Unmanned aerial vehicle safety relay method for guaranteeing timeliness of Internet of Things information

Abstract

The invention provides an unmanned aerial vehicle safety relay method for guaranteeing the timeliness of Internet of Things information, and the method comprises the steps: determining the average decoding error probability of an unmanned aerial vehicle according to the signal transmitting power of Internet of Things equipment, the position of the Internet of Things equipment, and the position of the unmanned aerial vehicle; determining the average decoding error probability of the eavesdropping device based on a preset threshold value of the average decoding error probability of the monitor, the position of the eavesdropping device and the position of the unmanned aerial vehicle; according to the average decoding error probability of the unmanned aerial vehicle and the average decoding error probability of the monitor, determining the time average information age of the monitor, and according to the average decoding error probability of the unmanned aerial vehicle and the average decoding error probability of the eavesdropping device, determining the time average information age of the eavesdropping device; determining an influence degree according to the time average information age of the eavesdropping device and the time average information age of the monitor; and optimizing the position of the unmanned aerial vehicle by taking maximization of the influence degree as a target until the optimal position of the unmanned aerial vehicle is obtained. According to the invention, the security of information transmission of the Internet of Things is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle communication, and in particular relates to a unmanned aerial vehicle secure relay method for ensuring the timeliness of Internet of Things information. Background Art

[0002] Relay communication technology is implemented by deploying relay nodes in the network. These relay nodes can receive signals from source nodes and resend them, thereby extending the transmission distance. In the field of modern communications, drones, as a flexible and efficient aerial platform, have been widely used to perform various communication relay tasks. The development of drone communication relay technology provides a new solution to solve communication problems in complex environments. However, in response to the security issues of IoT wireless networks, related technologies use the security outage probability (SOP) to measure the security performance of the network. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a drone security relay method and device that ensures the timeliness of Internet of Things information, so as to meet the need to further improve communication security during relay communication.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] The present invention provides a drone security relay method for ensuring the timeliness of Internet of Things information, which is applied to a system consisting of an Internet of Things device, a drone, a monitor and an eavesdropping device, including: determining the average decoding error probability of the drone at the current position according to the signal transmission power of the Internet of Things device, the position of the Internet of Things device and the current position of the drone; determining the average decoding error probability of the eavesdropping device when the drone sends a target communication signal at the current position based on a preset threshold of the average decoding error probability of the monitor, the position of the eavesdropping device and the current position of the drone; determining the time average information age at the monitor according to the average decoding error probability of the drone at the current position and the average decoding error probability of the monitor, and ... The average decoding error probability of the drone at the current position and the average decoding error probability of the eavesdropping device are used to determine the time average information age at the eavesdropping device; the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor is determined according to the time average information age at the eavesdropping device and the time average information age at the monitor; it is judged whether the influence degree obtained at the current position of the drone meets the maximization convergence condition. If not, the influence degree is maximized as the goal, and the optimal transmission power of the drone is less than the maximum power and the position of the drone and the position of the Internet of Things device meet the preset conditions. The next iteration optimization is performed on the position of the drone until the optimal position of the drone is determined.

[0006] Optionally, the target communication signal is a signal sent by the UAV at an optimal transmission power at a current position. Based on a preset threshold of the average decoding error probability of the monitor, the position of the eavesdropping device and the current position of the UAV, the average decoding error probability of the eavesdropping device when the UAV sends the target communication signal at the current position is determined, including: solving the optimal transmission power of the UAV at the current position with the average decoding error probability of the monitor as the preset threshold; determining the average signal-to-noise ratio of the eavesdropping device with the optimal transmission power of the UAV at the current position, the position of the eavesdropping device and the current position of the UAV; and determining the average decoding error probability of the eavesdropping device using the average signal-to-noise ratio of the eavesdropping device as a parameter of the signal-to-noise ratio probability density function.

[0007] Optionally, the average decoding error probability of the drone at the current position is determined based on the signal transmission power of the IoT device, the position of the IoT device and the current position of the drone, including: determining the average signal-to-noise ratio of the drone at the current position based on the signal transmission power of the IoT device, the position of the IoT device and the current position of the drone; and using the average signal-to-noise ratio of the drone at the current position as a parameter of the signal-to-noise ratio probability density function to determine the average decoding error probability of the drone at the current position.

