CN110087293B - Low-energy-consumption distributed event detection wireless sensor network construction method - Google Patents
Low-energy-consumption distributed event detection wireless sensor network construction method Download PDFInfo
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Abstract
The invention provides a method for constructing a low-energy-consumption distributed event detection wireless sensor network, which belongs to the technical field of wireless sensor networks and comprises the following steps: s1, establishing corresponding models aiming at different events to accurately depict detection performance; s2, providing a distributed event detection algorithm and an optimization method; s3, providing a distributed dynamic guarantee method for detecting the service quality; and S4, a high-reliability event transmission method under the dynamic dormancy condition is provided. The construction method of the low-energy-consumption distributed event detection wireless sensor network forms a plurality of basic theories of low-energy-consumption distributed event detection of the wireless sensor network, breaks through a plurality of key technologies of distributed event detection, and contributes to the practicability and industrialization development of the wireless sensor network.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a low-energy-consumption distributed event detection wireless sensor network construction method.
Background
Wireless Sensor Networks (WSNs) are leading-edge hot research fields which are currently receiving great attention internationally and relate to multidisciplinary high-degree intersection and knowledge high-degree integration. Advances in sensor technology, micro-electromechanical systems, modern networks, and wireless communications have facilitated the creation and development of modern wireless sensor networks. The wireless sensor network expands the information acquisition capability of people, connects physical information of an objective world with a transmission network, and provides the most direct, most effective and most real information for people in the next generation of network.
The wireless sensor network has the excellent characteristics of low power consumption, low cost, self-organization, easy deployment and the like, has an unlimited and wide application prospect in the military field and the civil field, can serve the aspects of social life, and provides a brand-new solution for various practical problems and requirements. Applications of wireless sensor networks can be broadly divided into two main categories: environmental monitoring and event detection.
The environmental monitoring application is concerned with various parameters of the target environment, such as temperature, humidity, and light level. The nodes regularly collect and report environmental information to the base station, and examples of the application include wild animal habitat research, volcano monitoring and the like. Event detection applications concern the occurrence of an abnormal event, an abnormal change in the environment, such as a fire, gas leak, pollution discharge, etc. Unlike environmental monitoring, the main task of an event detection sensor network is to capture the occurrence of an event and report it to a network base station. After the user knows that the abnormal event occurs, the user can take corresponding measures in time to prevent serious harm from occurring. Typical applications of event detection include forest fire prevention, environmental pollution prevention, battlefield safety monitoring and the like.
The design and implementation of wireless sensor networks for low power consumption and efficient event detection encounter many new problems and challenges. First, the event has features of randomness, unpredictability, etc., and the event may occur at any place and any time in the target area. Second, there are differences in real world events that exhibit different characteristics. Third, events are dynamic, and the attributes of an event may change dynamically over time. Fourth, the design of wireless sensor networks must achieve low energy consumption, enabling long-term effective detection of target areas. Finally, the wireless sensor network often adopts the strategies of intensive deployment and redundant delivery, and how to manage node redundancy and process high-density deployment is also a big challenge newly provided.
Therefore, how to construct a low-energy-consumption distributed event detection wireless sensor network is one of the key points and difficulties.
Disclosure of Invention
To solve at least one of the above problems, the present invention provides: a low-energy-consumption distributed event detection wireless sensor network construction method comprises the following steps:
s0: analyzing and summarizing different events to obtain the commonalities and differences therein;
s1, establishing corresponding models aiming at different events to accurately depict detection performance;
s2, providing a distributed event detection algorithm and an optimization method;
s3, providing a distributed dynamic guarantee method for detecting the service quality;
s4, providing a high-reliability event transmission method under the dynamic dormancy condition;
s5: and (4) carrying out actual verification and performance analysis on the theoretical analysis result and the designed algorithm through a test platform.
Preferably, the step S1 specifically includes the steps of:
s11, establishing corresponding event model, detection model and sensor network system model aiming at different events;
s12: revealing the relation between the detection performance and the key system parameter;
s13: revealing the relation between the detection performance and the system energy consumption;
the step S2 specifically includes the steps of:
s21: analyzing the influence of node awakening arrangement on detection performance under the given energy consumption condition;
s22: studying the complexity of the optimal wake-up schedule;
s23: designing a distributed optimization algorithm to determine the awakening time of the node;
s24: optimizing the detection performance of the system;
the step S3 specifically includes the steps of:
s31: defining an event detection service quality;
s32: providing a guarantee method for distributed detection service quality;
s33: dynamically maintaining a quality of service of the sensor network;
the step S4 specifically includes: according to the dynamic dormancy of the nodes, a data transmission method aiming at event detection is designed to realize high-energy-efficiency and high-reliability event transmission service.
