A method and system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization.

Abstract

This invention discloses a method and system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization. By abstracting the two-dimensional scene into a circle and the three-dimensional scene into a spherical model, and initializing the number of nodes and communication parameters, sensing nodes and a central marker transmitting node are randomly deployed according to a Poisson distribution. An interference-based PDR estimator is constructed to achieve rapid assessment of network reliability. Based on this, with a preset PDR threshold as a constraint, an improved bare-bones particle swarm optimization algorithm is used to efficiently iterate within the combination space of node number and communication parameters, outputting a deployment scheme that meets PDR requirements and minimizes the number of nodes. This application, through modules such as scene abstraction, node initialization, system construction, PDR estimation, particle swarm optimization, and scheme configuration, enables low-cost, highly reliable, and real-time adjustable sensing network deployment in resource-constrained hazardous environments.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things and wireless communication technology, and in particular relates to a method and system for minimizing the number of sensing nodes in dangerous scenarios based on Packet Delivery Ratio (PDR) optimization. Background Technology

[0002] In hazardous scenarios, wireless communication-based environmental monitoring systems have become crucial for ensuring personnel safety and reducing accident losses due to their flexible deployment and real-time capabilities. Existing systems primarily employ wireless LAN technology compliant with the IEEE 802.11 standard. These systems collect environmental data such as temperature, pressure, humidity, and location information through distributed sensing nodes and transmit the data to neighboring nodes, aggregation nodes, or command centers via broadcast or multicast to collaboratively complete hazard warnings and emergency dispatch.

[0003] In node deployment, traditional research typically relies on static planning based on coverage area or signal strength thresholds. One approach utilizes Voronoi diagrams to determine sensing unit locations by minimizing the distance between adjacent nodes, aiming to maximize coverage area and reduce coverage redundancy. Another approach sets a minimum signal strength threshold based on the relationship between Received Signal Strength Indication (RSSI) and distance, assuming a fixed radius for the effective coverage area of ​​a node. However, these two static assumptions fail to adequately reflect the complexity and dynamic changes of wireless channels in practical applications. Factors such as signal shadowing fading, multipath effects, and inter-node interference can lead to vastly different communication quality, causing deployment schemes to often fail to achieve the expected reliability in real hazardous environments. Crucially, both too many and too few communication nodes degrade system performance. Too many nodes not only increase hardware costs but also reduce the overall efficiency of the wireless communication system due to mutual interference; conversely, insufficient nodes may lead to coverage blind spots, affecting effective data transmission and system reliability. Under resource-constrained conditions, optimizing the deployment of sensing nodes becomes a key aspect of designing wireless communication systems for hazardous scenarios.

[0004] To evaluate the reliability of wireless broadcast networks, Packet Delivery Ratio (PDR) is widely used. PDR defines the ratio of the number of neighboring nodes that successfully receive broadcast data from a node with a specific marker to its total number of neighbors as a key performance indicator, directly reflecting network connectivity and transmission stability. For example, in flood monitoring scenarios, if a sensing node detects a sudden change in water level and sends an alarm, but the PDR is below the target level, there is a risk that some rescue forces will not receive the warning in time, affecting overall command efficiency and personnel safety. Therefore, optimizing the deployment of communication systems based on PDR has significant theoretical and practical implications.

[0005] Currently, there are two main technical approaches to PDR analysis: simulation and theoretical modeling. Simulation tools (such as NS2 and OMNeT++) can achieve high-precision simulations based on detailed channel models and protocol stacks, considering various factors such as path loss, shadowing fading, and interference source distribution to evaluate node layout and communication strategies. However, as the area size and number of nodes increase, the computational load of simulation rises sharply, not only causing long simulation processes but also making it difficult to meet the real-time requirements for rapid response to deployment schemes in hazardous scenarios.

[0006] In contrast, theoretical analysis models offer advantages such as fast computation speed and low resource consumption by simplifying channel and protocol processes and providing mathematical approximations of PDR. However, most current models, in multi-dimensional space (two-dimensional plane or three-dimensional solid) scenarios, still rely on regular geometric assumptions or only consider path loss, often neglecting the impact of the distribution of concurrent marker transmitting nodes on the signal-to-interference-plus-noise ratio (SINR), and failing to fully consider the constraints of hidden terminal effects and CSMA / CA sensing mechanisms on concurrent access. This results in significant deviations between the estimated PDR and the measured results.

[0007] Given the current state of the technology, how to construct a comprehensive solution that can quickly and accurately evaluate PDR and optimize the number of nodes deployed and communication parameters accordingly is a key technical bottleneck in the design of wireless monitoring systems for hazardous scenarios. Summary of the Invention

[0008] In view of this, the present invention aims to solve the technical problems that existing wireless sensing network deployments in hazardous scenarios cannot simultaneously achieve assessment accuracy, real-time performance, and deployment cost. It provides a method and system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization. By constructing an interference-based PDR estimator and an improved particle swarm optimization algorithm, the number of nodes is accurately minimized while ensuring communication reliability.

[0009] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0010] The first objective of this application is to disclose a method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization, comprising the following steps:

[0011] S1: The two-dimensional hazardous scene area to be monitored is abstracted into a circular model with the center of the area as the center and the radius as R, or the three-dimensional hazardous scene area is abstracted into a spherical model with the center of the area as the center and the radius as R.

