A source address independent distributed state monitoring method

Abstract

This invention discloses a source-address-independent distributed state monitoring method. Based on a source-address-independent random access protocol, all sensors share a common codebook, and the fusion center only needs to recover the transmitted message sequence without concern for the identity information of active sensors. First, the sensors are activated to observe some state variables and transmit the observation results uplink to the fusion center. The fusion center processes the observation information, estimates the global state vector, and broadcasts a reliability index for the state variable estimation. Then, the sensors determine their activation probability in the next time slot based on the reliability index of the estimated observable state variables. The task is completed when the estimation of all state variables in the system is reliable. This scheme achieves efficient and accurate observation of the system's global state vector using a small number of time slots.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more particularly to a source address-independent distributed state monitoring method. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), the number of IoT devices has increased significantly. A large number of IoT devices can support diverse IoT applications, but providing fast and reliable access is crucial. Traditional unlicensed random access technology is unsuitable for IoT scenarios. This is because in unlicensed random access schemes, each potential user is assigned a specific pilot sequence, and with a large number of potential users, the pilot overhead is unacceptable. To address this issue, a source-address-independent random access technology has been proposed. In this scheme, all potential users share a common codebook, and the base station only needs to recover the transmitted message sequence without caring about the identity of active users. Since users do not need to transmit pilot sequences, this scheme avoids high pilot overhead and meets the key requirements of IoT scenarios.

[0003] In IoT scenarios, there are various applications, one of which is distributed state monitoring. In distributed state monitoring tasks, multiple sensors at different locations observe the system's output and send the results to a fusion center to estimate the global system state vector. For a typical linear system, each sensor can only acquire observations of a portion of the state variables in the global state vector. The fusion center focuses only on the state information embedded in the observations, not on which sensor sent them. Due to the passive nature of sensor transmission, uplink transmission can be performed using a source-address-independent random access protocol. However, since a single sensor can only acquire observations of a portion of the state variables, and only a few sensors are active at any given time, the observation of the global state vector through transmission in a single time slot is insufficient. Furthermore, the detection process may result in missed or false detections, leading to inaccuracies in the global state vector observation. The insufficiency and inaccuracy of state variable observations necessitate observations in multiple time slots to obtain an accurate observation of the system's global state vector. However, in existing source-address-independent random access protocols, to address the problem of excessively high common codebook dimensionality, users first divide the information sequence into several sub-blocks and then add check bits to establish check relationships between different information sub-blocks. At the receiving end, all transmitted information sub-blocks are first detected, and then a tree decoder is used to decode and reassemble the original information sequence. This requires a large number of parity bits to ensure decoding accuracy, resulting in a low transmission rate. Due to the low transmission rate and the need for observation across multiple time slots, the efficiency of completing distributed state monitoring tasks is low. Therefore, designing a scheme that enables sensors to quickly and accurately estimate the global state vector of the system becomes a crucial issue. Summary of the Invention

[0004] The purpose of this invention is to propose an efficient, source-address-independent distributed state monitoring method for distributed state monitoring tasks in IoT scenarios.

[0005] The specific technical solution adopted in this invention is as follows:

[0006] A source address-independent distributed state monitoring method, characterized by comprising the following steps:

[0007] S1. Activate the sensor, acquire observations of the system status of the entire monitoring area and generate a status index. Concatenate the observation results and status index of each status into an information sub-block, encode the information sub-block and send it to the fusion center through a wireless channel.

[0008] S2. The fusion center detects and estimates the information sub-blocks corresponding to the codewords transmitted by the sensor and the corresponding superimposed channel gain values ​​from the received signal.

[0009] S3. The fusion center obtains an estimated value of each state variable and a reliability index of the estimated value based on several observations of each state variable from multiple sensors and the channel gain of each observation. When the reliability index of the state variable is greater than the set reliability threshold, it indicates that the observation of the state variable is reliable.

[0010] S4. Divide the entire monitoring area into different regions. The fusion center broadcasts the reliability index of each state variable estimate. If the estimates of all observable state variables in a region are reliable, then the state variable estimation for that region is complete. The fusion center instructs the sensors in those regions where state estimation has not been completed to activate in the next time slot. All sensors determine their activation probability in the next transmission process based on the reliability index of the observable state variables.

[0011] S5. Continue executing steps S1 to S4 until the fusion center obtains a reliable estimate of all state variables of the monitoring area system.

