a storage medium and a wireless sensor network.
Background
Currently, when a wireless sensor network classifies collected data according to a certain type and transmits the data, the data may be homogeneous data with the same property or heterogeneous data with different properties. In practical applications, the data demand is often heterogeneous data such as images, videos and the like, the data types are heterogeneous and the data dimension is large, so that the requirement on the transmission function of the node is higher. In the process of data transmission, a node generally takes the measure of performing aggregation and packaging processing on collected data and data transmitted by child nodes, that is, a data aggregation technology. For the universality of the network, the nodes in the network are all heterogeneous: different node energies, different functions, different communication capabilities, etc. The tree topology has stronger survivability, and the maximum connected subgraph is generally the basis for researching the topology attributes. In the data transmission of the network, the degree, the edge and the weight of the node in the multilayer network have an internal relation and an internal rule with the structure of the network topology, the terminal node, the routing node and the gateway node can be switched, the fast access and transmission of big data are researched, and the dynamic state to each network source node and the service quality requirement meeting the data transmission at any time are provided. The optimal topology has stronger data transmission capacity, dynamic maintenance and reconstruction of the optimal topology are necessary to adapt to a rapidly changing application scene, the survivability of a multi-layer sensing network under the influences of electromagnetic interference, non-line-of-sight transmission (namely, obstacles need to be avoided in the data transmission process) and refraction, reflection, absorption and the like of signals in the transmission process can be enhanced, the survivability of large-scale dynamic networking is enhanced to the maximum extent, and a network data safety and real-time transmission technology oriented to an actual combat environment is realized.
In the topology research of the current mainstream wireless sensor network, the topology optimization design of the network is often performed for a specific network target, for example, the main target of the topology control algorithm design based on the MST is to maximize the service life of the network, which is also a main research target. Although this algorithm considers multiple objective optimizations, it only optimizes the energy and robustness of the network. In practical applications, heterogeneous WSNs are increasingly widely used, topology control algorithms are advanced, and for differences of the heterogeneous WSNs, a topology algorithm suitable for a heterogeneous sensor network needs to be provided to solve more complex practical problems. The prior art has the following defects: (1) it is difficult to extend the local optimization to the global optimization through the network, reflecting the performance of the maximum connected subgraph of the study graph to obtain the properties of the whole graph. Especially for distributed multi-layer networks, there is less research. (2) The network data transmission topology constructed under the heterogeneous big data background has weak survivability, and the routing topology structure of the whole network is realized through the maintenance and reconstruction of the local network topology structure, so that the big data transmission technology under some application backgrounds is difficult to realize. (3) The universality of the data aggregation technology is not high, and the constructed network has certain difficulty in ensuring the authenticity and accuracy of data and removing the redundancy of the data. Meanwhile, low delay of data transmission and low energy consumption operation of nodes in the network are guaranteed, and the technical difficulty in prolonging the service life of the network is high.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the prior art is difficult to extend from local optimization to global optimization through a network.
(2) The network data transmission topology constructed under the heterogeneous big data background in the prior art has low survivability.
(3) The universality of the data aggregation technology in the prior art is not high, and the authenticity and the accuracy of data are ensured by a constructed network; meanwhile, low delay of data transmission and low energy consumption operation of nodes in the network are guaranteed, and the technical difficulty in prolonging the service life of the network is high.
The difficulty in solving the above problems and defects is: considering a suitable topology, a data aggregation approach, is challenging due to the uncertainty, complexity, and dimensionality of the data to be transmitted of the signal propagation medium.
The significance of solving the problems and the defects is as follows: the realization of the real-time transmission of big data with low energy consumption under the optimal heterogeneous network topology structure is significant, and plays a great role in the development of science and technology. Basic service networks such as internet of things, intelligent transportation, modern medical treatment and the like acquire required information through rapid transmission of data, and signal noise is often generated due to network damage, hardware loss, geographic position complexity and the like in the transmission process of the data, so that inaccuracy of finally acquired data or data delay is caused. However, these repair costs are relatively high and are difficult to avoid again after repair, so it is very important to dynamically maintain the optimal topology quickly under the existing conditions to satisfy the safe big data transmission, which can satisfy both the random access, real-time, accuracy of data transmission and the robustness of the network, and is also the focus of the research of the present invention, the practical problem of design, strong applicability and certain support for the research of multiple practical application research fields.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a data aggregation method, a data aggregation system, a storage medium and a wireless sensor network.
