KR101931803B1 - Method of data collection using uav in wireless sensor network and device with this method - Google Patents

Abstract

The present invention is to perform regional density-aware data collection (RDDC) to support fast data collection. According to an embodiment of the present invention, provided are a method for collecting regional density-aware data using a UAV in a wireless sensor network and a device applied with the same. The method comprises the steps of: dividing a field sensed by a UAV into a plurality of regions; calculating regional node densities of the divided regions; determining a minimum competition window value for each sensor node included in the regions by using the calculated regional node densities; and collecting data by allocating the determined minimum competition window value to the sensor nodes.

Description

TECHNICAL FIELD [0001] The present invention relates to a method of collecting data using a UAV in a wireless sensor network, and a device to which the method is applied.

The present invention relates to a method of collecting data using a UAV in a wireless sensor network and more particularly to a method of collecting data by allocating different minimum contention window values to a plurality of areas in a field detected by a UAV, It is about the device to which this method is applied.

The sensor network is used to transmit information within the sensor communication system. A sensor network operating in a wireless radio frequency channel is known as a Wireless Sensor Network (WSN). A WSN typically includes a multi-spatially distributed sensor node and one or several sinks or base stations. Sensor nodes sense physical information and send sensed information to a sink as a measurement sample. The entire set of measurements is transmitted. The sink can query information and coordinate the function of the sensor node to provide coordination.

Wireless sensor networks consist of sensor nodes that use limited energy sources such as batteries. These sensor nodes basically have to guarantee a lifetime of several months to several years and at the same time, it is difficult to recharge and replace due to battery consumption, so it is most important to manage energy efficiently. Since wireless sensor networks have low data generation rate, it is unnecessary for the sensor node to keep the idle listening for intermittent data communication, which may unnecessarily use the radio and consume energy. In particular, energy consumption in sensor nodes is mostly due to the use of radio for data transmission and reception, so it is essential to reduce idle listening for energy efficiency.

In general, a sensor network collects data from sensor nodes constituting a sensor network, and provides application services using the collected data. Since the sensor network is located in the area network of a huge infrastructure network, collected data is terminated in the local network and used for application service in the server. Generally, the data generated in the sensor network is transmitted to the server of the local network, and the server uses the data for the required service. In this way, the sensing data of the sensor network composed of the regional network of the infrastructure is collected at the server, and it is located at the center of the infrastructure and there are many problems for the service through the data processing of the sensor.

Sensor data from each local network server collects data coming from the local network, which causes a lot of load due to the process of collecting the local sensor data from the server and the conversion operation to transmit to the collecting device. This steadily increases. Due to such a problem, when the amount of log is increased, the system may be shut down when the throughput is increased. As a result, data latency may occur and it is difficult to perform the big data service of the sensor. In order to solve this problem, a method of adaptively adjusting the value of the minimum contention window (CW _min ) of the sensor node according to the local node density of the sensing field is required.

SUMMARY OF THE INVENTION The present invention has been made to solve the above problems, and it is an object of the present invention to provide a method and apparatus for adaptively adjusting a value of a minimum contention window (CW _min ) of a sensor node according to a local node density of a sensing field, And to perform the Regional Density-Aware Data Collection (RDDC).

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention, and are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Can be considered.

There is provided a method of collecting local density recognition data using a UAV in a wireless sensor network and dividing a field detected by the UAV into a plurality of areas in an apparatus to which the method is applied, Calculating local node densities of the plurality of divided regions; Determining a minimum contention window value for each sensor node included in the plurality of regions using the calculated regional node density; And collecting data by allocating the determined minimum contention window value to the sensor node.

In a method for collecting local density recognition data using a UAV in a wireless sensor network, the field is determined based on an altitude of the UAV and a maximum transmission range of the sensor node, the maximum area in which the UAV can collect data .

In a method for collecting local density recognition data using a UAV in a wireless sensor network, the total throughput processed in each of the divided regions is determined based on Equation (1)

[Equation 1]

Where T _i is the total throughput, n _i is the number of sensor nodes in each region, E [P] is the expected value of the transmitted payload size, σ is the duration of the empty slot time, T _s is the average time of successful transmission, T _C is the average time of the collision and τ _i is the transmission probability in the random time slot of each divided region.

