CN111148117A - LEACH protocol cluster head selection method based on position and energy correlation - Google Patents

LEACH protocol cluster head selection method based on position and energy correlation Download PDF

Info

Publication number
CN111148117A
CN111148117A CN202010061801.4A CN202010061801A CN111148117A CN 111148117 A CN111148117 A CN 111148117A CN 202010061801 A CN202010061801 A CN 202010061801A CN 111148117 A CN111148117 A CN 111148117A
Authority
CN
China
Prior art keywords
node
cluster
cluster head
energy
nodes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010061801.4A
Other languages
Chinese (zh)
Other versions
CN111148117B (en
Inventor
刘拥民
刘一鸣
罗皓懿
杨钰津
王靖枫
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Central South University of Forestry and Technology
Original Assignee
Central South University of Forestry and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Central South University of Forestry and Technology filed Critical Central South University of Forestry and Technology
Priority to CN202010061801.4A priority Critical patent/CN111148117B/en
Publication of CN111148117A publication Critical patent/CN111148117A/en
Application granted granted Critical
Publication of CN111148117B publication Critical patent/CN111148117B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H04W16/225—Traffic simulation tools or models for indoor or short range 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
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a method for selecting LEACH protocol cluster heads based on the mutual relation of position and energy, which comprises the following steps of A, calculating the optimal value of the number K of cluster heads, and dividing the whole wireless sensor network into K cluster groups through K-Means; B. establishing a hierarchical region model in a cluster group; C. setting an energy threshold value, and selecting a cluster head according to the comparison of the energy in the cluster; D. and generating an energy consumption model, and selecting a common node close to the sink node to directly transmit the data to the sink node. The invention can solve the defects of the prior art, effectively reduce the total energy consumption of the network, improve the stable operation time of the network and prolong the service life of the whole wireless sensor network.