The invention is realized in such a way, and discloses a data aggregation method, which divides nodes into different layers according to the hop count of the nodes in a network, and the nodes of the different layers select relay nodes with a certain proportion; different initial energies are set for different layers of nodes, and due to the fact that data packets of different nodes have different sizes, corresponding data aggregation coefficients are adopted according to actual data requirements of a network during data transmission; and dynamically updating the topological structure of the tree in real time in the network operation process so as to prolong the service life of the nodes.
Further, the data aggregation method comprises:
firstly, after wireless sensor nodes are arranged in a network, a minimum routing tree is constructed from a Sink node by using a greedy algorithm according to the distance, and the network is layered from the Sink node according to the communication radius of the nodes;
secondly, selecting a certain proportion of nodes in each layer as relay nodes;
thirdly, setting the energy of each layer of nodes;
and fourthly, performing local routing tree reconstruction after the network runs for a period of time, returning to the first step when the service life of the network is not terminated, and otherwise, terminating the network to output final data information.
Further, the first step includes: layering the network nodes according to the distance between the nodes in the network and the Sink node, gradually establishing a minimum distance routing tree from the Sink node according to the minimum distance by using a greedy algorithm, and selecting the aggregation coefficient of each layer:
Figure BDA0002418210490000031
wherein hopmaxThe maximum hop count of the node in the network, hop (i) is the hop count of node i.
Further, the second step includes: for all nodes, the proportion of randomly selected relay nodes in each layer is as follows:
Figure BDA0002418210490000041
where ρ isn-hopIs the proportion of the i-th relay node, nn-hopThe number of the ith layer node is N, and the total number of the nodes is N;
the number of the ith layer of relay nodes is as follows:
Figure BDA0002418210490000042
further, the third step includes: the node with the largest hop count has energy EinitialDefinition of energy possessed by nodes at different hop levelsComprises the following steps:
Figure BDA0002418210490000043
wherein Ei-hopIs the energy of all nodes at the ith hop level.
Further, the fourth step includes: the method for reconstructing the local routing tree of the network comprises the steps of reselecting and adjusting child nodes and father nodes of nodes in the network, calculating the energy consumption required by each node for collecting, aggregating and forwarding data multiplied by the updated round number after the network runs for a certain round number, and performing same-layer transfer operation on the child nodes if the energy consumption is more than or equal to the residual energy of the nodes or the nodes are compared; the specific operation method for transferring the child nodes comprises the following steps: and searching the node with the highest residual energy in the same layer of the father node in the communication radius as a relay father node to complete local route reconstruction.
It is another object of the present invention to provide a program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising: dividing the nodes into different layers according to the hop counts of the nodes in the network, and selecting relay nodes in a certain proportion from the nodes in the different layers; different initial energies are set for different layers of nodes, and due to the fact that data packets of different nodes have different sizes, corresponding data aggregation coefficients are adopted according to actual data requirements of a network during data transmission; and dynamically updating the topological structure of the tree in real time in the network operation process so as to prolong the service life of the nodes.
Another object of the present invention is to provide a data aggregation system implementing the data aggregation method, the data aggregation system including:
the network layering module is used for constructing a minimum routing tree from a Sink root node by using a greedy algorithm according to the distance and layering the network from the Sink node according to the communication radius of the node;
the relay node selection module is used for selecting a certain proportion of nodes in each layer as relay nodes;
the node energy setting module is used for setting the energy of each layer of nodes;
and the local routing tree reconstruction module is used for reconstructing the local routing tree after the network runs for a period of time.
Another object of the present invention is to provide a wireless communication system incorporating the data aggregation system.
Another object of the present invention is to provide an application of the data aggregation method in data processing of a wireless sensor network.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention extends to global optimization through local optimization of the network, reflects the performance of the maximum connected subgraph of the research graph, and can obtain the property of the whole graph. The research on the distributed multi-layer network provides a theoretical basis and a new idea. The heterogeneous nodes are used for completing the transmission of the data of different types, and the transmission mode can meet the requirements of real-time transmission and data accuracy. In the topology optimization process, the thesis adopts a mode of adjusting the structure of the network through three steps, namely, relay node selection, heterogeneous energy setting and dynamic local tree structure adjustment, and the network has a strong topology structure after the three steps of optimization means.
The invention constructs the optimal network data transmission topology under the heterogeneous big data background, and realizes the routing topology structure of the whole network through the maintenance and reconstruction of the local network topology structure, thereby being more beneficial to the big data passing technology under some application backgrounds.