In the method of collecting the local density recognition data using the UAV in the wireless sensor network, the transmission probability is calculated using the following equation (2)

&Quot; (2) "

Here, τ _i is the transmission probability in the random time slot of each divided region, n _i is the number of sensor nodes in each region, W _i is the CW _min value of each divided region, and m is the maximum backoff step.

In the method of collecting the local density recognition data using the UAV in the wireless sensor network, the CW _min value of each divided region is determined using Equation (3)

&Quot; (3) "

Here, W _i represents the CW _min value of each divided area, and Th _i represents the total throughput.

A method for collecting local density recognition data using a UAV in a wireless sensor network, comprising the steps of: assigning an ID to the sensor nodes included in the divided plurality of areas, ID, and collects the data.

According to the embodiments of the present invention, the following effects can be expected.

Through the local density recognition data collection method for adaptively adjusting the value of the minimum contention window (CW _min ) of the sensor node according to the local node density of the present invention to support fast data collection, when the unmanned altitude is 50 m, Shows a 9.4% shorter latency and 4.3% higher total throughput compared to traditional data collection. When the altitude of the UAV is 70m, the delay and total throughput of RDDC are improved by 9.2% and 2.6%, respectively. Accordingly, the latency time can be reduced and a high total throughput can be secured through the local density recognition data collection method of the present invention.

The effects obtainable in the embodiments of the present invention are not limited to the effects mentioned above, and other effects not mentioned can be obtained from the description of the embodiments of the present invention described below by those skilled in the art Can be clearly understood and understood. In other words, undesirable effects of implementing the present invention can also be obtained by those skilled in the art from the embodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. It is to be understood, however, that the technical features of the present invention are not limited to the specific drawings, and the features disclosed in the drawings may be combined with each other to constitute a new embodiment. Reference numerals in the drawings refer to structural elements.
1 is a configuration diagram of a wireless sensor network 10 according to an embodiment.
2 is a flowchart illustrating an embodiment in which local density recognition data collection operates as an embodiment of the present invention.
FIG. 3 is a flowchart illustrating an embodiment of the region segmentation step according to an embodiment of the present invention.
FIG. 4 shows an embodiment in which the UAV determines a field in the region segmentation step, according to an embodiment of the present invention.
5 is a diagram illustrating an example of dividing a field into one or more areas and a movement path of an unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 6 is a diagram showing a simulation result of the present invention, showing a change in delay with an increase in the number of sensor nodes.
FIG. 7 is a diagram showing the simulation result of the present invention, showing a change in total throughput when the number of sensor nodes increases.
Figs. 8 and 9 are diagrams showing simulation results of the present invention, showing the delay and total throughput when the altitude of the UAV is 70 m.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. Also, in certain cases, there may be a term selected arbitrarily by the applicant, in which case the meaning thereof will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term, not on the name of a simple term, but on the entire contents of the present invention.

The following embodiments are a combination of elements and features of the present invention in a predetermined form. Each component or characteristic may be considered optional unless otherwise expressly stated. Each element or characteristic may be embodied in a form that is not combined with other elements or features, and some of the elements and / or features may be combined to form an embodiment of the present invention. Further, the order of the operations described in the embodiments of the present invention may be changed. Some configurations or features of certain embodiments may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments.

In the description of the drawings, there is no description of procedures or steps that may obscure the gist of the present invention, nor is any description of steps or steps that can be understood by those skilled in the art.

Throughout the specification, when an element is referred to as "comprising" or " including ", it is meant that the element does not exclude other elements, do. Also, the terms " part, "" module," and the like, as used in the specification, mean a unit for processing at least one function or operation, Software. ≪ / RTI > Also, the terms " a or ", "one "," the ", and the like are synonyms in the context of describing the invention (particularly in the context of the following claims) Can be used in the sense of including both singular and plural, unless the context clearly dictates otherwise

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following detailed description, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the invention and is not intended to represent the only embodiments in which the invention may be practiced.

In addition, the specific terminology used in the embodiments of the present invention is provided to help understanding of the present invention, and the use of such specific terminology can be changed into other forms without departing from the technical idea of the present invention.

The present invention is to propose a local density recognition data collection method using a UAV in a large scale wireless network. Hereinafter, a wireless sensor network according to the present invention will be described.

1 is a configuration diagram of a wireless sensor network 10 according to an embodiment.

The wireless sensor network (10) includes a spatially distributed autonomous sensor node (11). Each sensor node 11 can communicate with one or several sensor nodes 11. The sensor cooperatively communicates with a coordinator node (base station) 12 via a wireless communication device, such as a radio frequency channel, at a location that receives data received by the sensor over the wireless network.

Large-scale wireless sensor networks (WSNs) are widely used for a variety of intelligent services such as environmental monitoring, smart cities, connected vehicles, smart farms, and battlefield surveillance. Most intelligent services deploy sensor nodes to monitor changes in the surrounding environment. Therefore, data collection is regarded as one of the most important research topics for decades, regarded as the basic function of large scale WSN.

So far, much research has been done on data collection of large WSNs. Generally, sensor nodes send data to static sinks using multihop communication for data collection. However, this method causes a long delay by multi-hop relay and path selection, thereby reducing network performance.

Also, sensor nodes close to the sink tend to consume more energy because of frequent data reception and transmission, which also shortens the network lifetime. Much research has been done on data collection to solve the above problem.

Multiple sinks can reduce latency by selecting a minimum number of hop sinks among multiple sinks, and increase network lifetime through load balancing between sensor nodes. However, if you do not want to duplicate sinks, you need a sink deployment strategy. Mobile sinks are widely used for data collection for efficient data collection. Unlike traditional sinks, mobile sinks move toward a deployed sensor node along a specified path for data collection. This approach uses only one mobile sink and one hop communication. This is a cost-effective solution that improves network performance, such as throughput, latency, and network lifetime. However, mobile sinks have mobility using ground transportation (e.g., vehicles). Therefore, the path of the mobile sink is greatly limited by the traffic a.

The present invention suggests a method of locally dense data collection (RDDC) using an unmanned aerial vehicle in a large scale wireless sensor network (WSNs). In the present invention, the local density recognition data collection method is designed to support fast data collection by adaptively adjusting the value of the minimum contention window (CW _min ) of the sensor node according to the regional node density of the field detected by the UAV. The local density recognition data collection method adaptively adjusts the CW _min value of the sensor node according to the local node density so that the data can be collected quickly in a large scale WSN. The local node density is a key parameter of the local density recognition data collection method, which is determined by the number of sensor nodes in the partitioned area.

Hereinafter, the method of collecting the local density recognition data will be described in detail.

2 is a flowchart illustrating an embodiment in which local density recognition data collection operates as an embodiment of the present invention.

The local density recognition data collection method consists of region segmentation, node scanning, and data collection.

The field sensed by the UAV can be divided into one or more regions (S210). In the segmentation step, the UAV divides the detected field into several regions and assigns the region ID to each sensor node according to the region to which each sensor node belongs.

In the node scanning step, the local node density of each region can be calculated (S220, hereinafter referred to as a node scanning step).

Finally, the data may be collected in consideration of the local node density of each divided region (S230, hereinafter collecting data). In the data collection step, different CW _min values are assigned to each sensor node considering the local node density of each region.

FIG. 3 is a flowchart illustrating an embodiment of the region segmentation step according to an embodiment of the present invention.

In the area segmentation step, an area where the UAV can collect data can be determined as a field (S211). In the present invention, a "field" may represent a region determined by a maximum area (i.e., a data collection range) at which a UAV can collect data at a particular location. An example in which the field is determined will be described again with reference to FIG.

In the region segmentation step, the UAV can divide the detection field into one or more regions and calculate the size thereof (S212).

IDs can be assigned to the sensor nodes in each of the divided regions and regions (S213).

FIG. 4 shows an embodiment in which the UAV determines a field in the region segmentation step, according to an embodiment of the present invention.