[0008] Optionally, the signal-to-noise ratio probability density function is:

[0009]

[0010] in, They represent the average signal-to-noise ratios of drones, monitors, and eavesdropping devices in LoS and NLoS scenarios, respectively. U stands for drone, M stands for monitor, E stands for eavesdropping device, P stands for D represents the signal transmission power of IoT devices, a U,D represents the large-scale fading between drones and IoT devices, κ represents the Rice coefficient, and σ 2 represents the distribution parameter of additive Gaussian white noise, P U represents the signal transmission power of the drone, a U,M represents the large-scale fading between the drone and the monitor, a U,E Represents the large-scale fading between the drone and the eavesdropping device.

[0011] Optionally, taking the average signal-to-noise ratio of the drone at the current position as a parameter of the signal-to-noise ratio probability density function, determining the average decoding error probability of the drone at the current position includes:

[0012]

[0013] in, The average decoding error probability of device k, where k is any one of the drones, eavesdropping devices, and surveillance cameras, (a) is the first-order Riemann integral approximation, m represents the number of bits of information contained in each update sent by the IoT device, and n represents the number of bits encoded into the block length.

[0014] Optionally, determining the time average information age at the monitor according to the average decoding error probability of the drone at the current position and the average decoding error probability of the monitor, and determining the time average information age at the eavesdropping device according to the average decoding error probability of the drone at the current position and the average decoding error probability of the eavesdropping device, comprises:

[0015]

[0016] in, represents the time average information age of device k, where k is any of the eavesdropping devices and monitors. represents the average decoding error probability of the drone at its current location, The average decoding error probability of device k, where k is any of the eavesdropping devices and the monitor, λ represents the frequency of data packets generated by the IoT device, and μ represents the distribution range parameter of the service time at the monitor and the eavesdropping device that follows the exponential distribution.

[0017] Optionally, determining the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor according to the time average information age at the eavesdropping device and the time average information age at the monitor includes:

[0018]

[0019] in, represents the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor, represents the time-averaged information age at the eavesdropping device, Represents the time-averaged information age at the monitor.

[0020] Optionally, with the goal of maximizing the influence, the optimal transmission power of the drone is less than the maximum power and the position of the drone and the position of the IoT device meet preset conditions, and the position of the drone is iteratively optimized until the optimal position of the drone is determined, including:

[0021]

[0022] sP U ≤P max ,

[0023] rU,D ∈(0,r),

[0024] l U,D >0

[0025] Among them, P U Represents the transmitting power of the UAV, P max Indicates the maximum transmit power limit of the drone. represents the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor, (r U,D ,l U,D ) represents the relative position coordinates of the drone and the IoT device, and r represents the maximum limit range of the horizontal coordinate in the relative position coordinates of the drone and the IoT device.

[0026] Optionally, taking the average decoding error probability of the monitor as a preset threshold, the optimal transmission power of the UAV at the current position is solved, including: constructing a function that subtracts the average decoding error probability function of the monitor and the preset threshold as the function to be solved; using the secant method to solve the zero point of the function to be solved, and when the function to be solved meets the convergence condition, the zero point is obtained; and obtaining the optimal transmission power of the UAV at the current position based on the zero point.

[0027] Optionally, the optimal transmission power of the drone at the current position is obtained based on the zero point:

[0028]

[0029] in, Indicates the optimal transmission power of the drone at its current location, represents the zero point, κ represents the Rice coefficient, σ 2 represents the distribution parameter of additive white Gaussian noise, a U,M Represents the large-scale fading between the drone and the monitor.

[0030] The embodiment of the present invention proposes a drone security relay method for ensuring the timeliness of Internet of Things information, and utilizes the air-to-ground (A2G) line-of-sight channel characteristics of the drone to enhance the transmission reliability. In this method, the age of information (AoI) is used as an indicator of signal transmission, which highlights the reliability of signal transmission while also emphasizing the real-time nature of the signal. The method provided in this embodiment jointly analyzes the position and transmission power of the drone in the presence of a hidden eavesdropper, and proposes an optimization problem based on the AoI model, which aims to use the timeliness of information to reflect the security performance of the network. Finally, the optimization problem is solved by using an iterative method, and by optimizing the hovering position and transmission power of the drone in the air, a reliable transmission solution is provided for remote devices in the Internet of Things that lack infrastructure for communication coverage, and it is ensured that the hidden eavesdropper on the ground has a high decoding error rate, thereby ensuring the reliability and security of information transmission.