The invention applies flexible data aggregation technology, thereby not only ensuring the authenticity and accuracy of the data, but also removing the redundancy of the data. Meanwhile, low delay of data transmission and low energy consumption operation of nodes in the network are guaranteed, the service life of the network is prolonged, the operation abstraction of the actual network is a mathematical problem, the result is obtained by establishing a model and solving the model, the innovation is strong, and a new idea is provided for cross research of mathematics and engineering problems.
The tree topology optimization algorithm constructed by the invention is based on the transmission of heterogeneous data, the transmission of the heterogeneous data has higher requirements on network topology, and the service quality requirements of the transmission of the heterogeneous data are difficult to meet by a common homogeneous network.
Drawings
Fig. 1 is a flowchart of a data aggregation method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a data aggregation system provided in an embodiment of the present invention;
in the figure: 1. a network layering module; 2. a relay node selection module; 3. a node energy setting module; 4. and a local routing tree reconstruction module.
Fig. 3 is a flowchart of an implementation of a data aggregation method according to an embodiment of the present invention.
Fig. 4(a) and 4(b) are tree topology data transmission models provided by the embodiment of the present invention.
Fig. 5(a) and 5(b) are diagrams illustrating selection effects of a network relay node according to an embodiment of the present invention.
Fig. 6(a) and fig. 6(b) are schematic diagrams of energy consumption effects provided by embodiments of the present invention.
Fig. 7 is a diagram illustrating the effect of setting the initial energy provided by the embodiment of the present invention.
Fig. 8(a), fig. 8(b) and fig. 8(c) are diagrams illustrating the effect of load balancing adjustment of nodes in a heterogeneous network according to an embodiment of the present invention.
Fig. 9 is a diagram illustrating an effect of selecting an optimal round number according to an embodiment of the present invention.
Fig. 10 is a graph showing the effect of different algorithms provided by the embodiment of the present invention on different numbers of rounds and ratios of surviving nodes.
Fig. 11 is a schematic diagram of comparing life cycles of different algorithms at different nodes according to an embodiment of the present invention.
Fig. 12 is a schematic diagram of the comparison of the number of data packets in different algorithms according to the embodiment of the present invention.
Fig. 13 is a schematic diagram of energy variance comparison at 200 nodes for different algorithms provided by an embodiment of the present invention.
FIG. 14 is a diagram illustrating comparison of energy variances at 300 nodes for different algorithms provided by embodiments of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a data aggregation method, system, storage medium, and wireless sensor network, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the data aggregation method provided by the present invention includes the following steps:
s101: after the wireless sensor nodes are arranged in the network, a greedy algorithm is used for constructing a minimum routing tree from a Sink root node according to the distance, and the network is layered from the Sink node according to the communication radius of the nodes;
s102: selecting a certain proportion of nodes in each layer as relay nodes;
s103: setting the energy of each layer of nodes;
s104: and (4) after the network operates for a period of time, local routing tree reconstruction is carried out, the service life of the network is not terminated, the S101 is returned, and otherwise, the network is terminated to output final data information.
As shown in fig. 2, the data aggregation system provided by the present invention includes:
and the network layering module 1 is used for constructing a minimum routing tree from a Sink root node by using a greedy algorithm according to the distance and layering the network from the Sink node according to the communication radius of the node.
And the relay node selection module 2 is used for selecting a certain proportion of nodes in each layer as relay nodes.
And the node energy setting module 3 is used for setting the energy of each layer of nodes.
And the local routing tree reconstruction module 4 is used for reconstructing the local routing tree after the network runs for a period of time.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
As shown in fig. 3, the data aggregation method provided by the present invention specifically includes the following steps:
firstly, layering network nodes according to the distance between the nodes in the network and a Sink node. And gradually establishing a minimum distance routing tree from a Sink root node according to the minimum distance by using a greedy algorithm, and then selecting the aggregation coefficient of each layer. Specifically, the formula is shown as follows:
Figure BDA0002418210490000081
wherein hopmaxThe maximum hop count of the node in the network, hop (i) is the hop count of node i.
Step two, in order to ensure that more nodes are selected as relay nodes in the layer with more nodes, and less nodes are selected as relay nodes in the layer with less nodes, for all the nodes, the proportion of randomly selecting the relay nodes in each layer is as follows:
Figure BDA0002418210490000082
where ρ isn-hopIs the proportion of the i-th relay node, nn-hopThe number of the ith layer node is shown, and N is the total node number.
The number of the ith layer of relay nodes is as follows:
Figure BDA0002418210490000083
step three, assuming that the node energy with the maximum hop count is EinitialMeanwhile, the invention considers the influence of the aggregation ratio rho, and the energy of the nodes in different hop-number layers is defined as:
Figure BDA0002418210490000084
wherein Ei-hopIs the energy of all nodes at the ith hop level.