The field can be calculated using the altitude of the UAV 410 and the maximum transmission range of the sensor node 420. In the embodiment of FIG. 4, it is assumed that the UAV and sensor nodes use an omnidirectional antenna and fixed transmit power to simplify field calculation, which is data collection coverage. Since the range of the field is circular, the size of the field can be calculated using Equation 1 below.

[Equation 1]

Here, c represents the size of the field, r represents the radius of the field, tr represents the maximum transmission range of the sensor node 420, and h represents the altitude of the UAV.

5 is a diagram illustrating an example of dividing a field into one or more areas and a movement path of an unmanned aerial vehicle according to an embodiment of the present invention.

As shown in FIG. 5, depending on the size of the data collection range, the entire sensing field of the WSN is divided into several areas. The field detected through the grid partitioning method can be divided into a plurality of square areas having the same size. Each zone maintains a unique zone ID, and each sensor node can be assigned a zone ID according to the zone to which it belongs.

In the node scanning stage, the UAV calculates the local node density of each region. The UAV can move along the center of each area and receive a hello message from the sensor node. In FIG. 5, the bold line represents the movement path of the UAV, and the UAV can move in the order of the ID values of the area. The UAV can calculate the local node density of the corresponding region by calculating the number of hello messages received from the sensor node having the same local ID. The regional node density of each region is proportional to the number of nodes belonging to the region because all regions have the same size.

At the node scanning stage, unnecessary overhead can occur in data collection. However, the node scanning step is performed only once when the data collection process is started or when the arrangement of the sensor nodes is changed. In a large WSN, the location of the sensor nodes is almost unchanged, so the scan overhead can be ignored during the long data collection period.

In the data collection stage, the UAV can collect data from sensor nodes using the movement path of the node scanning stage. At the beginning of the data collection phase, the UAV can assign a different CW _min value to each sensor node and notify it, taking into consideration the local node density of each area. The purpose of the CW _min adjustment is to mitigate collisions between sensor nodes in high density areas and to reduce unnecessary latency for channel access. Therefore, the CW _min adjustment of local density aware data collection can effectively maximize total throughput. It is assumed that each sensor node operates in a saturated state, and communication between the UAV and the sensor node is performed with a carrier sense multiple access with Collision Avoidance (CSMA / CA).

It is also assumed that UAV can collect data from sensor nodes in all regions. Thus, the total throughput in each area can be calculated via equation (2).

&Quot; (2) "

Where T _i is the total throughput, n _i is the number of sensor nodes in each region, E [P] is the expected value of the transmitted payload size, σ is the duration of the empty slot time, T _s is the average time of successful transmission, T _C is the average time of the collision and τ _i is the transmission probability in the random time slot of each divided region.

Here, the transmission probability τ _i can be calculated through Equation (3).

&Quot; (3) "

Here, τ _i is the transmission probability in the random time slot of each divided region, n _i is the number of sensor nodes in each region, W _i is the CW _min value of each divided region, and m is the maximum backoff step.

Using equations (2) and (3), we can derive a function that defines the relationship between total throughput and CW _min . Equation 4 can then be used to obtain the adjusted CW _min that maximizes the total throughput of each segmented region.

&Quot; (4) "

Here, W _i represents the CW _min value of each divided area, and Th _i represents the total throughput.

In the following, the experimental simulation is performed to evaluate the performance of the local density recognition data collection method. The performance of the local density recognition data collection method is compared with the performance of existing data collection to verify the superiority of the present invention. The following describes the simulation scenarios, settings, and results in detail.

This simulation was performed in various scenarios. To investigate the effect of elevation of UAV on delay and total throughput, simulations were conducted at two different elevations of UAV 50 m and 70 m.

In addition, the number of sensor nodes was set to change from 100 to 1,000 in each simulation. Using this setting, we examined the performance (ie, latency and total throughput) of the local density awareness data collection method as the number of sensor nodes increased.

In the simulation the sensor nodes are placed randomly in the entire sensing area of 1,000 × 1,000m ^2. UAV and sensor nodes communicate with each other using IEEE 802.11n MAC / PHY and use a data rate of 130Mbps. The transmission range of the sensor node is set to 100 m. Therefore, when the altitude of the UAV is 50m, the length of one side of the area is 173.2m, and when the altitude of UAV is 70m, it is 142.8m. Detailed simulation parameters are shown in Table 1.

Simulation parameters. Parameter Value Parameter Value MAC / PHY model IEEE 802.11n PHY header 34 Bytes Data rate 130 Mbps MAC header 16 Bytes Size of the sensing field 1,000 × 1,000 m ² Payload 5,120 Bytes Number of sensor nodes 100 - 1,000 CW _min 0 - 1,023 Transmission range 100 m CW _max 1,023 Altitude of the UAV 50 m, 70 m SIFS 10 μs Slot time 20 μs DIFS 50 μs

FIG. 6 is a diagram showing a simulation result of the present invention, showing a change in delay with an increase in the number of sensor nodes.

In FIG. 6, the UAV is set to 50 m. The local density recognition data collection method adjusts the CW _min value of the sensor node adaptively according to the node density of each region. Therefore, the local density recognition data collection method obtains a shorter delay than the existing data collection, _min (i.e., 31) is used.

In particular, when the number of sensor nodes is 100, the delay of local density recognition data collection method is 21.7% shorter than the delay of existing data collection because the number of sensor nodes is small. In other words, the fixed value of CW _min in existing data collection results in a long latency in channel access. RDDC can reduce the collision between sensor nodes by increasing the value of CW _min when the number of sensor nodes increases. Therefore, if the number of sensor nodes exceeds 500, the delay of the local density recognition data collection method does not change much, but the delay of existing data collection becomes longer because the number of collision between sensor nodes increases.

FIG. 7 is a diagram showing the simulation result of the present invention, showing a change in total throughput when the number of sensor nodes increases.

In FIG. 7, the UAV is set to 50 m. In Figure 7, since the UAV receives data from a larger number of sensor nodes, the total throughput increases as the number of sensor nodes increases. The local density recognition data collection method can reduce the channel access latency and mitigate the collision between the sensor nodes in the high density region.

Thus, local density awareness data collection methods have higher total throughput than traditional data collection. In this simulation, local density-aware data collection methods average 4.3% higher total throughput than traditional data collection.

Especially, when the number of sensor nodes is 1,000, local density recognition data collection method obtains 7.6% higher total throughput than existing data collection method. This is because the local density recognition data collection method significantly reduces the number of collisions by assigning a larger CW _min value to the sensor node.

Figs. 8 and 9 are diagrams showing simulation results of the present invention, showing the delay and total throughput when the altitude of the UAV is 70 m.

Due to the high altitude of the UAV, the size of this field is smaller than when the altitude is 50 m. Therefore, the number of regions increases in the entire sensing region, and the number of sensor nodes in the region decreases. Therefore, RDDC can achieve 2.2% shorter latency and 35.4% lower total throughput compared to 50m altitude. Also in Figures 8 and 9, RDDC can achieve a 9.2% shorter delay and 2.6% higher total throughput than conventional data collection due to the CW _min adjustment.

Fig. 10 is a block diagram for explaining a UAV related to the present invention, and Fig. 11 is a conceptual diagram according to an embodiment of a UAV related to the present invention. The above-described method of collecting the local density recognition data can operate in the UAV described below.

10, the UAV 1100 includes a wireless communication unit 1110, a sensing unit 1120, a camera 1130, an output unit 1140, a memory 1150, a GPS module 1160, an image processing unit 1170, A control unit 1180, and a power supply module 1190. [0042]

The components shown in FIG. 10 are not essential, so that the UAV 1100 having more or fewer components can be implemented.

Hereinafter, the components will be described in detail.

The wireless communication unit 1110 is a module for providing a wireless communication function between the UAV 1100 and an external device and enables wireless communication with a mobile terminal for controlling the UAV 1100 or a wearable device worn on the target object. For this, the wireless communication unit 1110 may include a mobile communication module for connecting to a mobile communication network, a wireless Internet module for connecting to a wireless Internet network, and a short-range communication module for enabling a wireless local area connection.

