CN109547966B - Routing planning and fault diagnosis backup method for wireless sensor network of power transmission line - Google Patents
Routing planning and fault diagnosis backup method for wireless sensor network of power transmission line Download PDFInfo
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Abstract
A route planning and fault diagnosis backup method of a wireless sensor network of a power transmission line is optimized aiming at three aspects of clustering, route planning and fault backup, a uniform clustering algorithm based on energy balance and energy consumption optimization is provided in the aspect of clustering, two factors of residual energy and communication load are considered, and then the distribution of cluster heads is homogenized according to distance factors; a data forwarding mechanism based on service differentiation is provided in the aspect of routing planning, and under the condition of meeting the time delay requirement, corresponding paths are distributed to services with different time delay requirements for data forwarding; a fault backup mechanism based on residual energy and optimal energy consumption is provided in the aspect of fault backup and is divided into a link backup part and a node backup part. The method of the invention can balance the communication load of each cluster head under the condition of meeting the service time delay constraint requirement and reliability, reduce the total energy consumption in the data transmission process and finally prolong the life cycle of the network.
Description
Technical Field
The invention belongs to the technical field of power internet of things, relates to a wireless sensor network of a power transmission line, and discloses a path planning and fault backup method based on power transmission line internet of things sensing and monitoring application in energy consumption balance.
Background
The power industry is one of the fields with wider application of the internet of things technology, such as on-line monitoring of power transmission lines, intelligent substations, intelligent machine rooms and the like, and sensor equipment in the internet of things (IoT) can meet the application of intelligent home, body/health monitoring, environment monitoring, state-based maintenance and the like. The power transmission line is a power link with the largest power grid asset, the widest distribution, the complex and severe equipment operation environment and multiple external force influence factors. Due to the equipment safety problems of the power transmission line, such as overhigh operating temperature of a wire, sag change, windage discharge, breeze vibration, tower inclination and the like, the installation of effective sensing equipment on the power transmission line is urgently needed. The fault on-line monitoring system transmits various equipment parameters to the data processing center through the Internet of things gateway, then processes various equipment data by adopting an advanced data analysis technology, and judges whether the power transmission line has faults or not. This provides the basis for the grid safety decision.
In order to solve the development situation of the prior art, the existing papers and patents are searched, compared and analyzed, and the following technical information with high relevance to the invention is screened out:
the technical scheme 1: an energy-efficient wireless sensor network routing method, patent publication No. CN101360051B, relates to an energy-efficient hierarchical routing method, the key steps of which are as follows: first, the protocol initializes. Through a flooding mode, the Sink node acquires network topology and residual energy information of each node in the network, the sensor node acquires hop count from the Sink node and neighbor node information, and after a protocol is initialized, the Sink node can calculate total residual energy and network average energy of the network; each sensor node knows own neighbor node information and hop count from each neighbor node to the Sink node, and creates conditions for cluster establishment; second, cluster establishment. Dividing a sensor network cluster by using a repeated segmentation method according to the preset number of cluster heads, ensuring that sensor nodes with residual energy higher than the average energy of the network become cluster heads, finishing clustering work, enabling the geographical positions of the cluster head nodes to be uniformly distributed, and enabling the number of nodes in each cluster to be basically the same so as to balance network node loads; third, inter-cluster routing. And performing inter-cluster routing by adopting an ant colony algorithm, wherein the inter-cluster routing comprises forming a routing table of cluster head nodes, calculating link pheromone concentration and selecting an information transmission link.
The technical scheme 2 is as follows: a low-power self-adaptive clustering multi-hop wireless sensor network topology control method based on fuzzy decision, patent publication No. CN 104394566A, relates to a hierarchical routing planning method based on LEACH protocol, the key steps of the method are as follows: first, cluster head election. The present invention uses the same strategy of randomly selecting cluster heads as in the LEACH protocol. During initialization, each sensor node N randomly generates a number from 0 to 1, and if the random number is lower than a threshold value T (N), the sensor node is selected as a cluster head. Second, clustering. After the cluster heads are selected in each area, each sensor node selects which cluster to join according to the strength of the received signals. If no cluster head exists, the sensor node directly communicates with the base station, otherwise the sensor node which is not the cluster head generally acts within the range of the cluster. Third, inter-cluster routing. After the cluster structure and cluster head selection, a communication path to the base station is selected for each cluster head and the sensor node directly communicating with the base station, and a fuzzy set and fuzzy decision theory is introduced here, wherein the idea of the algorithm is as follows: 1) dividing all cluster heads and sensor nodes directly communicating with a base station into three sets, and measuring the membership degrees of the sensor nodes by using different membership functions respectively; 2) judging to directly remove the sensor nodes with the membership degree of 0, and taking an intersection to reduce the range of the candidate sensor nodes; 3) and (4) scoring and adding the rest sensor nodes by using a fuzzy decision theory to obtain the highest score as the relay node of the next hop.
Technical scheme 3: a wireless sensor network fault diagnosis method is disclosed, wherein the patent publication number is CN102340811A, which relates to the field of wireless sensor networks and fault diagnosis, and the method comprises the following key steps: firstly, acquiring data measured by sensor nodes in a wireless sensor network in real time on line; secondly, detecting the fault of a single sensor node by utilizing the time-space characteristics of the data; thirdly, detecting the fault of the whole wireless sensor network by utilizing the correlation before the adjacent nodes; fourthly, performing attribute reduction on the parameters acquired in the second step and the third step; fifthly, obtaining a fault diagnosis result of the wireless sensor network according to the attribute reduction result of the step four.
The analysis aiming at the prior art has the following problems:
the technical scheme 1 adopts an energy-efficient wireless sensor network routing method, and aiming at a layered sensor network architecture, an ant colony algorithm is adopted to realize a sensor network routing protocol, which is characterized in that: determining whether the node has qualification of competing for cluster heads according to the residual energy; according to the geographical positions of the nodes, cluster division is carried out by using a repeated segmentation method, so that the geographical positions of all clusters are uniform and the number of nodes in the clusters is close; the cluster member nodes transmit information to the cluster head in a single-hop mode; selecting a routing link according to the pheromone concentration on each link by adopting an ant colony algorithm, carrying out inter-cluster routing, and transmitting data of each cluster to a Sink node; along with the operation of the network, the pheromone concentration of the link is periodically calculated, and the node routing table is updated according to the pheromone concentration, so that the routing information is maintained. The method adopts the ant colony algorithm on the inter-cluster routing to ensure the reasonability of selecting the link. However, the ant colony algorithm has too long convergence time and is not suitable for application in large-scale sensor network scenes.
The technical scheme 2 adopts a fuzzy decision-based low-power consumption self-adaptive clustering multi-hop wireless sensor network topology control method, which is characterized in that one round is taken as a cycle, and each round comprises an establishing stage and a stabilizing stage; the establishing stage comprises three steps of cluster head election, clustering and inter-cluster routing; the step of cluster head election uses the same strategy of randomly selecting cluster heads in the LEACH protocol. The randomness of cluster head selection may cause the node distribution to be not ideal and affect the performance of data transmission in a stable phase. And transmitting the data. In the data transmission stage, the cluster head node selects the next hop relay node by using fuzzy decision, and the method optimizes the selection of the next hop node in data transmission, so that the next hop node which has more residual energy and is closer to the sink node is more likely to become the relay node. However, the method does not distinguish the service data, and both the low QoS service and the high QoS service select a shorter path for transmission, which causes redundant energy consumption and network congestion.
Technical scheme 3 adopts a fault diagnosis method based on measured data space-time characteristics in the WSN, and the method detects the fault of a single node by using the space-time characteristics of data measured by sensor nodes in a wireless sensor network and realizes the rapid and correct fault detection of all nodes in the network by using the correlation among the nodes. And obtaining a diagnosis decision table according to the application environment and the fault characteristics of the wireless sensor network, carrying out attribute reduction on the diagnosis decision table by using an improved rough concentrated induction attribute reduction algorithm, and establishing a set of fault classification method by using a Hamming network. Although the method can detect the sensor nodes in real time, the method does not consider the fault repairing condition of the sensor nodes and does not have a fault node backup mechanism, so that the method is not suitable for the condition that the cost of manually repairing fault nodes and links in the transmission line of the Internet of things is high, and when a large number of nodes break down, the method can have a great influence on the performance of the whole network.
Disclosure of Invention
The invention aims to overcome the defects of path transmission and fault safety schemes of wireless sensor networks in the prior art, and aims to research a route planning method for a wireless sensor network of a power transmission line, wherein the method is based on a layered wireless sensor network, firstly, a uniform and efficient clustering mode is researched to lay a cushion for high-performance data transmission of subsequent cluster head nodes, and then, when the cluster head nodes transmit data, the appropriate cluster head nodes can be selected according to service priority to carry out relaying, so that an optimized end-to-end route is formed, load balance is realized, and the life cycle of the network is prolonged.
The technical scheme of the invention is as follows: a route planning method of wireless sensor network of transmission line, carry on the uniform clustering to the sensor network first, then while carrying on the data transmission, the cluster head node collects the information that the node sends of adjacent cluster head and node in the cluster, and carry on the route based on business differentiation and transmit, including the following part: 1) cluster head selection, namely establishing a cluster head selection mechanism based on two factors of energy and node communication load, so that nodes with high residual energy and large communication load are more likely to become cluster head nodes; 2) cluster head distribution, namely optimizing the selected cluster heads, setting the optimal distance range of adjacent cluster head nodes, and enabling the cluster head nodes to be distributed more uniformly by increasing and deleting the cluster head nodes on the basis of the optimal distance range; 3) and data transmission, namely routing planning is carried out based on service priority, the cluster head carries out priority sequencing on the received data packets of different services, and different paths are planned according to the service priority of the data packets for data forwarding.
Further, a fault diagnosis method of the routing planning method of the wireless sensor network of the power transmission line is also provided, fault diagnosis is carried out based on an information negotiation sensing protocol SPIN, and three types of messages are used for carrying out: a probe PRO message, a reply REP message and a DATA DATA message,
the first step is as follows: the sensor node firstly sends PRO information to a next-hop neighbor node of the sensor node, and the information is used for detecting whether the next-hop node is normal or not;
the second step is that: after the next hop node receives the PRO information, replying REP information to the source node, and going to the third step; if the source node does not receive the REP information in a period of time, the next hop node is considered to be in fault, other sensor nodes are informed of the fault condition of the node through a flooding method, and the source node resends the data information according to a fault tolerance mechanism and goes to the first step;
the third step: and after receiving the REP information, the source node sends the DATA information to the next hop node.
Furthermore, a fault backup method of the routing planning method of the wireless sensor network of the power transmission line is also provided, which comprises two parts, namely a link backup and a fault backup, wherein the link backup is based on a data forwarding mode, and each node forwards data to a different path, so that the link backup is realized; node backup is performed according to fault diagnosis: during data transmission, fault node backup is carried out based on residual energy and optimal energy consumption, when cluster head nodes carry out data forwarding, if the cluster head nodes of the next hop of a selected path die, the path is planned again, and cluster head nodes of other paths are selected to carry out data forwarding, wherein in the node backup stage of each period, the cluster nodes with the maximum weight are selected as backup nodes in each cluster in a weight setting mode, and after the cluster head nodes die, the backup cluster head nodes replace the cluster head nodes to carry out data aggregation and forwarding.
The invention is based on the layered routing algorithm, improves two stages of clustering and routing planning of the traditional layered algorithm, and provides a fault backup mechanism. In the clustering stage, the invention provides a uniform clustering algorithm based on energy balance and optimal energy consumption, two factors of residual energy and communication load are considered, and then the distribution of cluster heads is homogenized according to distance factors. In the route planning stage, the invention provides a data forwarding mechanism based on service differentiation, under the condition of meeting the delay requirement, corresponding paths are distributed to services with different delay requirements for data forwarding, and finally, under the condition of meeting the service delay constraint requirement, the communication load of each cluster head node is balanced, the total energy consumption in the data transmission process is reduced, and the life cycle of the network is finally prolonged. In the aspect of fault backup of route planning, the invention provides a fault diagnosis mechanism and a backup resource sharing mechanism. The fault diagnosis mechanism is similar to a three-way handshake protocol, and can be used for diagnosing whether the node is in fault. The backup resource sharing mechanism comprises two aspects of link backup and node backup. Because the method of the invention realizes multi-path planning in the aspect of data transmission, if the main path fails, the data packet can still be forwarded by other paths. Meanwhile, a backup cluster head node with a larger weight exists in each cluster by setting the weight, and when the cluster head node dies, the backup cluster head node replaces the cluster head node to carry out data aggregation and forwarding.
The technical innovation points of the invention are as follows:
1) the method comprises a uniform clustering mechanism based on energy balance and energy consumption optimization, a data forwarding mechanism based on service differentiation and a fault node backup mechanism based on residual energy and optimal energy consumption;
2) a clustering mechanism is provided to plan the whole sensor network topological structure, and the mechanism considers the residual energy of the nodes and the communication load of the nodes in the selection of cluster head nodes, so that the nodes with high residual energy and large communication load are more likely to become the cluster head nodes; in cluster head distribution, the mechanism sets the optimal distance range of adjacent cluster head nodes according to the whole network scale and the node distribution condition, and makes the cluster head nodes more uniformly distributed by increasing and deleting the cluster head nodes based on the optimal distance range;
3) a data forwarding mechanism is provided to plan the data transmission of the whole network, cluster head nodes forward data according to the mechanism, received data packets of different services are subjected to priority ranking, and the data packets of different priorities are forwarded in a mode which is in accordance with the load balance of the whole network according to the residual energy condition and the distance of adjacent cluster head nodes, so that the life cycle of the whole sensor network is prolonged;
4) a failure backup mechanism is provided to alternate dead cluster head nodes, the mechanism is divided into two parts of link backup and failure backup, the link backup is based on the data forwarding mode designed by the invention, and each node forwards data to a different path, thereby playing the role of link backup; the node backup is realized by setting a weight, when a cluster head node fails, the cluster node with the maximum weight is used as a backup cluster head node to replace the failed cluster head node for data aggregation and data forwarding, and finally the reliability of the data transmission of the whole network is met.
The invention provides a power transmission line Internet of things sensing and monitoring application path planning and fault backup method (EHRPFB) based on energy consumption balance, which has the advantages that:
the method of the invention optimizes the clustering, the routing planning and the fault backup. In the aspect of clustering, the invention provides a uniform clustering algorithm based on energy balance and optimal energy consumption, two factors of residual energy and communication load are considered, and then the distribution of cluster heads is homogenized according to distance factors. In the aspect of route planning, the invention provides a data forwarding mechanism based on service differentiation, and under the condition of meeting the time delay requirement, corresponding paths are distributed to services with different time delay requirements for data forwarding. In the aspect of fault backup, the invention provides a fault backup mechanism based on residual energy and optimal energy consumption. The mechanism is divided into a link backup part and a node backup part. The link backup is based on the data forwarding mode designed by the invention, and each node can forward data to different paths, thereby playing the role of link backup; the node backup is performed by setting a weight, and when a cluster head node fails, the cluster node with the largest weight is used as a backup cluster head node to replace the failed cluster head node for data aggregation and data forwarding. The evaluation result shows that the algorithm provided by the invention can balance the communication load of each cluster head under the condition of meeting the service delay constraint requirement and reliability, reduce the total energy consumption in the data transmission process and finally prolong the life cycle of the network.
Drawings
Fig. 1 is a schematic diagram of a topological structure of a long-chain transmission line according to the method of the present invention.
FIG. 2 is a schematic diagram of a link fault tolerance mechanism according to the present invention.
FIG. 3 is a schematic diagram of a node fault tolerance mechanism according to the present invention.
Fig. 4 is a flow chart of the uniform clustering mechanism based on energy balance and energy consumption optimization according to the present invention.
Fig. 5 is a flow chart of a data forwarding mechanism based on service priority according to the present invention.
Fig. 6 is a flow chart of the failure backup mechanism based on the remaining energy and the optimal energy consumption according to the present invention.
FIG. 7 is a graph comparing the clustering results of the I-LEACH algorithm of the present invention with those of the prior art, showing node death in round 1 of clustering, (a) being the I-LEACH algorithm and (b) being the method of the present invention.
FIG. 8 is a graph comparing the clustering results of the I-LEACH algorithm of the present invention with those of the prior art, showing node death in the 30 th round of clustering, (a) being the I-LEACH algorithm and (b) being the method of the present invention.
FIG. 9 is a graph comparing the clustering results of the I-LEACH algorithm of the present invention with those of the prior art, showing node death in the 50 th round of clustering, (a) being the I-LEACH algorithm and (b) being the method of the present invention.
FIG. 10 is a comparison of the number of rounds of death of node deaths for the present invention and the I-LEACH algorithm.
Fig. 11 is a schematic diagram of a CRLA inter-cluster routing algorithm and a routing plan based on an ant colony algorithm, where (a) is the ant colony algorithm and (b) is the CRLA algorithm.
Fig. 12 is a schematic diagram of service differentiation-based routing planning according to the present invention, (a) for higher service priority services, and (b) for lower service priority services.
Fig. 13 shows the node death situation of each round of the three algorithms of the service differentiation-based routing planning method, the CRLA inter-cluster routing algorithm, and the ant colony algorithm-based method of the present invention, and shows the mean value of the multiple tests.
Fig. 14 is a line graph formed by residual energy variances of all surviving nodes in each round according to the three algorithms of the service differentiation-based routing planning method, the CRLA inter-cluster routing algorithm, and the ant colony algorithm.
Fig. 15 is a comparison graph of efficiency of the backup mechanism under different node failure probabilities, (a) the node failure probability p is 0.001, (b) the node failure probability p is 0.005, (c) the node failure probability p is 0.01, and (d) the node failure probability p is 0.05.
Detailed Description
Research shows that the hierarchical routing planning algorithm in the wireless sensor network has the following advantages compared with a non-hierarchical routing planning algorithm: 1) self-adaptability: continuous monitoring and data acquisition are realized through the periodic rotation of cluster head nodes and the joining or exiting of cluster members. 2) Energy conservation: because the base station is far away from the network, the communication between the nodes and the base station is the operation with the highest energy consumption, and after the network is clustered, the cluster head is responsible for sending the data of the whole cluster to the base station, so that the number of the nodes communicated with the base station is reduced, and the energy consumption of the network is greatly reduced. 3) And (3) eliminating data redundancy: a large amount of data redundancy exists in the WSN, and the cluster head can perform data fusion and compression operation to eliminate the redundancy before transmitting the data of the cluster to the base station, so that the communication traffic with the base station is further reduced. 4) Robustness: the nodes are selected as cluster head nodes in a self-organizing mode, the current intra-cluster information is collected and is forwarded to the base station after fusion, and the load of the network is uniformly distributed in the whole network, so that the energy consumption in the communication process is greatly reduced, and the robustness of the network is enhanced. 5) Local/global optimization: compared with other routing protocols, the clustering algorithm can not only perform fusion optimization on local information. But also global information can be optimized. 6) And (3) expandability: the clustering algorithm is easily combined with other routing algorithms, thereby improving the performance of the routing algorithm.
Therefore, aiming at the fault detection of the power transmission line, the invention puts the gravity on the route planning method for researching the sensor network of the power transmission line, and the method is a path planning and fault backup method (EHRPFB) for power transmission line internet of things sensing and monitoring application based on energy consumption balance, and has three sub-mechanisms of a node clustering strategy, a data forwarding strategy and a fault backup strategy. Firstly, the method comprises a node clustering strategy based on energy balance and energy consumption optimization. Preferentially selecting nodes with high residual energy and high traffic load as cluster head nodes, and then uniformly distributing the cluster head nodes. Next, the method of the present invention includes a load balancing based data forwarding policy to differentiate services and select appropriate routes for different services based on service priority and delay requirements. The method also comprises a fault backup strategy based on the residual energy and the optimal energy consumption, and the mechanism is divided into a link backup part and a node backup part. The link backup is based on the data forwarding mode designed by the invention, and each node can forward data to different paths, thereby playing the role of link backup; the node backup is performed by setting a weight, and when a cluster head node fails, the cluster node with the largest weight is used as a backup cluster head node to replace the failed cluster head node for data aggregation and data forwarding. In a word, the method of the invention can reduce the total energy consumption of data transmission and prolong the life cycle of the sensor network on the premise of ensuring the delay requirement.
The practice of the invention is described in detail below.
(1) Wireless sensor network architecture
The schematic diagram of the topological structure in the power transmission line is shown in fig. 1, and data transmission is carried out from a sensor to a sink node through ZigBee. And the nodes surrounded by the circles are cluster head nodes, and after receiving a plurality of service data packets, the cluster head nodes perform routing forwarding according to different paths under the condition of meeting the requirement of service time delay. After the data is transmitted to the sink node, the sink node may transmit the data to the substation in an optical fiber composite gateway (OPGW) mode. In addition, the sink node can also transmit data to the substation by adopting two long-distance wireless communication modes, namely a wireless private network (LoRa) and a wireless public network (NB-IoT).
The topological structure of the sensor of the power transmission line is a link type structure, and the link type network structure has the characteristics of special structure and low complexity. It is known that each transmission line has three phases, namely A, B, C three phases, and sensor nodes are respectively and fixedly deployed on the three phases of each line tower. We deploy a hierarchical network model such as a network topology map and make the following assumptions: 1) all sensors are static, although monitoring services may be different, the frequency of sending data at ordinary times is the same, the size of the data is basically consistent, and when an emergency situation is monitored, the data is sent immediately, and the data has higher priority. 2) All sensors know their own remaining energy and adjust their transmission power according to the communication distance. 3) The link is symmetrical and the radio signal has the same energy attenuation in all directions. 4) All sensors can operate in a forwarding (cluster head) mode and a sensing mode.
(2) Network energy model
Energy of a sending end is used for sending data, energy of a receiving end is used for receiving the data, and a free space model is assumed to be adopted for energy dissipation between two nodes during data transmission. Therefore, when t bit data is sent between two nodes with a distance d (unit: meter), the energy consumed by the sending end is as follows:
Econsum(t,d)＝t·(Eelec+ε·dindex) (1)
wherein E iselecEnergy representing loss of the transmitting circuit, d is the Euclidean distance between two nodes, d0Is a distance threshold value, epsilonfsAnd εmpThe energy required for power amplification in the two models is respectively.
The energy consumed by the node for receiving the t bit is as follows:
Er(t)＝t·Eelec (2)
wherein E isrThe energy required to receive the t bit data.
(3) The invention relates to a route planning method
3.1) optimal number of cluster head nodes
The initial energy of all nodes is the same, and other parameters of all nodes are the same except for different sensing services. It is assumed here that N sensor nodes are uniformly distributed on a three-phase line of the power transmission line, the size of the area is set to L · W, and the sink node is arranged on one side of the power transmission line. In each clustering period, each cluster node sends t bits of data to the cluster head. According to energy consumption equation (1), the total energy consumption of the whole network per clustering period is equal to:
wherein d isave-sinkRepresents the average distance from the cluster head node to the sink node, dave-chRepresents the average distance from the cluster node to the corresponding cluster head node, and M represents the number of cluster head nodes.
Since all nodes are uniformly distributed, the following formula can be obtained:
the optimal number of cluster head nodes obtained by deriving the number M of cluster head nodes in equation (3) and making it equal to 0 is:
3.2) optimal rotation period for each node to become cluster head node
The invention sets TroFor each cluster head node rotation period, i.e. after each node is selected as a cluster head node with probability p, at least T is neededroThe cluster head node can be selected again according to the probability p after a clustering period, and then
Assuming that the probability that the ith node becomes a cluster head node in the r-th clustering period is as follows:
wherein p isaveIs the probability of each node becoming a cluster head in an ideal state, Ei(r) is the residual energy of the ith node in the round r,
the average remaining energy at round r for all nodes is expressed as follows:
wherein, R is the life cycle of the whole network under ideal conditions, and can be expressed by the following formula:
then in the r-th round, the rotation period of the ith node can be represented by:
wherein E isroundGiven by formula (3), EtotalTo be integratedThe initial total energy of each sensor network, expressed as follows:
Einitthe initial residual energy is the same for each node.
3.3) selecting Cluster head node
The cluster head selection process is mainly divided into two stages, wherein a pre-cluster head node is generated in the first stage, and the pre-cluster head node generated in the first round is adjusted in the second stage, so that the real cluster head node is finally determined.
In the first stage, the selection of the pre-cluster-head node refers to a LEACH protocol cluster head selection formula, and two factors are considered on the basis, one is the residual energy of the sensor node, and the other is the communication cost of the node and other nodes in the communication range. The election process of the pre-cluster head node is as follows: the node generates a random number, the number is between 0 and 1, and if the random number is smaller than a threshold value T (n), the node is selected as a pre-cluster-head node in the current round. The expression of T (n) is as follows:
in the formula, G is a node set of an unselected cluster head in the optimal rotation period of each node, and a node t (n) not belonging to the set is 0; r is the current number of rounds.
The average residual energy formula of the node is as follows:
suppose a node n is within a distance range dmaxIf there are k adjacent nodes in the range, and each adjacent node sends the data of the related service and contains t bit data, then receives the number of each nodeAccording to corresponding energy consumption as Er(t)＝t·EelecThen, for the central node n, the total energy consumption for receiving t bits of data sent by the adjacent nodes is:
the average energy consumption of each node in the whole network for receiving the information of the adjacent nodes is as follows:
in the present invention, it is considered that DmaxCommunication range for forwarding data packets for cluster head nodes in multi-hop inter-cluster routing, DmaxDerived from the following.
Suppose a communication distance d from a cluster node to a cluster head nodechSatisfies the following conditions:
then:
factors are added to account for the particularities of the transmission line topology
And
so that d is the finalmaxSatisfies the following conditions:
wherein, L is the length of the scene, Lm is the transverse interval of sensor placement, W is the width of the scene, Wm is the longitudinal interval of sensor placement, and M is the number of cluster head nodes.
dmaxFor optimal communication distance from cluster node to cluster head node, dmaxAnd DmaxThe relationship is as follows:
in the second stage, each pre-cluster head node broadcasts information of the pre-cluster head nodes to the periphery, and if the distance between the two pre-cluster head nodes is smaller than the set distance DminIf the weight of the pre-cluster head node is smaller than the weight of the cluster head node, the pre-cluster head node is decreased to be a common node, and the pre-cluster head node with larger weight is increased to be a cluster head node. Minimum distance DminIs determined by the following formula:
the weight ω (n) of a node is determined by:
in the formula, alpha, beta and χ are respectively the residual energy of the node, and the node receives the energy consumption of the related service data packet sent by the adjacent node and the weight coefficient of the distance from the sink node.
After the cluster head node is determined, the cluster head node sends invitation information to other surrounding nodes, and each common node belongs to the cluster head node which is closest to the cluster head node in the communication range. If no cluster head node exists in the communication range, the node becomes an un-home node.
And in the nodes which are not owned, the cluster heads are regenerated, and the cluster head generation probability is as follows:
wherein M 'is the desired number of cluster heads M minus the actual number of cluster heads, and N' isThe number of non-homed nodes is,
average residual energy in non-homed nodes, Er-total(n, t)' is the received energy consumption in the non-homed node,
energy consumption to receive neighbor node information for an average of each of the non-homed nodes.
3.4) delay constraints for data Transmission
The invention assumes that there are multiple services in the whole network, and marks the kth service as BKIts delay requirement is
The time D taken for the sensor to transmit the data packet to the sink node from the sensing dataKShould satisfy
Assuming that the time delay of each cluster head node for processing the data packets of the same service is constant, assuming that the queuing time of the data packets at each cluster head node is equal and also constant, and assuming that the distance of the data packets during channel transmission is negligible compared with the speed, D is calculatedave＝Dqueue+Dprocess+DtransmissionSet to a constant value, Dqueue,Dprocess,DtransmissionThe queuing delay, the processing delay and the propagation delay of the same service are respectively, so that the delay constraint can be converted into the hop constraint:
3.5) Fault diagnosis mechanism based on information Negotiation Sensing Protocol (SPIN)
Three types of messages are used in the fault diagnosis mechanism to probe the PRO message, reply REP message and DATA message.
The first step is as follows: the sensor node first sends PRO information to its next-hop neighbor node, which is used to detect whether the next-hop node is normal.
The second step is that: after the next hop node receives the PRO information, replying REP information to the source node, and going to the third step; and if the source node does not receive the REP information in a period of time, the next hop node is considered to have a fault, and other sensor nodes are informed of the fault condition of the node through a flooding method. And the source node resends the data information according to the fault tolerance mechanism and goes to the first step.
The third step: and after receiving the REP information, the source node sends the DATA information to the next hop node.
3.6) failure backup mechanism
The path planning of the invention only relates to cluster head nodes, so the fault-tolerant mechanism only considers the fault tolerance of the cluster head nodes. And periodically carrying out a fault diagnosis mechanism between the sensor nodes to determine whether the link is normal.
1) Link backup mechanism based on multi-path planning
According to the EHRPFB algorithm, the routing table of each cluster head node comprises a plurality of paths to the destination node, when a certain intermediate node in a certain path dies, the cluster head node updates the routing table, and the cluster head node forwards data according to the new routing table information.
As shown in fig. 2, the routing table of the cluster head node 7 includes two paths, which are 7-3-5-sink node and 7-14-10-18-sink node. When the node 5 in the main path has a fault, the node 3 senses the fault of the node 5 through a fault diagnosis mechanism and informs other sensor nodes of the fault of the node through a flooding method. And after knowing that the node 5 has a fault, the node 7 updates the routing table and forwards the data to be forwarded through the backup path.
2) Fault node backup mechanism based on residual energy and optimal energy consumption
According to the EHRPFB algorithm, the sensor network is clustered, and the cluster node with the largest weight is selected as a backup node in each cluster. The cluster head node sends the routing table to the backup cluster head node, and sends the updated routing table to the backup cluster head node after updating the routing table each time. The weight formula is as follows:
wherein w is the proportion of node energy, Eave-remainIs the average remaining energy of nodes within a cluster, daveIs the average distance between the node in the cluster and other nodes,
is the average of the average distances of all nodes and is expressed as follows:
λ is the number of nodes in the cluster, dijIs the distance between node i and node j.
And when the cluster head node dies, the backup cluster head node replaces the original cluster head node, the rest nodes in the cluster transmit data to the backup cluster head node, and meanwhile, the backup cluster head node receives the data forwarded by other cluster head nodes and forwards the data.
As shown in fig. 3, the cluster head node 3 and the nodes 2,4, and 9 belong to the same cluster, and the node 9 is a backup cluster head node. When the cluster head node 3 fails, the backup cluster head node finds that the cluster head node fails through a failure diagnosis mechanism, other sensor nodes are informed through a flooding method, and the node 9 replaces the node 3 to carry out data aggregation and forwarding. In the process of data transmission, if the node 3 fails, the cluster head node 7 will update the routing table from 7-3-5-sink node to 7-9-5-sink node.
The following specifically describes the method of carrying out the present invention.
The invention provides a uniform clustering mechanism based on energy balance and optimal energy consumption. Firstly, setting the optimal communication distance d between nodes according to the attenuation power formulamax. Setting clusterDistance between heads is DmaxWithin is the neighbor cluster head node, DminIs limited by the minimum distance between cluster heads. Secondly, clustering is carried out on the network, nodes are randomly selected as pre-cluster head nodes according to a probability formula (12), and the rest nodes are used as common nodes. Thirdly, if the distance between the pre-cluster head nodes is less than DminThen the node with larger weight becomes a cluster head node, and the node with smaller weight is reduced to a common node. Fourthly, if the number of the cluster head nodes is less than the expected number, all the common nodes belong to the cluster head nodes which are nearest to the common nodes in the communication range, and the rest common nodes become non-home nodes; otherwise, if no non-home node exists or the number of the cluster head nodes is larger than or equal to the expected value, all the common nodes belong to the cluster head node closest to the common nodes, and the clustering stage is finished. Fifthly, cluster head nodes are regenerated in the non-home nodes according to a probability formula (22), all the non-home nodes belong to the cluster head nodes which are closest to the non-home nodes in the communication range, and the rest nodes are still non-home nodes. Go to step four. The flow of the uniform clustering mechanism is shown in fig. 4.
The invention also provides a data forwarding mechanism based on the service priority. In each round of data transmission stage, the cluster head simultaneously receives the node data in the cluster and other cluster head forwarding data, and distinguishes the data packets of various services according to time delay priority. Each data packet carries a timestamp, the effective time of the data packet is limited by hop count constraint, and the hop count constraint is reduced by 1 every time the data packet is forwarded. In the data forwarding process, a cluster head firstly directly forwards a data packet with hop number constraint of 1 to a sink node; after that, the cluster head selects the next hop cluster head node of the shortest path under Dijkstra algorithm for the service with the highest requirement on the delay constraint to carry out data forwarding, and marks the next hop cluster head node; then considering the secondary priority service, preferentially selecting the shortest path under the Dijkstra algorithm from the unmarked nodes and marking the nodes of the next hop cluster under the condition of meeting the time delay requirement; if the shortest path meeting the time delay requirement does not exist in the unmarked nodes, the cluster head node selects a path meeting the time delay requirement from the marked nodes; if the path which meets the time delay requirement still does not exist, the shortest path is selected for data forwarding; and repeating the steps until all the data packets are forwarded. The flow of the data forwarding mechanism is shown in fig. 5.
On the basis of the previous route planning, the invention also provides a fault node backup mechanism based on the residual energy and the optimal energy consumption. When the cluster head node carries out data forwarding, if the cluster head node of the next hop of the selected path is found dead, the path is re-planned, and the cluster head nodes of other paths are selected to carry out data forwarding. In the node backup stage of each period, a backup cluster head node with a larger weight exists in each cluster by setting the weight. And when the cluster head node is dead, the backup cluster head node replaces the cluster head node to carry out data aggregation and forwarding. The backup mechanism of the cluster head node is shown in fig. 6.
The following is illustrated by way of example:
(1) parameter setting
To demonstrate the effectiveness of the present invention, experimental evaluations were performed for validation. The experiment sets the position of a target node, the communication distance between nodes, initial energy, transmission energy consumption, the number of nodes and the like.
TABLE 1 parameter settings
| Parameter(s) | Value taking | Parameter(s) | Value taking |
| Number of |
90 | Energy consumption for data transmission E0(J) | 2×10^(-8) |
| Expected |
90*0.9 | Transmission energy consumption (J/(bit × m)) | 50E0 |
| Cluster head node generation probability P | 0.1 | Receiving energy consumption (J/bit) | 50E0 |
| Node distribution area A (m)2) | 2×3000 | Convergence energy consumption (J/bit) | 5E0 |
| Common node transmission radius r (m) | 300 | Control information (bit) | 32 |
| Cluster head node transmission radius R (m) | 600 | Data information (bit) | 4000 |
| Initial energy of node (J) | 1500J | Higher priority traffic hop count constraint (hop) | 10 |
| Destination node position (m) | (3000,1) | Lower priority traffic hop count constraint (hop) | 15 |
(2) Results of the experiment
The invention mainly carries out experimental evaluation from three aspects of the algorithm. In a clustering stage experiment, the algorithm of the invention is mainly compared and analyzed with an I-LEACH algorithm in the distribution condition of dead nodes and the death degree of the nodes, and the I-LEACH algorithm modifies a calculation formula of a threshold value T (n) on a cluster head selection mechanism of the LEACH algorithm, so that the more the neighbor nodes are, the larger the residual energy is, and the higher the probability that the nodes closer to a convergent node become cluster head nodes is. In the data forwarding stage, the algorithm of the invention is mainly compared and analyzed with the ant colony algorithm and the CRLA algorithm in a routing planning mode, a node death degree and a node energy consumption balance degree. The ant colony algorithm is an intelligent algorithm, and a planned path is continuously converged to an optimal path in a mode of searching the path and leaving pheromones. The CRLA algorithm is an improvement of the LEACH-C algorithm, a single-hop and multi-hop combined algorithm is executed in the aspect of routing among clusters, and after all data are less than a certain threshold value from a sink node, the data are transmitted to the sink node through the single hop. In the fault backup stage, the invention mainly utilizes the node fault rate to evaluate the proposed fault backup mechanism.
1) Uniform clustering strategy based on energy balance and energy consumption optimization
As shown in fig. 7 to 9, which are graphs of results of clustering experiments performed by the I-LEACH algorithm and the EHRPFB algorithm, respectively, a sink node is located at the rightmost end, a cluster head node is denoted by a "+", a cluster node is denoted by an "o", a line segment denotes a cluster head node to which each cluster node belongs, a hollow node is a survival node, and a solid node is a death node.
Running I-LEACH and EHRPFB algorithms, and respectively recording node death conditions of 1 st, 30 th and 50 th rounds of clustering by the two algorithms, wherein the results are shown in figures 7-9:
it can be seen that in each round before 50 rounds, the number of dead nodes of the EHRPFB algorithm is significantly less than that of the I-LEACH algorithm; by round 50, the nodes of the I-LEACH algorithm have mostly died. In addition, it can be found that the dead nodes of the EHRPFB algorithm are spread from the center of the link to both sides, while the dead nodes of the I-LEACH algorithm are spread from a direction away from the aggregation node, because the cluster head nodes of the EHRPFB algorithm are distributed more uniformly, and the nodes located in the middle of the link consume energy more quickly due to the larger number of packets to be forwarded; in the I-LEACH algorithm, because the number of cluster head nodes far away from the sink node is too small, and the cluster heads adopt single-hop transmission to send data to the sink node, the cluster heads far away from the sink node can die quickly. In contrast, EHRPFB algorithm has a slower node death rate than I-LEACH algorithm, which is evident from the figure.
In order to more intuitively compare the energy performance and the load balance of the two clustering methods, the node death conditions of each round of the two clustering algorithms are recorded during experimental evaluation, and the death rounds of the first node, 30% node, 50% node and 80% node of the two clustering algorithms are compared. In order to ensure the reliability of the experimental results, all data are the mean values of the experimental results of a plurality of times. The results were plotted in bar graph form as shown in FIG. 10.
As can be seen from the figure, the node death time of the EHRPFB algorithm is later than that of the I-LEACH algorithm when the two algorithms are compared. Therefore, compared with the I-LEACH algorithm, the EHRPFB algorithm effectively prolongs the service life of the network, and improves the load balance and the network connectivity within the network survival time.
2) Data forwarding mechanism based on service priority
The CRLA inter-cluster routing algorithm, the ant colony algorithm-based routing algorithm and the service priority-based routing algorithm are respectively subjected to experimental evaluation, the sink node is located at the rightmost end, each sensor node can spontaneously generate and forward a data packet, and equivalently, each node can possibly detect data in an actual situation. In the experiment, the clustering algorithm of the three path planning algorithms uses an EHRPFB clustering stage algorithm. The experimental results are shown in 11-12.
The paths generated by the different path planning algorithms and the hop counts are recorded and compared, as shown in table 2.
TABLE 2 comparison of paths and hop counts generated by different path planning algorithms
Fig. 11 and 12 show the characteristics of path selection under different algorithms, and table 2 records the paths in the figures. It can be seen that in the ant colony algorithm, after a certain number of iterations, the planned path converges to a shorter path. After all data in the CRLA algorithm is less than a certain threshold value from the sink node, the data is transmitted to the sink node through single hop. All services of the two inter-cluster routing planning modes are transmitted by a single path. In the algorithm based on traffic differentiation, fig. 12(a) shows a traffic with a higher traffic priority, and when a traffic with a high priority selects a path, the shortest path is directly selected; fig. 12(b) shows the traffic with lower priority, and according to the path selection algorithm proposed in the present invention, the traffic with low priority may be "far away" without selecting the shortest path when selecting the path.
In experimental evaluation, node death was recorded for each round of the three algorithms, and the results of the mean of multiple trials are shown in fig. 13. It can be seen that the dead time of the nodes based on the service differentiation algorithm is later than that of the other two routing algorithms by comparison of the three algorithms, so that the energy efficiency is better. The inter-cluster routing algorithm based on service differentiation adopts a load-balanced data forwarding path for data packets with low time delay requirement, and the energy consumption of the nodes is balanced, so that the number of dead nodes in each clustering period is optimized.
In addition, the residual energy variance of all survival nodes of each round of the three algorithms is recorded and is made into a line graph, and the result of the mean value of a plurality of tests is shown in fig. 14. It can be seen that the residual energy variance of the traffic differentiation algorithm is overall minimal. This is because the node energy consumption of the CRLA algorithm depends on the selection of the transmission threshold, and the node death is fast because the transmission energy consumption of the node near the threshold is large. In the other two algorithms, the service distinguishing algorithm is used for multi-path forwarding of different service data, so that the node energy consumption is more balanced, the number of surviving nodes in each clustering period is optimized, and the purpose of prolonging the life cycle of the network is finally achieved.
3) Failure node backup mechanism
Because the routing planning mode of the algorithm has the particularity of multipath selection, the EHRPFB algorithm has link fault tolerance. Therefore, the section mainly demonstrates and analyzes the fault tolerance mechanism of the node. The average node efficiency after the round of clustering, i.e. the effective working time of the node/data transmission time after each round of clustering, is recorded, as shown in fig. 15.
As can be seen from fig. 15, the efficiency of the node with the backup mechanism is significantly higher than that of the node without the backup mechanism, because when the cluster head node fails, the nodes in the whole cluster will fail. However, the backup resource sharing mechanism based on reliability used by the invention enables backup cluster head nodes to exist in each cluster, and when the cluster head nodes are in failure, the backup cluster head nodes can replace the cluster head nodes to carry out data aggregation and forwarding, thereby greatly reducing the influence of the failure nodes on the efficiency of other nodes of the whole network.
Claims (7)
1. A route planning method of wireless sensor network of transmission line is characterized in that the sensor network is clustered evenly, then when data transmission is carried out, a cluster head node collects information sent by adjacent cluster head nodes and nodes in the cluster, and route forwarding based on service differentiation is carried out, and the route planning method comprises the following steps: 1) cluster head selection, namely establishing a cluster head selection mechanism based on two factors of energy and node communication load, so that nodes with high residual energy and large communication load are more likely to become cluster head nodes; 2) cluster head distribution, namely optimizing the selected cluster heads, setting the optimal distance range of adjacent cluster head nodes, and enabling the cluster head nodes to be distributed more uniformly by increasing and deleting the cluster head nodes on the basis of the optimal distance range; 3) data transmission, routing planning is carried out based on service priority, the cluster head carries out priority sequencing on received data packets of different services, and different paths are planned according to the service priority of the data packets for data forwarding;
the cluster head selection and the cluster head distribution are specifically as follows: setting the initial energy of all nodes in the network to be the same, setting the parameters of all nodes to be the same except for different sensing services, setting N sensor nodes to be uniformly distributed on a three-phase line of a power transmission line, setting the size of an area to be L.W, arranging a sink node on one side of the power transmission line, and in each clustering period, sending t-bit data to a cluster head by each cluster node, wherein according to an energy consumption formula (1), the total energy consumption of the whole network in each clustering period is equal to:
wherein E iselecEnergy representing loss of the transmitting circuit, dave-sinkRepresents the average distance from the cluster head node to the sink node, dave-chRepresenting the average distance, ε, of a cluster node to the corresponding cluster head nodefsAnd εmpThe energy required for the corresponding power amplification, M represents the number of cluster head nodes,
since all nodes are uniformly distributed, the following formula is obtained:
by deriving the cluster head node number M in formula (3) and making it equal to 0, the optimal cluster head node number is obtained as:
after the number of cluster head nodes is determined, T is setroFor each cluster head node rotation period, i.e. after each node is selected as a cluster head node with probability p, at least T is neededroThe cluster head node can be selected again according to the probability p after a clustering period, and then
The probability that the ith node becomes a cluster head node in the r-th clustering period is set as follows:
wherein p isaveIs the probability of each node becoming a cluster head in an ideal state, Ei(r) is the residual energy of the ith node in the round r,
the average remaining energy at round r for all nodes is expressed as follows:
wherein, R is the life cycle of the whole network under an ideal state:
then in the r-th round, the rotation period of the ith node is represented by:
wherein E isroundGiven by formula (3), EtotalThe initial total energy for the entire sensor network is expressed as follows:
Einitthe initial residual energy of each node is the same;
the cluster head selection process is divided into two stages, wherein a first stage generates pre-cluster head nodes, a second stage adjusts the pre-cluster head nodes generated in the first round, and finally real cluster head nodes are determined:
in the first phase, the election process of the pre-cluster head node is as follows: if the random number is smaller than a threshold value T (n), the node is selected as a pre-cluster head node in the current round, and the expression of T (n) is as follows:
in the formula, G is a node set of an unselected cluster head in the optimal rotation period of each node, and a node t (n) not belonging to the set is 0; r is the number of the current wheel,
the average residual energy formula of the node is as follows:
let a certain node n be in a distance range dmaxThere are k adjacent nodes in the range, and the data of each adjacent node sending related service contains t bit data, and the energy consumption for receiving each node data is Er(t)＝t·EelecThen, for the node n, the total energy consumption for receiving t bits of data sent by the adjacent node is:
the average energy consumption of each node in the whole network for receiving the information of the adjacent nodes is as follows:
Dmaxcommunication range for forwarding data packets for cluster head nodes in multi-hop inter-cluster routing, DmaxDerived from the following:
suppose a communication distance d from a cluster node to a cluster head nodechSatisfies the following conditions:
then:
factors are added to account for the particularities of the transmission line topology
And
so that d is the finalmaxSatisfies the following conditions:
dmaxfor optimal communication distance from cluster node to cluster head node, dmaxAnd DmaxThe relationship is as follows:
in the second stage, each pre-cluster head node broadcasts information of the pre-cluster head nodes to the periphery, and if the distance between the two pre-cluster head nodes is smaller than the set distance DminIf the weight of the pre-cluster head nodes is smaller than the weight of the pre-cluster head nodes, the pre-cluster head nodes with smaller weight are decreased to be common nodes, the weight of the pre-cluster head nodes is increased to be cluster head nodes, and the minimum distance D is between the pre-cluster head nodes and the cluster head nodesminIs determined by the following formula:
the weight ω (n) of a node is determined by:
in the formula, alpha, beta and χ are respectively the residual energy of the node, and the node receives the energy consumption of the related service data packet sent by the adjacent node and the weight coefficient of the distance from the sink node.
2. The routing planning method of the wireless sensor network of the power transmission line according to claim 1, wherein after the cluster head node is determined, the cluster head node sends invitation information to other surrounding nodes, each common node belongs to the cluster head node closest to the communication range, if no cluster head node exists in the communication range, the node becomes an un-homed node, and in the un-homed node, the cluster head is regenerated, and the cluster head generation probability is as follows:
wherein M 'is the value obtained by subtracting the actual number of cluster heads from the expected number of cluster heads M, N' is the number of non-home nodes,
average residual energy in non-homed nodes, Er-total(n, t)' is the received energy consumption in the non-homed node,
energy consumption to receive neighbor node information for an average of each of the non-homed nodes.
3. The routing planning method of the wireless sensor network of the power transmission line according to claim 1, wherein the data transmission is based on service priority for routing planning, in each data transmission stage, the cluster head simultaneously receives node data in the cluster and other cluster head forwarded data, and distinguishes data packets of various services according to time delay priority, each data packet carries a timestamp, the effective time of the data packet is limited by hop constraint, and the hop constraint is reduced by 1 for each forwarding of the data packet; in the data forwarding process, a cluster head firstly directly forwards a data packet with hop number constraint of 1 to a sink node; after that, the cluster head selects the next hop cluster head node of the shortest path under Dijkstra algorithm for the service with the highest requirement on the delay constraint to carry out data forwarding, and marks the next hop cluster head node; then considering the secondary priority service, preferentially selecting the shortest path under the Dijkstra algorithm from the unmarked nodes and marking the nodes of the next hop cluster under the condition of meeting the time delay requirement; if the shortest path meeting the time delay requirement does not exist in the unmarked nodes, the cluster head node selects a path meeting the time delay requirement from the marked nodes; if the path which meets the time delay requirement still does not exist, the shortest path is selected for data forwarding; and repeating the steps until all the data packets are forwarded.
4. The method according to claim 3, wherein the delay requirement is that, assuming that there are multiple services in the whole network, the Kth service is denoted as BKIts delay requirement is
The time D taken for the sensor to transmit the data packet to the sink node from the sensing dataKShould satisfy
Setting the time delay of each cluster head node for processing the data packets of the same service as a constant, setting the equal queuing time of the data packets in each cluster head node as a constant,
is a constant value, Dqueue，Dprocess，DtransmissionRespectively queuing delay, processing delay and propagation delay of the Kth service, thereby converting the delay constraint into hop constraint, wherein the hop constraint of the Kth service is as follows:
5. the method of claim 1, wherein the fault diagnosis is performed based on the information negotiation sensing protocol SPIN, and three types of messages are used: a probe PRO message, a reply REP message and a DATA DATA message,
the first step is as follows: the sensor node firstly sends PRO information to a next-hop neighbor node of the sensor node, and the information is used for detecting whether the next-hop node is normal or not;
the second step is that: after the next hop node receives the PRO information, replying REP information to the source node, and going to the third step; if the source node does not receive the REP information in a period of time, the next hop node is considered to be in fault, other sensor nodes are informed of the fault condition of the node through a flooding method, and the source node resends the data information according to a fault tolerance mechanism and goes to the first step;
the third step: and after receiving the REP information, the source node sends the DATA information to the next hop node.
6. The fault backup method of the route planning method of the power transmission line wireless sensor network according to claim 5, characterized by being divided into two parts, namely a link backup and a fault backup, wherein the link backup is based on a data forwarding mode, and each node performs data forwarding to a different path, thereby playing a role of the link backup; node backup is performed according to fault diagnosis: during data transmission, fault node backup is carried out based on residual energy and optimal energy consumption, when cluster head nodes carry out data forwarding, if the cluster head nodes of the next hop of a selected path die, the path is planned again, and cluster head nodes of other paths are selected to carry out data forwarding, wherein in the node backup stage of each period, the cluster nodes with the maximum weight are selected as backup nodes in each cluster in a weight setting mode, and after the cluster head nodes die, the backup cluster head nodes replace the cluster head nodes to carry out data aggregation and forwarding.
7. The fault backup method of the routing planning method of the power transmission line wireless sensor network according to claim 6, characterized in that after the sensor network is clustered, a cluster node with the largest weight is selected as a backup node in each cluster, the cluster head node sends the routing table to the backup cluster head node, and sends the updated routing table to the backup cluster head node after updating the routing table each time, and the weight formula of the nodes is as follows:
wherein w is the proportion of node energy, Eave-remainIs the average remaining energy of nodes within a cluster, daveIs the average distance between the node in the cluster and other nodes,
is the average of the average distances of all nodes and is expressed as follows:
λ is the number of nodes in the cluster, dijIs the distance between node i and node j;
and when the cluster head node dies, the backup cluster head node replaces the original cluster head node, the rest nodes in the cluster transmit data to the backup cluster head node, and meanwhile, the backup cluster head node receives the data forwarded by other cluster head nodes and forwards the data.
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| CN111917637B (en) * | 2019-05-09 | 2022-05-17 | 华为技术有限公司 | Data message sending method and device |
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| CN110247791B (en) * | 2019-05-22 | 2022-04-01 | 北京英视睿达科技股份有限公司 | Environment monitoring system and updating method for continuous work online updating |
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| CN113573392B (en) * | 2021-07-13 | 2022-11-22 | 电子科技大学中山学院 | Energy-saving communication method under abnormal state of gateway of Internet of things |
| CN114189874B (en) * | 2021-12-13 | 2024-04-16 | 国家电网有限公司 | 5G-based network construction method for power transmission line online monitoring system |
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