Data Transmission Method

Abstract

To avoid packet collisions at an aggregation station even if the number of sensors that transmit data increases, thereby preventing the occurrence of packet loss.SOLUTION: There is provided a data transmission method in which, in a data transmission target area 10 where a plurality of sensors 30 are installed, observation data from each sensor 30 is assigned to an index corresponding to a frame so as to be transmitted to an aggregation station 40 by a PLIM method, the frame being formed of a plurality of time slots and a use channel. The data transmission method executes: advance preparation step (S1) of aggregating, as a sensor observation data occurrence probability distribution, an observation probability for the observation data at each sensor 30 and the position of an observation target; step (S2) of using the sensor observation data occurrence probability distribution based on the observation data within the data transmission target area 10 to calculate the index having a minimum packet collision probability at the aggregation station 40 by the PLIM method; and step (S3) of transmitting the observation data to the aggregation station 40 by the PLIM method to which the index is applied.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a data transmission method. [Background technology]

[0002] In recent years, the Internet of Things (IoT) has become widespread. In the IoT, attention has been focused on Low Power Wide Area (LPWA), a communication method that enables long-distance communication with low power consumption. In order to resolve the decrease in throughput in LPWA, some of the inventors of this application have proposed, in Patent Document 1 (JP 2022-85522 A) and Non-Patent Document 1, etc., a data transmission method using a packet-based index modulation method (so-called Packet Level Index Modulation (PLIM) method) that sets the time slot for transmitting packets and the channel to be used based on the information bit sequence to be transmitted. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-85522 [Non-patent literature]

[0004] [Non-Patent Document 1] National University Corporation, University of Electro-Communications, "New communication method to solve IoT issues - Increased data transmission volume with low power consumption - │," [online], July 9, 2021, National University Corporation, University of Electro-Communications, [Retrieved June 22, 2022], Internet<URL:https: / / www.uec.ac.jp / news / announcement / 2021 / 20210709_3546.html> Summary of the Invention [Problem to be solved by the invention]

[0005] In the data transmission methods disclosed in Patent Document 1 and Non-Patent Document 1, as shown in Figure 12, when a sensor receives radio waves from a radio wave source and transmits data to an aggregate station, an index having multiple index numbers is created based on the time slot and channel used by the sensor. The data transmitted by the sensor to the aggregate station is then associated with the index, thereby improving data transmission throughput. However, as the number of sensors increases, so-called packet collisions become more likely to occur, as shown in Figure 13, in which multiple sensors simultaneously transmit the same index number to the aggregate station. When such packet collisions occur, it becomes impossible for the aggregate station to decode the data (packet loss).

[0006] Therefore, attempts have been made to avoid packet collisions at the aggregation station by randomly assigning index numbers to each sensor, which correspond to the data that the sensor transmits to the aggregation station.However, because the index numbers are simply assigned randomly, there are problems with the success rate of avoiding packet collisions at the aggregation station varying, and as the number of sensors increases, packet collisions cannot be avoided at the aggregation station, making packet loss more likely. [Means for solving the problem]

[0007] Therefore, the present invention aims to avoid packet collisions at the aggregation station and prevent packet loss even when the number of sensors transmitting data increases by mathematically optimizing the assignment of data to be transmitted and index numbers in the indexes used in the PLIM method.

[0008] That is, the present invention is a data transmission method for transmitting observation data from a plurality of sensors installed in a data transmission area to an aggregation station by a PLIM (Packet Level Index Modulation) method, which packetizes the observation data from each of the sensors, assigns the packetized observation data to any of indexes corresponding to frames formed by a plurality of time slots and channels in use, and transmits the packetized observation data to the aggregation station, Each of the sensors is a preparation step of aggregating the observation probability of the numerical value of the observation data at each of the sensors and the position of the observation target as a sensor observation data occurrence probability distribution; Each of the sensors is calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM scheme used by each of the sensors using the sensor observation data occurrence probability distribution based on the observation data by each of the sensors within the data transmission target area; teeth, and transmitting the observation data to the aggregation station by the PLIM method to which the index is applied.

[0009] This allows mathematical optimization of the assignment of observation data to be transmitted and index numbers, thereby avoiding packet collisions at the aggregation station and preventing packet loss even when the number of sensors transmitting observation data increases.

