CN111787506A - Trusted data collection method based on unmanned aerial vehicle in wireless sensor network - Google Patents

Trusted data collection method based on unmanned aerial vehicle in wireless sensor network Download PDF

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CN111787506A
CN111787506A CN202010695768.0A CN202010695768A CN111787506A CN 111787506 A CN111787506 A CN 111787506A CN 202010695768 A CN202010695768 A CN 202010695768A CN 111787506 A CN111787506 A CN 111787506A
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蒋博
刘安丰
滕浩钧
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Central South University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a trusted data collection method based on an unmanned aerial vehicle in a wireless sensor network. In each round of data collection, firstly, nodes are randomly selected according to initial credibility, or nodes with high credibility are selected as cluster heads according to credibility data obtained through evaluation, then various unmanned aerial vehicle flight path schemes are randomly generated according to the serial numbers of the cluster heads, paths with the best effect are selected through comprehensive evaluation indexes, and after the unmanned aerial vehicle finishes data collection, the node credibility is evaluated, malicious nodes are identified, and data collection quality is improved. Meanwhile, the method adopts the unmanned aerial vehicle to complete data collection, so that the energy consumption of the core node in the network is effectively reduced, and the service life of the network is prolonged.

Description

Trusted data collection method based on unmanned aerial vehicle in wireless sensor network
Technical Field
The invention relates to the field of wireless communication, in particular to a trusted data collection method based on an unmanned aerial vehicle in a wireless sensor network.
Background
The wireless sensor network is a communication network composed of a plurality of sensors, the sensors are often distributed in different geographical areas, sense a certain specific phenomenon and collect related data, and then the data are uniformly sent to a target node, namely a sink node, through the wireless sensor network, and the sink node is responsible for collecting the sensed data of the network.
Because energy consumption of the sensor is mainly focused on data transmission and data reception, nodes closer to the sink node often undertake more data forwarding tasks, so that energy consumption is higher, and when the nodes die due to energy exhaustion, functions of the wireless sensing network are damaged and cannot normally operate, so that the service life of the wireless sensing network is defined as the time spent by the death of the first node in the network. In order to balance the energy consumption of the network and prolong the service life of the network, a plurality of invention methods are provided at present, for example, a movable aggregation node can continuously change the position of the aggregation node, so that the energy consumption center of the network is continuously changed, and the energy consumption of the whole network is balanced, but the method has certain limitation. In a real environment, the deployment position of the sensor is often fixed, and the cost of moving the sensor is large, so that the method is not practical.
With the maturity of unmanned aerial vehicle technique, combine unmanned aerial vehicle and wireless sensor network into for new research hotspot, accomplish the collection of network data through unmanned aerial vehicle, can reduce the energy consumption of node by a wide margin, prolong the network life-span. However, the position of the cluster head in the network is fixed in the current method, and it is not considered that the cluster head node still bears more data forwarding tasks, so that a further optimization space exists, and meanwhile, few methods consider that untrusted malicious nodes exist in the network, and the nodes can seriously affect the data collection quality of the whole network.
Disclosure of Invention
The invention provides the wireless sensor network data collection method based on the unmanned aerial vehicle, which can effectively reduce the energy consumption of network nodes, prolong the service life of the network and accurately evaluate the reliability of the nodes in the network.
In order to achieve the purpose, the invention provides a credible data collection method based on an unmanned aerial vehicle, which comprises the following steps: step one, selecting cluster head nodes of a network according to the reliability of the nodes, and clustering the network, so that each cluster comprises one cluster head, and other nodes are added into different clusters according to a proximity principle; step two, comprehensively considering the reliability of cluster heads, network energy consumption and flight distance of the unmanned aerial vehicle, selecting an unmanned aerial vehicle data collection path, enabling the unmanned aerial vehicle to sequentially pass through each cluster head on the path, sending sensing data of a node in a cluster where the cluster head is located to the unmanned aerial vehicle by the cluster head, and finally collecting and sending the data to a sink node by the unmanned aerial vehicle; and thirdly, re-evaluating the reliability of the nodes in the network, and re-selecting the nodes with high reliability as cluster head nodes of the network in the next round of data collection process according to the evaluation result. When the next round of data collection starts, returning to the step one; according to the credible data collection method based on the unmanned aerial vehicle, the network energy consumption can be effectively reduced, the service life of the network is prolonged, and the credibility of the network collected data is improved.