[0012] S2: Initialize the number of sensing nodes N and communication parameters, and calculate the node density accordingly;

[0013] S3: Establish a communication system within the circular or spherical model, randomly deploy N sensing nodes according to a Poisson distribution, and set a marker transmitting node T at the center of the model;

[0014] S4: Construct an interference-based PDR estimator for deployed nodes to evaluate the PDR value of the communication system in different time periods and the overall PDR value:

[0015] S5: With the overall PDR value reaching or exceeding a preset threshold as a constraint, an improved bare bone particle swarm optimization algorithm is adopted. By iteratively updating the particle positions, which include the combination of communication parameters and the number of nodes, the minimum number of sensing nodes and deployment scheme that meet the PDR requirements are obtained.

[0016] Furthermore, step S4 includes:

[0017] S41: Calculate the average sensing distance of the marked transmitting node T using the path loss model based on the transmitting power, transmitting end gain, receiving end gain, and receiving power threshold.

[0018] S42: Confirm the set of concurrent sending nodes V(T) and the set of hidden terminal nodes H(T) of the marker sending node T, wherein the nodes within the average sensing distance around the marker sending node T are divided into the set of concurrent sending nodes V(T), and the remaining nodes are added to the set of hidden terminal nodes H(T).

[0019] S43: Based on the transmission probability of the concurrent transmission area and the hidden terminal area, and the random marking mechanism, select the set Φ of nodes that transmit simultaneously with the marked transmission node T from the set. T ;

[0020] S44: Based on the node set Φ T Calculate the signal-to-noise ratio (SINR) of the marker transmitting node T at the receiving node;

[0021] S45: For each data packet sent by the marker sending node T, calculate the packet reception rate PRR(r,p) of data packet p;

[0022] S46: Take a preset time as the time period, average the PRR of all data packets within the time period, and obtain the PRR(t,p) for the preset time period.

[0023] S47: Take the arithmetic mean of PRR(t,p) for all time periods to obtain the overall PDR index of the communication system.

[0024] Furthermore, in step S41, the average sensing distance, i.e., the average sensing range r E The calculation method is shown in the following formula:

[0025]

[0026] Where d0 is the reference distance, α is the path loss exponent, and P t For transmission power, G t For the transmitter gain, G r Let P be the receiver gain, η be a constant determined by the sender, and P be a constant. th The power threshold is used for sensing.

[0027] Furthermore, in step S42, the area surrounding the marker sending node T, which is outside one time the average sensing distance but within two times the average sensing distance, is defined as the hidden terminal area, and the nodes in the hidden terminal area are the set of hidden terminal nodes H(T).

[0028] Furthermore, in step S43, a random marker mark with a value range of [0,1] is generated for each network node, and the probability threshold π0 for the concurrent transmission region and the probability threshold p for the hidden terminal region are set respectively. t m i For node i's mark, select the node that sends the mark simultaneously with node T, according to the following rules:

[0029] Rule 1: For a node i, i∈V(T) in a concurrent sending region, when m i When π < 0, the node is considered to have sent the tag simultaneously with the tag-sending node T, and the set of nodes is denoted as .

[0030]

[0031] Where V(T) is the set of nodes in the concurrent sending region;

[0032] Rule 2: For a node i, i∈H(T) in a hidden terminal region, when m i <p t At that time, the node is considered to have sent the tag simultaneously with the tag-sending node T, and the set of nodes is denoted as .

[0033]

[0034] Where H(T) is the set of nodes in the hidden terminal region;

[0035] The set Φ of nodes that send data simultaneously with T T for:

[0036] Furthermore, in step S44, the formula for calculating the signal-to-noise ratio (SINR) of the transmitting node T at the receiving node is as follows:

[0037]

[0038] Where P r (T,j) represents the signal power received by receiver node j from self-tagged transmitter node T, calculated according to the Nakagami channel fading model, and I(j) represents the signal power received by receiver node j from the set of nodes Φ. T The sum of interference power of internal nodes, where N0 is the background noise power.

[0039] Furthermore, in step S45, for each data packet p sent by the marker sending node T, the packet reception rate PRR(r,p) of the data packet p is calculated using the following formula:

[0040]

[0041] Where r represents the receiving range of the marker sending node T, p represents a certain data packet, N(r) is the number of nodes within the receiving range r of the marker sending node T, and M... j M is the event in which the signal-to-interference-plus-noise ratio (SINR) and the received power threshold of receiving node j are simultaneously satisfied. j Represented as:

[0042] M j ={P r (T,j)≥P th &SINR j ≥θ}

[0043] Among them, P r (T,j) represents the power of the signal received by receiving node j from the marker transmitting node T, SINR. j Let θ be the signal-to-noise ratio (SNR) of receiving node j, and θ be the SINR threshold.

[0044] Furthermore, in step S46, with one second as a time interval, for each data packet sent within one second, PDR(t) is calculated, where PDR(t,p) is estimated using the following estimator:

[0045]

[0046] Where t is time, p represents a certain data packet, and the average value of PDR(t,p) of all data packets obtained within this time period is used as PDR(t).

[0047] Furthermore, in step S5, the improved bare bone particle swarm optimization algorithm includes:

[0048] S51: Initialize the PDR target threshold ε:

[0049] S52: Initialize the particles by using the combination of the number of nodes and communication parameters as the positions of each particle;

[0050] S53: Calculate the fitness PDR of the corresponding particle based on its position and step S4.

[0051] S54: Obtain particles whose fitness meets the requirements, i.e., PDR>ε, and form a set P;

[0052] S55: Select the particle in set P that minimizes the number of nodes as the current optimal particle;

[0053] S56: All particles are updated once to the current best particle position;

[0054] S57: Repeat S53–S56 until the transmission parameters converge or the maximum number of iterations is reached;

[0055] S58: The parameters shown by the current optimal particle are used as the minimum number of nodes N and the optimal communication parameters.