Description

LEACH protocol cluster head selection method based on position and energy correlation
Technical Field
The invention belongs to the technical field of wireless sensor network communication, and particularly relates to a method for selecting a LEACH protocol cluster head based on a mutual relation between a position and energy.
Background
Wireless sensor networks have been developed based on micro-electromechanical systems (MEMS), systems on a chip (SoC), wireless communication, and low power embedded technologies. With the rapid development of the internet of things technology, wireless sensor networks have been widely applied in the fields of military, industry, agriculture, forestry and the like. A wireless sensor network is a network consisting of a plurality of sensor nodes with data storage and data transmission capabilities. These sensor nodes are typically randomly distributed throughout the data acquisition transmission area. When a node transmits a packet, it consumes node power. Since the energy supply of the nodes in these networks is very limited, how to ensure that each node can balance the global energy consumption of the load in the whole network, thereby prolonging the whole life cycle of the network, remains a hot technical problem at present.
Routing protocols of wireless sensor networks are mainly divided into two categories: a flat routing protocol and a layered routing protocol. The traditional low-energy adaptive clustering hierarchy protocol (LEACH) is one of the most widely used classical clustering protocols. The whole network is divided into a plurality of different clusters, after each round of random election, a cluster head node is selected from each cluster, a common node in each cluster transmits data to the cluster head node, and then the cluster head node fuses and transmits the data to a sink node. The LEACH protocol plays an important role in the overall wireless sensor network to save energy consumption. However, the LEACH protocol still has some disadvantages:
(1) cluster head selection is not reasonable.
Since all cluster heads are generated by random selection, the positions of the cluster head nodes and the residual energy of the cluster head nodes are not considered, and the cluster head nodes may be too far away from the aggregation node to cause premature death of the nodes.
(2) The communication mode of the common node is single.
As a sink node in the cluster, it can only communicate with the cluster head node. If a normal node is closer to the sink node, it should communicate directly with the sink node to reduce unnecessary energy consumption costs.
(3) Difficulty in selecting p-value
In a protocol, the P value is typically defined manually. In practical applications, however, the number of clusters will directly affect the performance of the entire network and the lifetime of the entire network. When the cluster heads are too many, the total energy consumption of the network can be increased, and the original clustering can not be meaningful. If the cluster heads are too few, it means that there are too many sink nodes in each cluster, which easily causes network congestion and data transmission delay.
Disclosure of Invention
The invention aims to provide a method for selecting a LEACH protocol cluster head based on the mutual relation between position and energy, which can solve the defects of the prior art, effectively reduce the total energy consumption of a network, improve the stable operation time of the network and prolong the service life of the whole wireless sensor network.
The subject matter of the present invention includes the following steps,
A. calculating the optimal value of the number K of cluster heads, and dividing the whole wireless sensor network into K clusters through K-Means;
B. establishing a hierarchical region model in a cluster group;
C. setting an energy threshold value, and selecting a cluster head according to the comparison of the energy in the cluster;
D. and generating an energy consumption model, and selecting a common node close to the sink node to directly transmit the data to the sink node.
Preferably, in step a, the optimal value of the number k of cluster heads is calculated by,
randomly deploying n communication nodes, wherein the n communication nodes comprise k cluster head nodes; the base station is positioned in the center of the communication area; the size of the data packet sent by the common node in each period is m bits, the total energy consumption of the cluster head node is,
Ecluster＝eTx+eRx+eDa，
wherein e isTxIndicating the energy consumed by the cluster head to transmit data, eRxIndicating cluster headsEnergy consumed for receiving data from a common node, eDaEnergy consumed by the cluster head during data fusion;
since the base station is located at the center of the area, the communication distance is substantially less than d0，eTxIs based on a free-space model,
Figure BDA0002374743010000021
wherein the content of the first and second substances,
Figure BDA0002374743010000022
the average distance from the cluster head node to the base station is shown, and the nodes are randomly distributed when being set, then
Figure BDA0002374743010000023
Energy e used by cluster head node for receiving data from common nodes in clusterRxThe calculation method of (a) is that,
Figure BDA0002374743010000024
cluster head data fusion energy eDaThe calculation method of (a) is that,
Figure BDA0002374743010000031
wherein E isDAThe energy consumed by fusing unit data by the cluster head nodes is represented, and the total energy consumption of data of the common nodes except the cluster head nodes is calculated by the method,
Figure BDA0002374743010000032
wherein the content of the first and second substances,
Figure BDA0002374743010000033
is the average distance from the common node to the cluster head node, expressed as,
Figure BDA0002374743010000034
the total energy consumption of each node of the whole wireless sensor network is,
Eall＝k·Ecluster+(n-k)·Enormal，
Eallthe minimum value k of (a) is,
Figure BDA0002374743010000035
preferably, in step B, the hierarchical region model in the cluster is established by,