The invention has the beneficial effects that: the invention uses the unmanned aerial vehicle to complete data collection and transmission, thereby reducing the energy consumption of data transmission of the nodes and prolonging the service life of the network. In the process of cluster head election, only the nodes with high reliability are selected as cluster heads, so that the reliability of network data is guaranteed, in the process of multi-round data acquisition, the reliability data of each node in the network are continuously updated, the nodes with high reliability are selected as cluster heads again, the true reliability of each node is iteratively approached through the method, malicious nodes are identified, and the data quality of the network is guaranteed.
Drawings
Fig. 1 is a schematic view of a flight path based on an unmanned aerial vehicle according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of total energy consumption of a wireless sensor network according to an embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating comparison of the lifetime of a wireless sensor network according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating comparison of average flight distances of the unmanned aerial vehicle according to the embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating comparison of accuracy rates of data collected by the unmanned aerial vehicle according to the embodiment of the present invention.
Detailed Description
In this embodiment, all sensing nodes in the wireless sensor network are randomly deployed in a square area, and this embodiment mainly includes the following contents.
Step one, selecting cluster head nodes of a network according to the reliability of the nodes, and clustering the network, so that each cluster comprises one cluster head, and other nodes are added into different clusters according to a proximity principle. Specifically, first, a threshold value of the node reliability is set to Q, and when a certain node u is presentiReliability q ofiAnd when the node is not less than Q, the node is allowed to become a cluster head candidate. And then randomly selecting N nodes from the cluster head candidates as the finally determined cluster head. In addition, in the initial state of the network, the trust information of the network nodes is not known, so that the trust values of all the nodes in the network are set to be 0.5 in the initial state, namely whether the nodes are credible or not is not determined, cluster heads are randomly selected from all the nodes in the first round of data acquisition, then the network trust information is continuously updated in the multiple rounds of data acquisition, and the cluster heads are determined according to the method.
And step two, comprehensively considering the reliability of the cluster heads, network energy consumption and the flight distance of the unmanned aerial vehicle, selecting an unmanned aerial vehicle data collection path, wherein the unmanned aerial vehicle sequentially passes through each cluster head on the path, the cluster heads send the sensing data of the nodes in the cluster where the cluster heads are located to the unmanned aerial vehicle, and the unmanned aerial vehicle finally gathers the data and sends the data to the sink nodes. Firstly, according to the determined cluster head list, randomly arranging and generating a plurality of groups of initial paths. Then, the evaluation indexes of the possible paths are calculated, and the calculation method is as follows:
Figure BDA0002590889920000021
Figure BDA0002590889920000022
Figure BDA0002590889920000023
Figure BDA0002590889920000031
wherein
Figure BDA0002590889920000032
And the comprehensive evaluation index of the path i, the integral reliability of the network corresponding to the path i, the network energy consumption and the flight distance of the unmanned aerial vehicle are represented respectively. EiRepresentative node uiThe energy consumption of (2) is reduced,
Figure BDA0002590889920000033
indicating the starting point u of the flight of the dronemAnd node r0A distance between um+1Representing the end of flight of the drone.
And when the evaluation indexes of all the initial paths are calculated, selecting one path with the maximum evaluation index as the flight path of the unmanned aerial vehicle. After the data collection process starts, the unmanned aerial vehicle can fly through all cluster head nodes in sequence according to the cluster head access data given on the flight path, and data collection is completed.