[0031] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0033] Figure 1 This is a specific example flow chart of a drone security relay method for ensuring the timeliness of Internet of Things information in the present invention;

[0034] Figure 2 A system composed of an Internet of Things device, a drone, a monitor, and an eavesdropping device in an embodiment of the present invention;

[0035] Figure 3 is a graph of error-free AoI at receiver k in an embodiment of the present invention;

[0036] Figure 4 is a graph of an AoI curve with bit errors at a receiver k in an embodiment of the present invention;

[0037] Figure 5 Schematic diagram of AoI difference area in an embodiment of the present invention;

[0038] Figure 6 A schematic diagram of a data packet processing flow in an embodiment of the present invention;

[0039] Figure 7 It is a pseudo code diagram of the algorithm in the embodiment of the present invention;

[0040] Figure 8 A comparative experimental data diagram of the method proposed in the embodiment of the present invention and the traditional method;

[0041] Fig. 9 It is a principle block diagram of a specific example of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0045] This embodiment provides a drone security relay method that ensures the timeliness of IoT information. Figure 1 As shown, including:

[0046] S101, determining an average decoding error probability of the drone at the current location according to the signal transmission power of the IoT device, the location of the IoT device, and the current location of the drone;

[0047] S102, determining an average decoding error probability of the eavesdropping device when the drone sends a target communication signal at the current location based on a preset threshold of the average decoding error probability of the monitor, the location of the eavesdropping device, and the current location of the drone;

[0048] S103, determining the time average information age at the monitor according to the average decoding error probability of the drone at the current position and the average decoding error probability of the monitor, and determining the time average information age at the eavesdropping device according to the average decoding error probability of the drone at the current position and the average decoding error probability of the eavesdropping device;

[0049] S104, determining the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor according to the time average information age at the eavesdropping device and the time average information age at the monitor;

[0050] S105, determining whether the influence degree obtained by the UAV at the current position satisfies the maximization convergence condition. If not, continue to maximize the influence degree as the goal, the optimal transmission power of the UAV is less than the maximum power and the position of the UAV and the position of the IoT device meet the preset conditions as restrictions, and perform the next iterative optimization on the position of the UAV until the optimal position of the UAV is determined.

[0051] Exemplarily, the method proposed in this embodiment is applied to, for example, Figure 2 As shown in the figure, it is a system composed of IoT devices, drones, monitors and eavesdropping devices, in which the IoT device (Device) deployed in the edge area needs to send status information to the monitor (Monitor) to complete the information update. However, due to the limited transmission power of the Device, coupled with the long-distance path loss and the non-line of sight (NLoS) communication channel, the signal sent by the Device cannot reach the Monitor. In order to achieve reliable communication between the Device and the Monitor, the unmanned aerial vehicle (UAV) is deployed in the air as a relay, and the decode-and-forward method is used to decode the received signal from the Device and forward it to the Monitor from the new package to achieve the reliability of signal transmission.

[0052] However, due to the obvious floating position and broadcast channel of UAV, the information forwarded by it can be stolen by illegal users, i.e. eavesdroppers (Eav), causing serious security problems. Moreover, in order to avoid being discovered, Eav will hide behind a shelter, which means that the channel between Eav and UAV is NLoS at this time. In addition, this embodiment considers the worst case, that is, Eav hides directly below UAV to steal the signal forwarded by UAV to the greatest extent. This embodiment takes advantage of the line of sight channel (LoS) and uses power control at UAV to stabilize the receiving SNR at Monitor to reduce the probability of decoding errors. At the same time, the floating position of UAV is optimized and solved under the premise of ensuring reliable transmission to maximize the security performance of the network.

[0053] This embodiment establishes a two-dimensional Cartesian coordinate system with the Device as the origin, and the positions of the UAV, Device, EAV and Monitor can be represented as L U =(r U,D,l U,D ), L D =(0,0),L E =(r U,D ,0) and L M =(r,0), where r U,D represents the distance between Device and Eav, and r is the distance between Device and Monitor, both in m. Therefore, the UAV and object k∈{D,E,M} can be expressed as:

[0054] d U,k =||L U -L k ||2. (1.1)

[0055] Since the worst case Eav position is considered, we have d U,E = l U,D .