[0056] The second objective of this application is to disclose a system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization, comprising:

[0057] Physical Scene Abstraction Module: Used to abstract two-dimensional hazardous scene areas into circular models or three-dimensional hazardous scene areas into spherical models;

[0058] Node Quantity and Communication Parameter Initialization Module: Used to initialize the number of sensing nodes N and the communication parameters of each node according to the area size and preset communication requirements, and to calculate the node density;

[0059] Communication system construction module: used to deploy N homogeneous sensing nodes according to a Poisson distribution within an abstract model and set a marker sending node T at the center of the model;

[0060] PDR Estimator Module: Used to evaluate packet delivery rate (PDR) of a system based on a comprehensive information assessment system that considers path loss, Nakagami fading model, concurrent transmission, and hidden terminal effects.

[0061] An improved bare-bones particle swarm optimization algorithm module is used to perform particle swarm iterative search with the PDR reaching a preset threshold as a constraint to minimize the number of sensing nodes and optimize communication parameters.

[0062] Communication system configuration module: used to record the optimal number of nodes, communication parameters and corresponding PDR values ​​and output the final deployment configuration.

[0063] Compared with existing technologies, the method and system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization described in this invention have the following advantages:

[0064] 1. The method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization described in this invention significantly simplifies spatial representation and deployment calculation by abstracting complex terrain into a simple circular or spherical model and automatically determining the coverage radius with the center of the region as the center. It tightly couples node initialization, random deployment and performance evaluation, and combined with an interference-based PDR estimator, it can quickly and accurately evaluate network reliability without relying on highly complex simulation tools. Thus, while maintaining the model's universality, it greatly reduces computational overhead and supports rapid modeling and pre-deployment evaluation of hazardous scenarios of different scales and dimensions.

[0065] 2. The method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization described in this invention constructs a PDR estimator based on interference factors that integrates multi-dimensional factors such as path loss, channel fading, concurrent transmission, and hidden terminal effects. This enables fast and accurate online calculation of network reliability per second and overall network reliability without the need for a large simulation platform. It provides a realistic and reliable fitness index for deployment scheme optimization, enabling network reliability assessment to shift from empirical to refined evaluation.

[0066] 3. The method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization described in this invention adopts an improved bare-bones particle swarm optimization algorithm, which organically combines the PDR threshold constraint with the goal of minimizing the number of nodes. Through the efficient iterative convergence of the particle swarm in the space of node number and communication parameters, the minimum number of sensing nodes is finally obtained under the premise of meeting the preset communication quality requirements. This significantly reduces hardware investment and maintenance costs, while improving network throughput and stability, providing a low-cost, highly reliable, and real-time adjustable communication solution for resource-constrained hazardous scenarios. Attached Figure Description

[0067] Figure 1 This is a flowchart of the method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization, as described in an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram of the system for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization, as described in an embodiment of the present invention.

[0069] Figure 3 This is a flowchart illustrating the estimation process of the PDR estimator described in an embodiment of the present invention.

[0070] Figure 4 This is a flowchart of the improved bare bone particle swarm optimization algorithm described in an embodiment of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0072] In the description of this application, it should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0073] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and are not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0074] It should be noted that in the description of this application, the directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0075] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0076] like Figures 1-4 As shown, this application discloses a method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization, including the following steps:

[0077] S1: Abstract the two-dimensional dangerous scene area into a circle, and the three-dimensional dangerous scene area into a sphere. The abstracted scene covers the dangerous scene area with the center of the area as the center of the circle or the center of the sphere.

[0078] S2: Initialize the number of nodes N and communication parameters. The corresponding node density β is calculated based on the number of nodes and the area size.

[0079] S3: Establish a communication system based on an abstract dangerous scene area. Place N sensing nodes in the scene according to the Poisson distribution, and place a node at the center as the marker sending node T.

[0080] S4: Construct a packet delivery rate (PDR) estimator based on interference factors to evaluate the PDR value of the communication system in each time period:

[0081] S5: An improved bare bone particle swarm optimization algorithm is adopted. With the system PDR reaching or exceeding a preset threshold as a constraint, the particle positions, which include the combination of communication parameters and the number of nodes, are updated iteratively to obtain the minimum number of sensing nodes for the optimal deployment scheme that meets the PDR requirements.

[0082] This application discloses a method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization. First, the geometric boundaries of the hazardous scenario are extracted. Using the center point of the region as the center of a circle or sphere, a radius of a circle that can cover the two-dimensional scene region, or a radius of a sphere that can cover the three-dimensional scene region, is given. This simplifies the spatial model by abstracting the two-dimensional region into a circle covering the entire target area or the three-dimensional region into a sphere that can completely enclose the scene. Then, within the circular or spherical coverage area, it is assumed that all devices are isomorphic, i.e., all communication devices conform to IEEE standards. The 802.11 protocol initializes all nodes with the same transmission power, antenna gain, and other communication parameters. Based on the number of nodes and the area size, it initializes the initial distribution density and related communication parameters of the sensing nodes, including transmission power, transmitter gain, receiver gain, and receiver power threshold. Sensing nodes are deployed in the area according to a Poisson distribution, while a marker transmitter node T is established at the center for unified topology control. Then, a packet delivery rate (PDR) estimator based on interference analysis is constructed for the deployed node group. This estimator combines factors such as transmission power, receiver gain, path loss model, node concurrent transmission probability, and hidden terminal effect to calculate the average PDR per second and across all time periods to evaluate network reliability. The comparison result of the PDR with a preset threshold is used as a fitness function and imported into an improved Barebones PSO optimization algorithm. Under the constraint of satisfying or exceeding the PDR threshold, a global optimal solution is achieved through iterative search of a one-step movement strategy in the space of node number and communication parameters using particle swarm optimization, thus obtaining a deployment scheme that satisfies both communication quality and minimizes the number of sensing nodes.