the nodes in the cluster are divided into two layers according to the distance between the nodes and the base station, the specific layering rule is as follows,
Figure BDA0002374743010000036
wherein d isiDenotes the distance, d, from node i to the sink nodeminRepresenting the minimum distance from a node in a cluster to a sink node, dmaxRepresenting the maximum distance from the nodes in the cluster to the sink node, wherein tau is a control coefficient and is determined by the number of the nodes and the size of the cluster; according to the hierarchical region model in the cluster, the selection of cluster heads is arranged from high to low according to the distance and therefore the priority: nearest node, first layer area node and second layer area node.
Preferably, in the step C, when the cluster head is selected for the first time, the closest node in each cluster is selected as the cluster head; when the energy of the nearest node is less than the set threshold value
Figure BDA0002374743010000037
It is demoted to a common node; randomly selecting an energy greater than a threshold in a first layer
Figure BDA0002374743010000038
The node of (2) is upgraded to a cluster head node; if there are no qualified nodes in the first layer, then in the first layerSelecting energy in two layers to be larger than threshold value
Figure BDA0002374743010000039
Node of (2), upgrade to cluster head node, threshold
Figure BDA0002374743010000041
The calculation method of (a) is that,
Figure BDA0002374743010000042
wherein E isall(i) Represents the total energy in the ith cluster, n (i) represents the number of nodes in the ith cluster, dead (i) represents the number of dead nodes in the ith cluster, and δ is an adjustment factor.
Preferably, in step D, the total energy consumption of the model transmitting part is calculated by,
Figure BDA0002374743010000043
wherein E isTx(ij) represents the total energy consumption during the transmission from node i to node j, ETx-elec(ij) represents the energy consumption of the transmission module when node i is sent to node j; eTx-amp(ij) is the energy consumed by the amplifier module when node i sends to node j, m is the size and length of the sending packet, EelecIs the amount of energy consumed to receive or transmit a unit data packet. EpsilonfsIs an energy consumption parameter in the free space channel model, with the unit of J.bit-1·m-2。εampIs the energy consumption parameter of the multipath fading channel model, and the unit is J.bit-1·m-2。d0Is a threshold value of the transmission distance, d0The calculation method of (a) is that,
Figure BDA0002374743010000044
dijrepresenting the distance from node i to node j. dijThe calculation method of (a) is that,
Figure BDA0002374743010000045
the energy consumption of the node receiving the data is calculated by,
ERx＝m·Eelec。
preferably, in the step D, when one common node is closer to the sink node than all cluster head nodes, the node will jump out of the cluster structure in the round of selection and immediately become a special node in direct communication with the sink node; when the distance between the cluster head node and the sink node is larger than a threshold value phi, the data is directly transmitted to the sink node from the special node, so that the energy consumption of the whole network is reduced, and the life cycle of the network is effectively utilized; the threshold value phi is calculated by a method in which,
Figure BDA0002374743010000046
the method has the advantages that the residual energy of the nodes and the positions of the nodes are considered when the cluster heads are selected, and the whole wireless sensor network is divided into k clusters by calculating the theoretical optimal value of the number k of the cluster heads. Meanwhile, each cluster is divided into two levels. When the cluster head is selected for the first time, the nearest node in each cluster is selected to be promoted to the cluster head center. In each subsequent round of cluster head selection, conditions for upgrading to the cluster head nodes need to be determined according to the hierarchical model in the cluster and the residual energy of the nodes. Meanwhile, when the common node is closer to the sink node than the cluster head node, the common node jumps out of the cluster structure in the current round and becomes a special node which is directly communicated with the sink node. Compared with the traditional LEACH protocol, the invention can greatly prolong the service life of the whole network.
Drawings
Fig. 1 is a clustering diagram simulated using the conventional LEACH protocol.
Fig. 2 is a clustering diagram simulated using the improved LEACH protocol of the present application.
Fig. 3 is a schematic diagram of nodes that survive 1000 rounds and have failed using the conventional LEACH protocol.
Fig. 4 is a schematic diagram of nodes that survive 1000 rounds and have failed using the improved LEACH protocol of the present application.
Fig. 5 is a graph comparing the number of surviving nodes for three protocols.
Fig. 6 is a graph of the residual energy for three protocols.
Detailed Description
The method comprises the following steps of,
A. calculating the optimal value of the number K of cluster heads, and dividing the whole wireless sensor network into K clusters through K-Means;
B. establishing a hierarchical region model in a cluster group;
C. setting an energy threshold value, and selecting a cluster head according to the comparison of the energy in the cluster;
D. and generating an energy consumption model, and selecting a common node close to the sink node to directly transmit the data to the sink node.
In the step A, the optimal value of the number k of cluster heads is calculated by the following steps,
randomly deploying n communication nodes, wherein the n communication nodes comprise k cluster head nodes; the base station is positioned in the center of the communication area; the size of the data packet sent by the common node in each period is m bits, the total energy consumption of the cluster head node is,
Ecluster＝eTx+eRx+eDa，