And fourthly, the network carries out local routing tree reconstruction. The reconstruction method is to carry out reselection adjustment on the child nodes and the parent nodes of the nodes in the network. After the network runs a certain number of rounds, calculating the energy consumption required by each node for collecting, aggregating and forwarding data multiplied by the updated number of rounds, and if the energy consumption is more than or equal to the residual energy of the nodes or the nodes are compared, performing same-layer transfer operation on the child nodes of the nodes; the specific operation method for transferring the child nodes comprises the following steps: and searching the node with the highest residual energy in the same layer of the father node in the communication radius as a relay father node to complete local route reconstruction.
As shown in fig. 10, the energy consumption of the network is analyzed from a theoretical point of view:
first, for the DADADAT algorithm, for node i, parent node j, assume that there are n nodes in the network, relay node i receives liBit data and generate miThere are three aspects to the energy consumption of node i: the energy consumption for receiving data, the energy consumption for transmitting data and the energy consumption for aggregating data are specifically shown as the following formulas:
Figure BDA0002418210490000091
Erec.i＝li*Eelec；
Eda.i＝(li+mi)*ρi*Eelec；
and step two, the energy consumption of the node i is as follows:
Figure BDA0002418210490000092
the energy consumption of each round is:
Figure BDA0002418210490000093
Figure BDA0002418210490000094
assuming an average per nodeGenerating m bits of energy with an average distance d between nodesC-SFrom the above equation, the minimum value of energy can be derived as:
Figure BDA0002418210490000095
thirdly, the algorithm DA-L TRA provided by the invention assumes that k leaf nodes and n-k relay nodes exist in the network, and the number of the nodes is n at the i layeri-hopCalculating the number of relay nodes of the i layer as
Figure BDA0002418210490000096
Figure BDA0002418210490000097
Fourthly, for a leaf node j, wherein the relay node is o, the energy consumption is as follows:
Figure BDA0002418210490000098
fifthly, for a relay node p, wherein the relay node is q, the energy consumption is as follows:
Figure BDA0002418210490000099
sixthly, the energy consumption of the algorithm DA-L TRA is as follows:
Figure BDA0002418210490000101
the maximum value of the above formula is:
Figure BDA0002418210490000102
the maximum energy consumption of each round of the method is compared with the minimum energy consumption of a comparison algorithm DADAT, and the effectiveness of the algorithm in energy consumption is verified from a theoretical point of view.
The technical effects of the present invention will be described in detail with reference to experiments.
1. The simulation conditions comprise that the experimental environment is a Win764 bit system and MAT L AB 2012a software, the CPU is i7-4720HQ, the memory is 8.00GB, the proposed algorithm and the comparison algorithm are simulated in the same network environment, the initial setting of the network is the same, and the simulation parameters are shown in the following table.
TABLE 1 values of the experimental parameters
Figure BDA0002418210490000103
2. Simulation content and simulation result:
simulation 1: the model carries out simulation on the optimal reconstruction round number of the network topology. As shown in fig. 9, the number of the selected network-optimized update rounds is 90. This is because if the network is updated too frequently (less than the optimal value of 90), the nodes in the network will consume a large portion of energy to perform data operations, resulting in a reduction in the overall network lifetime; if the network topology is updated slowly, some nodes in the network which undertake excessive data forwarding will consume energy rapidly, so that the dead nodes appear early and the service life of the network is affected. During simulation, the optimal updating times of the network are selected and fixed, and the network utilization rate can be maximized by performing topology evolution in an optimal environment. It can be seen from the figure that the lifetime of the network is reduced by more than one time when the network update times reach more than 110 times or less than 70 times, which also shows that the data calculation energy consumption of the network and the energy consumption of the transmitted data have great influence on the lifetime of the network.
The method DA-L TRA effectively prolongs the service life of the network and delays the death time of the first node in the network by 3000 rounds as shown in FIGS. 10 and 11, because the DA-L TRA is better than the DADADAT algorithm in the aspects of setting the initial energy of the nodes and selecting the relay nodes under the condition of heterogeneous networks, and the load of the network is balanced by adopting a local tree reconstruction technology in the maintenance stage of a later tree.