Preferably, the attributes included in the event model in the step S11 include spatial attributes including physical size, coverage pattern and distribution characteristics and temporal attributes including duration and temporal distribution.
Preferably, the method of determining the physical size of an event is: and through carrying out statistical analysis on the historical data of the event, deducing a probability distribution function of the event size by using a parameter estimation method to represent the physical size.
Preferably, the method of determining the coverage pattern of an event is: and analyzing the complexity of the event, and adopting a corresponding shape model to represent the coverage mode according to the complexity.
Preferably, the method of determining said distribution characteristic of events is: and adopting a two-dimensional probability distribution function to represent the distribution characteristics for the event e, wherein the two-dimensional probability distribution function of the event e in the target area A is as follows:
preferably, the method of determining the duration of an event is: deriving a probability distribution function of the duration to represent the duration by statistical analysis of historical data of the event.
Preferably, the method of determining the temporal distribution of events is: a poisson process is used to represent the time distribution.
Preferably, the step S23 is specifically: adopting a distributed heuristic algorithm to ensure that the nodes exchange information with adjacent nodes when determining the awakening time, and dynamically adjusting the awakening time of the nodes on the basis of knowing the adjacent time so as to uniformly distribute the awakening time of the adjacent nodes;
the step S32 specifically includes: after the system is initialized, the nodes select preset aggregation activity probability, and the nodes gradually reduce the aggregation activity probability through a distributed iteration method, so that the distributed guarantee method corresponding to the aggregation activity probability at the moment is determined.
Preferably, the aggregate activity probability is greater than a minimum aggregate activity probability, wherein the aggregate activity probability is defined as: the probability that a node is within an effective monitoring distance of at least one active node is preset.
The construction method of the low-energy-consumption distributed event detection wireless sensor network forms a plurality of basic theories of low-energy-consumption distributed event detection of the wireless sensor network, breaks through a plurality of key technologies of distributed event detection, and contributes to the practicability and industrialization development of the wireless sensor network.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for constructing a low-energy distributed event detection wireless sensor network according to the present invention;
FIG. 2 is a flowchart of specific steps of a method for constructing a low-energy-consumption distributed event detection wireless sensor network according to the present invention;
FIG. 3 is a flowchart of specific steps of a method for constructing a low-power distributed event detection wireless sensor network according to the present invention;
FIG. 4 is a flowchart of specific steps of a method for constructing a low-power distributed event detection wireless sensor network according to the present invention;
fig. 5 is a node hierarchy diagram in a tree structure in the method for constructing a low-energy-consumption distributed event detection wireless sensor network according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The invention provides a low-energy-consumption distributed event detection wireless sensor network construction method shown in fig. 1-4, and the construction method is described in detail below with reference to fig. 1-4.
Step S0: analyzing and summarizing different events to obtain the commonalities and differences therein;
specifically, different events are analyzed and summarized, the formation mechanism and the expression form of the events are analyzed, commonalities and differences are extracted from the events, the commonalities are summarized and selected, and the unique differences of the specific events are recorded, so that the workload and the difficulty of classifying and summarizing all the events are reduced. In this embodiment, a big data analysis mode and the like may be adopted, and all events may be sorted according to various sorting bases such as time, place and the like, and of course, other methods may also be adopted, and are not described herein again.
Step S1, establishing corresponding models for different events to accurately depict detection performance; specifically, the method comprises the following steps:
s11, establishing corresponding event model, detection model and sensor network system model aiming at different events;
s12: revealing the relation between the detection performance and the key system parameter;
s13: and disclosing the relation between the detection performance and the system energy consumption.
In this step, the specific process is as follows:
the event model used for representing the event needs to embody important attributes of the event, including two attributes of space and time. The spatial attributes include physical size, coverage pattern, distribution characteristics, and the like. The physical size can be represented by a probability distribution function derived by a parameter estimation method through the statistical analysis of historical data of the event; the coverage patterns of events can be represented by corresponding shape models, simple events can be represented by point models, slightly complex events can be represented by disk models, and more complex events can be represented by specific shape models; the distribution characteristic of the event can be represented by a two-dimensional probability distribution function, specifically, the two-dimensional probability distribution function of the event e in the target area a is:
the time attributes include duration and time distribution, etc. Wherein the duration may be derived by statistical analysis of historical data of the event, a probability distribution function; and the temporal distribution of events can be represented by a poisson (Poison) process.
