CN108366409B - A Reliable Multipath Aggregate Routing Method Based on Energy Balance - Google Patents

Abstract

The invention relates to a reliable multi-path aggregation routing algorithm based on energy balance, and belongs to the technical field of wireless sensor networks. The algorithm includes: analyzing the characteristics of network data transmission, combining the density and location information of nodes, establishing a multi-hop convergence network energy consumption rate estimation model; using a node selection algorithm based on spatial correlation, in the event of low and high energy consumption rate Select more AMNs and fewer AMNs in the area; design an optimal path search algorithm based on forward forwarding node sets, and use this algorithm to design energy-balanced multi-path aggregation routing, including an optimal main path for data collection and each The optimal aggregation path of each AMN node, all aggregation paths converge on the aggregation nodes on the main path; the monitoring data reaches the aggregation node through the aggregation path, and after the aggregation node completes the data aggregation, it reaches the sink node through the main path. The invention can effectively balance the network energy consumption, prolong the network life and improve the reliability of event detection.

Description

A Reliable Multipath Aggregate Routing Method Based on Energy Balance

technical field

The invention belongs to the technical field of wireless sensor networks, and relates to a wireless sensor network routing method with reliable detection and high energy efficiency.

Background technique

The sensor nodes of wireless sensor networks are generally powered by batteries, and are usually deployed in harsh or unattended monitoring areas. It is very difficult to charge or replace batteries for nodes, and their energy resources are severely limited. Therefore, energy utilization efficiency and network life are Important performance indicators for wireless sensor networks. In addition, sensor nodes are usually used to monitor events, and sink nodes complete event detection by processing the data collected by sensor nodes, so event detection reliability is also an important performance indicator of wireless sensor networks.

Compared with the active mode, the energy consumption of the sensor node in the sleep mode will be greatly reduced, so the node should be kept in the sleep state as much as possible without affecting the network performance. Based on this, related scholars have proposed a variety of energy-efficient MAC layer protocols. The S-MAC protocol is one of the most widely used contention mode-based protocols, which reduces network energy consumption through sleep scheduling of virtual clusters. The S-MAC protocol and its improved protocols generally do not consider the spatial correlation between nodes. When an event occurs, the nodes monitoring the event compete for the channel and send data immediately, and usually, these nodes are spatially correlated. Many application scenarios do not require all monitoring nodes to send data. On the premise of ensuring network detection reliability indicators, waking up as few monitoring nodes as possible will effectively reduce network energy consumption and prolong network life. Based on this, related scholars have proposed protocols such as CC-MAC and SC-MAC based on spatial correlation.

The above protocols are mainly MAC layer protocols, which reduce network energy consumption by adjusting the node sleep period, the number of active nodes, and the number of packets sent, but do not consider optimizing methods above the MAC layer to improve network energy efficiency. Events in the network are monitored cooperatively by multiple nodes. For densely deployed sensor nodes, the data collected by each node is not only correlated, but also has a large amount of redundant information. Data aggregation technology can effectively integrate relevant data, eliminate redundant data, reduce network data transmission volume, reduce network energy consumption and data transmission delay.

Existing methods mainly take network energy consumption and network lifetime as optimization indicators, and do not consider the optimization of detection reliability. Related research shows that the event detection reliability of sink nodes is positively correlated with the number of monitoring nodes. Under the premise of ensuring the reliability of network detection, selecting fewer active monitoring nodes can reduce network energy consumption and prolong network life, but the detection quality will also decrease. And more monitoring nodes means more energy consumption, which will reduce the network lifetime. At present, there are few methods that take into account network energy efficiency and detection reliability.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a wireless sensor network routing method with reliable detection and energy efficiency. To achieve the above object, the present invention provides the following technical solutions:

A reliable multi-path aggregation routing method based on energy balance is applied to an event-driven wireless sensor network with uneven deployment of large-scale nodes; the algorithm specifically includes the following steps:

S1: After an event occurs somewhere in the network, the event source S is the center of the circle, and the node sensing range _rs is the radius to form the event domain;

S2: Estimate the energy consumption rate of event domain nodes through a multi-hop aggregation network energy consumption rate estimation model;