Simulation 3: the algorithm provided by the invention compares the number of the data packets with the existing similar algorithm under the same simulation data, so that the effectiveness, the packet loss rate and the like of the data can be reflected. As shown in fig. 12, the number of the final obtained data packets is the most under 200 nodes and 300 nodes, because the method adjusts the selection manner of the relay node according to the actual requirement of the network layer, and selects a part of the nodes to become the relay nodes in different layers according to the requirement of the aggregation ratio, thereby achieving the requirement of low packet loss rate of the data packets.
And (4) simulation: the algorithm provided by the invention is compared with the existing similar algorithm in the same simulation data, so that the load balance of the nodes can be reflected. As shown in fig. 13 and fig. 14, the energy consumption variance of the nodes of the algorithm of the present invention is the lowest under different nodes, i.e. the load balance is due to the compared algorithms. This is because the present invention uses a local routing tree tuning technique that extends from local optimization to global topology optimization.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A data aggregation method is characterized in that the data aggregation method divides nodes into different layers according to the hop count of the nodes in a network, and the nodes of the different layers select relay nodes with a certain proportion; different initial energies are set for different layers of nodes, and due to the fact that data packets of different nodes have different sizes, corresponding data aggregation coefficients are adopted according to actual data requirements of a network during data transmission; and dynamically updating the topological structure of the tree in real time in the network operation process so as to prolong the service life of the nodes.
2. The data aggregation method of claim 1, wherein the data aggregation method comprises:
firstly, after wireless sensor nodes are arranged in a network, a minimum routing tree is constructed from a Sink node by using a greedy algorithm according to the distance, and the network is layered from the Sink node according to the communication radius of the nodes;
secondly, selecting a certain proportion of nodes in each layer as relay nodes;
thirdly, setting the energy of each layer of nodes;
and fourthly, performing local routing tree reconstruction after the network runs for a period of time, returning to the first step when the service life of the network is not terminated, and otherwise, terminating the network to output final data information.
3. The data aggregation method of claim 2, wherein the first step comprises: layering the network nodes according to the distance between the nodes in the network and the Sink node, gradually establishing a minimum distance routing tree from the Sink node according to the minimum distance by using a greedy algorithm, and selecting the aggregation coefficient of each layer:
Figure FDA0002418210480000011
wherein hopmaxThe maximum hop count of the node in the network, hop (i) is the hop count of node i.
4. The data aggregation method of claim 2, wherein the second step comprises: for all nodes, the proportion of randomly selected relay nodes in each layer is as follows:
Figure FDA0002418210480000012
where ρ isn-hopIs the proportion of the i-th relay node, nn-hopThe number of the ith layer node is N, and the total number of the nodes is N;
the number of the ith layer of relay nodes is as follows:
Figure FDA0002418210480000021
5. the data aggregation method of claim 2, wherein the third step comprises: the node with the largest hop count has energy EinitialThe energy possessed by the nodes at different hop-count levels is defined as:
Figure FDA0002418210480000022
wherein Ei-hopIs the energy of all nodes at the ith hop level.
6. The data aggregation method of claim 2, wherein the fourth step comprises: the method for reconstructing the local routing tree of the network comprises the steps of reselecting and adjusting child nodes and father nodes of nodes in the network, calculating the energy consumption required by each node for collecting, aggregating and forwarding data multiplied by the updated round number after the network runs for a certain round number, and performing same-layer transfer operation on the child nodes if the energy consumption is more than or equal to the residual energy of the nodes or the nodes are compared; the specific operation method for transferring the child nodes comprises the following steps: and searching the node with the highest residual energy in the same layer of the father node in the communication radius as a relay father node to complete local route reconstruction.
7. A program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising: dividing the nodes into different layers according to the hop counts of the nodes in the network, and selecting relay nodes in a certain proportion from the nodes in the different layers; different initial energies are set for different layers of nodes, and due to the fact that data packets of different nodes have different sizes, corresponding data aggregation coefficients are adopted according to actual data requirements of a network during data transmission; and dynamically updating the topological structure of the tree in real time in the network operation process so as to prolong the service life of the nodes.
8. A data aggregation system for implementing the data aggregation method according to any one of claims 1 to 6, the data aggregation system comprising:
the network layering module is used for constructing a minimum routing tree from a Sink root node by using a greedy algorithm according to the distance and layering the network from the Sink node according to the communication radius of the node;
the relay node selection module is used for selecting a certain proportion of nodes in each layer as relay nodes;
the node energy setting module is used for setting the energy of each layer of nodes;
and the local routing tree reconstruction module is used for reconstructing the local routing tree after the network runs for a period of time.
9. A wireless communication system incorporating the data aggregation system of claim 8.
10. Use of the data aggregation method according to any one of claims 1 to 6 in data processing in a wireless sensor network.
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