Factors that affect detection performance include event attributes, detection models, node wake-up schedules, and the like.
Events are first characterized in the form of particles, when the distribution of sensor nodes follows a typical random uniform distribution. Firstly, assuming that the awakening arrangement of the nodes is independent and random, the total number of the nodes is n, the detection distance of the nodes is r, and the average detection delay of the system can be obtained through analysis:
when the duration of the event is less than the wake-up period, the event may not be detected. Let the duration of the event be d. Since there will be holes in the target area, that is, no node can cover these areas, and such factors are removed, the detection probability is:
υ(d)＝Pr{e is detected|e is covered}。
assuming that the detection set size of e is X and the number of detection nodes is Y, the detection probability is:
υ(d)＝1-Pr{Y＝0|X≥1}
＝1-(1-dπr2/τcycle)n+(1-πr2)n。
the energy consumption of the sensor node mainly comes from three modules, namely a processor, a sensing device and a communication circuit, and the energy consumption is respectively set as follows: rhoP、ρSAnd ρR. Based on the energy consumption model of the system, the relationship between the detection performance and the service life can be obtained. Assuming that the initial state energy is E and the duty cycle distributions of the sensing device and the communication module are δ and ψ, the operating life of the system is:
by combining the obtained theoretical equation of the detection probability and the detection time delay, the relation between the detection performance and the service life is as follows:
And
When more event attributes (such as event coverage size), node distribution, detection models and the like are involved, the detection performance is different, and the correctable analysis method can obtain more accurate performance analysis.
Step S2, providing a distributed event detection algorithm and an optimization method;
specifically, the method comprises the following steps:
s21: analyzing the influence of node awakening arrangement on detection performance under the given energy consumption condition;
s22: studying the complexity of the optimal wake-up schedule;
s23: designing a distributed optimization algorithm to determine the awakening time of the node;
s24: optimizing the detection performance of the system;
in this step, the specific process is as follows:
under the condition of given energy consumption requirements, the target area a is configured with n nodes, S ═ {1,2,3, …, n }, and the position of the node i is (x)i,yi) (ii) a Examination point p (x)p,yp) Is detected by the delay L (x)p,yp) Let the detection set be U (x)p,yp) Then the detection delay at this point is:
wherein k ═ U (x)p,yp) I, set U (x)p,yp) The awakening time of the internal node is sequenced to be wp1,wp2,wp3,…,wpk。
A site set:
the goal of the system is to optimize the detection delay for m sites within the area, then the detection performance optimization can be described as the following constrained optimization problem:
inputting: a node set S ═ {1,2,3, …, n }, and a target location set P ═ 1,2,3, …, m };
and (3) outputting: the wake-up time W of the node is { W1, W2, W3, …, wn };
the target is as follows: minimization
Constraint conditions are as follows:
research has shown that the optimization problem described above is NP-complete.
When n and m are large, a distributed heuristic algorithm is considered for solving the optimization problem, namely the awakening time of the adjacent nodes is uniformly distributed as much as possible, the nodes are required to exchange information with the adjacent nodes when the awakening time is determined, and the awakening time of the nodes is dynamically adjusted on the basis of knowing the adjacent time.
Step S3, providing a distributed dynamic guarantee method for detecting the service quality;
specifically, the method comprises the following steps:
s31: defining an event detection service quality;
s32: providing a guarantee method for distributed detection service quality;
s33: dynamically maintaining a quality of service of the sensor network;
in this step, the specific process is as follows:
for event detection at a target location, the quality of service is defined as the probability and delay of detection at that location. The service quality of the detection delay sets a soft upper bound: given the highest upper delay bound, DL, fully characterized by a Cumulative Distribution Function (Cumulative Distribution Function), the event detection delay is less than DL.
Defining: if the following conditions are met, the random time delay D1 is smaller than D2, which is marked as D1 ≦ D2,
The user gives the goal of quality of service: a lower detection probability limit υ 0 and an upper detection delay limit L0. A probability-based method is provided for the point event model, namely, the nodes judge whether the given place reaches the service quality target according to the localized indexes. The probability that the node is in the active state is probabilistic, and the probability parameter of the node i is marked as delta i, so that the probability of being in the dormant state is 1-delta i.