[0056] Then, the A2G channel between UAV and k can be modeled as:

[0057]

[0058] Among them, a U,k is large-scale fading, g U,k is small-scale fading. U,k It can be further expressed as:

[0059]

[0060] in, is the free space path loss at a distance of 1 m, η c (c∈{LoS,NLoS}) is the path loss factor, which has different values ​​in NLoS and LoS. In addition, the Rician channel model and the Rayleigh channel model are used to describe small-scale fading, so for g U,k It can be expressed as:

[0061]

[0062] where κ is the Rice coefficient, is a complex constant representing the LoS propagation environment. Random variable Represents the fading in NLoS propagation environment.

[0063] The transfer is divided into two phases:

[0064] Phase 1: Device uses channel h U,D Transmit information to UAV, but due to the transmission power P at the Device DThis will result in decoding errors at the UAV.

[0065] Phase 2: If the information at the UAV is decoded successfully, the information will be transmitted through channel h U,M The data is transmitted to the Monitor. Since the UAV uses probability control to ensure that a stable SNR can be maintained at the Monitor, this embodiment assumes that the decoding error probability at the Monitor is a constant value.

[0066] Assume that the channel between Device, Monitor and UAV is a LoS channel.

[0067] In summary, the signal-to-noise ratio (SNR) at the receiver k∈{E,M} can be expressed as:

[0068]

[0069] Among them, P U is the transmit power at the UAV, σ n ~CN(0,σ 2 ) represents additive complex Gaussian white noise (AWGN). In addition, in the first stage of transmission, the SNR at the UAV can be similarly expressed as:

[0070]

[0071] Obviously, the position of the UAV can have a huge impact on the decoding performance at the Monitor and Eav. If the UAV is closer to the Device, then in the second phase, the UAV needs a larger transmission power to communicate with the Monitor, but this will increase the probability of the message being successfully decoded by the Eav. On the contrary, if the UAV is closer to the Monitor, although the transmission power can be appropriately reduced to improve the security of the signal, the transmission signal of the Device is limited, and the average decoding error probability of the signal in the first phase at the UAV will increase. Since the height of the UAV, that is, the distance between the UAV and the EAV is one of the factors to be considered, and it is expected that the probability of successful decoding at the Monitor is much higher than that at the Eav, the UAV needs to be deployed in a reasonable position.

[0072] Based on the above derivation conclusion, this embodiment determines the optimal location for UAV deployment in the following manner:

[0073] First, the signal transmission performance of Monitor and Eav is analyzed according to the location of the UAV. In order to obtain accurate performance analysis, this embodiment determines the average decoding error probability of UAV and Eav according to the distribution of instantaneous channel and noise. Since the average decoding error probability at Monitor is a fixed value, it is not analyzed here.

[0074] In this embodiment, the receiver k∈{D,E,M} is about γ k The probability density function (PDF) can be expressed as:

[0075]

[0076] in, is the average SNR in LoS and NLoS scenarios.

[0077] For the above probability density function, the specific derivation process is given as follows:

[0078] According to formula (1.4), the complex random variable g U,k can be written as:

[0079]

[0080] in, is an imaginary unit, is to satisfy a 2 +b 2 =1 complex constant value, are independent random variables. Then we can get:

[0081]

[0082] Obviously and From this we can further obtain The amplitude is:

[0083]

[0084] According to the knowledge of central chi-square distribution and non-central chi-square distribution, we can get:

[0085]

[0086]

[0087] in, is a central chi-square distribution with one degree of freedom v, is a noncentral chi-squared distribution with one degree of freedom v and one noncentral parameter ω. x is the independent variable of the distribution function.

[0088] Similarly, AWGNs n can be written as:

[0089]

[0090] in are independent and identically distributed random variables, then their amplitude can be written as:

[0091]

[0092] Based on the knowledge of central F distribution and non-central F distribution, the linear combination of the above random variables can be expressed as:

[0093]

[0094] Among them, F(v1,v2;x) is a central F distribution with two degrees of freedom v1 and v2, F(v1,v2,ω;x) is a noncentral F distribution with two degrees of freedom v1 and v2 and a noncentral parameter ω, and x is the independent variable of the distribution function. The specific expressions of the central and noncentral F distributions are as follows:

[0095]

[0096] in, is the Gamma function.

[0097] Substituting equation (1.10) and equation (1.14) into equation (1.5) and equation (1.6), we can obtain:

[0098]

[0099] Will You can get:

[0100]

[0101] Among them are:

[0102]

[0103] The infinite series in formula (1.22) can be further written as:

[0104]

[0105] Finally, we get:

[0106]

[0107] Thus, the derivation is completed and formula (1.7) is obtained.