[0012] The encoding method described in step S1 is as follows:

[0013] Setting Codebook in Representing the complex field, each column of A represents a codeword, and there are a total of [number] codewords. There are 1, and the length of each codeword is L. c The entire system has a total of N. o There are states, and the j-th state vector is used... It indicates that the length is b s Bits; the index vector of this state is used It indicates that the length is b I Bit; information sub-block Length N m =b I +b s Bits. Divide a time slot into T sub-time slots; for the t-th sub-time slot, the k-th activated sensor will transmit N bits. m Bit information sub-blocks are mapped to values ​​ranging from 1 to integers The k-th activated sensor will access the codebook A. The codewords represented by the column are sent to the fusion center.

[0014] The detection and estimation method described in step S2 is as follows:

[0015] In IoT scenarios, there are a total of K total One sensor, only K in one transmission process a One sensor is activated, i.e., K a <<K total The received signal can be expressed as Y = AΔH + Z = AX + Z, where This represents the channels of all sensors; The selection matrix is ​​represented by the element δ in the Δ matrix. n,k Indicates whether the k-th sensor transmitted the n-th codeword; Z represents noise and follows a complex Gaussian distribution with a mean of 0; vector Each non-zero element represents the channel gain from the sensor transmitting this codeword to the fusion center;

[0016] Compressed sensing is used to recover vector X from the received signal Y, obtaining the codewords transmitted by the sensor and the corresponding set of channel gain amplitudes. Then, based on the codeword index, it is converted into a binary vector, which represents the information sub-block transmitted by the user. In the t-th sub-time slot within the l-th time slot, for the n-th codeword, it is converted into a binary vector to obtain the information sub-block. The corresponding channel gain amplitude of this codeword is Where |·| represents the amplitude value, and the subscript (l,t) represents the t-th sub-slot within the l-th time slot; let the detected codeword index set be... The detected information sub-block set is The set of superimposed channel gains is

[0017] The reliability index calculation method mentioned in step S3 is as follows:

[0018] Within the l-th time slot, it is possible to obtain the data within T sub-time slots. and from One of the elements can obtain the b-th observation value of the j-th state variable. The subscript (l) represents the l-th time slot; if the j-th state variable is observed by different sensors, the different channel gain amplitudes corresponding to the same observation value are superimposed to obtain the corresponding superimposed channel gain amplitude. Finally, after observations in the l-th time slot, all observed values ​​for the j-th state variable constitute an observation set. The superimposed channel gain magnitude corresponding to each observation constitutes a set of superimposed channel gain magnitudes. Among them |·| c Indicates the number of elements in a set;

[0019] The estimation of the j-th state variable is treated as a classification problem; all elements in the superimposed channel gain amplitude set are concatenated into a vector and used as the input to the softmax function, and the output is the index of the estimated value of the j-th state variable. Represented as

[0020]

[0021] Where f(x) is the truncation and scaling of the sigmoid function, it can be expressed as:

[0022]

[0023] Where the domain of x is 0≤x≤1, and the parameter p is the scaling factor; This indicates that for each g, the corresponding Take The maximum g; the estimated value can be expressed as make This represents the reliability index for the estimated value of the j-th state variable, and sets the threshold for the number of observations and the reliability threshold.

[0024] It can be represented as

[0025]

[0026] in This represents the number of observations of the j-th state obtained after transmission through the l-th time slot, where c is the threshold for the number of observations. Represents the q-th state variable of the j-th state variable. j The channel gain magnitude corresponding to each observation; ∈ is the reliability threshold, then This indicates that the observation of the j-th state is reliable.

[0027] The process described in step S4 is as follows:

[0028] make The activation probability of the k-th sensor in the (l+1)-th time slot is then It can be represented as Among them O k This represents the set of state variable indices that the k-th sensor can observe, i.e. O k It is {1,2,...,N} o A subset of}; This represents the weight of the reliability index of the j-th state variable in relation to the activation probability of the k-th sensor in the (l+1)-th time slot.

[0029] The entire region to be observed is divided into N a The nth region a The set of state indices that sensors can observe within a given area is: State variable estimation is performed on a regional basis. If the estimation of all observable state variables within a region is reliable, then the state variable estimation for that region can be considered complete.