[0083] This application discloses a method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization. By simplifying the spatial model of hazardous scenarios into circular or spherical coverage areas, it significantly reduces computational complexity and enhances the versatility of deployment schemes. Furthermore, by combining an interference-based PDR estimator, it can quickly and accurately assess network reliability without relying on highly complex simulation tools. The improved Barebones PSO optimization algorithm minimizes the number of nodes while meeting a preset PDR threshold, thus reducing hardware investment and subsequent maintenance costs. In addition, the algorithm adaptively adjusts communication parameters to effectively suppress inter-node interference, improving coverage efficiency and ensuring system stability and real-time performance. This enables efficient and reliable sensing network deployment in resource-constrained hazardous environments and provides solid technical support for emergency early warning and decision-making.

[0084] As a preferred example of this application, step S4 includes:

[0085] S41: Calculate the average sensing distance of the marked transmitting node T using the path loss model based on the transmitting power, transmitting end gain, receiving end gain, and receiving power threshold.

[0086] S42: Confirm the set of concurrent sending nodes V(T) and the set of hidden terminal nodes H(T) of the marker sending node T, wherein the nodes within the average sensing distance around the marker sending node T are divided into the set of concurrent sending nodes V(T), and the remaining nodes are added to the set of hidden terminal nodes H(T).

[0087] S43: Based on the transmission probability of the concurrent transmission area and the hidden terminal area, and the random marking mechanism, select the set Φ of nodes that transmit simultaneously with the marked transmission node T from the set. T ;

[0088] S44: Based on the node set Φ T Calculate the signal-to-noise ratio (SINR) of the marker transmitting node T at the receiving node;

[0089] S45: For each data packet sent by the marker sending node T, calculate the packet reception rate PRR(r,p) of data packet p;

[0090] S46: Take a preset time as the time period, average the PRR of all data packets within the time period, and obtain the PRR(t,p) for the preset time period.

[0091] S47: Take the arithmetic mean of PRR(t,p) for all time periods to obtain the overall PDR index of the communication system.

[0092] The interference factor PDR estimator described in this application first calculates the average sensing distance of the marked transmitting node T using a path loss model, based on preset transmit power, transmit end gain, receive end gain, and receive power thresholds, combined with known path loss exponents and reference distances in the environment. Then, based on the obtained distances, the network nodes are divided into a set of concurrent transmitting nodes V(T) and a set of hidden terminal nodes H(T) to simulate direct contention interference and the hidden terminal effect, respectively. Finally, based on the concurrent transmission probability and hidden terminal transmission probability defined in the CSMA / CA protocol, and combined with a random labeling mechanism, a set Φ of nodes that actually transmit simultaneously is selected from the above node set. T Then, the Nakagami channel fading model is used to calculate the received power from the transmitting node T to each receiving node, and the transmit power of all interfering nodes is summarized to obtain the signal-to-interference-plus-noise ratio (SINR) of each receiving link. Next, in each preset time period (e.g., 1 second), the reception success rate of each transmitted data packet is determined based on whether the SINR and received power meet the threshold conditions, and the packet reception rate is calculated. The average of all packet reception rates in the time period is used to obtain the time period PDR. Finally, the PDR values ​​of all time periods are accumulated and averaged to obtain the PDR of the entire network communication system in the time domain of interest, which is used as a reliability evaluation index for subsequent optimization algorithms.

[0093] As a preferred example of this application, in step S41, the average sensing distance, i.e., the average sensing range r E The calculation method is shown in the following formula:

[0094]

[0095] Where d0 is the reference distance, usually 1 meter, α is the path loss exponent, and P t For transmission power, G t For the transmitter gain, G r Let P be the receiver gain, η be a constant determined by the sender, and P be a constant. th The power threshold is used for sensing.

[0096] In the example of this application, the MAC layer of IEEE 802.11 uses the CSMA / CA protocol to coordinate distributed access to the channel by each node. Based on key parameters such as transmit power, transmitter gain, receiver gain, and sensing power threshold required by the communication scenario, the sensing mechanism forms a sensing area (also an interference protection area) for the marked transmitting node T to avoid interference caused by simultaneous transmission from nearby nodes. Combining a preset reference distance and environmental path loss index, the radius of the node's sensing protection area is defined as the average sensing distance. The parameters are substituted into the average sensing range r through a mathematical model. E The formula is solved precisely. This calculation not only establishes an interference protection boundary for the marked sending node T in the network based on the actual equipment performance and environmental attenuation characteristics, but also enables the system to dynamically adjust the sensing range directly using real-time communication parameters without relying on complex simulation tools. This provides an objective and accurate physical basis for the subsequent partitioning of the concurrent sending node set V(t) and the hidden terminal node set H(T).

[0097] This application constructs an interference protection zone in hazardous scenarios using precisely calculated average sensing distance. This accurately reflects changes in the real channel environment during node deployment and communication, effectively avoiding concurrent transmission conflicts between neighboring nodes. Combined with the distributed avoidance mechanism of the CSMA / CA protocol, it further improves the success rate of network data frame transmission. At the same time, this calculation method comprehensively considers multiple factors such as path attenuation, device gain, and threshold settings, significantly improving the accuracy of PDR estimation. This provides a reliable fitness evaluation index for the subsequent Barebones PSO optimization algorithm.