wherein e isTxIndicating the energy consumed by the cluster head to transmit data, eRxRepresenting the energy consumed by the cluster head to receive data from the regular node, eDaEnergy consumed by the cluster head during data fusion;
since the base station is located at the center of the area, the communication distance is substantially less than d0，eTxIs based on a free-space model,
Figure BDA0002374743010000051
wherein the content of the first and second substances,
Figure BDA0002374743010000052
the average distance from the cluster head node to the base station is shown, and the nodes are randomly distributed when being set, then
Figure BDA0002374743010000061
Energy e used by cluster head node for receiving data from common nodes in clusterRxThe calculation method of (a) is that,
Figure BDA0002374743010000062
cluster head data fusion energy eDaThe calculation method of (a) is that,
Figure BDA0002374743010000063
wherein E isDAThe energy consumed by fusing unit data by the cluster head nodes is represented, and the total energy consumption of data of the common nodes except the cluster head nodes is calculated by the method,
Figure BDA0002374743010000064
wherein the content of the first and second substances,
Figure BDA0002374743010000065
is the average distance from the common node to the cluster head node, expressed as,
Figure BDA0002374743010000066
the total energy consumption of each node of the whole wireless sensor network is,
Eall＝k·Ecluster+(n-k)·Enormal，
Eallthe minimum value k of (a) is,
Figure BDA0002374743010000067
in step B, the method for establishing the hierarchical region model in the cluster is as follows,
the nodes in the cluster are divided into two layers according to the distance between the nodes and the base station, the specific layering rule is as follows,
Figure BDA0002374743010000068
wherein d isiDenotes the distance, d, from node i to the sink nodeminRepresenting the minimum distance from a node in a cluster to a sink node, dmaxRepresenting the maximum distance from the nodes in the cluster to the sink node, wherein tau is a control coefficient and is determined by the number of the nodes and the size of the cluster; according to the hierarchical region model in the cluster, the selection of cluster heads is arranged from high to low according to the distance and therefore the priority: nearest node, first layer area node and second layer area node.
In the step C, when the cluster head is selected for the first time, the nearest node in each cluster is selected as the cluster head; when the energy of the nearest node is less than the set threshold value
Figure BDA0002374743010000071
It is demoted to a common node; randomly selecting an energy greater than a threshold in a first layer
Figure BDA0002374743010000072
The node of (2) is upgraded to a cluster head node; selecting an energy greater than a threshold in a second layer if there are no eligible nodes in the first layer
Figure BDA0002374743010000073
Node of (2), upgrade to cluster head node, threshold
Figure BDA0002374743010000074
The calculation method of (a) is that,
Figure BDA0002374743010000075
wherein E isall(i) Represents the total energy in the ith cluster, n (i) represents the number of nodes in the ith cluster, dead (i) represents the number of dead nodes in the ith cluster, and δ is an adjustment factor.
In step D, the total energy consumption of the model transmitting part is calculated by the following steps,
Figure BDA0002374743010000076
wherein E isTx(ij) represents the total energy consumption during the transmission from node i to node j, ETx-elec(ij) represents the energy consumption of the transmission module when node i is sent to node j; eTx-amp(ij) is the energy consumed by the amplifier module when node i sends to node j, m is the size and length of the sending packet, EelecIs the amount of energy consumed to receive or transmit a unit data packet. EpsilonfsIs an energy consumption parameter in the free space channel model, with the unit of J.bit-1·m-2。εampIs the energy consumption parameter of the multipath fading channel model, and the unit is J.bit-1·m-2。d0Is a threshold value of the transmission distance, d0The calculation method of (a) is that,
Figure BDA0002374743010000077
dijrepresenting the distance from node i to node j. dijThe calculation method of (a) is that,
Figure BDA0002374743010000078
the energy consumption of the node receiving the data is calculated by,
FRx＝m·Eelec。
in the step D, when a common node is closer to the sink node than all cluster head nodes, the node jumps out of the cluster structure in the round of selection and immediately becomes a special node which is in direct communication with the sink node; when the distance between the cluster head node and the sink node is larger than a threshold value phi, the data is directly transmitted to the sink node from the special node, so that the energy consumption of the whole network is reduced, and the life cycle of the network is effectively utilized; the threshold value phi is calculated by a method in which,
Figure BDA0002374743010000081
the simulation is carried out on MATLAB R2016a, and the specific setting parameter data of the network simulation is shown in the following table:
Figure BDA0002374743010000082
referring to fig. 1 and 2, the conventional LEACH protocol randomly generates 100 nodes. Because the position information of the cluster heads is not considered, the distribution of 8 cluster heads is unreasonable, the classification effect is poor, and the load of the cluster heads is unbalanced. The invention calculates the optimal cluster number, divides 100 nodes into 10 classes, and has good clustering effect, reasonable cluster head distribution and balanced cluster head load.
Since the conventional LEACH protocol does not consider its location when selecting a cluster head, it results in randomness in the selection of its cluster head. As shown in fig. 3, the death of nodes is randomly distributed and unpredictable in the middle and late stages of the network lifecycle, which causes unnecessary trouble to the maintenance of the entire wireless sensor network. Comparing fig. 3 with fig. 4, it can be seen that the order of death of the nodes of the present invention is better than the order of death of the nodes using the conventional LEACH protocol. Since the present invention takes into account the location of the cluster head and the remaining energy when selecting the cluster head. The deaths of the nodes are regular, with the first dead node being a node that is far away from the aggregation node.