S3: According to the energy consumption rate of the event domain nodes, allocate the corresponding number of AMNs (Active monitoring nodes, active monitoring nodes) to the event domain. The allocation principle is: the lower the energy consumption rate of the event domain, the greater the number of AMNs; The higher the energy consumption rate, the smaller the number of AMNs; suppose the number of AMNs allocated to the event domain is m;

S4: The node selection algorithm based on spatial correlation is adopted, and m nodes are selected as AMNs; AMNs change from dormant state to active state, and continuously monitor events, while ordinary nodes in the event domain keep dormant state;

S5: Select the AMN closest to the sink node as the starting node of the main path, and take this node as the source node, and use the optimal path search algorithm based on the forward forwarding node set to search for the optimal path to the sink node, which is is the main path;

S6: Select the node in the main path that is outside the hotspot area and has the most remaining energy as the aggregation node; in addition to the starting node of the main path, centrifugal routing and the optimal path search algorithm based on the forward forwarding node set are used to design the remaining m-1 nodes Aggregation paths from AMNs to aggregation nodes;

S7: The monitoring data of all AMNs reach the aggregation node through the planned aggregation path. After the aggregation node completes the data aggregation, it is transmitted to the sink node through the main path.

Further, in the step S2, the multi-hop convergence network energy consumption rate estimation model is:

Events occur randomly in a circular network with a radius of R; in a cycle, the probability of an event per unit area in the network is η; the total number of packets generated by each event is n; the reporting frequency of the network is f, that is, an event occurs After that, the number of monitoring packets sent by the AMNs in the event domain to the sink node o per unit time; when the sink node receives the data of n packets, the event detection is completed, and the AMNs in the event domain are reset to ordinary nodes; the monitoring of AMNs The data is aggregated at the node b hops away from the event source; the node transmission radius is r; now assume that the distance between node i and the sink node is l, and l=hr+x, where h represents the number of hops, and x represents the distance less than 1 hop , the node density in this area is ρ _l , then in each cycle, the number of data packets P _l transmitted by this node is:

表示数据聚合中的遗忘因子。Among them, z represents the number of hops from the node in the edge area of the network to the sink node, and c represents the correlation coefficient in the data aggregation,

Represents the forgetting factor in data aggregation.

为：The probability that the event domain node with distance l from the sink node is selected as the AMN

for:

Among them, m _l represents the number of AMNs in the event area, and _rs represents the node sensing radius.

The energy consumption of a node to transmit a data packet is e, the energy consumption power in the active mode is ω _a , and the energy consumption in the sleep mode is negligible, then the energy consumption E _l of the node in this area in each cycle is:

Further, the step S3 specifically includes: according to the energy consumption rate of the event domain nodes, assigning the corresponding number of AMNs to the event domain, and the allocation principle is: the lower the energy consumption rate of the event domain, the more the number of AMNs; the energy consumption of the event domain The higher the consumption rate, the smaller the number of AMNs; usually, the area one hop away from the sink node, that is, the hotspot area, has the highest energy consumption rate. The event domain of the region allocates the minimum number of AMNs m _hot to reduce the energy consumption of the hot region; in other regions of the network, we adjust the number of AMNs to make the energy consumption rate of different regions approach the hot region, improve the network energy utilization efficiency and detection reliability Based on this, the formula for the number of AMNs in non-hotspot areas is:

Among them, ρ _hot represents the density of nodes in the hotspot area, and P _hot represents the number of data packets transmitted by the nodes in the hotspot area.

Further, in the step S4, the node selection algorithm based on spatial correlation specifically includes the following steps:

Step1: Set the initial correlation radius _rc = _rs , the initial AMN number atc = 0;

Step2: randomly select a node in the event domain S _event as AMN, atc=atc+1; if atc=m, the algorithm ends, otherwise, execute Step3;

Step3: Take all _AMNs as the center and rc as the radius to form a spatial correlation area S _corr . The nodes in this area are marked as relevant nodes and remain dormant; if there are still non-correlated nodes in the event domain S _event , Then randomly select the next AMN in the remaining nodes, atc=atc+1, if atc=m, the algorithm ends, otherwise continue to execute Step3; if there are no irrelevant nodes in the event domain S _event , then _rc = _rc - r _step , atc=0, re-execute Step2, where _{r step} is the length of each shortening of rc.

