KR102495114B1 - Method and apparatus for controlling of tactical sensor network, computer-readable storage medium and computer program - Google Patents

Abstract

An embodiment is a method for controlling a tactical sensor network performed in a tactical sensor network control apparatus, comprising: searching for a plurality of routes from a source node to a destination node to transmit tactical data; The method may include calculating a Q-learning value for the Q-learning value and selecting the path through which the tactic data is to be transmitted based on the Q-learning value.
The embodiment has an effect of effectively transmitting tactical data without loss by selecting a highly reliable transmission path in a tactical sensor network.

Description

Tactical sensor network control method and apparatus, computer readable recording medium and computer program

Embodiments relate to a method and apparatus for controlling a tactical sensor network.

In general, in a tactical sensor network, mission-critical data generated from various tactical sensors must be safely and quickly transmitted to a gateway. To this end, the characteristics of the resource-constrained sensor node must be considered, and reliability required according to mission criticality and priority characteristics of real-time/non-real-time tactical data must be guaranteed. However, tactical sensor networks can be exposed to various cyberattacks that steal key sensor data or interfere with data transmission. For example, our military equipment equipped with security equipment can be hijacked and abused, such as performing a black hole attack or DoS (Denial of Services) attack. There may be a situation where you can't do it and you can't even receive it.

Recently, various learning-based trust evaluation methods have been studied to solve this problem. However, studies to perform learning considering the characteristics of tactical sensor networks and to ensure the reliability of mission-critical data are at an insufficient level.

Korean Registered Patent Publication No. 10-2167028 (registered on October 12, 2020)

In order to solve the above problems, an object of the embodiment is to provide a method and apparatus for controlling a tactical sensor network for effectively transmitting important tactical data to a destination node.

An embodiment is a method for controlling a tactical sensor network performed in a tactical sensor network control apparatus, comprising: searching for a plurality of routes from a source node to a destination node to transmit tactical data; The method may include calculating a Q-learning value for the Q-learning value and selecting the path through which the tactic data is to be transmitted based on the Q-learning value.

In the step of selecting the path, the importance of the tactic data is determined, and if the importance of the tactic data is greater than or equal to a reference value, the tactic data may be transmitted through a path having the highest Q-learning value.

If the importance of the tactic data is less than the reference value, the tactic data may be transmitted through a path having the highest Q-learning value or a randomly selected path among the remaining paths.

The Q-learning value may be calculated based on reliability of nodes existing on the path, residual energy values of nodes existing on the path, and a trust evaluation value.

The reliability may be an average value of a weighted summation result of node reliability evaluated by a previous node of the evaluation target node and node reliability evaluated by at least one neighboring node of the evaluation target node.

In addition, the tactical sensor network control apparatus of the embodiment includes a memory in which a tactical sensor network control program is stored, and a processor executing the tactical sensor network control program stored in the memory, wherein the processor includes a source node to transmit tactical data. Searching for a plurality of paths from to a destination node, calculating a Q-learning value for each of the plurality of paths found, and selecting the path to transmit the tactic data based on the Q-learning value. there is.

In addition, an embodiment is a computer readable recording medium storing a computer program, which, when executed by a processor, searches for a plurality of paths from a source node to a destination node to transmit said data; , calculating a Q-learning value for each of the plurality of paths found, and selecting the path through which the tactic data is to be transmitted based on the Q-learning value, by the processor It may contain instructions to perform.

In addition, an embodiment is a computer program stored on a computer-readable recording medium, which, when executed by a processor, includes the steps of: searching for a plurality of paths from a source node to a destination node to transmit said data; , calculating a Q-learning value for each of the plurality of paths found, and selecting the path through which the tactic data is to be transmitted based on the Q-learning value, by the processor It may contain instructions for performing.

The embodiment has an effect of effectively transmitting tactical data without loss by selecting a highly reliable transmission path in a tactical sensor network.

In addition, the embodiment has an effect of selecting a transmission path with higher reliability by using the Q-learning value.

1 is a block diagram illustrating a tactical sensor network system according to an embodiment.
2 is a block diagram illustrating a tactical sensor network control device according to an embodiment.
3 is a block diagram illustrating a tactical sensor network control method according to an embodiment.
4 is a flowchart illustrating detailed operations of a tactical sensor network according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the drawings.

1 is a block diagram showing a tactical sensor network system according to an embodiment, FIG. 2 is a block diagram showing a tactical sensor network control device according to an embodiment, and FIG. 3 is a block diagram showing a tactical sensor network control method according to an embodiment. FIG. 4 is a flowchart illustrating detailed operations of a tactical sensor network according to an embodiment.

Referring to FIG. 1 , a tactical sensor network system 100 according to an embodiment may include a tactical sensor network 110 and a tactical sensor network controller 130 that controls transmission of tactical data of the tactical sensor network.

The tactical sensor network 100 may include a source node 111 , a neighbor node 113 , and a destination node 115 . Tactics data may be transmitted from source node 111 to destination node 115 . The neighbor node 113 may include a plurality of neighbor nodes. A plurality of neighboring nodes 113 may establish transmission paths between the source node 111 and the destination node 115 . For example, the plurality of neighboring nodes 113 may establish five transmission paths between the source node 111 and the destination node 115, but the number of transmission paths is not limited thereto.

The tactical sensor network 110 may include a sensor node 117 . The sensor node 117 may sense information of each node and transmit the sensed information to a destination node. The number of sensor nodes 117 may be equal to the number of neighboring nodes, but is not limited thereto.

Tactical data is transmitted from the source node 111 to the destination node 115, and the tactical data may be delivered to the destination node 115 through a highly reliable transmission path.

The tactical sensor network controller 130 may measure the reliability of the transmission path and determine the tactical data transmission path based on the measured reliability.

As shown in FIG. 2 , the tactical sensor network control device 130 may include a memory 131 , a processor 133 , and a communication unit 135 .

The memory 131 may store various data for overall operation, such as a tactical sensor network control program. Specifically, the memory 131 may store a plurality of application programs driven by the tactical sensor network control device 130, data and commands for operating the tactical sensor network control device.

In addition, the memory 131 includes various values measured by the tactical sensor network control device 130, for example, node trust value (NTV), remaining energy value of nodes (Energy), and trust evaluation value (ETV, Energy based Trust Value). Value) may be stored.

The memory 131 may include magnetic storage media or flash storage media, but is not limited thereto.

The communication unit 135 may be a device including hardware and software necessary for transmitting and receiving signals such as control signals or data signals with other network devices through wired or wireless connections.

The communication unit 135 may transmit/receive values measured in the transmission path and tactical sensor network control device with other network devices.

The communication unit 135 may perform communication using a Low Power Wireless Network (LPWN) and a Low Power Wide Area Network (LPWAN) such as 3G, LTE, and 5G, as well as NB-IoT, LoRa, SigFox, and LTE-CAT1. there is.

The communication unit 135 may perform communication using a communication method using a wired local area network (LAN) as well as a wireless LAN such as WiFi 80211a/b/g/n. In addition to this, the communication unit may perform communication with an electric range or an external device using a communication method such as NFC or Bluetooth.

Here, the communication unit 135 is not an essential component of the tactical sensor network control device, and may be mounted or unmounted on the tactical sensor network control device 130 as needed.

The processor 133, as a kind of central processing unit, can control the entire operation of the tactical sensor network controller 130 for providing a transmission path.

The processor 133 may include any type of device capable of processing data. Here, a 'processor' may refer to a data processing device embedded in hardware having a physically structured circuit to perform functions expressed by codes or instructions included in a program, for example. As an example of such a data processing device built into hardware, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated (ASIC) circuit) and a processing device such as a field programmable gate array (FPGA), but is not limited thereto.

The processor 133 may select a highly reliable transmission path in the process of transmitting tactical data from the source node to the destination node. The detailed operation of the processor 133 is as follows.

As shown in FIG. 3, the tactical sensor network control method includes the steps of searching a plurality of routes from a source node to a destination node to transmit tactical data (S100), and a queue for each of the plurality of routes searched. - It may include measuring a running value (S200) and selecting the path through which the tactic data will be transmitted based on the Q-learning value (S300).

In step S100, the source node may broadcast a RREQ (Route REQuest) message for requesting a transmission path search and trust evaluation value before transmitting tactical data to the destination node. The received neighbor node may respond with a credit value and broadcast an RREQ message. When a route search and trust evaluation request arrives to the destination, appropriate compensation can be given. Depending on each reward, each node can update its queue learning value.

In step S200, a Q-learning value for each of a plurality of paths may be measured. The source node can evaluate the Q-learning value by observing the behavior of its neighbors and sensor nodes through a promiscuous mode.

Here, the Q-learning value may be calculated based on a reliability value (NTV), a residual energy (Energy) value of each node, and a trust evaluation value (ETV).

The reliability value (NTV) is the average value of the weighted summation of the node reliability evaluated by the previous node of the evaluation target node and the node reliability evaluated by at least one neighboring node of the evaluation target node for the evaluation target node. can For example, a node reliability value (NTV) may be calculated using a packet forwarding ratio. The reliability value (NTV) may be represented by a value between 0 and 1.