[0010] Furthermore, the advance preparation step is preferably a step of dividing the data transmission target area into a plurality of divided areas, and aggregating the observation probabilities for the numerical values ​​of the observation data at each of the sensors in a specific divided area as a sensor observation data occurrence probability distribution by divided area, for each of the divided areas, and the step of calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM method used by each of the sensors is preferably a step of selecting the most similar sensor observation data occurrence probability distribution by divided area from the sensor observation data occurrence probability distributions by divided area based on the observation data by each of the sensors in the data transmission target area, and a step of calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM method used by each of the sensors using the selected sensor observation data occurrence probability distribution by divided area.

[0011] This makes it possible to assign more detailed observation data to be transmitted and index numbers according to the conditions of the observation area.

[0012] Furthermore, it is preferable that the observation data is quantized by a plurality of observation data numbers corresponding to each required numerical range, and the step of calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM method used by each of the sensors is performed based on the following formula:

number

[0013] This makes it possible to reduce the amount of calculation required to minimize the packet collision probability. [Effects of the Invention]

[0014] According to the configuration of the present invention, by mathematically optimizing the assignment of observation data to be transmitted and index numbers, packet collisions at the aggregation station can be avoided even if the number of sensors transmitting observation data increases, and packet loss can be prevented. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic configuration diagram of a data transmission system according to a first embodiment. [Figure 2] FIG. 3 is a flowchart showing an example of an implementation procedure of a data transmission method in the first embodiment. [Figure 3] FIG. 10 is an explanatory diagram of mapping data in which a histogram showing quantized data and frequencies of RSSI values ​​for each sensor obtained in the advance preparation step in the first embodiment is Gaussian distributed; [Figure 4] 10 shows the parameters of a comparative simulation of the packet collision probability versus the number of sensors when data transmission is performed in the first embodiment and the packet collision probability versus the number of sensors in the prior art. [Figure 5] This is the result of a comparative simulation conducted using the specifications shown in Fig. 4. [Figure 6] FIG. 11 is an explanatory diagram of mapping data obtained by converting a histogram of the occurrence probability of RSSI values ​​in each sensor into a Gaussian distribution when the radio wave source is located in the first divided area in the advance preparation step in the second embodiment. [Figure 7] FIG. 11 is an explanatory diagram of mapping data obtained by converting a histogram of the occurrence probability of RSSI values ​​in each sensor into a Gaussian distribution when the radio wave source is located in the second divided area in the advance preparation step in the second embodiment. [Figure 8] FIG. 11 is an explanatory diagram of mapping data obtained by converting a histogram of the occurrence probability of RSSI values ​​in each sensor into a Gaussian distribution when the radio wave source is located in the third divided area in the advance preparation step in the second embodiment. [Figure 9]FIG. 11 is an explanatory diagram of mapping data obtained by converting a histogram of the occurrence probability of RSSI values ​​in each sensor into a Gaussian distribution when the radio wave source is located in the fourth divided area in the advance preparation step in the second embodiment. [Figure 10] 10 shows the parameters of a comparative simulation of the packet collision probability versus the number of sensors when data transmission is performed in the second embodiment and the packet collision probability versus the number of sensors in the prior art. [Figure 11] This is the result of a comparative simulation performed using the specifications shown in Fig. 10. [Figure 12] This is the first explanatory diagram of the prior art. [Figure 13] This is the second explanatory diagram of the prior art. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, an embodiment of a data transmission method according to the present invention will be described with reference to the drawings. Note that in the following embodiment, the data transmission method will be described based on a data transmission system 50 in which the observation target is a radio wave source 20, but the present invention is not limited to this embodiment.

[0017] (First embodiment) A data transmission system 50 in this embodiment includes one or more radio wave sources 20 in a data transmission target area 10, multiple sensors 30 that receive radio waves emitted from the radio wave sources 20, and an aggregation station 40 that aggregates observation data transmitted from the multiple sensors 30. The multiple sensors 30 in this embodiment quantize the signal strength (hereinafter referred to as RSSI value) of the radio waves received as observation data, assign the quantized data to an index number in the PLIM method, and transmit the data to the aggregation station 40. The greatest feature of the present invention is that the allocation of index numbers used by the multiple sensors 30 is optimized to minimize conflicts in the aggregation station 40 between index numbers transmitted from the multiple sensors 30.

[0018] As shown in Fig. 1, the data transmission system 50 of this embodiment includes at least one radio wave source 20, a first sensor 32, a second sensor 34, a third sensor 36, and a fourth sensor 38 in a data transmission target area 10, and an aggregate station 40 installed at one location. Hereinafter, the first sensor 32, the second sensor 34, the third sensor 36, and the fourth sensor 38 may be collectively referred to as "sensors 30." Note that the number of sensors 30 installed in the data transmission system 50 is not limited to the number shown in this embodiment. Each step will be explained below with reference to the flow diagram shown in Fig. 2.

[0019] In order to optimize the allocation of index numbers used by each sensor 30, a preparation step (S1) is performed to compile quantized data of RSSI values ​​and the distribution of their occurrence frequency (sensor observation data occurrence probability distribution: hereinafter referred to as mapping data 60) for each sensor 30 when each radio wave source 20 transmits radio waves within the data transmission target area 10. By providing such a preparation step, it is possible to analyze the frequency of use of quantized data of RSSI values ​​for each sensor 30 (reception probability (observation probability) for the received signal strength of observation data received by the sensor 30) depending on the geographical conditions, etc., of the data transmission target area 10.

[0020] FIG. 3 is an explanatory diagram of mapping data 60 obtained by converting quantized data (RSSI numbers: observation data numbers) of RSSI values ​​and histograms showing frequency counts for the first sensor 32, the second sensor 34, the third sensor 36, and the fourth sensor 38, obtained in the advance preparation step, into a Gaussian distribution. While the mapping data 60 in FIG. 3 has been converted into a Gaussian distribution, it may also be converted into another probability distribution or left as a histogram. The number of events used in creating FIG. 3 is not particularly limited, but to ensure reliability, it is preferable that the number of events be 100 or more (case 100 or more). By collecting the distribution of the frequency of occurrence of RSSI numbers for each sensor 30 in this way, it is possible to understand the tendency of RSSI number selection by each sensor 30 in the data transmission target area 10.

[0021] Next, a step (S2) of optimizing the design of indexes used in the PLIM method used in the data transmission target area 10 of this embodiment is executed using the mapping data 60 shown in FIG. 3 . Specifically, this step calculates indexes that minimize the probability that the RSSI values ​​likely to occur in each of the first sensor 32 to the fourth sensor 38 will have the same index number. To achieve this, in this embodiment, the design is performed as a problem of minimizing the collision of index numbers of quantized data of RSSI values ​​of two sensors. This makes it possible to express the target of optimization design as a quadratic model, and to create a calculation formula for a quadratic integer programming problem such as the following equation. The calculation formula for such a quadratic integer programming problem can be solved by a known method. Specifically, a mathematical optimization solver such as Gurobi Optimizer can be used.

number

[0022] Note that constraint 1 in the above equation states that, for all i and all j, the i-th sensor must assign the j-th observation data number (here, the quantized data number of the RSSI value) to one of the index numbers. Similarly, constraint 2 states that, for all i and all k, the i-th sensor must assign at most one observation data number (the quantized data number of the RSSI value) to the k-th index number. Similarly, constraint 3 states that the maximum number of duplicated indexes is the number of sensors. Constraints 1 to 3 make it possible to prevent the calculation of meaningless solutions (where all elements are 0) to the quadratic integer programming problem equation.

[0023] By solving the mapping data 60 shown in Figure 3 as the quadratic integer programming problem shown in the above equation, it is possible to calculate the optimal combination of quantized data numbers and index numbers (numbers indicating positions within a frame separated by time slots and channels in use) of RSSI values ​​for each sensor 30. A step (S3) of transmitting data in a PLIM system using indexes having index numbers calculated (designed) in this way is executed. The RSSI value data (packet data) transmitted from each sensor 30 in this way is aggregated in the aggregation station 40, and a step (S4) of decoding the data in a predetermined procedure is executed.

[0024] A comparative simulation was performed using the parameters shown in Figure 4 to compare the packet collision probability (probability of packet data loss) versus the number of sensors when data is transmitted from each sensor 30 to one aggregation station 40 using the index obtained by solving the above equation, and the packet collision probability versus the number of sensors in the conventional technology. Figure 5 shows the results of this comparative simulation. Note that the conventional technology in this case is a data transmission method using the PLIM method, in which index numbers are randomly assigned to each sensor. As shown in Figure 5, when the allowable packet collision probability is set to 5%, the conventional technology reaches its limit when the number of sensors is four. However, in this embodiment, even when the number of sensors is five, the packet collision probability can be kept in the 4% range. Furthermore, the comparative simulation results in this embodiment clearly show that the packet collision probability is always lower than that of the conventional technology, even when the number of sensors is increased.

[0025] As described above, according to the data transmission method of the present embodiment, it is possible to assign index numbers based on the reception characteristics of the RSSI values, which are the observation data of each sensor 30 in the data transmission target area 10. This minimizes the probability of packet collisions at the aggregation station 40 for packet data transmitted from each sensor 30 by the data transmission method using the PLIM method, and it is possible to increase the reliability of decoding of the transmitted data at the aggregation station 40 even when transmitting data from multiple sensors 30 to one aggregation station 40.

[0026] (Second embodiment) In this embodiment, a step is provided for further detailing the mapping data 60 (reception probability for each piece of observation data) obtained in the advance preparation step in the first embodiment. That is, as shown in Figures 6 to 9, this embodiment is characterized in that it includes a step of dividing the data transmission target area 10 into a plurality of divided areas and tallying up the occurrence probability of the RSSI value of each sensor 30 for each divided area in which the radio wave source 20 is located. Here, an embodiment will be described in which the data transmission target area 10 is divided into four divided areas, and one sensor (first sensor 32 to fourth sensor 38) is disposed in each divided area.

[0027] 6 is an explanatory diagram of first mapping data 62 obtained by converting a histogram of the occurrence probability of RSSI values ​​for each sensor 30 into a Gaussian distribution when the radio wave source 20 is located in the first divided area 12. From the first mapping data 62 shown in FIG. 6, when the radio wave source 20 is located in the first divided area 12, the RSSI value obtained by the first sensor 32 located in the first divided area 12 (closest to the first divided area 12) often occurs as a high numerical value. Furthermore, the RSSI value obtained by the fourth sensor 38 located in the fourth divided area 18, which is the farthest from the first divided area 12, often occurs as a low numerical value. Furthermore, it can be said that the RSSI values ​​obtained by the second sensor 34 and the third sensor 36 located in the second divided area 14 and the third divided area 16, which are intermediate between the first divided area 12 and the fourth divided area 18, often occur as intermediate values ​​between the RSSI value obtained by the first sensor 32 and the RSSI value obtained by the fourth sensor 38.

[0028] 7 is an explanatory diagram of second mapping data 64 obtained by converting a histogram of the occurrence probability of RSSI values ​​for each sensor 30 into a Gaussian distribution when the radio wave source 20 is located in the second divided area 14. From the second mapping data 64 shown in FIG. 7, when the radio wave source 20 is located in the second divided area 14, the RSSI value obtained by the second sensor 34 located in the second divided area 14 (closest to the second divided area 14) often occurs as a high numerical value. Furthermore, the RSSI value obtained by the third sensor 36 located in the third divided area 16, which is the farthest from the second divided area 14, often occurs as a low numerical value. Furthermore, it can be said that the RSSI values ​​obtained by the first sensor 32 and the fourth sensor 38 located in the first divided area 12 and the fourth divided area 18, which are intermediate between the second divided area 14 and the third divided area 16, often occur as intermediate values ​​between the RSSI value obtained by the second sensor 34 and the RSSI value obtained by the third sensor 36.

[0029] 8 is an explanatory diagram of third mapping data 66 obtained by converting a histogram of the occurrence probability of RSSI values ​​for each sensor 30 into a Gaussian distribution when the radio wave source 20 is located in the third divided area 16. From the third mapping data 66 shown in FIG. 8, when the radio wave source 20 is located in the third divided area 16, the RSSI value obtained by the third sensor 36 located in the third divided area 16 (located closest to the third divided area 16) often occurs as a high numerical value. Furthermore, the RSSI value obtained by the second sensor 34 located in the second divided area 14, which is located farthest from the third divided area 16, often occurs as a low numerical value. Furthermore, it can be said that the RSSI values ​​obtained by the first sensor 32 and the fourth sensor 38 located in the first divided area 12 and the fourth divided area 18, which are located intermediate between the third divided area 16 and the second divided area 14, often occur as intermediate values ​​between the RSSI value obtained by the second sensor 34 and the RSSI value obtained by the third sensor 36.

[0030] 9 is an explanatory diagram of fourth mapping data 68 obtained by converting a histogram of the occurrence probability of RSSI values ​​for each sensor 30 into a Gaussian distribution when the radio wave source 20 is located in the fourth divided area 18. From the fourth mapping data 68 shown in FIG. 9, when the radio wave source 20 is located in the fourth divided area 18, the RSSI value obtained by the fourth sensor 38 located in the fourth divided area 18 (located closest to the fourth divided area 18) often occurs as a high numerical value. Furthermore, the RSSI value obtained by the first sensor 32 located in the first divided area 12, which is located farthest from the fourth divided area 18, often occurs as a low numerical value. Furthermore, it can be said that the RSSI values ​​obtained by the second sensor 34 and the third sensor 36 located in the second divided area 14 and the third divided area 16, which are located intermediate between the fourth divided area 18 and the first divided area 12, often occur as an intermediate value between the RSSI value of the fourth sensor 38 and the RSSI value of the first sensor 32.

[0031] 6 to 9, the first mapping data 62 to the fourth mapping data 68 are obtained by converting the histograms of the occurrence probability of the RSSI values ​​of each sensor 30 into a Gaussian distribution, but they may be converted into other probability distributions or left as histograms. As described above, the preparation step of this embodiment involves dividing the data transmission target area 10 into a plurality of divided areas and tallying up the probability distribution of the RSSI values ​​of each sensor 30 with respect to radio waves emitted from the radio wave source 20 in each divided area. The first mapping data 62, second mapping data 64, third mapping data 66, and fourth mapping data 68 of the RSSI values ​​of each sensor 30 with respect to radio waves emitted from the radio wave source 20 in each divided area shown in FIGS. 6 to 9 correspond to the divided area-by-divided area sensor observation data occurrence probability distributions. By obtaining the probability distribution of the RSSI values ​​of each sensor 30 for radio waves generated from the radio wave source 20 in each divided area, the position of the radio wave source 20 (in which divided area the radio wave source 20 is located) can be estimated based on the RSSI value of each sensor 30 in the data transmission target area 10.

[0032] If the position of the radio wave source 20 in the data transmission target area 10 can be identified in this manner, it is possible to grasp in more detail the tendency of the probability of the RSSI value that each sensor 30 can take. After the data transmission target area 10 is divided into a plurality of divided areas in this manner, the first mapping data 62 to fourth mapping data 68 of the RSSI values ​​of each sensor 30 shown in Figures 6 to 9 are calculated, and then the process proceeds to the next processing step.

[0033] Specifically, the system designer compares the RSSI value status of each sensor 30 that receives radio waves from a certain radio wave source 20 in the data transmission target area 10 with the first mapping data 62 to fourth mapping data 68 of the RSSI values ​​of each sensor 30 shown in FIGS. 6 to 9 in a state in which the data transmission target area 10 is divided into a plurality of divided areas, which was compiled in the advance preparation step. The system designer then selects one of the first mapping data 62 to fourth mapping data 68 that has the highest similarity, and performs optimization design of the index to be used in the PLIM method to be used in the data transmission target area 10 using the selected one of the first mapping data 62 to fourth mapping data 68. Specifically, this is a step of calculating an index that minimizes the probability that the RSSI values ​​that are likely to occur in each of the first sensor 32 in the first divided area 12 to the fourth sensor 38 in the fourth divided area 18 will have the same index number. Specifically, as in the first embodiment, a solution to the above equation, which is a calculation formula for a quadratic integer programming problem, may be calculated.

[0034] A comparative simulation was performed using the parameters shown in FIG. 10 to compare the packet collision probability versus the number of sensors when data transmission is performed using the PLIM method using indexes with index numbers calculated (designed) in this way, and the packet collision probability versus the number of sensors in the conventional technology. FIG. 11 shows the results of this comparative simulation. The comparative simulation results shown in FIG. 11 are simulation results when the data transmission target area 10 is divided into nine parts. As shown in FIG. 11, when the allowable value for packet collision probability is set to 5%, the conventional technology reaches its limit at four sensors. However, when the data transmission target area 10 is divided into nine parts, the packet collision probability can be suppressed to the 3% range even when the number of sensors is six. It is also clear that the simulation results for this embodiment are lower than those for the conventional technology and the first embodiment, which always have the same number of sensors, even when the number of sensors increases.

[0035] 11 also shows the simulation results when the data transmission target area 10 is divided into 16, 25, and 36 parts and the index used in the PLIM method for the data transmission target area 10 is optimized. As is clear from FIG. 11, the packet collision probability at the aggregation station 40 can be reduced as the data transmission target area 10 is divided into more parts. When the allowable packet collision probability is 5%, the number of sensors that can be deployed in the conventional technology is four, whereas when the data transmission target area 10 is divided into 36 parts and the index is optimized, the number of sensors that can be deployed is eight. In other words, when the number of channels used by each sensor 30 is the same, the number of sensors that can be deployed in the data transmission target area 10 is doubled, and the frequency utilization efficiency in data transmission is doubled.