[0030] The fusion center only needs to instruct sensors distributed in other areas to activate in the next time slot; during transmission in the next time slot, it is desirable to minimize the activation of regions so that all observed state variables are covered; this problem can be modeled as a set coverage optimization problem, as follows:

[0031]

[0032]

[0033]

[0034] matrix Represents the state variables that can be observed in different regions; For elements of matrix B, Indicates the nth a Can the sensors within each region acquire observations of the j-th state variable? Indicates selecting the nth... a The cost to each region The value is set to 1; Indicates whether to select the nth... a One region; This indicates that at least one sensor within the activated region acquires an observation of the j-th state variable;

[0035] The greedy algorithm is used to solve the optimization problem of this set cover, let Let represent a set whose elements are indices of state variables that need to be observed in the (l+1)th time slot; Let the set be a collection of indices containing the indices of regions with unreliable state variable estimates after the l-th time slot observation; in the set... Within, each time the nth is selected a There are n regions, where n are regions a It can be represented as Indicates the nth a The set of state indices that sensors can observe within a region; then index n a From the set Remove and add to collection The set This represents the set of indices of the regions to be activated in the (l+1)th time slot; repeat the above selection process until... in Represents all n a belong ∪· indicates all Take the union; final The index of the region to be activated in the (l+1)th time slot;

[0036] Within a transmission time slot, the number of activated sensors will be controlled; distributed in the... The first in the region The activation probability of each sensor is Indicates the nth a The set of sensor indexes within a region, where p0 represents the activation probability of the sensor in the first time slot, and the specific value needs to be set according to the scenario. This represents the activation probability of the k-th sensor within the (l+1)-th time slot, based on... Get; except for the nth a Sensors in areas outside of the designated area remain dormant during the next transmission time slot.

[0037] The beneficial effects of this invention are as follows: The source-address-independent distributed state observation method proposed in this invention employs a multi-slot transmission scheme, solving the problem of insufficient and inaccurate global state vector observation caused by single-slot transmission. Furthermore, it addresses the issue of existing methods using a large number of parity bits, resulting in low code rates and low efficiency in completing distributed state monitoring tasks. This scheme achieves efficient and accurate observation of the system's global state vector using only a small number of time slots. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of a distributed status monitoring scenario provided in an embodiment of the present invention;

[0039] Figure 2This is a graph showing the relationship between the average number of transmission time slots and the signal-to-noise ratio when comparing the distributed state monitoring method provided in this embodiment of the invention with the baseline method under different values ​​of the number of state variables.

[0040] Figure 3 This is a graph showing the relationship between the minimum mean square error of state variable estimation and the maximum allowable number of transmission time slots when comparing the distributed state monitoring method provided in this embodiment of the invention with the baseline method under different values ​​of the number of state variables. Detailed Implementation

[0041] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] In this embodiment, the scenario diagram of distributed state monitoring is as follows: Figure 1 As shown in the diagram, the scenario features a fusion center with multiple sensors distributed throughout. Within each time slot, only a small number of sensors are activated, acquiring observations of the system's state vector and uploading these observations to the fusion center using a source-address-independent random access protocol. The fusion center processes the received signals, estimates the system's global state vector, and broadcasts the reliability index of each state variable estimate to all sensors. The sensors determine their activation probability in the next transmission time slot based on the reliability index of the estimated observable state variables. The transmission process continues until the fusion center has obtained reliable estimates of all system state variables.

[0043] This embodiment provides a source address-independent distributed state monitoring method, which includes the following steps:

[0044] 1) In IoT scenarios, after activating the device to obtain observations of the system status, the observation results and status index of each status are concatenated into information sub-blocks, and the information sub-blocks are encoded and sent to the fusion center through a wireless channel.

[0045] In this step, the encoding method is as follows:

[0046] Setting Codebook in Representing the complex field, each column of A represents a codeword, and there are a total of [number] codewords. There are 1, and the length of each codeword is L. c The entire system has a total of N. o There are states, and the j-th state vector is used... It indicates that the length is b s Bits; the index vector of this state is used It indicates that the length is b I Bit; information sub-block Length N m =b I +b sBits; Divide a time slot into T sub-time slots; For the t-th sub-time slot, the k-th activated sensor will transmit N bits. m Bit information sub-blocks are mapped to values ​​ranging from 1 to integers The k-th activated sensor will access the codebook A. The codewords represented by the column are sent to the fusion center.

[0047] 2) The fusion center detects and estimates the information sub-blocks corresponding to the codewords transmitted by the sensors and the corresponding superimposed channel gain values ​​from the received signals.

[0048] In this step, the detection and estimation methods are as follows:

[0049] In IoT scenarios, there are a total of K total One sensor, only K in one transmission process a One sensor is activated, i.e., K a <<K total The received signal can be expressed as Y = AΔH + Z = AX + Z, where This represents the channels of all sensors; The selection matrix is ​​represented by the element δ in the Δ matrix. n,k Indicates whether the k-th sensor transmitted the n-th codeword; Z represents noise and follows a complex Gaussian distribution with a mean of 0; vector Each non-zero element represents the channel gain from the sensor transmitting this codeword to the fusion center;

[0050] Compressed sensing is used to recover vector X from the received signal Y, obtaining the codewords transmitted by the sensor and the corresponding set of channel gain amplitudes. Then, based on the codeword index, it is converted into a binary vector, which represents the information sub-block transmitted by the user. In the t-th sub-time slot within the l-th time slot, for the n-th codeword, it is converted into a binary vector to obtain the information sub-block. The corresponding channel gain amplitude of this codeword is Where |·| represents the amplitude value, and the subscript (l,t) represents the t-th sub-slot within the l-th time slot; let the detected codeword index set be... The detected information sub-block set is The set of superimposed channel gains is

[0051] 3) The fusion center obtains the estimated value of each state variable and the reliability index of the estimated value based on several observations of each state variable and the channel gain of each observation.

[0052] In this step, the reliability index is calculated as follows:

[0053] Within the l-th time slot, it is possible to obtain the data within T sub-time slots. and from One of the elements can obtain the b-th observation value of the j-th state variable. The subscript (l) represents the l-th time slot; if the j-th state variable is observed by different sensors, the different channel gain amplitudes corresponding to the same observation value are superimposed to obtain the corresponding superimposed channel gain amplitude. Finally, after observations in the l-th time slot, all observed values ​​for the j-th state variable constitute an observation set. The superimposed channel gain magnitude corresponding to each observation constitutes a set of superimposed channel gain magnitudes. Among them |·| c Indicates the number of elements in a set;

[0054] The estimation of the j-th state variable is treated as a classification problem; all elements in the superimposed channel gain amplitude set are concatenated into a vector and used as the input to the softmax function, and the output is the index of the estimated value of the j-th state variable. Represented as

[0055]

[0056] Where f(x) is the truncation and scaling of the sigmoid function, it can be expressed as:

[0057]

[0058] Where the domain of x is 0≤x≤1, and the parameter p is the scaling factor; This indicates that for each g, the corresponding Take The maximum g; the estimated value can be expressed as make This represents the reliability index for the estimated value of the j-th state variable, and sets the threshold for the number of observations and the reliability threshold.

[0059] It can be represented as

[0060]

[0061] in This represents the number of observations of the j-th state obtained after transmission through the l-th time slot, where c is the threshold for the number of observations. Represents the q-th state variable of the j-th state variable. j The channel gain magnitude corresponding to each observation; ∈ is the reliability threshold, then This indicates that the observation of the j-th state is reliable.

[0062] 4) The fusion center broadcasts the reliability index of each state variable estimate, and all sensors determine their activation probability in the next transmission process based on the reliability index of the observable state variables.

[0063] In this step, the sensor determines the activation probability as follows:

[0064] make The activation probability of the k-th sensor in the (l+1)-th time slot is then It can be represented as Among them O k This represents the set of state variable indices that the k-th sensor can observe, i.e. O k It is {1,2,...,N} o A subset of}; This represents the weight of the reliability index of the j-th state variable in relation to the activation probability of the k-th sensor in the (l+1)-th time slot.

[0065] The entire region to be observed is divided into N a The nth region a The set of state indices that sensors can observe within a given area is: State variable estimation is performed on a regional basis. If the estimation of all observable state variables within a region is reliable, then the state variable estimation for that region can be considered complete.

[0066] The fusion center only needs to instruct sensors distributed in other areas to activate in the next time slot; during transmission in the next time slot, it is desirable to minimize the activation of regions so that all observed state variables are covered; this problem can be modeled as a set coverage optimization problem, as follows:

[0067]

[0068]

[0069]

[0070] matrix Represents the state variables that can be observed in different regions; For elements of matrix B, Indicates the nth a Can the sensors within each region acquire observations of the j-th state variable? Indicates selecting the nth... a The cost to each region The value is set to 1; Indicates whether to select the nth... a One region; This indicates that at least one sensor within the activated region acquires an observation of the j-th state variable;