Defining Aggregate Activity probability (Aggregate Activity): the aggregate activity probability for a location q is defined as the probability that the point is within the effective detection distance of at least one active node.
When the activity probability does not change over time, the aggregate activity probability for site q can be calculated by definition:
After having defined the aggregate activity probability, the quality of detection of the location q may be represented by the aggregate activity probability. Assuming that the duration of the event e is τ e, the detection probability can be expressed as:
the detection latency in aggregate activity probability may be expressed as:
where M is the number of time slots before detection and M is a random variable. Combining the probability distribution function of M, the probability distribution function of time delay can be obtained:
In summary, the minimum aggregate activity probability φ can be calculated from the minimum detection probability and the longest detection delay0：
υe＝f(φ(qe))≥υ0；
Therefore, in order to satisfy the service quality both of the detection probability and the time delay, it is necessary to ensure that the aggregation activity probability is greater than phi0。
The distributed guarantee method is characterized in that the activity probability of the distributed guarantee method is gradually determined through information exchange among nodes, namely, after the system is initialized, the nodes select a larger and conservative activity probability, and then the nodes gradually reduce the activity probability in a distributed iteration mode, but the minimum aggregation activity probability is ensured.
Step S4, providing a highly reliable event transmission method under the dynamic dormancy condition;
data transmission of event detection needs to include design requirements such as low energy consumption, transmission reliability and timeliness, and can be specifically divided into real-time application and non-real-time application: in real-time application, the transmission delay should be as small as possible to ensure the timeliness of transmission; in non-real-time applications, tolerable delay should be used to minimize the energy consumption of the node to prolong the operating life of the system.
In real-time applications, active Routing (Proactive Routing) may be used to minimize transmission delay. As shown in fig. 5, a tree structure is established in the network, and the base station is the root of the tree. The time is divided into frames with equal intervals, and the allocation of time slots and the wakeup arrangement of the nodes follow the following principle:
1) the receiving time slot of the parent node is aligned with the transmitting time slot of the child node. Therefore, the father node can timely receive the data of the child node when the child node sends the data;
2) the transmission slots of the parent node should be as close as possible to the transmission slots of the child node. The event can be transmitted to the base station node as fast as possible from the source node along the path of the tree;
3) the transmission time slot of a node should be staggered from the reception time slots of neighboring nodes other than the parent node. Reducing the collision of wireless transmission to the maximum extent, and simultaneously combining with a Carrier Sense Multiple Access (CSMA) method, before the data transmission is started, checking a channel to avoid the data transmission in progress;
4) if no data is received, the node should go to sleep in the subsequent transmission time slot.
In non-real-time applications, Reactive Routing (Reactive Routing) may be employed. Each node is normally in a sleep power saving state, but needs to intermittently enter a receiving state to monitor a channel to see whether a new data transmission is required. When the source node detects an event, the source node sends a long Preamble Signal (Preamble Signal) on the channel to wake up the neighbor to complete data transmission.
Due to the unreliability of wireless transmission and the dynamic property of the sensor nodes, data is easily lost in the transmission process. In order to achieve reliable transmission of event data, reliability enhancement measures must be taken. In this embodiment, a mechanism of multi-backup transmission may be adopted, and a plurality of independent backups are simultaneously transmitted when event data is transmitted.
In addition, when the sleep state of the communication circuit is arranged, the work time of the communication circuit and the sleep state of the sensing device are arranged together as much as possible so as to reduce the overall energy consumption of the node system. That is, a data transmission method for event detection is designed according to the dynamic dormancy of nodes to realize an energy-efficient and highly reliable event transmission service.
Step S5, carrying out actual verification and performance analysis on the theoretical analysis result and the designed algorithm through a test platform;
specifically, a proper simulation platform is selected according to different requirements, wherein the simulation platform comprises an MATLAB numerical calculation platform, a TOSIM, an NS2 and an autonomously developed simulator, so that the scientificity and the accuracy of a theoretical analysis result are verified on one hand; and the other side studies the performance of the algorithm under large-scale network conditions.
In this embodiment, a wireless sensor network prototype system with about 100 nodes can be established on the basis of simulation, so as to implement various algorithms in the present invention, study the performance and adaptability of the algorithms under real and complex conditions on a real wireless sensor network test platform, find out possible problems, and provide corresponding countermeasures.
The construction method of the low-energy-consumption distributed event detection wireless sensor network forms a plurality of basic theories of low-energy-consumption distributed event detection of the wireless sensor network, breaks through a plurality of key technologies of distributed event detection, and contributes to the practicability and industrialization development of the wireless sensor network.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundary of the appended claims, or the equivalents of such scope and boundary.
Claims (1)
1. A low-energy-consumption distributed event detection wireless sensor network construction method is characterized by comprising the following steps:
s0: analyzing and summarizing different events to obtain the commonalities and differences therein;
s1, establishing corresponding models aiming at different events to accurately depict detection performance;
s2, providing a distributed event detection algorithm and an optimization method;
s3, providing a distributed dynamic guarantee method for detecting the service quality;
s4, providing a high-reliability event transmission method under the dynamic dormancy condition;
s5: carrying out actual verification and performance analysis on a theoretical analysis result and a designed algorithm through a test platform;
the step S1 specifically includes the steps of:
s11, establishing corresponding event model, detection model and sensor network system model aiming at different events;
s12: revealing the relation between the detection performance and the key system parameter;
s13: revealing the relation between the detection performance and the system energy consumption;
in this step, the specific process is as follows:
the event model used for representing the event needs to embody the important attributes of the event, including two attributes of space and time, wherein the space attributes include physical size, coverage mode and distribution characteristics, and the physical size can be represented by using a parameter estimation method to deduce a probability distribution function of the event size through statistical analysis of historical data of the event; the coverage patterns of events can be represented by corresponding shape models, simple events can be represented by point models, slightly complex events can be represented by disk models, and more complex events can be represented by specific shape models; the distribution characteristic of the event can be represented by a two-dimensional probability distribution function, specifically, the two-dimensional probability distribution function of the event e in the target area a is:
the temporal attributes include duration and temporal distribution, wherein a probability distribution function of the duration is derived through statistical analysis of historical data of the event; while the temporal distribution of events can be represented by a poisson process,
factors that affect detection performance include event attributes, detection models and node wakeup schedules,
firstly, describing an event in a particle form, wherein the distribution of sensor nodes follows typical random uniform distribution, firstly, assuming that the awakening arrangement of the nodes is independent and random, and assuming that the total number of the nodes is n and the detection distance of the nodes is r, the average detection delay of the system can be obtained through analysis:
when the duration of the event is less than the wakeup period, the event may not be detected, the duration of the event is d, and since the target area may have a vulnerability, that is, no node can cover the target area, and such factors are removed, the detection probability is:
υ(d)＝Pr{e is detected|e is covered}
assuming that the detection set size of e is X and the number of detection nodes is Y, the detection probability is:
υ(d)＝1-Pr{Y＝0|X≥1}
＝1-(1-dπr2/τcycle)n+(1-πr2)n
the energy consumption of the sensor node mainly comes from three modules, namely a processor, a sensing device and a communication circuit, and the energy consumption is respectively set as follows: rhoP、ρSAnd ρRBased on an energy consumption model of the system, a relation between detection performance and working life can be obtained, and assuming that energy in an initial state is E and duty cycle distributions of a sensing device and a communication module are delta and psi, the working life of the system is as follows:
by combining the obtained theoretical equation of the detection probability and the detection time delay, the relation between the detection performance and the service life is as follows:
And
When more event attributes, node distribution and detection model conditions are involved, the detection performance is different, and the correctable analysis method can obtain more accurate performance analysis;
the step S2 specifically includes the steps of:
s21: analyzing the influence of node awakening arrangement on detection performance under the given energy consumption condition;
s22: studying the complexity of the optimal wake-up schedule;
s23: designing a distributed optimization algorithm to determine the awakening time of the node;
s24: optimizing the detection performance of the system;
in this step, the specific process is as follows:
under the condition of given energy consumption requirements, the target area a is configured with n nodes, S ═ {1,2,3, …, n }, and the position of the node i is (x)i,yi) (ii) a Examination point p (x)p,yp) Is detected by the delay L (x)p,yp) Record the detection set as U (x)p,yp) Then the detection delay at that point is:
wherein k ═ U (x)p,yp) I, set U (x)p,yp) The awakening time of the internal node is sequenced to be wp1,wp2,wp3,…,wpk，
A site set:
the goal of the system is to optimize the detection delay of m sites within the area, then the detection performance optimization can be described as the following constrained optimization problem:
inputting: a node set S ═ {1,2,3, …, n }, and a target location set P ═ 1,2,3, …, m };