[0036] As described above, according to the data transmission method of this embodiment, packet collisions can be clearly prevented not only in the data transmission method of the prior art but also in the data transmission method of the first embodiment.

[0037] Although the present invention has been described above based on the embodiments, the present invention is not limited to the above embodiments. For example, in the above embodiments, the data transmission method using the data transmission system 50 in which the observation target is the radio wave source 20 is described, but the observation target is not limited to the radio waves transmitted from the radio wave source 20. Other examples of observation targets include the amount of heat from a heat source, the water level from a water source, the wind force from a wind source, and the seismic intensity from a seismic source.

[0038] Furthermore, in the above embodiment, as shown in FIG. 1, an example is given in which the aggregation station 40 is installed outside the range of the data transmission target area 10, but it is also possible to adopt a configuration in which the aggregation station 40 is installed within the range of the data transmission target area 10.

[0039] Furthermore, in the second embodiment, the data transmission target area 10 is divided into a first divided area 12 to a fourth divided area 18, and a first sensor 32 to a fourth sensor 38 are disposed in each divided area, but the sensor 30 does not have to be disposed in each of the first divided area 12 to the fourth divided area 18. The sensors 30 can also be disposed within the data transmission target area 10 in an arrangement layout independent of the division shapes of the first divided area 12 to the fourth divided area 18. Furthermore, as shown in FIGS. 10 and 11, the number of divided areas (the number of divisions of the data transmission target area 10) and the number of disposed sensors 30 do not have to match.

[0040] Furthermore, in the above embodiment, the observation data to be transmitted is a quantized data number obtained by quantizing the RSSI value of the reception strength of the radio wave received by the sensor 30 from the radio wave source 20 within a required numerical range, but the observation data transmitted from the sensor 30 to the aggregation station 40 is not limited to this form. The observation data received by each sensor 30 may be transmitted directly from each sensor 30 to the aggregation station 40.

[0041] Furthermore, the configuration of the present embodiment described above may be appropriately combined with any of the modified examples described in the specification or other known configurations. [Explanation of symbols]

[0042] 10: Data transmission area 12: First division area, 14: Second division area, 16: Third division area, 18: 4th division area 20: Radio source (observation target) 30: Sensor 32: First sensor, 34: Second sensor, 36: Third sensor, 38: Fourth sensor 40: Aggregation station 50: Data transmission system 60: Mapping data (sensor observation data occurrence probability distribution) 62: First mapping data (probability distribution of sensor observation data by divided area), 64: Second mapping data (probability distribution of sensor observation data by divided area), 66: Third mapping data (probability distribution of sensor observation data by divided area), 68: 4th mapping data (probability distribution of sensor observation data by divided area)

Claims

1. A data transmission method for transmitting observation data to a central station by a PLIM (Packet Level Index Modulation) method in a data transmission area in which a plurality of sensors are installed to observe an observation target, the observation data from each of the sensors is packetized, and the packetized observation data is assigned to any of indexes corresponding to frames formed by a plurality of time slots and channels in use, the method comprising: a preparation step in which each of the sensors compiles an observation probability for the numerical value of the observation data at each of the sensors and the position of the observation target as a sensor observation data occurrence probability distribution; each of the sensors calculates the index that minimizes the packet collision probability at the aggregation station in the PLIM scheme used by each of the sensors, using the sensor observation data occurrence probability distribution based on the observation data by each of the sensors within the data transmission target area; each of the sensors transmitting the observation data to the aggregation station using the PLIM method to which the index is applied; A data transmission method characterized by carrying out the above steps.

2. The advance preparation step includes: a step of dividing the data transmission target area into a plurality of divided areas, and aggregating the observation probabilities for the numerical values ​​of the observation data at each of the sensors in a specific divided area as a sensor observation data occurrence probability distribution by divided area for each of the divided areas, The step of calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM scheme used by each of the sensors includes: selecting the most similar sensor observation data occurrence probability distribution for each divided area from the sensor observation data occurrence probability distributions for each divided area based on the observation data obtained by each of the sensors within the data transmission target area; 2. The data transmission method according to claim 1, further comprising the step of calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM method used by each of the sensors, using the selected sensor observation data occurrence probability distribution for each divided area.

3. the observation data is quantized by a plurality of observation data numbers corresponding to each required numerical range, 3. The data transmission method according to claim 1, wherein the step of calculating the index that minimizes the packet collision probability at the aggregation station in the PLIM scheme used by each of the sensors is performed based on the following equation: [Equation 3]

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