[0098] As a preferred example of this application, in step S42, the area surrounding the marker sending node T, which is between one and two times the average sensing distance, is defined as the hidden terminal region, and the nodes in the hidden terminal region are the set of hidden terminal nodes H(T). In the example of this application, the distance between each node other than the marker sending node T and the marker sending node T is calculated. If the distance is less than the average sensing distance, the node is added to the set of concurrent sending nodes V(T); otherwise, the node is added to the set of hidden terminal nodes H(T). This application calculates the distance between the marker sending node T and each node in the network in real time, classifies nodes whose distance does not exceed the average sensing distance into a set of concurrent sending nodes V(T), and coordinates their access through a schedulable timing mechanism before data transmission to avoid competition between nearby nodes. Nodes whose distance is between one and two times the average sensing distance are classified into a set of hidden terminal nodes H(T), and their interference on the ongoing transmission link is reduced by dynamically adjusting their access window or time slot priority. This enables regionalized management and seamless cooperation of different interference sources in the same physical space, thereby significantly improving communication success rate and system throughput, reducing retransmissions and latency, and ensuring reliable and efficient data acquisition and transmission in resource-constrained and dangerous scenarios.

[0099] As a preferred example of this application, in step S43, a random marker mark with a value range of [0,1] is generated for each network node, and the probability threshold π0 for the concurrent transmission region and the probability threshold p for the hidden terminal region are set respectively. t m i For node i's mark, select the node that sends the mark simultaneously with node T, according to the following rules:

[0100] Rule 1: For a node i, i∈V(T) in a concurrent sending region, when m i When π < 0, the node is considered to have sent the tag simultaneously with the tag-sending node T. The set of such nodes is denoted as .

[0101]

[0102] Where V(T) is the set of nodes in the concurrent sending region;

[0103] Rule 2: For a node i, i∈H(T) in a hidden terminal region, when m i <p t At that time, the node is considered to have sent the tag simultaneously with the tag-sending node T, and the set of such nodes is denoted as .

[0104]

[0105] Where H(T) is the set of nodes in the hidden terminal region;

[0106] Then, the set Φ of nodes that send data simultaneously with T is... T for

[0107] In the example of this application, a random label ranging from zero to one is generated for each node in the network except for the label-sending node T. Then, based on the region type to which the node belongs, different probability thresholds are applied to compare the random labels. In the concurrent transmission region, when a node's label is less than π0, it is selected and added to the simultaneous transmission set; while in the hidden terminal region, when a node's label is less than p... t At the same time, it is also selected and added to the simultaneous transmission set. Through the above single random marking and dual probability screening process, the system can flexibly distinguish nodes with different interference risks and dynamically control their transmission behavior during each data transmission, so as to effectively avoid transmission collisions between nodes while ensuring random access characteristics and load distribution.

[0108] As a preferred example of this application, in step S44, the formula for calculating the signal-to-noise ratio (SINR) of the transmitting node T at the receiving node is as follows:

[0109]

[0110] Where P r (T,j) represents the signal power received by receiver node j from self-tagged transmitter node T, calculated according to the Nakagami channel fading model, and I(j) represents the signal power received by receiver node j from the set of nodes Φ. T The sum of interference power of internal nodes, where N0 is the background noise power.

[0111] In this application, the wireless channel between the marker transmitting node T and the receiving node j is modeled using pre-set Nakagami fading parameters, and a corresponding fading gain value is generated. This gain value is then multiplied by the transmit power and antenna gain to accurately calculate the signal power P received by the receiving node j from the marker transmitting node T. r (T,j), and calculate the set of nodes in the concurrent sending region in the same way. and the set of nodes within the hidden terminal area The interference power at each receiving node j is calculated, and these interference powers are summed to obtain the total interference power I(j). Finally, P is... r (T,j) is divided by the sum of I(j) and the preset background noise N0 to obtain the signal-to-noise ratio (SINR). jThis value truly reflects the ratio of signal to multi-source interference, and can more accurately reflect the signal quality and interference level of the receiving node in complex environments, thereby significantly improving the accuracy of communication performance evaluation and providing accurate input for subsequent PRR and PDR evaluations.

[0112] As a preferred example of this application, in step S45, for each data packet p sent by the marker sending node T, the packet reception rate PRR(r,p) of the data packet p is calculated using the following formula:

[0113]

[0114] Where r represents the receiving range of the marker sending node T, p represents a certain data packet, N(r) is the number of nodes within the receiving range r of the marker sending node T, and M... j M is the event in which the signal-to-interference-plus-noise ratio (SINR) and the received power threshold of receiving node j are simultaneously satisfied. j Represented as:

[0115] M j ={P r (T,j)≥P th &SINR j ≥θ}

[0116] Among them, P r (T,j) represents the power of the signal received by receiving node j from the marker transmitting node T, SINR. j Let θ be the signal-to-noise ratio (SNR) of receiving node j, and θ be the SINR threshold.

[0117] In the example of this application, for each data packet p, the successful reception events of all neighboring nodes within the reception range r of the marked sending node T are first counted. Successful reception is only considered when the received power reaches a preset threshold and the signal-to-noise-interference ratio is not lower than the SINR threshold. Then, the PRR(r,p) of the data packet is directly calculated by the ratio of the number of successfully received nodes to the total number of neighboring nodes, thereby accurately quantifying the actual coverage effect of each data packet.

[0118] As a preferred example of this application, in step S46, for each data packet sent within a second, PDR(t) is calculated, with one second as a time interval, where PDR(t,p) is estimated using the following estimator:

[0119]

[0120] Where t is time, p represents a certain data packet, and the average value of PDR(t,p) of all data packets obtained within this time period is used as PDR(t).

[0121] In the example of this application, PDR(t) is generated by taking the arithmetic mean of the PDR values ​​of all data packets within the time period t, in units of consecutive time intervals t.

[0122] In step S46, the present invention generates PDR(t) by taking the arithmetic mean of the PRR values ​​of all data packets within a continuous time interval t. Then, the PDR(t) values ​​of multiple time intervals are accumulated and arithmetically averaged to obtain the overall PDR. This comprehensively evaluates the data transmission reliability and stability of the network in different time periods, provides an accurate and reliable fitness index for subsequent deployment of optimization algorithms, significantly reduces the retransmission rate, improves throughput and transmission stability, and ensures that environmental data can be delivered in real time and reliably in dangerous scenarios.

[0123] As a preferred example of this application, in step S5, the improved bare bone particle swarm optimization algorithm includes:

[0124] S51: Initialize the PDR target threshold ε:

[0125] S52: Initialize the particles by using the combination of the number of nodes and communication parameters as the positions of each particle;

[0126] S53: Calculate the fitness PDR of the corresponding particle based on its position and step S4.

[0127] S54: Obtain particles whose fitness meets the requirements, i.e., PDR>ε, and form a set P;

[0128] S55: Select the particle in set P that minimizes the number of nodes as the current optimal particle;

[0129] S56: All particles are updated once to the current best particle position;

[0130] S57: Repeat S53–S56 until the transmission parameters converge or the maximum number of iterations is reached;

[0131] S58: Use the parameters shown by the current optimal particle as the minimized N and the optimal communication parameters.

[0132] In the example of this application, in order to minimize the number of nodes deployed in the network and optimize communication parameter settings while ensuring wireless communication reliability, this application uses the reliability index (PDR) of the wireless communication system as the fitness target. An improved bare-bones particle swarm optimization algorithm iteratively searches for the optimal combination of node number and communication parameters. Each particle represents a deployment scheme, and its fitness is calculated based on the PDR value obtained in step S4 after initialization. After filtering out the set of particles that meet the target threshold, the particle with the fewest nodes is selected as the current optimal scheme, and all particles are guided to converge directionally. Finally, under the premise of satisfying communication reliability, the minimum deployment scale and the optimal transmission parameter configuration are found, significantly improving the deployment efficiency and energy efficiency ratio of the wireless sensing system. In hazardous environments, it can achieve high-reliability communication support for stable data acquisition and remote transmission in environments with minimal resource configuration. Furthermore, this algorithm has significant advantages in parameter convergence speed and search accuracy, and can adapt to the dual requirements of communication quality and system simplicity in complex scenarios, possessing broad practical application prospects and engineering promotion value.

[0133] This application discloses a method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization. By abstracting complex terrain into a simple circular or spherical model, the complexity of scene representation and computation is effectively reduced. Based on this, a unified communication parameter initialization and Poisson distribution method are used to deploy isomorphic sensing nodes and establish a marker transmitting node T, ensuring the random coverage characteristics of the network. Combined with a path loss model, the average sensing distance is accurately calculated to delineate interference protection zones and distinguish between concurrent marker transmitting nodes and hidden terminal nodes, thus forming an interference partitioning mechanism that reflects the actual wireless channel environment. In terms of performance evaluation, by constructing a PDR estimator that considers path loss, channel fading, concurrent transmission, and hidden terminal effects, the network reliability in different time periods can be accurately quantified without the need for a large simulation platform. The improved bare-bones particle swarm optimization algorithm is guided by PDR as a fitness function to perform convergence search in the combination space of node number and communication parameters. While ensuring that the network communication reliability meets the preset threshold, the particle position is dynamically adjusted to gradually approach the global optimum and ultimately minimize the number of deployed nodes. The method for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization disclosed in this application not only significantly reduces equipment investment and operation and maintenance costs, but also improves the flexibility and robustness of network deployment. It is particularly suitable for the actual needs of resource-constrained and network structure-requiring rapid adjustment in hazardous scenarios, providing a stable, efficient and scalable solution for emergency monitoring and remote data transmission systems.

[0134] This application also discloses a system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization, including:

[0135] Physical Scene Abstraction Module: Used to abstract two-dimensional hazardous scene areas into circular models or three-dimensional hazardous scene areas into spherical models;

[0136] Node Quantity and Communication Parameter Initialization Module: Used to initialize the number of sensing nodes N and the communication parameters of each node according to the area size and preset communication requirements, and to calculate the node density;

[0137] Communication system construction module: used to deploy N homogeneous sensing nodes in the abstract model according to a Poisson distribution and set a marker sending node T at the center of the model;

[0138] PDR Estimator Module: Used to evaluate packet delivery rate (PDR) of a system based on a comprehensive information assessment system that considers path loss, Nakagami fading model, concurrent transmission, and hidden terminal effects.

[0139] An improved bare-bones particle swarm optimization algorithm module is used to perform particle swarm iterative search with the PDR reaching a preset threshold as a constraint to minimize the number of sensing nodes and optimize communication parameters.

[0140] Communication system configuration module: used to record the optimal number of nodes, communication parameters and corresponding PDR values ​​and output the final deployment configuration.

[0141] This application discloses a PDR-optimized system for minimizing the number of sensing nodes in hazardous scenarios. This system executes the PDR-optimized method for minimizing the number of sensing nodes in hazardous scenarios as described in the above embodiments. Through a modular, layered design, it achieves clear responsibilities and concise interfaces. In the physical scene abstraction module, it innovatively uses a circular or spherical model to replace complex terrain representations, significantly reducing spatial computational overhead. Based on the region of the two-dimensional or three-dimensional hazardous scenario, it provides the radius of a circle that can cover the two-dimensional scene region, or the radius of a sphere that can cover the three-dimensional scene region, with the center point of the region as the center of the circle or sphere. The node number and communication parameter initialization module initializes the number of nodes N and the communication parameters. The corresponding node density can be calculated based on the number of nodes and the region size. In the examples of this application, it is assumed that all devices are isomorphic, that is, it is assumed that all communication devices conform to IEEE standards. The 802.11 protocol initializes all nodes with the same transmit power, antenna gain, and other communication parameters, thereby improving deployment consistency by uniformly setting communication parameters for homogeneous devices and automatically calculating node density in the node initialization module. The communication system construction module establishes a communication system against an abstract two-dimensional or three-dimensional scene region, placing N sensing nodes in this scene according to a Poisson distribution, with one node placed at the center as a marker transmitting node T. The Poisson distribution model ensures random node coverage, facilitating subsequent reliability assessment. The PDR estimator module evaluates the PDR value based on communication parameters such as the number of nodes, transmit power, transmitter gain, receiver gain, and receiver power threshold, and integrates path loss and channel fading. The concurrent / hidden terminal effect enables online rapid calculation of PDR, eliminating the reliance on large simulation tools; the improved bare-bones particle swarm optimization algorithm module uses an improved bare-bones particle swarm optimization algorithm to minimize the number of sensing nodes and select appropriate communication parameters, and incorporates the PDR index and node minimization into the fitness constraint, which greatly improves the optimization efficiency and convergence speed, so that the system meets the PDR requirements while minimizing the number of sensing nodes; the communication system configuration module records the number of nodes that meet the conditions, communication parameters, and achievable PDR, which is used to guide the construction of communication systems in actual scenarios. It can automatically record and output the optimal deployment scheme to facilitate engineering implementation. Overall, it realizes the deployment of a low-cost, highly reliable, and real-time adjustable hazardous scenario sensing network.

[0142] The system disclosed in this application, which minimizes the number of sensing nodes in hazardous scenarios based on PDR optimization, significantly reduces computational and deployment complexity by organically integrating scenario modeling, deployment planning, performance evaluation, and optimization algorithms. It also obtains accurate PDR evaluation results in real time without the need for large-scale simulation. The improved bare-bones particle swarm optimization algorithm quickly converges to the minimum node deployment and optimal parameter configuration while ensuring communication reliability. This effectively saves hardware costs and energy consumption and improves network throughput and stability, thus providing an efficient, reliable, and easy-to-implement environmental data acquisition and transmission solution for resource-constrained hazardous scenarios.

[0143] This invention discloses a method and system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization. It is an innovative solution designed to improve the performance of wireless communication systems in complex environments. Its core lies in combining a precisely constructed PDR estimator with an improved Bare-bones Particle Swarm Optimization (BBPSO) algorithm. This achieves accurate calculation and minimization of the required number of sensing nodes and optimized communication parameter configuration while ensuring data transmission quality. This application designs an interference-based PDR estimator and proposes an improved BBPSO algorithm to adjust the number of nodes and communication parameters, optimizing PDR to meet requirements while minimizing the number of nodes. It is particularly suitable for the design of wireless communication systems in two-dimensional and three-dimensional hazardous scenarios, including the design of the PDR estimator, the method for minimizing the number of sensing nodes, and the selection of communication parameters. This invention not only improves the reliability and efficiency of wireless communication systems in hazardous scenarios but also provides strong technical support for emergency response, disaster management, and public safety, possessing profound social significance and broad application prospects.

[0144] This invention discloses a method and system for minimizing the number of sensing nodes in hazardous scenarios based on Packet Delivery Ratio (PDR) optimization. Through PDR evaluation and optimization, it estimates the minimum number of sensing nodes and communication parameter settings required to meet the needs in two-dimensional and three-dimensional hazardous scenarios. The implementation method is illustrated using the bare-bones particle swarm optimization algorithm as an example. This method and system are not only adaptable to two-dimensional applications, such as post-earthquake rubble search and rescue and forest fire monitoring, but can also be extended to three-dimensional spaces, such as high-rise building fire rescue and aerial drone swarm environmental monitoring, demonstrating high multidimensional adaptability and wide applicability. It can also be applied to various scenarios such as vehicle-to-everything (V2X) networks and drone networks.

[0145] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization, characterized in that, Includes the following steps: S1: The two-dimensional hazardous scene area to be monitored is abstracted into a circular model with the center of the area as the center and the radius as R, or the three-dimensional hazardous scene area is abstracted into a spherical model with the center of the area as the center and the radius as R. S2: Initialize the number of sensing nodes N and communication parameters; S3: Establish a communication system within the circular or spherical model, randomly deploy N sensing nodes according to a Poisson distribution, and set a marker transmitting node T at the center of the model; S4: Construct a packet delivery rate (PDR) estimator based on interference factors for deployed nodes to evaluate the PDR value of the communication system in each time period and the overall PDR value: Step S4 includes: S41: Calculate the average sensing distance of the marked transmitting node T using the path loss model based on the transmitting power, transmitting end gain, receiving end gain, and sensing power threshold. S42: Confirm the set of concurrent sending nodes for the marker sending node T. and hidden terminal node set Among them, the nodes within the average sensing distance around the marker sending node T are divided into a set of concurrent sending nodes. The remaining nodes in the hidden terminal area are added to the hidden terminal node set. ; S43: Based on the transmission probability of the concurrent transmission area and the hidden terminal area, and the random marking mechanism, from the set of concurrent transmission nodes... and hidden terminal node set Select the set of nodes that send the tag simultaneously with node T. ; S44: Based on the node set Calculate the signal-to-noise ratio (SINR) of the marker transmitting node T at the receiving node; S45: For each data packet sent by the marker sending node T, calculate the packet reception rate of data packet p. ,in, To mark the receiving range of the sending node T, This refers to a specific data packet; S46: Using a preset time period as the time interval, calculate the packet reception rate for each data packet p obtained in step S45. Determine the corresponding data packets within this time period. and all data packets within that time period. Take the average to obtain the PDR(t) for the preset time period, where t is time; S47: For multiple time periods The cumulative values ​​are taken as the arithmetic mean to obtain the overall PDR index of the communication system. S5: With the overall PDR index reaching or exceeding a preset threshold as a constraint, an improved bare bone particle swarm optimization algorithm is adopted. By iteratively updating the particle positions, which include the combination of communication parameters and the number of nodes, the minimum number of sensing nodes and deployment scheme that meet the PDR requirements are obtained.

2. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to claim 1, characterized in that, In step S41, the average sensing distance is the average sensing range. The calculation method is shown in the following formula: in, For reference distance, The path loss index. For transmission power, For the transmitter gain, For receiver gain, A constant determined by the sender. The power threshold is used for sensing.

3. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to claim 1, characterized in that, In step S42, the area surrounding the marker sending node T, which is outside one average sensing distance but within two average sensing distances, is defined as the hidden terminal area.

4. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to claim 1, characterized in that, In step S43, a random marker (mark) with a value range of [0,1] is generated for each network node, and a probability threshold for the concurrent transmission region is set for each node. and the probability threshold of hidden terminal areas , For nodes The mark is selected from the nodes that send it simultaneously with the mark-sending node T, according to the following rules: Rule 1: For node i in a concurrent sending region, ,when At that time, the node is considered to have sent the tag simultaneously with the tag-sending node T, and the set of nodes is denoted as . : in It is a collection of nodes in the concurrent sending region; Rule 2: For node i in a hidden terminal region, ,when At that time, the node is considered to have sent the tag simultaneously with the tag-sending node T, and the set of nodes is denoted as . : in It is a collection of nodes in the hidden terminal region; Then, the set of nodes that send at the same time as T. for: .

5. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to claim 1, characterized in that, In step S44, the formula for calculating the signal-to-noise ratio (SINR) of the transmitting node T at the receiving node is as follows: in Let I(j) be the signal power received by receiver node j from self-tagged transmitter node T, calculated based on the Nakagami channel fading model. The sum of the interference power of the internal nodes, This represents the background noise power.

6. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to claim 1, characterized in that, In step S45, for each data packet p sent by the marker sending node T, the packet reception rate PRR(r,p) of data packet p is calculated using the following formula: in, This indicates the number of nodes within the receiving range r of the sending node T. The event is defined as the signal-to-interference-plus-noise ratio (SINR) and received power of receiving node j simultaneously satisfying a threshold condition. Represented as: in, The power of the signal received by receiving node j from the marker transmitting node T. To sense the power threshold, The signal-to-noise ratio (SNR) of receiving node j. This is the SINR threshold.

7. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to claim 1, characterized in that, In step S46, a time interval of one second is defined, and for each data packet sent within that second, where... Estimate using the following estimator:

8. The method for minimizing the number of sensing nodes in a hazardous scenario based on PDR optimization according to any one of claims 1 to 7, characterized in that, In step S5, the improved bare bone particle swarm optimization algorithm includes: S51: Initialize PDR target threshold : S52: Initialize the particles by using the combination of the number of nodes and communication parameters as the positions of each particle; S53: Calculate the fitness of the corresponding particle based on its position and step S4, whereby the fitness is the overall PDR index of the communication system. S54: Obtain particles whose fitness meets the requirements, i.e. This forms a set P; S55: Select the particle in set P that minimizes the number of nodes as the current optimal particle; S56: All particles are updated once to the current best particle position; S57: Repeat S53–S56 until the transmission parameters converge or the maximum number of iterations is reached; S58: Use the parameters shown by the current optimal particle as the minimum number of nodes and the optimal communication parameters.

9. A system for minimizing the number of sensing nodes in hazardous scenarios based on PDR optimization, characterized in that, A method for performing the PDR-based optimization method for minimizing the number of sensing nodes in a hazardous scenario as described in any one of claims 1 to 8, comprising: Physical Scene Abstraction Module: Used to abstract two-dimensional hazardous scene areas into circular models or three-dimensional hazardous scene areas into spherical models; Node Quantity and Communication Parameter Initialization Module: Used to initialize the number of sensing nodes N and the communication parameters of each node according to the area size and preset communication requirements, and to calculate the node density; Communication system construction module: used to deploy N homogeneous sensing nodes according to a Poisson distribution within an abstract model and set a marker sending node T at the center of the model; PDR Estimator Module: Used to evaluate packet delivery rate (PDR) of a system based on a comprehensive information assessment system that considers path loss, Nakagami fading model, concurrent transmission, and hidden terminal effects. An improved bare-bones particle swarm optimization algorithm module is used to perform iterative particle swarm search with the packet delivery rate (PDR) reaching a preset threshold as a constraint to minimize the number of sensing nodes and optimize communication parameters. Communication system configuration module: used to record the minimum number of sensing nodes, communication parameters and corresponding PDR values ​​and output the final deployment configuration.

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