As shown in fig. 5, the first node death of the conventional LEACH protocol occurs in round 856, the first node death of the conventional LEACH-C protocol occurs in round 1021, whereas the first node death of the present invention occurs in round 1035. Under the same experimental condition, compared with the stable operation period of the traditional LEACH protocol, the stable operation period of the invention has 179 more turns, and is improved by 20.9 percent. The main reasons are: the remaining energy factor and the position factor are comprehensively considered in the selection process of the cluster head, so that the death of the cluster head caused by premature energy exhaustion is avoided.
Fig. 6 further illustrates the effectiveness of the present invention, which has the greatest remaining energy throughout the process and the life cycle of the network is greatly extended. The traditional LEACH protocol exhausts all energy in the 1405 th round, the traditional LEACH-C protocol exhausts all energy in the 1256 th round, and under the same condition, the energy is not exhausted until the 2197 th round, and the service life of the whole network is increased by nearly 56.3%. Due to the poor clustering capability of the traditional LEACH protocol, some nodes need to span a long distance to transmit data to a cluster head, and the nodes need to consume a large amount of energy. The invention can optimize and select the position of the cluster head, and simultaneously mark the common nodes which are closer to the sink node than all the cluster head nodes as special nodes. Such special nodes may transmit data directly to the sink node. The measure not only reduces the energy consumption of the cluster head node, but also reduces the energy consumption of the common node, and effectively prolongs the life cycle of the whole network.
The method is used in an environment monitoring system of the east garden bamboo park of the university of technology of the Chinese and south forestry. The system consists of a computer, a coordinator node and thirty terminal nodes with various sensors. The CC2530 is selected as a node chip, and meanwhile, the coordinator node is required to be capable of performing RS232 serial port communication with the control host. The equipment required by the system is roughly divided into two parts of software and hardware, and specifically comprises the following steps:
(1) hardware equipment:
the chip with the RS232 serial port communication function is one ZigBee node of the CC 2530;
thirty common ZigBee nodes with chips of CC 2530;
thirty DTH temperature and humidity sensor modules;
SmartRF04EB downloader one;
a plurality of power supply lines;
a plurality of heels of Dupont lines.
(2) Software equipment:
ZStack-CC2530-2.5.1a；
SmartRF Flash Programmer 1.12.8；
ZigBee Sensor Monitor 1.2.0。
the specific parameters of the common ZigBee node are as follows:
Figure BDA0002374743010000101
a temperature and humidity sensor terminal node is formed by the DHT11 sensor module and the CC2530 common node. A total of thirty nodes were randomly deployed within an experimental environment having an area size of 100 square meters. The aim is to resemble the simulation experimental environment as much as possible.
Two kinds of ZigBee devices (coordinator and terminal device) in the system respectively play different roles in the ZigBee network.
(1) The ZigBee coordinator: the gateway serving as a gateway in the ZigBee network is responsible for establishing the whole ZigBee network, and realizes data communication with a computer through a serial port, and is similar to a wireless sensor network sink node;
(2) ZigBee terminal equipment: being a terminal node in the ZigBee network, the terminal node is positioned at the 'end' of the network, and is similar to a common node in a wireless sensor network.
The purchased ZigBee device supports secondary development, and the programmed program is burned into a chip through a downloader. And after the ZigBee Sensor Monitor software is opened by the control host and the specified COM port is selected, the software starts to run.
In order to obtain the test effect more accurately and quickly, the terminal node is set to send data every 1s, and meanwhile, the terminal node never enters a sleep period. First, fifteen consecutive days of data are sent using the conventional LEACH routing protocol, and an average remaining power of 78.4% is observed for the node. And then, the same experimental environment of the same equipment is adopted, and a brand new battery is replaced. Fifteen consecutive days of data packet transmission are carried out, and the average power left by the nodes is observed to be 84.9%.
According to experimental data, the improved protocol is adopted to improve the actual network life cycle by 43 percent, which is very similar to the data predicted by MATLAB simulation experiment.
Under the condition of giving priority to the position distance, the optimal value of the clustering center number when the energy consumption of the cluster head is the lowest is calculated, and meanwhile, the optimal path when the special node transmits data is calculated. Meanwhile, a threshold phi is set, so that the nodes with the difference between the cluster head nodes and the sink nodes larger than the threshold phi jump out of the cluster structure, and data are directly transmitted to the sink nodes. And finally, verifying the effectiveness of the theory by using a simulation experiment. Simulation results show that: compared with the traditional LEACH and LEACH-C protocol, the invention greatly prolongs the death time of the first node, effectively improves the overall survival time of the network, greatly reduces the total energy consumption of the network, obviously improves the stable operation time of the network and greatly prolongs the service life of the network.