[0071] The greedy algorithm is used to solve the optimization problem of this set cover, let Let represent a set whose elements are indices of state variables that need to be observed in the (l+1)th time slot; Let the set be a collection of indices containing the indices of regions with unreliable state variable estimates after the l-th time slot observation; in the set... Within, each time the nth is selected a There are n regions, where n are regions a It can be represented as Indicates the nth a The set of state indices that sensors can observe within a region; then index n a From the set Remove and add to collection The set This represents the set of indices of the regions to be activated in the (l+1)th time slot; repeat the above selection process until... in Represents all n a belong ∪· indicates all Take the union; final The index of the region to be activated in the (l+1)th time slot;

[0072] Within a transmission time slot, the number of activated sensors will be controlled; distributed in the... The first in the region The activation probability of each sensor is Indicates the nth a The set of sensor indexes within a region, where p0 represents the activation probability of the sensor in the first time slot, and the specific value needs to be set according to the scenario. This represents the activation probability of the k-th sensor within the (l+1)-th time slot, based on... Get; except for the nth a Sensors in areas outside of the designated area remain dormant during the next transmission time slot.

[0073] 5) Continue executing steps 1) through 4) until the fusion center obtains a reliable estimate of all state variables of the system.

[0074] Computer simulations show that: Figure 2 As shown, the distributed state monitoring scheme of the present invention significantly reduces the average number of transmission time slots required to complete the global state observation of the system under the same signal-to-noise ratio, compared with the baseline random activation scheme. Figure 3 This demonstrates that the distributed state monitoring scheme proposed in this invention, compared to the baseline random activation scheme, can significantly reduce the minimum mean square error of state variable estimation after a small number of time slot transmissions. These advantages are mainly because the scheme utilizes the global state vector estimation results from each time slot to guide the sensor's transmission in the next time slot. Therefore, the source-address-independent distributed state monitoring scheme proposed in this invention provides an efficient method for estimating the system's global state vector.

[0075] The above embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A source address-independent distributed state monitoring method, characterized in that, Includes the following steps: S1. Activate the sensor, acquire observations of the system status of the entire monitoring area and generate a status index. Concatenate the observation results and status index of each status into an information sub-block, encode the information sub-block and send it to the fusion center through a wireless channel. S2. The fusion center detects and estimates the information sub-blocks corresponding to the codewords transmitted by the sensor and the corresponding superimposed channel gain values ​​from the received signal. S3. The fusion center obtains an estimated value of each state variable and a reliability index of the estimated value based on several observations of each state variable from multiple sensors and the channel gain of each observation. When the reliability index of the state variable is greater than the set reliability threshold, it indicates that the observation of the state variable is reliable. S4. Divide the entire monitoring area into different regions. The fusion center broadcasts the reliability index of each state variable estimate. If the estimates of all observable state variables in a region are reliable, then the state variable estimation for that region is complete. The fusion center instructs the sensors in those regions where state estimation has not been completed to activate in the next time slot. All sensors determine their activation probability in the next transmission process based on the reliability index of the observable state variables. S5. Continue executing steps S1 to S4 until the fusion center obtains a reliable estimate of all state variables of the monitoring area system.

2. The source address-independent distributed state monitoring method according to claim 1, characterized in that, The specific steps of the encoding method in step S1 are as follows: S1.1 Setting up the codebook ,in Represents the field of complex numbers. Each column represents a codeword, and there are a total of 100 codewords. Each codeword has a length of [number]. The entire system has a total of The state, the first Each state vector is used It indicates that the length is Bits; the index vector of this state is used It indicates that the length is Bit; information sub-block Length is Bit; S1.2, Divide a time slot into Each sub-time slot; for the first of them The first time slot One activated sensor, which will send... Bit information sub-blocks are mapped to values ​​ranging from arrive integers ; S1.3, No. An activation sensor will activate the codebook. The first in The codewords represented by the column are sent to the fusion center.

3. The source address-independent distributed state monitoring method according to claim 2, characterized in that, The detection and estimation method described in step S2 is as follows: In IoT scenarios, there are a total of One sensor, only one during a single transmission. One sensor is activated, that is The received signal can be represented as ,in This represents the channels of all sensors; Represents the selection matrix. Elements in the matrix Indicates the first Did the sensor transmit the first...? Each code character; It is noise and follows the mean of The complex Gaussian distribution; vector Each non-zero element represents the channel gain from the sensor transmitting this codeword to the fusion center; Using compressed sensing to receive signals Recover the vector The codewords transmitted by the sensor and the corresponding set of channel gain amplitudes are obtained; then, based on the codeword index, they are converted into a binary vector, which represents the information sub-block sent by the user; in the first... The first time slot within the [number] time slot For each sub-slot, for the first Each codeword is converted into a binary vector to obtain the information sub-block. The corresponding channel gain amplitude of this codeword is ,in Indicates the amplitude value, subscript Indicates the first The first time slot within the [number] time slot Each sub-slot; let the set of detected codeword indices be... The detected information sub-block set is The set of superimposed channel gains is .