)이 필요한데 많은 양의 센서 데이터가 발생할 때에도 병목현상으로 인해 신뢰도 값은 감소할 수 있으므로 유연한 임계점(

)이 필요하다. 여기서, 임계점은 수학식 1과 같이 결정될 수 있다.If neighboring nodes normally forward the data transmitted by each sensor node, the reliability value (NTV) increases, and if packets are maliciously dropped or not forwarded due to network load, the reliability value may decrease. At this time, the critical point for determining malicious nodes (

) is required, but even when a large amount of sensor data is generated, the reliability value may decrease due to a bottleneck, so a flexible threshold (

) is required. Here, the critical point may be determined as in Equation 1.

[Equation 1]

(Here, Linkbandwidth means the total link bandwidth usage, Cruurentbandwidth means the current link bandwidth usage, and u means an arbitrary value of the evil node)

The critical point may be set to 0 or more and 1 or less, more specifically, 0 or more and 0.5 or less. This can be set differently according to the intention and judgment of the operator to determine the evil node.

Through this, if the current link bandwidth usage is low, the link state is judged to be smooth, and the threshold is raised, and even if the malicious node performs a drop attack with a low probability, it can be quickly detected. In addition, if an abnormal packet transmission behavior such as DoS (Denial of Service) is detected, it can be determined as an abnormal behavior.

Sensor nodes may calculate an energy based trust value (ETV) through a reliability value and a residual energy value.

The trust evaluation value (ETV) can be calculated by Equation 2.

[Equation 2]

(여기서, ETV는 신뢰 평가값을 의미하고, Energy는 노드에 남아 있는 잔여 에너지량을 의미하며, w1, w2는 가중치를 의미함)

(Here, ETV means the trust evaluation value, Energy means the amount of remaining energy remaining in the node, and w1 and w2 mean the weights)

은 잔여에너지를 기반으로 수학식 3과 같이 결정되며

는

기반으로 아래 식과 같이 계산될 수 있다. The higher the reliability evaluation value (ETV), the better the reliability. Considering the characteristics of a resource-constrained device, we focus on flexible weighting based on the remaining energy.

Is determined by Equation 3 based on the residual energy

Is

Based on this, it can be calculated as follows.

[Equation 3]

In a tactical sensor network, a flexible weight module considering energy can be essentially utilized. Nodes with sufficient energy are weighted to increase the reliability factor of the network, and nodes with insufficient energy are weighted with energy factors to ensure survivability.

As described above, a local trust value (Localtrust) may be calculated using the calculated trust evaluation value (ETV). The local confidence value is as shown in Equation 4.

[Equation 4]

(여기서, n은 주변 노드의 개수를 의미하고, j는 임의의 j 노드를 의미함)

(Here, n means the number of neighboring nodes, and j means an arbitrary j node)

The local reliability value calculated as above may be a result of reflecting the result of evaluating the corresponding node j by the number of neighboring nodes for time t.

Also, the destination node can give an additional global trust value when the packet arrives normally. The confidence value derived in this way may be calculated and reflected as a reward value for learning the Q-Value.

Local confidence values can be reflected in the Q-learning equation (Q). The Q-learning equation (Q) is equal to Equation 5.

[Equation 5]

(여기서, s는 state로 데이터를 송신하기 위한 센서 노드를 의미하고, a는 action으로 데이터를 송신 할 다음 노드를 의미하며, Reward 값은 로컬 신뢰 평가 값과 글로벌 신뢰 평가 값의 합을 의미하고, r는 할인계수(Discount Factor)를 의미함)

(Here, s means the sensor node to transmit data as a state, a means the next node to transmit data as an action, and the Reward value means the sum of the local trust evaluation value and the global trust evaluation value, r means the discount factor)

Here, the global trust evaluation value can be any value, and if there is no global trust evaluation value in the reward value, only the local trust evaluation value can be obtained.

The reward value has a good trust evaluation value, and you can receive a better reward value if you arrive at the destination.

As described above, when the Q-learning value is measured, the step of selecting a path through which tactical data will be transmitted based on the Q-learning value (S300) can be performed. Here, the importance of tactical data can be considered. When the importance of tactical data is high, tactical data may be transmitted through a transmission path having a high Q-learning value. On the other hand, when the importance of tactical data is low, tactical data may be transmitted through a randomly selected transmission path.

As shown in FIG. 4 , the source node may check a multi-path configured to transmit tactical data and determine whether a preset timer has expired (S10).

If the timer has not expired, the source node may check the trust value, the remaining energy value, and the trust evaluation value (S15), and check whether the importance of the tactics data is high (S16).

When the timer expires, it can be checked whether it is the destination node (S11). If it is determined to be the destination node, the global trust value can be reflected in the reward function of Q-learning (S12).

If it is not the destination node, the reliability and residual energy value may be updated (S13), and the trust evaluation value may be calculated using the weight (S14).

Subsequently, a step (S16) of determining whether the importance of tactical data is high may be performed. If the importance of tactic data is high, the tactic data may be transmitted by selecting a node having the largest Q-learning value (S18). Queue-learning can be calculated using reliability, residual energy amount of a node, and trust evaluation value.

If the importance of tactical data is low, it can be checked whether the critical point is 0.1 or higher (S17). If the critical point is 0.1 or more, data can be transmitted to a transmission node having a high queue-learning value (S18).

On the other hand, if the critical point is less than 0.1, tactical data may be transmitted through a randomly selected node (S19).

As described above, the tactic data transmitted to the destination node may reflect the local reliability value to the reward of Q-learning (S20).

Various embodiments of the present document are software (eg, machine-readable storage media) (eg, memory (internal memory or external memory)) including instructions stored in a storage medium readable by a machine (eg, a computer). : program). A device is a device capable of calling a stored command from a storage medium and operating according to the called command, and may include an electronic device according to the disclosed embodiments. When the command is executed by the control unit, the control unit may perform a function corresponding to the command directly or by using other components under the control of the control unit. An instruction may include code generated or executed by a compiler or interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, non-temporary means that the storage medium does not contain a signal and is tangible, but does not distinguish whether data is stored semi-permanently or temporarily in the storage medium.

According to an embodiment, a method according to various embodiments disclosed in this document may be included in a computer program product and provided.

According to an embodiment, a computer readable recording medium storing a computer program, comprising: searching for a plurality of paths from a source node to a destination node to transmit tactical data; Instructions for causing a processor to perform a method comprising calculating a cue-learning value for , and selecting the path through which the tactic data is to be transmitted based on the cue-learning value can include

According to one embodiment, a computer program stored on a computer-readable recording medium includes: searching for a plurality of paths from a source node to a destination node to transmit tactical data; Instructions for causing a processor to perform a method comprising calculating a cue-learning value for , and selecting the path through which the tactic data is to be transmitted based on the cue-learning value can include

Although the above has been described with reference to drawings and embodiments, it is understood that those skilled in the art can modify and change the embodiments in various ways without departing from the technical spirit of the embodiments described in the claims below. You will be able to.

100: tactical sensor network system
110: tactical sensor network
130: tactical sensor network control unit
131: memory
133: processor
135: communication department

Claims (8)

A tactical sensor network control method performed in a tactical sensor network control apparatus,
searching a plurality of routes from the source node to the destination node to transmit tactical data;
Calculating a Q-learning value for each of the plurality of paths based on a reliability evaluation value calculated from reliability and residual energy values of nodes existing in each of the plurality of paths; and
and selecting a path through which the tactical data is to be transmitted from among the searched plurality of paths based on the Q-learning value. According to claim 1,
In the step of selecting the path,
The tactical sensor network control method of determining the importance of the tactic data, and transmitting the tactic data through a path having the highest Q-learning value if the importance of the tactic data is greater than or equal to a reference value. According to claim 2,
If the importance of the tactic data is less than the reference value, the tactical sensor network control method of transmitting the tactic data through a path having the highest Q-learning value or a randomly selected path among the remaining paths. delete According to claim 1,
The reliability is an average value of a weighted summation result of node reliability evaluated by a previous node of the evaluation target node and node reliability evaluated by at least one neighboring node of the evaluation target node, with respect to the evaluation target node. control method. a memory in which a tactical sensor network control program is stored;
A processor executing the tactical sensor network control program stored in the memory;
the processor,
In order to transmit tactical data, a plurality of paths from a source node to a destination node are searched, and based on a reliability evaluation value calculated from reliability and residual energy values of nodes existing on each of the plurality of paths, A tactical sensor network control device that calculates a Q-learning value for each of a plurality of searched paths and selects a path to transmit the tactical data from among the plurality of searched paths based on the Q-learning value. A computer-readable recording medium storing a computer program,
When the computer program is executed by a processor,
searching a plurality of routes from the source node to the destination node to transmit tactical data;
Calculating a Q-learning value for each of the plurality of paths based on a reliability evaluation value calculated from reliability and residual energy values of nodes existing in each of the plurality of paths; and
A computer-readable recording medium comprising instructions for causing the processor to perform an operation including selecting a path through which the tactical data will be transmitted from among the searched plurality of paths based on the Q-learning value. As a computer program stored on a computer-readable recording medium,
When the computer program is executed by a processor,
searching a plurality of routes from the source node to the destination node to transmit tactical data;
Calculating a Q-learning value for each of the plurality of paths based on a reliability evaluation value calculated from reliability and residual energy values of nodes existing in each of the plurality of paths; and
A computer program comprising instructions for causing the processor to perform an operation including selecting a path through which the tactic data is to be transmitted from among the plurality of discovered paths, based on the Q-learning value.

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