The sensing unit 1120 may include at least one of an impact sensor 1211 and an elevation sensor 1122. In addition, an acceleration sensor, a gyro sensor, and the like may be further included. The impact sensor 1121 is a sensor that detects impact information applied to the UAV 1100. When an impact of a predetermined reference value or more is detected, it is determined that a dangerous situation has occurred and an external output can be provided.

The altitude detection sensor 1122 is a sensor for detecting a change in the altitude of the UAV 1100. When the altitude change of the altitude of the UAV 1100 is equal to or greater than a preset reference value, .

The UAV 1100 may include at least one camera 1130 and may photograph an image during operation or stop of operation through the camera 1130 and store the captured image in the memory 1150.

The output unit 1140 may include at least one of a display unit 1141, an acoustic output unit 1142, an IR output unit 1143, and an LED output unit 1144.

The display unit 1141 may have a mutual layer structure with the touch sensor or may be integrally formed to realize a touch screen. Such a touch screen is capable of providing an output interface between the UAV 1100 and a user, while being enabled as a user input providing an input interface between the UAV 1100 and the user.

The audio output unit 1142 can output audio data received from the wireless communication unit 1110 or stored in the memory 1150. The sound output unit 1142 may include a receiver, a speaker, a buzzer, and the like.

The IR output unit 1143 may include at least one infrared sensor, and may drive the infrared sensor according to the set control signal to output infrared rays. At this time, the infrared output time and the output direction can be adjusted.

The LED output unit 1144 may include at least one LED, and may control the LED output unit to output a set symbol, letter, number, etc. according to a set control signal.

The memory 1150 stores data supporting various functions of the UAV 1100. The memory 1150 may store a plurality of application programs (application programs or applications) driven by the UAV 1100, data for operation of the UAV 100, and commands. At least some of these applications may be downloaded from an external server via wireless communication.

The GPS module 1160 is a module for acquiring the position (or the current position) of the UAV 1100. By utilizing the GPS module, the position of the UAV can be obtained by using a signal transmitted from the GPS satellite.

The image processing unit 1170 may analyze the data of the image captured by the camera 1130 to detect a target object or to process the captured image such as filtering the image quality of the captured image.

In addition to the operations associated with the application program, the control unit 1180 typically controls the overall operation of the UAV 1100. The control unit 1180 may process or process signals, data, information, and the like input or output through the above-mentioned components, or may drive an application program stored in the memory 1150 to provide or process appropriate information or functions to the user. In particular, the local density recognition data collection method of the present invention can be operated by the control unit 1180.

In addition, the controller 1180 may control at least some of the components illustrated in FIG. 10 to drive an application program stored in the memory 1150. In addition, the controller 1180 can operate at least two of the components included in the UAV 1100 in combination with each other for driving the application program.

Under the control of the controller 1180, the power supply unit 1190 supplies power to the components included in the UAV 1100 by receiving external power and internal power. The power supply unit 1190 includes a battery, which may be an internal battery or a replaceable battery.

At least some of the respective components may operate in cooperation with each other to implement a method of operation, control, or control of a UAV according to various embodiments described below.

Also, the operation, control, or control method of the UAV can be implemented on the UAV by driving at least one application program stored in the memory 1150. [

Referring to FIG. 11, the disclosed unmanned aerial vehicle 1100 has a body of a small flying object. However, the present invention is not limited thereto, and can be applied to various structures such as a spherical shape and a disc shape.

The UAV 1100 includes a case (for example, a frame, a housing, etc.) which forms an appearance. As shown in the figure, the UAV 1100 has various electronic components arranged in the inner space of the case. A display unit 1141 is disposed on a front surface of the UAV so that information can be output.

As shown in the figure, an opening for exposing the camera 1130, the acoustic output unit 1142, the IR output unit 1143, and the LED output unit 1144 to the outside may be provided under the case of the UAV.

Such a case may be formed by injection molding a synthetic resin, or may be formed of a metal such as stainless steel (STS), aluminum (Al), titanium (Ti) or the like.

Meanwhile, the above-described embodiments may be embodied in the form of a computer-readable recording medium storing computer-executable instructions and data. At least one of the command and data may be stored in the form of program code, and when executed by the processor, a predetermined program module may be generated to perform a predetermined operation.