Claims (6)

1. A method for selecting LEACH protocol cluster head based on the correlation between position and energy is characterized by comprising the following steps,
A. calculating the optimal value of the number K of cluster heads, and dividing the whole wireless sensor network into K clusters through K-Means;
B. establishing a hierarchical region model in a cluster group;
C. setting an energy threshold value, and selecting a cluster head according to the comparison of the energy in the cluster;
D. and generating an energy consumption model, and selecting a common node close to the sink node to directly transmit the data to the sink node.
2. A method of location and energy correlation based LEACH protocol cluster head selection as claimed in claim 1 wherein: in the step A, the optimal value of the number k of cluster heads is calculated by the following steps,
randomly deploying n communication nodes, wherein the n communication nodes comprise k cluster head nodes; the base station is positioned in the center of the communication area; the size of the data packet sent by the common node in each period is m bits, the total energy consumption of the cluster head node is,
Ecluster＝eTx+eRx+eDa，
wherein e isTxIndicating the energy consumed by the cluster head to transmit data, eRxIndicating that cluster head receives number from general nodeAccording to the energy consumed, eDaEnergy consumed by the cluster head during data fusion;
since the base station is located at the center of the area, the communication distance is substantially less than d0，eTxIs based on a free-space model,
Figure FDA0002374743000000011
wherein the content of the first and second substances,
Figure FDA0002374743000000012
the average distance from the cluster head node to the base station is shown, and the nodes are randomly distributed when being set, then
Figure FDA0002374743000000013
Energy e used by cluster head node for receiving data from common nodes in clusterRxThe calculation method of (a) is that,
Figure FDA0002374743000000014
cluster head data fusion energy eDaThe calculation method of (a) is that,
Figure FDA0002374743000000015
wherein E isDAThe energy consumed by fusing unit data by the cluster head nodes is represented, and the total energy consumption of data of the common nodes except the cluster head nodes is calculated by the method,
Figure FDA0002374743000000016
wherein the content of the first and second substances,
Figure FDA0002374743000000021
is the average distance from the common node to the cluster head node, and is expressed as，
Figure FDA0002374743000000022
The total energy consumption of each node of the whole wireless sensor network is,
Eall＝k·Ecluster+(n-k)·Enormal，
Eallthe minimum value k of (a) is,
Figure FDA0002374743000000023
3. a method of location and energy correlation based LEACH protocol cluster head selection as claimed in claim 2, wherein: in step B, the method for establishing the hierarchical region model in the cluster is as follows,
the nodes in the cluster are divided into two layers according to the distance between the nodes and the base station, the specific layering rule is as follows,
Figure FDA0002374743000000024
wherein d isiDenotes the distance, d, from node i to the sink nodeminRepresenting the minimum distance from a node in a cluster to a sink node, dmaxRepresenting the maximum distance from the nodes in the cluster to the sink node, wherein tau is a control coefficient and is determined by the number of the nodes and the size of the cluster; according to the hierarchical region model in the cluster, the selection of cluster heads is arranged from high to low according to the distance and therefore the priority: nearest node, first layer area node and second layer area node.
4. A method of location and energy correlation based LEACH protocol cluster head selection as claimed in claim 3 wherein: in the step C, when the cluster head is selected for the first time, the nearest node in each cluster is selected as the cluster head; when the energy of the nearest node is less than the set threshold value
Figure FDA0002374743000000026
It is demoted to a common node; randomly selecting an energy greater than a threshold in a first layer
Figure FDA0002374743000000027
The node of (2) is upgraded to a cluster head node; selecting an energy greater than a threshold in a second layer if there are no eligible nodes in the first layer
Figure FDA0002374743000000028
Node of (2), upgrade to cluster head node, threshold
Figure FDA0002374743000000029
The calculation method of (a) is that,
Figure FDA0002374743000000025
wherein E isall(i) Represents the total energy in the ith cluster, n (i) represents the number of nodes in the ith cluster, dead (i) represents the number of dead nodes in the ith cluster, and δ is an adjustment factor.
5. The method of claim 4, wherein the method for selecting a LEACH protocol cluster head based on location and energy correlation comprises: in step D, the total energy consumption of the model transmitting part is calculated by the following steps,
Figure FDA0002374743000000031
wherein E isTx(ij) represents the total energy consumption during the transmission from node i to node j, ETx-elec(ij) represents the energy consumption of the transmission module when node i is sent to node j; eTx-amp(ij) is the energy consumed by the amplifier module when node i sends to node j, m is the size and length of the sending packet, EelecIs the energy consumed to receive or transmit a unit data packet; epsilonfsIs a free space channel modeThe energy consumption parameter in the form is J.bit-1·m-2；εampIs the energy consumption parameter of the multipath fading channel model, and the unit is J.bit-1·m-2；d0Is a threshold value of the transmission distance, d0The calculation method of (a) is that,
Figure FDA0002374743000000032
dijrepresents the distance from node i to node j; dijThe calculation method of (a) is that,
Figure FDA0002374743000000033
the energy consumption of the node receiving the data is calculated by,
ERx＝m·Eelec。
6. a method of location and energy correlation based LEACH protocol cluster head selection as claimed in claim 5 wherein: in the step D, when a common node is closer to the sink node than all cluster head nodes, the node jumps out of the cluster structure in the round of selection and immediately becomes a special node which is in direct communication with the sink node; when the distance between the cluster head node and the sink node is larger than a threshold value phi, the data is directly transmitted to the sink node from the special node, so that the energy consumption of the whole network is reduced, and the life cycle of the network is effectively utilized; the threshold value phi is calculated by a method in which,
Figure FDA0002374743000000034
CN202010061801.4A 2020-01-19 2020-01-19 LEACH protocol cluster head selection method based on position and energy correlation Active CN111148117B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010061801.4A CN111148117B (en) 2020-01-19 2020-01-19 LEACH protocol cluster head selection method based on position and energy correlation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010061801.4A CN111148117B (en) 2020-01-19 2020-01-19 LEACH protocol cluster head selection method based on position and energy correlation