and (3) outputting: the wake-up time W of the node is { W1, W2, W3, …, wn };
the target is as follows: minimization
Constraint conditions are as follows:
studies have shown that the above optimization problem is NP-complete,
when n and m are large, a distributed heuristic algorithm is considered for solving the optimization problem, namely the awakening time of the adjacent nodes is uniformly distributed as much as possible, the nodes are required to exchange information with the adjacent nodes when the awakening time is determined, and the awakening time of the nodes is dynamically adjusted on the basis of knowing the adjacent time; the step S3 specifically includes the steps of:
s31: defining an event detection service quality;
s32: providing a guarantee method for distributed detection service quality;
s33: dynamically maintaining a quality of service of the sensor network;
in this step, the specific process is as follows:
for event detection of a target location, service quality is defined as detection probability and detection delay of the location, and the service quality of the detection delay sets a soft upper bound: the highest delay upper bound DL is given and is completely described by the cumulative distribution function, the event detection delay is less than DL,
defining: if the following conditions are met, the random time delay D1 is smaller than D2, which is marked as D1 ≦ D2,
f (-) is a cumulative distribution function,
the user gives the goal of quality of service: detecting a lower probability limit upsilon 0 and an upper detection delay limit L0, and providing a probability-based method aiming at a point event model, namely judging whether a given point reaches a service quality target or not by a node according to a localized index, wherein the probability is that the node is in an active state, a probability parameter of a node i is marked as delta i, the probability of being in a dormant state is 1-delta i,
defining an aggregation activity probability: the aggregate activity probability for a location q is defined as the probability that the point is within the effective detection distance of at least one active node,
when the activity probability does not change over time, the aggregate activity probability for site q can be calculated by definition:
after having defined the aggregate activity probability, the detection quality of the location q can be represented by the aggregate activity probability: assuming that the duration of the event e is τ e, the detection probability can be expressed as:
the detection latency in aggregate activity probability may be expressed as:
wherein M is the time slot number before detection, M is a random variable, and the probability distribution function of the time delay can be obtained by combining the probability distribution function of M:
In summary, the minimum aggregate activity probability φ can be calculated from the minimum detection probability and the longest detection delay0：
υe＝f(φ(qe))≥υ0；
Therefore, in order to satisfy the service quality both of the detection probability and the time delay, it is necessary to ensure that the aggregation activity probability is greater than phi0；
The distributed guarantee method is that through the information exchange among the nodes, the activity probability of the nodes is gradually determined, namely after the system is initialized, the nodes select a larger and conservative activity probability, then through the distributed iteration mode, the activity probability is gradually reduced by the nodes, but the minimum aggregation activity probability is ensured;
the step S4 specifically includes: according to the dynamic dormancy of the node, a data transmission method aiming at event detection is designed to realize high-energy-efficiency and high-reliability event transmission service;
the data transmission of event detection needs to include low energy consumption, transmission reliability and timeliness design requirements, and can be specifically divided into real-time application and non-real-time application: in real-time application, the transmission delay should be as small as possible to ensure the timeliness of transmission; in non-real-time application, tolerable time delay is utilized to reduce the energy consumption of nodes as much as possible so as to prolong the service life of a system;
in real-time application, in order to shorten transmission delay as much as possible, active routing is adopted to establish a tree structure in a network, a base station is a tree root and divides time into frames with equal intervals, and the allocation of time slots and the awakening arrangement of nodes need to follow the following principle:
1) the receiving time slot of the father node is aligned with the sending time slot of the child node, so that the father node can timely receive the data of the child node when the child node sends the data;
2) the transmission time slot of the father node is close to the transmission time slot of the child node as much as possible, and the event can be transmitted to the base station node from the source node along the path of the tree as fast as possible;
3) the sending time slot of the node is staggered with the receiving time slot of the neighbor node except the father node, thereby reducing the conflict of wireless transmission to the maximum extent, and simultaneously, the method can be combined with a carrier sense multiple access method to check a channel to avoid the data transmission in progress before the data sending is started;
4) if no data is received, the node should go to sleep in the subsequent transmission slot,
in non-real-time application, reactive routing can be adopted, each node is in a sleep power-saving state at ordinary times, but needs to enter a receiving state to monitor a channel intermittently to check whether a new data transmission requirement exists, and after a source node detects an event, a neighbor is awakened to complete data transmission by sending a long preamble signal in the channel.
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