Further, in the step S5, the optimal path search algorithm based on the forward forwarding node set includes two parts: the search of the optimal path candidate set and the optimal path selection;

1) Search of the optimal path candidate set:

Suppose the source node is A and the destination node is B;

Step1: Select the node closest to B among the neighbor nodes of A as the preferred node of A, if B is the neighbor node of A, then B is the preferred node of A, A and B form a one-hop path, and the search ends; if the preferred node of A If the node is not B, then take the preferred node as the center of the circle, and r _o as the radius to form a circular screening domain, and the neighbor nodes of A in this area form the forward forwarding node set P _A of A; each node in A and P _A Both form a one-hop path, that is, A and P _A form multiple divergent paths;

Step2: Each node in P _A searches its own preferred node and forward forwarding node set in the same way as A, and forms a new hop of the path with each node of its forward forwarding node set; similarly, if a certain The preferred node of each node is B, then the node and B form the last hop on the path where it is located, and the path search for this node is completed;

Step3: The nodes in the newly formed forward forwarding node set in each step use the above method to continue the path search, and finally form multiple paths that converge to B, and these paths form the optimal path candidate set V(A, B);

2) Optimal path selection:

The path quality P(n) is evaluated by the performance index function, and the optimal path is selected from the optimal path candidate set; the performance index function comprehensively considers the average residual energy of path nodes E _ave (n) and the minimum residual energy of path nodes E _min (n) , path energy consumption E _con (n), path hop number H _hop (n) four factors, through the Min-Max normalization method to transform the data, and then use the linear weighted sum method to obtain; the performance index evaluation function is

Among them, λ, μ, β, and δ represent the weights of the four evaluation parameters, respectively, λ+μ+β+δ=1.

The main path planned in S5 is the main path for all AMNs to send monitoring data to the sink node, and the monitoring data of the AMNs must reach the main path through the aggregation path.

Further, the step S6 specifically includes: in addition to the starting node of the main path, the first hop of other AMNs is a discrete route, that is, selecting the node farthest from the event source S among the neighbor nodes as the second hop node to reduce collisions in the event domain and event detection delay; from the second hop node to the aggregation node, the optimal path search algorithm based on the forward forwarding node set is used to find the optimal path; the first hop discrete route and the subsequent optimal path together form the aggregation path.

The beneficial effects of the present invention are:

1) The present invention allocates more AMNs in the area with lower energy consumption rate, improves detection reliability and energy utilization efficiency; allocates less AMNs in the area with higher energy consumption rate, reduces the energy consumption rate in this area ;

2) The present invention takes the remaining energy of the path node, the energy consumption of the path transmission, and the number of path hops as the performance indicators, adopts the optimal path search algorithm based on the forward forwarding node set, and designs the multi-path aggregation route based on the energy consumption balance, which avoids the " "Energy Hole" to balance network energy consumption and prolong network life;

3) In the algorithm described in the invention, the monitoring data of AMNs is aggregated at the aggregation nodes outside the hotspot area, which reduces the energy consumption in the hotspot area, further balances the network energy consumption, and prolongs the network life.

Description of drawings

In order to make the purpose, technical solutions and beneficial effects of the present invention clearer, the present invention provides the following drawings for description:

1 is a schematic diagram of a reliable multi-path aggregation routing algorithm based on energy balance according to the present invention;

Fig. 2 is the flow chart of the reliable multi-path aggregation routing algorithm based on energy balance according to the present invention;

Fig. 3 is the energy consumption rate estimation model of the multi-hop convergence network according to the present invention;

Fig. 4 is the flow chart of the node selection algorithm based on spatial correlation according to the present invention;

5 is a schematic diagram of the optimal path search algorithm based on the forward forwarding node set according to the present invention;

Fig. 6 is the search flow chart of the optimal path candidate set according to the present invention;

FIG. 7 is an illustrative block diagram of the present invention.

Detailed ways

The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

FIG. 1 is a schematic diagram of the reliable multi-path aggregation routing algorithm based on energy balance according to the present invention. As shown in Figure 1, when an event occurs, the algorithm first uses the multi-hop converged network energy consumption rate estimation model to estimate the energy consumption rate of the event domain, and allocates the corresponding number of AMNs to the event domain according to the energy consumption rate; The node selection algorithm based on correlation selects the corresponding number of AMNs in the event domain; then, synthesizing the remaining energy of the path node, the path energy consumption, and the number of path hops, the optimal path search algorithm based on the forward forwarding node set is used to design the energy-balanced multiple Path aggregation routing, which includes an optimal main path for data collection and an optimal aggregation path for each AMN node, and all aggregation paths on the main path are aggregated by aggregation nodes; After the aggregation node completes data aggregation, it reaches the sink node through the main path.

FIG. 2 is a flow chart of the reliable multi-path aggregation routing algorithm based on energy balance according to the present invention. As shown in Figure 2, the algorithm specifically includes the following steps:

S1: After an event occurs somewhere in the network, the event source S is the center of the circle, and the node sensing range _rs is the radius to form the event domain;

S2: Estimate the energy consumption rate of event domain nodes through a multi-hop aggregation network energy consumption rate estimation model;

S3: According to the energy consumption rate of the event domain nodes, allocate the corresponding number of AMNs to the event domain. The allocation principle is: the lower the energy consumption rate of the event domain, the greater the number of AMNs; the higher the energy consumption rate of the event domain, the more AMNs The smaller the number. Suppose the number of AMNs allocated to the event domain is m;

S4: Adopt a node selection algorithm based on spatial correlation, and select m nodes as AMN. AMNs change from a dormant state to an active state, and continuously monitor events, while ordinary nodes in the event domain remain dormant;

S5: Select the AMN closest to the sink node as the starting node of the main path, and take this node as the source node, and use the optimal path search algorithm based on the forward forwarding node set to search for the optimal path to the sink node, which is is the main path;

S6: Select the node in the main path that is outside the hotspot area and has the most remaining energy as the aggregation node. Except for the starting node of the main path, centrifugal routing and the optimal path search algorithm based on the forward forwarding node set are used to design the aggregation path of the remaining m-1 AMNs to the aggregation node;

S7: The monitoring data of all AMNs reach the aggregation node through the planned aggregation path. After the aggregation node completes the data aggregation, it is transmitted to the sink node through the main path.

FIG. 3 is the energy consumption rate estimation model of the multi-hop aggregation network according to the present invention. Before describing the model in detail, a mathematical model to be used is first introduced: the lossless multi-hop aggregation model.

表示节点S_i和S_j的聚合结果；

表示节点S_i当前聚合结果；θ_i表示节点S_i所有输入数据和其自身数据的最终聚合结果。当节点S_i收到节点S_j的数据θ_j后，将自身数据与θ_j聚合。如果聚合的数据均是原始数据，即θ_j＝δ_j，则聚合公式为In this model, aggregation nodes aggregate continuously arriving data. δ _i represents the _unaggregated raw data of node Si;

represents the aggregation result of nodes S _i and S _j ;

represents the current aggregation result of node Si; _{θ i represents} the final aggregation result of all input data of node Si and its own data. When node S _i receives the data θ _j of node S _j , it aggregates its own data with θ _j . If the aggregated data are all original data, that is, θ _j = δ _j , the aggregation formula is

where c represents the correlation coefficient. If the aggregated data is not the original data, the aggregation formula is

和θ_j至少有一个不是原始数据。Among them, τ represents the forgetting factor, and the value range is (0, 1).

and at least one of θ _j is not the original data.

Next, the energy consumption rate estimation model of the multi-hop convergence network is described in detail, as shown in Figure 3:

The energy consumption of a node consists of three parts: 1) the energy consumption of the node transmitting data; 2) the non-communication energy consumption of the node in the active state; 3) the energy consumption of the node in the dormant state.

First, the energy consumption of the node to transmit data is calculated. Events occur randomly in a circular network with a radius of R. In a cycle, the probability of an event per unit area in the network is η, the total number of packets generated by each event is n, and the network reporting frequency is f. After the data of n packets is reached, the event detection is completed, and the AMNs in the time domain are reset to normal nodes and enter the sleep state.

As shown in Figure 3, we select a sector region V with radian θ→0 and width _dx →0 in the network. Node i is in this area, and its distance from the sink node is l, l=hr+x (h represents the number of hops, x represents the distance less than 1 hop). The node density in this area is ρ _l . The area V receives the data sent by the nodes in the fan-shaped area V', V"... which is far away from itself l+zr (z represents the number of hops from the node in the edge area of the network to the sink node).

According to the description of the algorithm described in the present invention, the data packets received by the area V can be divided into two parts: one part is the data within the distance area V b hops, and the data of this part is not aggregated; the other part is the distance beyond the area V b hops. data, the part of the data will be aggregated.

First count the number of packets in the first part. The area of region V is θld _x , and the data packets generated by itself are nθld _x λ; the data packets generated by region V' are nθ(l+r)d _x λ; and so on, the unaggregated data packets transmitted by region V are The total quantity is

P _l ¹ =nθd _x λ{l+(l+r)+...+(l+br)}

Then calculate the number of packets in the second part. If region V receives data from an area beyond its b hops, this part of the data will be aggregated, and the amount of data will be reduced compared to the original data. We adopt a lossless multi-hop aggregation model, assuming that data aggregation occurs at the confluence node g, according to the data aggregation model, the aggregation results of the first two packets are

After the first two packets are aggregated with the remaining n-2 packets, the aggregation result is

From this, the number of the second part of the data packets is calculated as

The number of nodes in area V is ρ _l θld _x , so the total number of data packets transmitted by node i in area V is

The probability that the event domain node with distance l from Sink is selected as AMN is

m _l represents the number of AMNs in the event area, and _rs represents the node sensing radius.

The energy consumption of a node to transmit a data packet is e, the energy consumption power in the active mode is ω _a , the energy consumption in the sleep state is far less than the communication energy consumption and the non-communication energy consumption in the active state. To simplify the calculation, here it is Leave it out. It can be obtained that the energy consumption of the node in this area in each cycle is

AMN quantity allocation principle: the lower the energy consumption rate of the event domain, the greater the number of AMNs; the higher the energy consumption rate of the event domain, the less the number of AMNs. Usually, the energy consumption rate of the hot spot area (the area one hop away from the sink node) is the highest. Therefore, under the premise of satisfying the detection reliability threshold Φ, we assign the least number of AMNs m _hot to the event domain of the hot spot area. , reducing the energy consumption of the hot zone. In other areas of the network, we adjust the number of AMNs to make the energy consumption rates in different areas approach the hot area, thereby improving the network energy utilization efficiency and detection reliability. From this, it can be seen that the distribution formula of the number of AMNs in non-hot spots is:

Among them, ρ _hot represents the density of nodes in the hotspot area, and P _hot represents the number of data packets transmitted by the nodes in the hotspot area. It is necessary to introduce a detection reliability evaluation model to obtain m _hot , which is described as follows:

Detection reliability evaluation model:

When an event S occurs, there are m monitoring nodes in the event domain that continuously send a total of n detection packets to the sink node. The reliability of event detection can be measured by the distortion value D(m, n) of event detection. The distortion formula is

是每个监测节点的监测数据与事件源信息S的方差；

是每个监测节点的观测噪声方差方；ρ(s，i)表示事件源与每个监测节点i(i＝1，2，...，m)的相关系数；ρ(i，j)表示每个监测节点i和j(j＝1，2,...,m)之间的相关系数。in,

is the variance between the monitoring data of each monitoring node and the event source information S;

is the observation noise variance square of each monitoring node; ρ(s, i) represents the correlation coefficient between the event source and each monitoring node i (i=1, 2, ..., m); ρ(i, j) represents Correlation coefficient between each monitoring node i and j (j=1, 2,...,m).

的高斯随机变量。每个监测节点对事件源S的监测数据是联合高斯随机变量(JGRV)，如下所示According to the relevant theory of the communication principle, the observation noise of the node is expected to be 0, and the variance is

a Gaussian random variable. The monitoring data of each monitoring node for the event source S is a joint Gaussian random variable (JGRV), as shown below

i＝1,...，mE(S _i )=0,

i=1,...,m

where d(i,j) is the distance between nodes i and j.

Fig. 4 is the flow chart of the node selection algorithm based on spatial correlation according to the present invention, as shown in Fig. 4, the algorithm specifically includes the following steps:

Step1: Set the initial correlation radius _rc = _rs , the initial AMN number atc = 0;

Step 2: Randomly select a node in the event domain S _event as AMN, atc=atc+1. If atc=m, the algorithm ends, otherwise, go to Step 3;

Step 3: Take all _AMNs as the center and rc as the radius to form a spatial correlation area S _corr , the nodes in this area are marked as correlation nodes and remain dormant. If there are still non-correlated nodes in the event domain S _event , the next AMN is randomly selected in the remaining nodes, atc=atc+1, if atc=m, Step 3 ends, otherwise, continue to execute Step 3; if the event domain S There is no irrelevant node in the _event , then rc = _rc -r _step , atc=0, and S32 is re-executed, where _{r step} is the length of each shortening of rc _.

FIG. 5 is the optimal path search algorithm based on the forward forwarding node set according to the present invention, and the algorithm includes two parts: the search of the optimal path candidate set and the optimal path selection.

1) Fig. 6 is the search flow chart of the optimal path candidate set, as shown in Fig. 6, the specific steps are as follows:

Suppose the source node is A and the destination node is B.

Step1: Select the node closest to B among the neighbor nodes of A as the preferred node of A, such as a ₂ in Figure 5 . If B is the neighbor node of A, then B is the preferred node of A, A and B form a one-hop path, and the search is over; if the preferred node of A is not B, then take the preferred node as the center and r _o as the radius to form a circle In this area, the neighbor nodes of A form a forward forwarding node set P _A of A, such as a ₁ , a ₂ , and a ₃ in FIG. 5 . Each node in A and P _A forms a one-hop path, that is, A and P _A form multiple divergent paths;

Step2: Each node in P _A , searches for its own preferred node and forward forwarding node set in the same way as A, and forms a new hop of the path with each node of the forward forwarding node set. The forwarding node set forms a new divergent path, such as a ₁ and b ₂ , b ₃ , a ₂ and b ₁ , b ₂ , a ₃ and b ₄ , b ₅ in FIG. 5 . Similarly, if the preferred node of a node is B, the node and B form the last hop on the path where it is located, and the path search for the node is completed;

Step3: The nodes in the newly formed forward forwarding node set in each step continue the path search using the above method, and finally form many paths that converge to B, and these paths form the optimal path candidate set V(A, B).

2) Optimal path selection:

The path quality P(n) is evaluated by the performance index function, and the optimal path is selected from the optimal path candidate set V(A, B). The performance index function comprehensively considers four factors: the average remaining energy of the path node E _ave (n), the minimum remaining energy of the path node E _min (n), the path energy consumption E _con (n), and the number of path hops H _hop (n). -Max normalization method to transform the data, and then use the linear weighted sum method to obtain. The performance index evaluation function is

Among them, λ, μ, β, and δ represent the weights of the four evaluation parameters, respectively, λ+μ+β+δ=1.

As shown in Figure 7, the energy-balanced multi-path aggregation route includes an optimal main path for data collection and an optimal aggregation path for each AMN node, and all aggregation paths converge on the aggregation nodes on the main path. The design method of multi-path aggregation routing is as follows:

Step1: After the selection of AMNs in the event domain is completed, select the AMN closest to the sink node as the starting node of the main path;

Step2: Take the starting node of the main path as the source node, and use the optimal path search algorithm based on the forward forwarding node set to search for the optimal path to the sink node, which is the main path;

Step3: Select the node on the main path that is outside the hot zone and has the most remaining energy as the aggregation node;

Step4: In addition to the starting node of the main path, the remaining m-1 AMNs select the node farthest from the event source S among the neighbor nodes as the second hop node (centrifugal routing);

Step5: Take the 2-hop node as the source node, and use the optimal path search algorithm based on the forward forwarding node set to search for the optimal path to the aggregation node;

Step 6: The discrete route in Step 4 and the optimal route in Step 5 together form an aggregation route.

Step7: The monitoring data of all AMNs reaches the aggregation node through the planned aggregation path. After the aggregation node completes the data aggregation, it is transmitted to the sink node through the main path.

Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should Various changes may be made in details without departing from the scope of the invention as defined by the claims.

Claims (6)

1. A reliable multipath aggregation routing method based on energy balance is characterized by being applied to an event-driven wireless sensor network deployed in a large-scale node non-uniform mode;

the method specifically comprises the following steps:

s1: after an event occurs at a certain position in the network, taking an event source S as a circle center, and sensing range r of the node_sForming an event field for the radius;

s2: estimating the energy consumption rate of the event domain nodes through a multi-hop aggregation network energy consumption rate estimation model;

s3: according to the energy consumption rate of the event domain node, allocating the corresponding AMNs (Active monitoring nodes) quantity for the event domain, wherein the allocation principle is as follows: the lower the energy consumption rate of the event domain, the greater the number of AMNs; the higher the energy consumption rate of the event domain, the lower the number of AMNs; the number of the distributed AMNs in the event domain is assumed to be m;

s4: selecting m nodes as AMN by adopting a node selection algorithm based on spatial correlation; the AMNs are converted from the dormant state to the active state, the event is continuously monitored, and meanwhile, the common node in the event domain keeps the dormant state;

s5: selecting the AMN closest to the Sink node as a main path starting node, taking the node as a source node, and searching the optimal path from the node to the Sink node by adopting an optimal path searching algorithm based on a forward forwarding node set, wherein the path is the main path;

s6: selecting a node which is outside a hot spot area in the main path and has the most residual energy as an aggregation node; except for a main path starting node, designing a convergent path from the residual m-1 AMNs to a polymerization node by adopting a centrifugal route and an optimal path searching algorithm based on a forward forwarding node set;

s7: and all the monitoring data of the AMNs reach the aggregation node through the planned aggregation path, and are transmitted to the Sink node through the main path after the aggregation node finishes data aggregation.

2. The reliable multipath aggregation routing method based on energy balancing of claim 1, wherein in the step S2, the energy consumption rate estimation model of the multi-hop aggregation network is:

events in a circular network with radius R occur randomly; within a period, the unit area in the network occursThe probability of an event is η; the total number of messages generated by each event is n; the reporting frequency of the network is f, namely the quantity of monitoring messages sent to the Sink node o by the event domain AMNs in unit time after an event occurs; after the Sink node receives the data of n messages, event detection is completed, and AMNs in an event domain are reset to be common nodes; the monitoring data of the AMNs are aggregated at a node b hops away from an event source; the node transmission radius is r; now, assume that the distance from the node i to the Sink node is l, and l ═ hr + x, where h denotes the number of hops, x denotes the distance less than 1 hop, and the node density in this region is ρ_lThe number P of data packets transmitted by the node per cycle_lComprises the following steps:

wherein z represents the hop count from the node of the network edge region to the Sink node, c represents the correlation coefficient in data aggregation,

Representing a forgetting factor in the data aggregation;

probability that event domain nodes with distance l from Sink node are selected as AMN

Comprises the following steps:

wherein m is_lNumber of AMNs representing event area, r_sRepresenting a node perception radius;

the energy consumption of a node for transmitting a data packet is e, and the energy consumption power in the active mode is omega_aIf the energy consumption in the sleep mode is ignored, the energy consumption E of the node in the area is calculated in each period_lComprises the following steps:

3. the reliable multipath aggregation routing method based on energy balancing as claimed in claim 2, wherein the step S3 specifically includes: allocating the corresponding AMNs quantity for the event domain according to the energy consumption rate of the event domain node, wherein the allocation principle is as follows: the lower the rate of energy consumption of the event domain, the greater the number of AMNs; the higher the rate of energy consumption of the event domain, the lower the number of AMNs; in general, the energy consumption rate of a region one hop away from a Sink node, i.e. a hot spot region, is the highest, so that on the premise of meeting a detection reliability threshold Φ, the number m of the AMNs which are allocated to the event domain of the hot spot region is the least_hotTo reduce the hot zone energy consumption; in other areas of the network, the energy consumption rate of different areas approaches to a hot area by adjusting the quantity of AMNs, so that the network energy utilization efficiency and the detection reliability are improved; based on this, the allocation formula of the quantity of AMNs in the non-hotspot region is as follows:

where ρ is_hotRepresents the node density, P, of the hot spot area_hotIndicating the number of data packets transmitted by the hot spot area nodes.

4. The reliable multipath aggregation routing method based on energy balancing as claimed in claim 3, wherein in the step S4, the node selection algorithm based on spatial correlation specifically includes the following steps:

step 1: let the initial correlation radius r_c＝r_sThe initial AMN number atc is 0;

step 2: random in event field S_eventSelecting a node as AMN, wherein atc is atc + 1; if atc is m, the algorithm ends, otherwise Step3 is executed;

step 3: using all AMNs as circle centers, r_cIs in the shape of a radiusRegion of spatial correlation S_corrNodes in the area are marked as correlation nodes and are kept dormant; if the event field S_eventIf the non-correlation nodes still remain in the node list, randomly selecting the next AMN from the remaining nodes, wherein atc is atc +1, if the atc is m, finishing the algorithm, and otherwise, continuing to execute Step 3; if the event field S_eventHas no non-correlation node in, then r_c＝r_c-r_stepAtc is 0, Step2 is re-executed, where r_stepIs r_cThe length of each shortening.

5. The reliable multipath aggregation routing method based on energy balancing as claimed in claim 4, wherein in the step S5, the optimal path searching algorithm based on the forward forwarding node set includes two parts of searching an optimal path candidate set and selecting an optimal path;

1) searching of the optimal path alternative set:

assuming that a source node is A and a destination node is B;

step 1: selecting a node closest to B in the neighbor nodes of A as an optimal node of A, if B is the neighbor node of A, then B is the optimal node of A, A and B form a one-hop path, and the search is finished; if the preferred node of A is not B, taking the preferred node as the center, r_oForming a circular screening domain for the radius, wherein the neighbor nodes of A in the region form a forward forwarding node set P of A_A(ii) a A and P_AEach node in (a) forms a one-hop path, i.e. a and P_AForming a plurality of divergent paths;

Step2：P_Asearching a self preferred node and a forward forwarding node set by each node in the node set by the same method as the method A, and forming a new path hop with each node in the forward forwarding node set; similarly, if the preferred node of a node is B, the node and B form the last hop on the path where the node is located, and the path search of the node is completed;

step 3: the nodes in the newly formed forward forwarding node set in each step continue path search by adopting the method, finally a plurality of paths converging to B are formed, and the paths form an optimal path alternative set V (A, B);

2) selecting an optimal path:

evaluating the path quality P (n) through a performance index function, and selecting an optimal path from the optimal path alternative set; performance index function comprehensively considering path node average residual energy E_ave(n) minimum path node remaining energy E_min(n) path energy consumption E_con(n) number of path hops H_hop(n) transforming the data by a Min-Max standardization method, and then obtaining the data by using a linear weighted sum method; the performance index evaluation function is

Wherein λ, μ, β, δ respectively represent weights of four evaluation parameters, λ + μ + β + δ being 1;

the main path planned in S5 is a main path through which all AMNs send monitoring data to the Sink node, and the monitoring data of the AMNs all need to reach the main path through the aggregation path.

6. The reliable multipath aggregation routing method based on energy balancing as claimed in claim 5, wherein the step S6 specifically includes: except for the initial node of the main path, the 1 st hop of other AMNs is a discrete route, namely, the node which is farthest away from the event source S in the neighbor nodes is selected as the 2 nd hop node, so that the collision and the event detection delay in the event domain are reduced; searching an optimal path from the 2 nd hop node to the aggregation node by adopting an optimal path searching algorithm based on a forward forwarding node set; the 1 st hop discrete route and the subsequent optimal route jointly form a convergent route.

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