Publications (2)

Publication Number Publication Date
CN111148117A true CN111148117A (en) 2020-05-12
CN111148117B CN111148117B (en) 2022-09-16

Family

ID=70526187

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010061801.4A Active CN111148117B (en) 2020-01-19 2020-01-19 LEACH protocol cluster head selection method based on position and energy correlation

Country Status (1)

Country Link
CN (1) CN111148117B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112714154A (en) * 2020-12-11 2021-04-27 国网宁夏电力有限公司吴忠供电公司 Routing control method and system for intelligent sensor for power grid monitoring
CN112888041A (en) * 2020-12-30 2021-06-01 电子科技大学 Wireless sensor network communication method based on non-uniform node deployment
CN113645633A (en) * 2021-08-06 2021-11-12 杭州海兴泽科信息技术有限公司 Backbone network planning method
CN115884082A (en) * 2023-02-21 2023-03-31 广东聚智诚科技有限公司 Map-based visual display system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20080070333A (en) * 2007-01-26 2008-07-30 삼성전자주식회사 Method for electing cluster head in sensor network and sensor node generation system in the sinsor network
CN106973419A (en) * 2017-03-03 2017-07-21 武汉工程大学 A kind of innovatory algorithm of LEACH agreements
CN107529201A (en) * 2017-08-29 2017-12-29 武汉中原电子信息有限公司 A kind of wireless sensor network data transmission method based on LEACH agreements
CN108696903A (en) * 2018-05-17 2018-10-23 昆明理工大学 A kind of LEACH algorithm optimization methods based on energy consumption balance
CN108848476A (en) * 2018-06-21 2018-11-20 河南科技大学 Power-efficient data assembly algorithms based on communication distance control in sensor network
CN109587753A (en) * 2018-11-26 2019-04-05 河南大学 The cluster head selection algorithm of improvement LEACH agreement based on targets threshold constraint