And thirdly, re-evaluating the reliability of the nodes in the network, and re-selecting the nodes with high reliability as cluster head nodes of the network in the next round of data collection process according to the evaluation result. Firstly, an evaluator (unmanned aerial vehicle) is assumed to be A, an evaluated person is assumed to be B, and the perception values of the evaluator and the evaluated person to the same phenomenon in the ith round of data perception and acquisition processes are respectively
Figure BDA0002590889920000034
And
Figure BDA0002590889920000035
by analyzing the phenomenon perceived by the network in advance, the upper limit of the difference of the node perception values is obtainedTheta, then if
Figure BDA0002590889920000036
B is considered to be completely untrustworthy. Otherwise, the credibility of the evaluation of B by the ith round data acquisition process A can be calculated
Figure BDA0002590889920000037
The formula is as follows:
Figure BDA0002590889920000038
after K-round data acquisition and confidence evaluation, a comprehensive confidence evaluation value q of B can be obtainedBThe calculation method is as follows:
Figure BDA0002590889920000039
namely, the comprehensive trust is the average value of the trust evaluation values of the node K rounds. At the same time, we specify a value nθIf in succession nθIn round trust evaluation, the change amplitude of the trust of a certain node is smaller than a threshold qI.e. | qi-qi-1|<qThen we consider the trust evaluation of the node to be completed and use the resulting integrated trust evaluation value of the node as its determined trust. In subsequent processes, the node is not subjected to confidence evaluation any more, so that the energy consumption of the unmanned aerial vehicle is further reduced.
The invention provides a credible data collection method based on an unmanned aerial vehicle in a wireless sensor network, which is characterized in that a flight path of the unmanned aerial vehicle is planned by selecting a cluster head node with high credibility and comprehensively considering various factors, the credibility of the network node is continuously evaluated in the process of multi-round data collection, malicious nodes in the network are identified, and the quality of data collected by the network is improved. The invention has the advantages that:
(1) the trusted data collection method based on the unmanned aerial vehicle effectively reduces the energy consumption of the nodes in the network and prolongs the service life of the network. The unmanned aerial vehicle replaces the node wireless communication to complete the collection of network data, the energy consumption of cluster head nodes and sink nodes in the network is greatly reduced, the high-energy-consumption nodes are prevented from dying in advance due to energy exhaustion, and therefore the service life of the network is prolonged.
(2) The credible data collection method based on the unmanned aerial vehicle can plan the unmanned aerial vehicle data collection path efficiently, comprehensively considers the average credibility of the cluster head, the energy consumption of the network and the flight distance of the unmanned aerial vehicle, and plans to obtain an unmanned aerial vehicle flight path with good effect.
(3) The credible data collection method based on the unmanned aerial vehicle can effectively identify malicious nodes in the network and accurately evaluate the credibility of different nodes, thereby improving the quality and reliability of network data collection. FIG. 2 shows the energy consumption of the network when the iteration turns of the algorithm are 102,5×102,103,5×103,2×104In turn, the total network energy consumption of the method is reduced by 0.8%, 14.7%, 23.2%, 19.7% and 10.6% compared with that of the general method. Therefore, the method of the invention effectively reduces the energy consumption of the network. The service life of the network is shown in fig. 3, and because the method of the invention effectively reduces the energy consumption of the network nodes, the average energy consumption of the cluster head nodes can be greatly reduced while efficiently planning the clustering and the flight path of the unmanned aerial vehicle, thereby prolonging the death time of the first node in the network, namely improving the service life of the network. Fig. 4 shows the average distance of the flight path of the unmanned aerial vehicle obtained in the method of the present invention, and as the algorithm starts to operate, the effect of obtaining the flight path is also rapidly improved, and when the iteration number of the algorithm is only 1000 rounds, the effect of the obtained flight path gradually approaches the effect of the optimal solution, and the optimization continues to approach the optimal solution in the subsequent iteration. Compared with the common method, the distance of the flight path of the unmanned aerial vehicle generated by the method is reduced by 11.2% on average. Fig. 5 shows the similarity between the data collected by the unmanned aerial vehicle and the real sensing data in the method of the present invention, and the higher the similarity is, the more reliable the collected data is, the method effectively identifies the malicious node, and avoids the malicious nodeThese nodes have an impact on data quality. In a network with malicious nodes, compared with a general method, the method provided by the invention has the advantage that the accuracy of the data collected by the network is improved by up to 32%.