The computer-readable recording medium may mean, for example, a magnetic storage medium such as a hard disk, an optical reading medium such as a CD and a DVD, or the like, and may be a memory included in a server accessible through a network . For example, the computer readable recording medium may be an electronic device or a memory of a server. Also, it may be a memory included in a terminal, a server, or the like connected to an electronic device or a server via a network.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. It will be possible. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

Claims (13)

Dividing a field detected by the UAV into a plurality of regions;
Calculating local node densities of the plurality of divided regions;
Determining a minimum contention window value for each sensor node included in the plurality of regions using the calculated regional node density; And
Collecting data by allocating the determined minimum contention window value to the sensor node; Lt; / RTI >
The total throughput processed in each of the divided regions is determined based on Equation (1)
[Equation 1]

Where T _i is the total throughput, n _i is the number of sensor nodes in each region, E [P] is the expected value of the transmitted payload size, σ is the duration of the empty slot time, T _s is the average time of successful transmission, T _C is the average time of the collision, and τ _i is the method of collecting the local density recognition data using the UAV in the wireless sensor network, which represents the transmission probability in the random time slot of each divided area The method according to claim 1,
Wherein the field is determined based on an altitude of the unmanned aerial vehicle and a maximum transmission range of the sensor node, the maximum area of which the unmanned aerial vehicle can collect data, How to collect delete The method according to claim 1,
The transmission probability is calculated using the following equation (2)
&Quot; (2) "

Where n _i is the number of sensor nodes in each region, W _i is the CW _min value of each segmented region, and m is the maximum backoff step, where τ _i is the transmission probability in the random time slot of each segmented region, A method for collecting local density recognition data using UAV in a wireless sensor network 5. The method of claim 4,
The CW _min value of each divided area is determined using Equation (3)
&Quot; (3) "

Here, W _i is the CW _min value of each divided area, Th _i is the total throughput, and the method of collecting the local density recognition data using the UAV in the wireless sensor network The method according to claim 1,
And assigning an ID to the sensor node included in the divided plurality of regions,
The method of collecting local density recognition data using a UAV in a wireless sensor network, wherein the collecting of the data comprises moving the UAV in order of ID and collecting the data A communication unit;
A memory for storing a program for operation of the UAV; And
The method comprising the steps of: dividing a field detected by a UAV into a plurality of areas, calculating a local node density of the plurality of divided areas, and outputting, by using the calculated regional node density, A controller for determining a minimum contention window value on a node-by-node basis, and allocating the determined minimum contention window value to the sensor node to collect data; Lt; / RTI >
Wherein,
Determining a total throughput processed in each of the divided regions based on Equation (1)
[Equation 1]

Where T _i is the total throughput, n _i is the number of sensor nodes in each region, E [P] is the expected value of the transmitted payload size, σ is the duration of the empty slot time, T _s is the average time of successful transmission, T _C is the mean time of the collision and τ _i is the local density recognition data in the wireless sensor network which represents the transmission probability in the random time slot of each divided region. 8. The method of claim 7,
Characterized in that the field is determined based on an elevation of the UAV and a maximum transmission range of the sensor node, the maximum area of which the UAV can collect data, collecting the local density recognition data in the wireless sensor network UAV delete 8. The method of claim 7,
Wherein,
The transmission probability is calculated using the following equation (2)
&Quot; (2) "

Where n _i is the number of sensor nodes in each region, W _i is the CW _min value of each segmented region, and m is the maximum backoff step, where τ _i is the transmission probability in the random time slot of each segmented region, And acquiring local density recognition data in a wireless sensor network 11. The method of claim 10,
Wherein,
The CW _min value of each of the divided regions is determined using Equation (3)
&Quot; (3) "

Where W _i is the CW _min value of each segmented region and Th _i is the total throughput. 8. The method of claim 7,
Wherein,
Collecting the local density recognition data in the wireless sensor network, assigning an ID to the sensor nodes included in the plurality of divided areas, and controlling the UAV to move in ID order and collect the data. UAV A computer readable nonvolatile recording medium on which a program for implementing the method of claim 1 is recorded.

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