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20080070333A (en) * 2007-01-26 2008-07-30 삼성전자주식회사 Method for electing cluster head in sensor network and sensor node generation system in the sinsor network
CN106973419A (en) * 2017-03-03 2017-07-21 武汉工程大学 A kind of innovatory algorithm of LEACH agreements
CN107529201A (en) * 2017-08-29 2017-12-29 武汉中原电子信息有限公司 A kind of wireless sensor network data transmission method based on LEACH agreements
CN108696903A (en) * 2018-05-17 2018-10-23 昆明理工大学 A kind of LEACH algorithm optimization methods based on energy consumption balance
CN108848476A (en) * 2018-06-21 2018-11-20 河南科技大学 Power-efficient data assembly algorithms based on communication distance control in sensor network
CN109587753A (en) * 2018-11-26 2019-04-05 河南大学 The cluster head selection algorithm of improvement LEACH agreement based on targets threshold constraint

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
严静静等: "基于粗糙C-均值聚类的能量均衡LEACH算法", 《计算机工程》 *
韦小铃等: "无线传感器网络LEACH路由协议的簇头多跳算法", 《广西科学院学报》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112714154A (en) * 2020-12-11 2021-04-27 国网宁夏电力有限公司吴忠供电公司 Routing control method and system for intelligent sensor for power grid monitoring
CN112888041A (en) * 2020-12-30 2021-06-01 电子科技大学 Wireless sensor network communication method based on non-uniform node deployment
CN112888041B (en) * 2020-12-30 2022-04-15 电子科技大学 Wireless sensor network communication method based on non-uniform node deployment
CN113645633A (en) * 2021-08-06 2021-11-12 杭州海兴泽科信息技术有限公司 Backbone network planning method
CN113645633B (en) * 2021-08-06 2023-10-31 杭州海兴泽科信息技术有限公司 Backbone network planning method and storage medium
CN115884082A (en) * 2023-02-21 2023-03-31 广东聚智诚科技有限公司 Map-based visual display system

Also Published As

Publication number Publication date
CN111148117B (en) 2022-09-16

Similar Documents

Publication Publication Date Title
CN111148117B (en) LEACH protocol cluster head selection method based on position and energy correlation
Latiff et al. Energy-aware clustering for wireless sensor networks using particle swarm optimization
Zarei et al. Novel cluster based routing protocol in wireless sensor networks
CN108966239B (en) Method for deploying relay nodes based on energy voidage
Antoo et al. EEM-LEACH: Energy efficient multi-hop LEACH routing protocol for clustered WSNs
Fakhet et al. New K-means algorithm for clustering in wireless sensor networks
Qubbaj et al. Review on LEACH protocol
Yuan et al. Congestion control for wireless sensor networks: A survey
Bakr et al. Comparison by Simulation of Energy Consumption and WSN Lifetime for LEACH and LEACH-SM
Alami et al. Routing-Gi: routing technique to enhance energy efficiency in WSNs
Sazak et al. An energy efficient MAC protocol for cluster based event driven WSN applications
Alla et al. Hierarchical adaptive balanced routing protocol for energy efficiency in heterogeneous wireless sensor networks
Song Probabilistic modeling of leach protocol and computing sensor energy consumption rate in sensor networks
Feng et al. Improvement of Energy Efficiency in Wireless Sensor Networks Using Low-energy Adaptive ClusteringHierarchy (LEACH)-based Energy Betweenness Model.
CN102325088A (en) Path protection method for optimizing node sleep under delay and shared risk link group restraint
CN110896542B (en) Heterogeneous network construction method under random multi-SINK mode
Mavromoustakis et al. A tier-based asynchronous scheduling scheme for delay constrained energy efficient connectivity in asymmetrical wireless devices
CN113162638A (en) SDWSN (software development station network) topology control algorithm based on non-cooperative game
Chen et al. New approach of energy-efficient hierarchical clustering based on neighbor rotation for RWSN
Ahmad et al. Energy-Efficient TDMA based Clustering Scheme for WSN: Energy-Efficient TDMA based Clustering Scheme for WSN
Jafarizadeh et al. An analytical study of low energy monitoring networks for large-scale data centers
Pamulapati WSN Energy Optimization: Evolutionary Approach
Parihar et al. Quadrant based routing protocol for improving network lifetime for WSN
He et al. Wireless sensor monitoring network optimization research in smart substation
Shankar et al. Energy utilization and security enhancement using particle swam optimization (PSO)

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant