CN107071811B - WSN fault-tolerant non-uniform clustering method based on fuzzy control - Google Patents
WSN fault-tolerant non-uniform clustering method based on fuzzy control Download PDFInfo
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Abstract
The invention relates to a wireless sensor network Clustering algorithm, in particular to a WSN fault-tolerant non-uniform Clustering algorithm DFUC (distributed Fuzzy controller based Unequal Clustering algorithm) based on Fuzzy control. The algorithm is based on a plurality of local parameters such as residual energy, node centrality, distance between a node and a base station and the like, and a cluster head forming opportunity and a cluster size value are calculated and output through a fuzzy controller, so that the optimal node becomes a cluster head and limits the size of the cluster, and member nodes form a backup cluster head list. The list is updated in real time by means of a TDMA mechanism so that the most suitable node becomes the backup cluster head. Once a cluster head fails, the result is that a backup cluster head is always guaranteed to replace the cluster head. The algorithm solves the problems of network energy consumption imbalance caused by random cluster size and neglect of fault tolerance of the traditional non-uniform clustering algorithm, reduces network energy consumption, prolongs the life cycle of the network and is suitable for practical application.
Description
Technical Field
The invention relates to a Wireless Sensor Network (WSN) non-uniform Clustering method, in particular to a WSN fault-tolerant non-uniform Clustering method DFUC (distributed fuzzy controller based on non-uniform Clustering) based on fuzzy control. The method inputs the node residual energy, the node centrality and the distance from the node to the base station into a fuzzy controller, and outputs a cluster head chance and a cluster size value through an IF-THEN rule in a reasoning mode, so that the node with the optimal performance becomes a cluster head and a cluster with a proper size is built. Data transmission is performed in a TDMA manner to tolerate temporary and permanent failure of cluster heads and member nodes. The load among clusters is effectively balanced, and the energy consumption of the network is reduced, so that the life cycle of the network is prolonged.
Background
The wireless sensor network is widely applied to occasions such as environment monitoring, emergency response, military monitoring, space exploration and the like. Efficient energy saving is an important challenge for WSNs, while clustering is one of the effective methods. The clustering method can improve the network expandability and the energy efficiency, reduce the routing delay and prolong the network life cycle.
The uniform clustering method generally adopts the steps of periodically and randomly selecting cluster head nodes to generate cluster heads with proper proportion, so that the network energy consumption is reduced. When the cluster head communicates with the base station in a multi-hop manner, the cluster head of the adjacent base station bears more data relay tasks and energy is exhausted prematurely, so that a 'hot spot' problem is generated. In order to solve the problems of 'hot spots' and the like existing in the uniform clustering method, a plurality of non-uniform clustering methods are provided, and the non-uniform clustering methods are proved to be superior to the uniform clustering methods in most network deployments. The non-uniform clustering method generally selects cluster heads according to factors such as node residual energy, node-to-base station distance, node degree, node-to-cluster head distance and the like, so that a network is divided into clusters with different sizes, and data are transmitted to a base station through intra-cluster single-hop and inter-cluster multi-hop communication modes. Load balancing is ensured by making different clusters have different cluster sizes, and the network life cycle is improved.
Both the existing uniform and non-uniform clustering methods can improve some of the network performance. However, they all assume that a node dies only when energy is exhausted. It may be practical whether a cluster head or a member node may fail for a number of reasons, such as power instability, physical damage, etc. The existing method adopts backup cluster heads to solve the problem, considers that the determined backup cluster heads always work normally and are nodes which are the most suitable to replace the cluster heads, and usually only considers two backup cluster heads. However, in some cases, the backup cluster head may consume more energy than other nodes. Also, considering a maximum of two backup cluster heads does not effectively maintain the existing clusters.
Disclosure of Invention
The invention aims to solve the technical problems that the cluster size cannot be determined and the fault-tolerant capability is weak in the existing non-uniform clustering method, the residual energy of nodes, the node centrality and the distance between the nodes and a base station are considered, the fuzzy controller and the local information of the nodes are used for calculating the cluster head forming opportunity of the nodes and the cluster size value, the nodes with the maximum opportunity value are formed into the cluster head, and whether a certain member node is added or not is determined according to the cluster size value, so that a cluster with a proper size is formed. In the data transmission stage, a TDMA mode is adopted to collect data in the clusters, temporary and permanent faults of cluster heads and member nodes are tolerated, a shortest path multi-hop mode is adopted to transmit data among the clusters, when the cluster heads have permanent faults, the optimal backup cluster heads become the cluster heads, and when the member nodes have permanent faults, the nodes are removed from the network. Therefore, the inter-cluster load is effectively balanced, and the network energy consumption is reduced, so that the network life cycle is improved.
The invention discloses a WSN fault-tolerant non-uniform clustering method DFUC based on fuzzy control, which comprises a network model, clustering and fault-tolerant parts. The network model provides a model for the DFUC method implementation, and specifically comprises a network model, an energy model and a fuzzy controller model. Clustering is to determine the cluster head forming opportunity and the cluster size value through a fuzzy controller which runs in each node in a distributed mode, so that a network is divided into a plurality of clusters with different sizes, and the node with the optimal performance becomes a cluster head, and specifically comprises two processes of cluster head election and cluster establishment. Fault tolerance is to maintain an established cluster by monitoring cluster heads and member nodes in a time division multiple access TDMA manner, i.e., when a cluster head fails permanently, the optimal backup cluster head becomes a cluster head, and when a member node fails permanently, the node is removed from the network.
The network model provides a model for the method implementation, wherein the network model determines that the network type is a static network, the network nodes are isomorphic and have unique identification, the approximate distance can be calculated through RSSI, and meanwhile, the node can obtain the distance from a base station and the node centrality through HELLO messages. The energy model provides a calculation model for the energy consumption of intra-cluster and inter-cluster data communication and is based on a wireless communication energy consumption model E under free spacetx,ErxBased on Etx,ErxTo calculate intra-cluster and inter-cluster energy consumption. Wherein the energy consumption in the cluster EintraConsisting of three parts, i.e. energy consumption E for the member nodes to communicate with the cluster headMemToChEnergy consumed by cluster head receiving data from its cluster member node EChrxAnd energy consumption of data fusion EDA. There are two cases of inter-cluster energy consumption, if the cluster head communicates directly with the base station, its energy consumption EsinterEnergy consumed for communication between the cluster head and the base station; if the cluster head communicates with the base station in a multi-hop manner, the energy consumption E of the cluster headminterThe energy consumed for communication between cluster heads plus the energy consumed for communication between the last cluster head and the base station. While the fuzzy controllerThe model adopts a Mandani fuzzy controller, which is simple and can produce better results. Fuzzy input residual energy, node centrality and distance between a node and a base station, and an inference engine performs control processing according to an IF-THEN rule base and outputs cluster head opportunity (Chance) and cluster Size (Size).
The clustering determines the cluster head forming opportunity and the cluster size value through a fuzzy controller which runs in each node in a distributed mode, so that the network is divided into a plurality of clusters with different sizes, and the node with the optimal performance becomes a cluster head, and specifically comprises two processes of cluster head election and cluster establishment. In the process of cluster head election, each node operates a fuzzy controller, and a clear input value is fuzzified into a proper linguistic variable by a fuzzy inference engine through a given membership function. THEN the fuzzy input variable is processed through an IF-THEN rule base, the output of the fuzzy inference engine is still a fuzzy linguistic variable, and the centroid method is adopted to solve the fuzzy, so that clear output quantity 'opportunity' and 'size' are obtained. In the process of establishing the cluster, all nodes broadcast a cluster head competition message CH _ CP to neighbor nodes in the communication radius of the nodes, the nodes have higher opportunity values than other nodes to become cluster heads, broadcast a competition SUCCESS message CH _ SUCCESS, after receiving the CH _ SUCCESS, the nodes update a nearby cluster head list and send a 'joining cluster message' CH _ JOIN to the cluster head nearest to the nodes. The cluster head receiving the CH _ JOIN message checks its "size" to determine whether to receive a new member. If the cluster member node is smaller than the value of 'size', sending back a 'successful joining message' CH _ JOIN _ SUCCESS, and joining the member into a backup cluster head list according to the value of 'opportunity'. Otherwise, sending back 'JOIN failure message' CH _ JOIN _ FAIL. When a certain node receives a CH _ JOIN _ FAIL message, if the cluster head list is not empty, the CH _ JOIN message is sent to the next nearest cluster head until the certain node is added into a certain cluster. In the worst case, if the cluster head list is empty, the node still cannot be added into a certain cluster, and then the node itself is selected as the cluster head. After the cluster is formed, according to the opportunity value of each node output by the fuzzy controller, a ' backup cluster head list ' is formed according to the principle that the opportunity value is from high to low, each cluster head broadcasts a ' backup cluster head list message ' CH _ Bch ' into the cluster, and the node receiving the CH _ Bch stores the backup cluster head list.
The fault tolerance is to maintain the established cluster by monitoring the cluster head and the member node in a time division multiplexing TDMA manner, that is, when the cluster head has a permanent fault, the optimal backup cluster head becomes the cluster head, and when the member node has a permanent fault, the node is removed from the network. Once the cluster is established, the cluster member nodes may begin data transmission based on the assigned time slots. To maintain the created cluster, the cluster members often compute their fuzzy controller outputs and send opportunity values to the cluster head along with the data. The cluster head updates the backup cluster head list based on the received opportunity value, and the updated list is periodically sent to the cluster members through a data request message to ensure that the most appropriate backup cluster head is updated in real time. Thereby enabling its members to be notified as soon as possible when a cluster head dies to avoid loss of data in the network, and when a member dies, the cluster head removes it from the backup cluster head list. Specifically, once a member receives a data request message in the allocated time slot, the member sends a data packet of the data request message, and if the cluster head does not receive the requested data in the frame tail, the member is marked with an error mark. The cluster head then issues another request in the next periodic time slot, and if the requested data has not been received, the member is considered to be permanently failed and removed from the backup cluster head list. Similarly, the member waits for the data request from the cluster head in the allocated time slot, and if the member does not successively receive the data request message at the end of the frame, the cluster head is considered to have a temporary failure. It will wait for the next corresponding time slot to receive the data request message, and if the data request message has not been received, it will consider that the cluster head has a permanent failure. Then, the member checks the backup cluster head list which is updated recently by the received cluster head, sends out a join cluster message by using the first node as the cluster head, and waits for a confirmation message. However, the node may also fail, and once the acknowledgement message is not received, a join cluster message is sent out for the cluster head by the next node in the list until a certain cluster head is finally joined.
From the above description, it can be seen that the WSN fault-tolerant non-uniform clustering method DFUC based on fuzzy control of the present invention includes three parts, namely a network model, clustering and fault tolerance. The method inputs the node residual energy, the node centrality and the distance from the node to the base station into a fuzzy controller, and outputs a cluster head chance and a cluster size value through an IF-THEN rule in a reasoning mode, so that the node with the optimal performance becomes a cluster head and a cluster with a proper size is built. Data transmission is performed in a TDMA manner to tolerate temporary and permanent failure of cluster heads and member nodes. The load among clusters is effectively balanced, and the energy consumption of the network is reduced, so that the life cycle of the network is prolonged.
Drawings
FIG. 1 is a diagram of a fuzzy controller structure according to the present invention
FIG. 2 is an input/output membership function of the fuzzy controller of the present invention
FIG. 3 is a fuzzy controller rule table of the present invention
FIG. 4 illustrates the data transmission and fault tolerance process of the present invention
FIG. 5 is a table of simulation parameters of the present invention
FIG. 6 is a comparison graph of the number of nodes of the network of the present invention
FIG. 7 is a graph of net energy remaining versus present invention
Detailed Description
The invention is further described in detail with reference to the accompanying drawings, and a WSN fault-tolerant non-uniform clustering method DFUC based on fuzzy control comprises three parts, namely a network model, clustering and fault tolerance. The network model provides a model for method implementation, and specifically comprises a network model, an energy model and a fuzzy controller model. Clustering is to determine the cluster head forming opportunity and the cluster size value through a fuzzy controller which runs in each node in a distributed mode, so that a network is divided into a plurality of clusters with different sizes, and the node with the optimal performance becomes a cluster head, and specifically comprises two processes of cluster head election and cluster establishment. Fault tolerance is to maintain an established cluster by monitoring cluster heads and member nodes in a time division multiple access TDMA manner, i.e., when a cluster head fails permanently, the optimal backup cluster head becomes a cluster head, and when a member node fails permanently, the node is removed from the network.
The network model provides a model for method implementation, and has the following properties:
firstly, all nodes are static after network deployment, the node energy is limited, and the base station energy is not limited;
the network nodes are isomorphic, namely have the same initial energy and the same processing, storage, sending and receiving capabilities;
calculating approximate distance between nodes by Receiving Signal Strength Indication (RSSI);
and fourthly, when the network deployment starts, the base station sends a HELLO message to the nodes of the whole network, and then each node broadcasts the HELLO message of the node by the communication radius R of the node. Based on the interactive information, each node calculates the distance from the base station and the node centrality;
each node has unique identifier, and the node set can be represented as S ═ S1,s2,...,snIn which s isiRepresenting the ith node.
The energy model provides a calculation model for data communication energy consumption and is based on a wireless communication energy consumption model E in free spacetx,ErxAnd has:
Erx＝l*Eelec (2)
wherein Etx,ErxRepresenting the energy consumed by the node for transmission and reception, l representing the number of data bits transmitted or received, EelecRepresenting the energy consumed by the circuit for transmitting or receiving a unit of data, epsilonfs、εmpRepresenting free space and multipath propagation model power consumption, respectively. d represents the distance between two nodes,a threshold value representing whether the transmission model is free space or multipath propagation.
For the clustered wireless sensor network, there are two main factors for the energy consumption in the data transmission phase, i.e. intra-cluster transmission and inter-cluster transmission. Wherein the intra-cluster transmission consists of three parts, as shown in formula (3), wherein EMemToChRepresenting member nodes and cluster headsEnergy consumption of communication, EChrxEnergy consumed to represent reception of data by a cluster head from its cluster member nodes, EDARepresenting the energy consumption of the data fusion.
Eintra＝EMemToCH+ECHrx+EDA (3)
The energy consumption of the member nodes in communication with the cluster heads is shown in (4), where n represents the number of cluster heads of the network, and miRepresenting the number of member nodes within a cluster i, which may not be equal for each cluster, and EtxIndicating the energy consumed by the node j in the ith cluster to send data to the cluster head.
The energy consumed by the cluster head for receiving data from the cluster member nodes is shown as the formula (5), wherein mi represents the number of member nodes in the ith cluster, n is the number of clusters in the network, and ErxIndicating the received energy.
The energy consumption of data fusion is shown in equation (6), where k represents the number of data bits, EpDbRepresenting the energy consumed by the unit data fusion.
Also, inter-cluster transmission includes two cases, if the cluster head communicates directly with the base station, its energy consumption is as shown in equation (7):
Esinter＝Etx(CH,BS) (7)
and energy consumption when the cluster head communicates with the base station through the multi-hop mode is as shown in formula (8):
wherein n ismIs cluster head to baseHop count of a station, CH(i)Indicating the ith cluster head on a multi-hop path from the cluster head to the base station.
While the fuzzy controller model adopts a Mandani fuzzy controller, which is simple and can produce better results, and the structure of the fuzzy controller model is shown in figure 1. The clear input value residual energy, the node centrality and the distance between the node and the base station are blurred into appropriate linguistic variables by the fuzzy inference engine through a given membership function. THEN the fuzzy input variable is controlled and processed in the inference system according to the IF-THEN rule base. The output quantity is the cluster head forming Chance (Chance) and the cluster Size (Size), the output of the fuzzy inference engine is still the fuzzy linguistic variable, and the fuzzy is solved by adopting the centroid method, so that clear output quantity 'Chance' and 'Size' values are obtained.
The clustering determines the cluster head forming opportunity and the cluster size value through a fuzzy controller which runs in each node in a distributed mode, so that the network is divided into a plurality of clusters with different sizes, and the node with the optimal performance becomes a cluster head, and specifically comprises two processes of cluster head election and cluster establishment. Initially in the cluster head election process, all nodes in the network are designated as cluster member nodes. Next, fuzzy linguistic variables are designated for the three input variables of "residual Energy", "node Centrality", and "node-to-base station distance", wherein the fuzzy linguistic variables of the residual Energy "and the node Centrality" are "low", "medium", and "high" (low, midle, high); the fuzzy linguistic variables of the node-to-base station Distance are "near", "medium" and "far" (near, middle, far). And the low, near, high and far adopt trapezoidal membership functions, and the middle of the fuzzy language adopts triangular membership functions. These membership functions are based on the results of the prior research experiments and our own experiments. The Chance "that the fuzzy output variable becomes a cluster head adopts nine fuzzy variables, namely," very low "," lower "," low medium "," high medium "," higher "," high "," very high "(very low, low, high, rater low, low medium, medium, high medium, rater high, high, very high). Wherein, the 'very low' and the 'very high' adopt a trapezoidal membership function, and other output linguistic variables adopt a triangular membership function. The second output variable cluster Size "employs seven fuzzy linguistic variables, which are" small "," medium "," large ", and" large "(very small, small, rater small, medium, rater large, large, very large). Wherein, the 'very small' and 'very large' adopt trapezoidal membership functions, and the others adopt triangular membership functions. The membership function of the input-output fuzzy variable is shown in fig. 2. The clean input values are blurred by the fuzzy inference engine into appropriate linguistic variables by a given membership function. The fuzzy input variables are THEN processed through the IF-THEN rule base. There are 27 rules, and the DFUC fuzzy IF-THEN rule is shown in FIG. 3. The output of the fuzzy inference engine is still a fuzzy linguistic variable, and the centroid method is adopted to solve the fuzzy, so that the clear output quantity 'opportunity' and 'size' are obtained.
During the cluster establishment process, a node may be in one of three states, i.e., member, cluster head, backup cluster head. After deployment, all nodes are in a member state, and a fuzzy controller is started to calculate the 'opportunity' and the 'size' of the cluster. Then, all nodes broadcast a cluster head competition message CH _ CP to neighbor nodes in the communication radius of the nodes, wherein the CH _ CP message is composed of a message type, a node ID and an opportunity value, and the message type indicates that the message is a cluster head competition message. Nodes with higher "chance" values than other nodes become cluster heads and broadcast a "contention SUCCESS message" CH _ SUCCESS, which consists of a message type, a node ID. After receiving CH _ SUCCESS, the node updates the nearby cluster head list, sends a 'joining cluster message' CH _ JOIN to the nearest cluster head, wherein the joining cluster message is composed of a message type, a node ID and a cluster head ID, and deletes the cluster head from the cluster head list. The cluster head receiving the CH _ JOIN message checks its "size" to determine whether to receive a new member. If the cluster member node is smaller than the value of 'size', sending back a 'successful joining message' CH _ JOIN _ SUCCESS, which is composed of a message type, a node ID, a member ID and an allocated time slot, and joining the member into a backup cluster head list according to the size of the opportunity value. Otherwise, sending back 'JOIN failure message' CH _ JOIN _ FAIL, which comprises message type, node ID and member ID, indicating that there is no new member node space. When a certain node receives a CH _ JOIN _ FAIL message, if the cluster head list is not empty, the CH _ JOIN message is sent to the next nearest cluster head until the node is added into a certain cluster. In the worst case, if the cluster head list is empty, the node still cannot be added into a certain cluster, and then the node itself is selected as the cluster head. After the cluster is formed, according to the opportunity value of each node output by the fuzzy controller, a backup cluster head list is formed according to the principle that the opportunity value is from high to low, each cluster head broadcasts a backup cluster head list message CH _ Bch into the cluster, and the message comprises a message type, a node ID and a backup cluster head list. And the node receiving the CH _ Bch stores a backup cluster head list, the node ID is the same as the ID of the first backup cluster head in the list, the node becomes a backup cluster head, and other nodes mark the node as the backup cluster head. Pseudo code for the DFUC method is as follows:
DFUC method
The fault tolerance is to maintain the established cluster by monitoring the cluster head and the member node in a time division multiplexing TDMA manner, that is, when the cluster head has a permanent fault, the optimal backup cluster head becomes the cluster head, and when the member node has a permanent fault, the node is removed from the network. Once the cluster is established, the cluster member nodes may begin data transmission based on the assigned time slots. At this stage, all sensor nodes are consuming energy, and therefore, energy exhaustion may occur both in the cluster head and in the cluster member. Once a member node in a cluster fails, the node centrality changes, which affects the probability of the node becoming a cluster head and the cluster size value, and if the cluster head node dies, the whole cluster coverage area cannot be monitored. Therefore, it is necessary to handle the failed cluster head and member nodes. To maintain the created cluster, the cluster members often compute their fuzzy controller outputs and send opportunity values to the cluster head along with the data. The cluster head updates the backup cluster head list based on the received opportunity value, and the updated list is periodically sent to the cluster members through a data request message to ensure that the most appropriate backup cluster head is updated in real time. Thereby enabling its members to be notified as soon as possible when a cluster head dies to avoid loss of data in the network, and when a member dies, the cluster head removes it from the backup cluster head list. The specific implementation uses TDMA to monitor cluster head and member, the process is shown in fig. 4. As can be seen from the figure, once a member receives a data request message, it sends its data packet, and if the cluster head does not receive the requested data at the end of the frame, it marks the member with an error. The cluster head then issues another request in the next periodic time slot, and if the requested data has not been received, the member is considered to be permanently failed and removed from the backup cluster head list. Similarly, the member waits for the data request from the cluster head in the allocated time slot, and if the member does not successively receive the data request message at the end of the frame, the cluster head is considered to have a temporary failure. It will wait for the next corresponding time slot to receive the data request message, and if the data request message has not been received, it will consider that the cluster head has a permanent failure. Then, the member checks the backup cluster head list which is updated recently by the received cluster head, sends out a join cluster message by using the first node as the cluster head, and waits for a confirmation message. However, the node may also fail, and once the acknowledgement message is not received, a join cluster message is sent out for the cluster head by the next node in the list until a certain cluster head is finally joined.
In order to verify the performance of the WSN fault-tolerant non-uniform clustering method DFUC based on fuzzy control, MATLAB is adopted for method simulation, simulation comparison is carried out on the method with LEACH, DUCF and WUCH methods, and the characteristics of the WSN fault-tolerant non-uniform clustering method DFUC based on fuzzy control in the aspects of residual energy and life cycle are analyzed. Setting nodes to be randomly deployed at 200 x 200m2And the base station coordinates are (100 ). The specific simulation parameters are shown in fig. 5. Each control packet is 25 bytes in size, and the data packet is 500 bytes in size, much larger than the control packet, and included in the simulationThe communication cost of these control packets is reduced. The desired cluster head percentage for LEACH is 0.1. The communication radius of the sensor nodes is 40m, so that all the nodes in the network can be enabled to be added into a cluster nearby. Fig. 6 is the change of the number of stored joints with the number of running rounds, and fig. 7 is the change of the remaining total energy with the number of running rounds. As can be seen from fig. 6, as the number of network operation rounds increases, the DFUC method can balance network energy consumption well compared with the other three methods, thereby effectively prolonging the life cycle of the network. The DFUC method comprehensively considers the residual energy of the nodes, the centrality of the nodes and the distance between the nodes and the base station to determine the sizes of the cluster heads and the clusters, improves the fault tolerance in the data transmission stage, and reduces the energy consumption of clustering again. Fig. 7 compares the change of the remaining total energy of the four methods with the increase of the number of operating rounds, and because various influencing factors are comprehensively considered in the cluster forming stage and the data transmission stage of the DFUC method, the DFUC method has small fluctuation and longer survival time.
It can be seen that the WSN fault-tolerant non-uniform clustering method DFUC based on fuzzy control of the invention calculates and outputs cluster head forming opportunities and cluster size values through a fuzzy controller based on a plurality of local parameters such as residual energy, node centrality, distance between a node and a base station, etc., so that an optimal node becomes a cluster head and limits the size of the cluster, and member nodes form a backup cluster head list. The list is updated in real time by means of a TDMA mechanism so that the most suitable node becomes the backup cluster head. Once a cluster head fails, the result is that a backup cluster head is always guaranteed to replace the cluster head. Simulation tests are carried out on the number of network storage nodes and the total network residual energy of the method, and results show that compared with LEACH, DUCF and WUCH methods, DFUC obtains a longer life cycle, and the performance of the DFUC is superior to that of other methods, so that the DFUC is more suitable for practical application.
Claims (3)
1. A WSN fault-tolerant non-uniform clustering method based on fuzzy control is characterized in that: the method comprises three parts of a network model, clustering and fault tolerance; based on a network model, considering the residual energy of the nodes, the node centrality and the distance between the nodes and a base station, calculating the probability of the nodes becoming cluster heads and the cluster size value by using a fuzzy controller and node local information, enabling the nodes with the maximum probability value to become cluster heads, and determining whether a member node is added or not according to the cluster size value, thereby forming a cluster with a proper size; in the data transmission stage, a TDMA mode is adopted to collect data in the clusters, temporary and permanent faults of cluster heads and member nodes are tolerated, data transmission is carried out between the clusters in a shortest path multi-hop mode, when the cluster heads have the permanent faults, the optimal backup cluster heads become the cluster heads, and when the member nodes have the permanent faults, the nodes are removed from the network, so that inter-cluster loads are effectively balanced, network energy consumption is reduced, and the life cycle of the network is prolonged.
2. The method of claim 1, wherein the method comprises the following steps: the clustering determines the cluster head forming opportunity and the cluster size value through a fuzzy controller which runs in each node in a distributed mode, so that the network is divided into a plurality of clusters with different sizes, and the node with the optimal performance becomes a cluster head, and specifically comprises two processes of cluster head election and cluster establishment; at the beginning of the cluster head election process, all nodes in the network are designated as cluster member nodes, and then fuzzy linguistic variables are designated for three input variables of ' residual Energy ', ' node Centrality ' and ' node-to-base station distance ', wherein the fuzzy linguistic variables of the residual Energy ' and the node Centrality ' centre ' are ' low ', ' medium ' and ' high ' (low, middle and high); fuzzy linguistic variables of the Distance between the node and the base station are near, middle and far (near, middle and far), trapezoidal membership functions are adopted for low, near, high and far, triangular membership functions are adopted for the fuzzy language, and the membership functions are based on the existing research experiment results and the own experiment results; the Chance "that the fuzzy output variable becomes a cluster head adopts nine fuzzy variables, namely" very low "," low middle "," high "," higher "," high "," very high "(very low, low, high, rater low, low medium, high, high), wherein" very low "and" very high "adopt a trapezoidal membership function, and other output linguistic variables adopt a triangular membership function; the Size "of the second output variable cluster adopts seven fuzzy linguistic variables which are respectively 'small', 'medium', 'large', 'very large' (very small, small, rater small, medium, rater large, large, very large), wherein the 'small' and the 'very large' adopt trapezoidal membership functions, and the others adopt triangular membership functions;
the clear input value is fuzzified into a proper linguistic variable through a given membership function by a fuzzy reasoning engine, THEN a fuzzy IF-THEN rule is formulated, the fuzzy input variable is processed through an IF-THEN rule base, the total number of the fuzzy input variable is 27, the output of the fuzzy reasoning engine is still a fuzzy linguistic variable, and a centroid method is adopted to solve the fuzzy, so that the clear output value 'opportunity' and 'size' are obtained;
in the process of establishing a cluster, a node may be in one of three states, namely a member, a cluster head and a backup cluster head, after deployment, all nodes are in the member state, a fuzzy controller is started to calculate the ' opportunity ' and the cluster ' size of the node to become the cluster head, then, all nodes broadcast a ' cluster head competition message ' CH _ CP to neighbor nodes in the communication radius of the node, wherein the CH _ CP message is composed of a message type, a node ID and an ' opportunity ' value, and the message type indicates that the node is a cluster head competition message; a node with higher probability value than other nodes becomes a cluster head and broadcasts a competition SUCCESS message CH _ SUCCESS, wherein the CH _ SUCCESS message is composed of a message type and a node ID, after the node receives the CH _ SUCCESS, the node updates a nearby cluster head list and sends a ' joining cluster message ' CH _ JOIN to a cluster head closest to the node, the CH _ SUCCESS message is composed of a message type, a node ID and a cluster head ID, the cluster head is deleted from the cluster head list, the cluster head receiving the CH _ JOIN message checks the ' size ' of the cluster head to judge whether to receive a new member, if the cluster member node is smaller than the ' size ' value, the ' successfully joining cluster head ' CH _ JOIN _ SUCCESS is sent back, the ' successfully joining cluster head is composed of the message type, the node ID, the member ID and an allocated time slot, the member is added into a backup cluster head list according to the probability value of the member, and otherwise, the ' joining failure message ' CH _ JOIN _ FAIL is sent back, and comprises the type message, The node ID and the member ID are formed, and the condition that no new member node is in space is indicated; when a certain node receives a CH _ JOIN _ FAIL message, if a cluster head list is not empty, the CH _ JOIN message is sent to the next nearest cluster head until the node is added into a certain cluster; in the worst case, when a cluster head list is empty, a node still cannot be added into a certain cluster, the node is selected as a cluster head, after the cluster is formed, a backup cluster head list is formed according to the probability value of each node output by a fuzzy controller and the principle that the probability value is from high to low, each cluster head broadcasts a backup cluster head list message CH _ Bch into the cluster, and the message comprises a message type, a node ID and a backup cluster head list; and the node receiving the CH _ Bch stores a backup cluster head list, the node ID is the same as the ID of the first backup cluster head in the list, the node becomes a backup cluster head, and other nodes mark the node as the backup cluster head.
3. The method of claim 1, wherein the method comprises the following steps: the fault tolerance is to monitor the cluster head and the member nodes in a time division multiplexing TDMA mode to maintain the established cluster, namely when the cluster head has a permanent fault, the optimal backup cluster head becomes the cluster head, when the member nodes have the permanent fault, the nodes are removed from the network, once the cluster is established, the cluster member nodes can start data transmission based on the allocated time slot, and at the stage, all the sensor nodes consume energy, so that the energy can be exhausted no matter the cluster head or the cluster members; once a member node in a cluster fails, the node centrality changes, the probability of the node becoming a cluster head and the cluster size value are influenced, and if the cluster head node dies, the whole cluster coverage area cannot be monitored, so that the failed cluster head and the member node are necessarily processed; in order to maintain the created cluster, the cluster member frequently calculates the fuzzy controller output of the cluster member, and simultaneously sends an opportunity value which becomes a cluster head to the cluster head along with data, the cluster head updates a backup cluster head list based on the received opportunity value, the updated list is periodically sent to the cluster member through a data request message to ensure that the most appropriate backup cluster head is updated in real time, so that the member can be informed as soon as possible to avoid the loss of the data in the network when the cluster head dies, and the cluster head removes the backup cluster head list when the member dies, and particularly, the cluster head and the member are monitored in a TDMA (time division multiple access) mode; the member sends a data packet once receiving a data request message, if the cluster head does not receive the requested data at the frame tail, the member is marked with an error, then the cluster head sends another request at the next periodic time slot, if the requested data is not received, the member is considered to be in permanent fault and removed from the backup cluster head list, similarly, the member waits for the data request from the cluster head at the allocated time slot, if the member does not successively receive the data request message at the frame tail, the cluster head is considered to be in temporary fault possibly, the member waits for the next corresponding time slot to receive the data request message, if the data request message is not received, the cluster head is considered to be in permanent fault, then the member checks the backup cluster head list updated recently by the cluster head received by the member, and sends a join cluster message by taking the first node as the cluster head, and waits for the acknowledgement message, however, the node may also fail, and once the acknowledgement message is not received, the next node in the list sends out a join cluster message for the cluster head until finally joining a certain cluster head.
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