4. The source address-independent distributed state monitoring method according to claim 3, characterized in that, The reliability index calculation method in step S3 is as follows: In the Within each time slot, it is possible to obtain Within each time slot and , from One of the elements can obtain the first element. The first state variable Observations Subscript Indicates the first The time slot; if the first time slot A state variable is observed by different sensors. The corresponding superimposed channel gain amplitude is obtained by superimposing the different channel gain amplitudes corresponding to the same observation value. Finally, after the first The observation in the first time slot, for the first All observations of a state variable constitute a set of observations. The superimposed channel gain amplitude corresponding to each observation constitutes the superimposed channel gain amplitude set. ,in Indicates the number of elements in a set; Will be for the first The estimation of the nth state variable is treated as a classification problem; all elements in the set of superimposed channel gain amplitudes are concatenated into a vector and used as the input to the softmax function, and then the output is the result of the estimation of the nth state variable. Index of state variable estimates , Represented as ， in The truncation and scaling of the sigmoid function can be expressed as follows: ， in The domain is ,parameter The scaling factor; This means for each corresponding , take The largest ; The estimated value can be expressed as ; make Indicates the first A reliability index for the estimated values ​​of each state variable is established, and a threshold for the number of observations and a reliability threshold are set. It can be represented as in Indicates after the first After the transmission of the time slot, the data for the first time slot is obtained. The number of observations for each state. The threshold for the number of observations, Indicates the first The first state variable The channel gain magnitude corresponding to each observation; If it is a reliability threshold, then Indicates the first The observations of each state are reliable.

5. The source address-independent distributed state monitoring method according to claim 4, characterized in that, Step S4 specifically includes: make , Then the first The sensor at the first Activation probability within a time slot , It can be represented as ,in Indicates the first The set of state variable indices that a sensor can observe, i.e. , express yes A subset of; Indicates the first The reliability index of the first state variable is related to the second state variable. The sensor at the first Contribution weight of activation probability within each time slot ; The entire area to be observed is divided into The first region, the first The set of state indices that sensors can observe within a given area is: State variable estimation is performed on a regional basis. If the estimation of all observable state variables within a region is reliable, then the state variable estimation for that region can be considered complete. The fusion center only needs to instruct sensors distributed in other areas to activate in the next time slot; during transmission in the next time slot, it is desirable to minimize the activation of regions so that all observed state variables are covered; this problem can be modeled as a set coverage optimization problem, as follows: ； matrix Represents the state variables that can be observed in different regions; For matrix elements, Indicates the first Can the sensors in the area acquire information about the first... Observations of state variables; Indicates selecting the first The cost to each region The value is set to 1; Indicate whether to select the first option One region; This indicates that at least one sensor within the activated area acquires data about the first... Observations of state variables; The greedy algorithm is used to solve the optimization problem of this set cover, let Let a set be a set whose elements are the elements at the i-th position. The index of the state variable that needs to be observed within each time slot; let Let a set be a collection of elements that contain the elements that have undergone the first step of the first step. Index of the region of unreliable state variable estimation after each time slot observation; in the set Inside, each time the first one is selected There are 10 regions, among which It can be represented as , Indicates the first A set of state indexes that sensors can observe within a given area; then the indexes are... From the set Remove and add to collection , where set Indicates the first The set of indices for regions to be activated within each time slot; repeat the above selection process until... ,in Indicates all belong , Indicates all Take the union; final Will be included in the The index of the region that needs to be activated within each time slot; Within a transmission time slot, the number of activated sensors will be controlled; distributed in the... The first in the region The activation probability of each sensor is ,in , , Indicates the first A set of sensor indexes within a region, wherein This indicates the activation probability of the sensor within the first time slot; the specific value needs to be set according to the scenario. Indicates the first Within the first time slot The activation probability of each sensor, according to Get; except for the first Sensors in areas outside of the designated area remain dormant during the next transmission time slot.

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