Claims (4)

1. A trusted data collection method based on an unmanned aerial vehicle in a wireless sensor network is characterized by comprising the following steps:
step one, selecting cluster head nodes of a network according to the reliability of the nodes, and clustering the network, so that each cluster comprises one cluster head, and other nodes are added into different clusters according to a proximity principle.
And step two, comprehensively considering the reliability of the cluster heads, selecting an unmanned aerial vehicle data collection path according to the network energy consumption and the flight distance of the unmanned aerial vehicle, enabling the unmanned aerial vehicle to sequentially pass through each cluster head on the path, sending the sensing data of the node in the cluster where the unmanned aerial vehicle is located to the unmanned aerial vehicle by the cluster head, and finally collecting and sending the data to the sink node by the unmanned aerial vehicle.
And thirdly, re-evaluating the reliability of the nodes in the network, and re-selecting the nodes with high reliability as cluster head nodes of the network in the next round of data collection process according to the evaluation result. And when the next round of data collection is started, returning to the step one.
2. The method for collecting trusted data based on unmanned aerial vehicle in wireless sensor network according to claim 1, wherein the specific steps of network clustering in step one are as follows: setting the threshold value of the node reliability as Q when a certain node uiReliability q ofiAnd when the node is not less than Q, the node is allowed to become a cluster head candidate. And then randomly selecting N nodes from the cluster head candidates as the finally determined cluster head. In addition, in the initial state of the network, the trust information of the network nodes is not known, so that the trust values of all the nodes in the network are set to be 0.5 in the initial state, namely whether the nodes are credible or not is not determined, cluster heads are randomly selected from all the nodes in the first round of data acquisition, then the network trust information is continuously updated in the multiple rounds of data acquisition, and the cluster heads are determined according to the method.
3. The method for collecting the credible data based on the unmanned aerial vehicle in the wireless sensor network according to claim 1, wherein the specific step of determining the flight trajectory of the unmanned aerial vehicle in the second step is as follows: firstly, according to the determined cluster head list, randomly arranging and generating a plurality of groups of initial paths. Then, the evaluation indexes of the possible paths are calculated, and the calculation method is as follows:
Figure FDA0002590889910000011
Figure FDA0002590889910000012
Figure FDA0002590889910000013
Figure FDA0002590889910000014
wherein
Figure FDA0002590889910000015
And the comprehensive evaluation index of the path i, the integral reliability of the network corresponding to the path i, the network energy consumption and the flight distance of the unmanned aerial vehicle are represented respectively. EiRepresentative node uiThe energy consumption of (2) is reduced,
Figure FDA0002590889910000016
indicating the starting point u of the flight of the dronemAnd node r0A distance between um+1Representing the end of flight of the drone.
And when the evaluation indexes of all the initial paths are calculated, selecting one path with the maximum evaluation index as the flight path of the unmanned aerial vehicle. After the data collection process starts, the unmanned aerial vehicle can fly through all cluster head nodes in sequence according to the cluster head access data given on the flight path, and data collection is completed.
4. The method for collecting trusted data based on unmanned aerial vehicle in wireless sensor network according to claim 1, wherein the specific method for evaluating the credibility of the nodes in the network in step three is as follows: firstly, an evaluator (unmanned aerial vehicle) is assumed to be A, an evaluated person is assumed to be B, and the perception values of the evaluator and the evaluated person to the same phenomenon in the ith round of data perception and acquisition processes are respectively
Figure FDA0002590889910000021
And
Figure FDA0002590889910000022
by analyzing the phenomena perceived by the network in advance, we obtain the upper limit theta of the node perception value difference, if
Figure FDA0002590889910000023
Figure FDA0002590889910000024
B is considered to be completely untrustworthy. Otherwise, the credibility of the evaluation of B by the ith round data acquisition process A can be calculated
Figure FDA0002590889910000025
The formula is as follows:
Figure FDA0002590889910000026
after K-round data acquisition and confidence evaluation, a comprehensive confidence evaluation value q of B can be obtainedBThe calculation method is as follows:
Figure FDA0002590889910000027
namely, the comprehensive trust is the average value of the trust evaluation values of the node K rounds. At the same time, we specify a value nθIf at all, is connected toN is continuous withθIn round trust evaluation, the change amplitude of the trust of a certain node is smaller than a threshold qI.e. | qi-qi-1|<qThen we consider the trust evaluation of the node to be completed and use the resulting integrated trust evaluation value of the node as its determined trust. In subsequent processes, the node is not subjected to confidence evaluation any more, so that the energy consumption of the unmanned aerial vehicle is further reduced.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112333648A (en) * 2020-11-11 2021-02-05 重庆邮电大学 Dynamic data collection method based on unmanned aerial vehicle
CN112506215A (en) * 2020-11-18 2021-03-16 广州工程技术职业学院 Internet of things data acquisition method and unmanned aerial vehicle
CN112564773A (en) * 2020-12-09 2021-03-26 南京航空航天大学 Unmanned aerial vehicle data collection method based on sub-network cooperation
CN112733170A (en) * 2021-01-14 2021-04-30 中南大学 Active trust evaluation method based on evidence sequence extraction
CN112911584A (en) * 2020-12-16 2021-06-04 中南大学 Method for avoiding black hole node attack based on detection route to obtain node trust value in energy collection wireless sensor network
CN113433967A (en) * 2021-06-07 2021-09-24 北京邮电大学 Chargeable unmanned aerial vehicle path planning method and system
CN114845306A (en) * 2022-04-21 2022-08-02 中南大学 Network trust state acquisition method based on active message detection
CN115348554A (en) * 2022-08-15 2022-11-15 中南大学 Method for collecting credible data in edge sensor network

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130225200A1 (en) * 2010-09-16 2013-08-29 Commissariat A L'energie Atomique Et Aux Energies Alternatives Method and Apparatus for the cooperative localization of transmitters and/or receivers on a mobile body
CN107548029A (en) * 2017-08-21 2018-01-05 河海大学常州校区 AUV methods of data capture in a kind of underwater sensing network based on sea water stratification
CN110049528A (en) * 2019-04-25 2019-07-23 华侨大学 Mobile trust data collection method based on trust value effectiveness in a kind of Sensor Network
CN110364031A (en) * 2019-07-11 2019-10-22 北京交通大学 The path planning and wireless communications method of unmanned plane cluster in ground sensors network
CN110839244A (en) * 2019-10-21 2020-02-25 华侨大学 Credible data collection method based on node trust value virtual force

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130225200A1 (en) * 2010-09-16 2013-08-29 Commissariat A L'energie Atomique Et Aux Energies Alternatives Method and Apparatus for the cooperative localization of transmitters and/or receivers on a mobile body
CN107548029A (en) * 2017-08-21 2018-01-05 河海大学常州校区 AUV methods of data capture in a kind of underwater sensing network based on sea water stratification
CN110049528A (en) * 2019-04-25 2019-07-23 华侨大学 Mobile trust data collection method based on trust value effectiveness in a kind of Sensor Network
CN110364031A (en) * 2019-07-11 2019-10-22 北京交通大学 The path planning and wireless communications method of unmanned plane cluster in ground sensors network
CN110839244A (en) * 2019-10-21 2020-02-25 华侨大学 Credible data collection method based on node trust value virtual force

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
BO JIANG ET AL.: "Trust based energy efficient data collection with unmanned aerial vehicle in edge network", 《WILEY SPECIAL ISSUE ARTICLE》 *
DARIUSH EBRAHIMI ET AL.: "UAV-Aided Projection-Based Compressive Data Gathering in Wireless Sensor Networks", 《IEEE INTERNET OF THINGS JOURNAL》 *
张瑞瑞: "基于无人机的无线传感器网络优化技术研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 *
王赛等: "一种基于UAV的无人海岛监控网络数据收集策略", 《电讯技术》 *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112333648B (en) * 2020-11-11 2021-11-02 重庆邮电大学 Dynamic data collection method based on unmanned aerial vehicle
CN112333648A (en) * 2020-11-11 2021-02-05 重庆邮电大学 Dynamic data collection method based on unmanned aerial vehicle
CN112506215A (en) * 2020-11-18 2021-03-16 广州工程技术职业学院 Internet of things data acquisition method and unmanned aerial vehicle
CN112564773A (en) * 2020-12-09 2021-03-26 南京航空航天大学 Unmanned aerial vehicle data collection method based on sub-network cooperation
CN112911584A (en) * 2020-12-16 2021-06-04 中南大学 Method for avoiding black hole node attack based on detection route to obtain node trust value in energy collection wireless sensor network
CN112733170A (en) * 2021-01-14 2021-04-30 中南大学 Active trust evaluation method based on evidence sequence extraction
CN112733170B (en) * 2021-01-14 2024-01-30 中南大学 Active trust evaluation method based on evidence sequence extraction
CN113433967A (en) * 2021-06-07 2021-09-24 北京邮电大学 Chargeable unmanned aerial vehicle path planning method and system
CN113433967B (en) * 2021-06-07 2022-11-25 北京邮电大学 Chargeable unmanned aerial vehicle path planning method and system
CN114845306A (en) * 2022-04-21 2022-08-02 中南大学 Network trust state acquisition method based on active message detection
CN114845306B (en) * 2022-04-21 2024-04-19 中南大学 Network trust state acquisition method based on active message detection
CN115348554A (en) * 2022-08-15 2022-11-15 中南大学 Method for collecting credible data in edge sensor network
CN115348554B (en) * 2022-08-15 2024-04-16 中南大学 Trusted data collection method in edge sensor network

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