[0108] Generally speaking, the data packet size in the IoT network is very small, so the short packet channel coding scheme can be used for reliable communication. This embodiment uses finite block length coding information theory to calculate the decoding error probability at the receiver. Assuming that each update sent by the Device contains m bits of information, which is encoded into a block length of n bits, the decoding error probability at k can be expressed as:

[0109]

[0110] in, is the Q function. For ease of analysis, the Q function can be approximated as:

[0111]

[0112] in, δ=2 m / n -1. Then, the average decoding error probability of k∈{D,E,M} can be approximated as:

[0113]

[0114] in, Now F γk (γ) is represented by the variable γ k The cumulative distribution function (CDF) of is:

[0115]

[0116] Then the average decoding error probability can be approximated as:

[0117]

[0118] right Using integration by parts we can get:

[0119]

[0120] And because:

[0121]

[0122] Finally, the closed-form expression of the average decoding error probability is obtained as:

[0123]

[0124] Where (a) is the first-order Riemann integral approximation,

[0125] This closed-form expression makes it easier to analyze the transmission performance of the system.

[0126] Therefore, based on the above receiver k∈{D,E,M} about γ k The probability density function and the average decoding error probability are derived, which gives the relationship between the probability density function and the average decoding error probability. Therefore, the average signal-to-noise ratio of the drone at the current position is determined based on the signal transmission power of the IoT device, the location of the IoT device and the current location of the drone; the average signal-to-noise ratio of the drone at the current position is used as the parameter of the signal-to-noise ratio probability density function to determine the average decoding error probability of the drone at the current position. Specifically, the location of the IoT device, the current location of the drone and the channel parameters are first substituted into (1.3) to obtain the large-scale fading of the drone and the IoT device at the current location, and then the large-scale fading data and the signal transmission power of the IoT device are substituted into Get the average signal-to-noise ratio of the drone at its current location, substitute the average signal-to-noise ratio of the current location into (1.28), and get the variable γ U The cumulative distribution function of Finally, substitute the cumulative distribution function into (1.32) to obtain the average decoding error probability of the drone at its current location.

[0127] Similarly, the average decoding error probability of the eavesdropping device can also be consistent with the average decoding error probability of the drone at the current position. Based on the optimal transmission power of the drone at the current position, the position of the eavesdropping device and the current position of the drone, the average decoding error probability of the eavesdropping device is determined when the drone sends the target communication signal at the current position. Prior to this, it is necessary to first obtain the optimal transmission power of the drone at the current position. In this embodiment, the function of the subtraction of the average decoding error probability function of the monitor and the preset threshold is constructed as the function to be solved; the secant method is used to solve the zero point of the function to be solved. When the function to be solved meets the convergence condition, the zero point is obtained; the optimal transmission power of the drone at the current position is obtained according to the zero point, as follows:

[0128] Assume a threshold value of decoding error probability as θ, then we have In order to prevent the data packet from being successfully decoded by Eav, P U Usually set to satisfy The value of . Therefore, we can get:

[0129]

[0130] because is a monotonically decreasing function on (0,1], so we have Then equation (1.33) has only one zero point. However, even if equation (1.33) has a zero point, its closed-form solution is not easy to find due to the complex form of the function, so this embodiment proposes a simple iterative method to find the zero point of equation (1.33).

[0131] Will If z is regarded as the independent variable, the secant method can be used to find the zero point of the function, and its expression is:

[0132]

[0133] Where (i) represents the i-th iteration. The convergence condition of the above formula is |z (i+1) -z (i) |≤ξ M ,ξ M is a certain tolerance. When equation (1.34) converges, the zero point is Then the optimal transmission power of the UAV at the current position is:

[0134]

[0135] The IoT network emphasizes the real-time nature of data information. Therefore, this embodiment adopts a new measurement method, namely, Age of Information (AoI), to evaluate the real-time nature of data on the Monitor and Eav terminals.

[0136] In order to understand the concept of AoI more intuitively, we first assume that no decoding errors will occur at the UAV. Figure 3 As shown in Figure 2, at time t, the AoI at the receiver k∈{E,M} can be expressed as:

[0137] Δ k (t) = tu k,I (t), (1.36)

[0138] Among them, u k,I (t) represents the time when the latest packet received at k is generated, and I represents the index of this packet. k,I Indicates the timestamp of the packet being decoded. Successful decoding will reset the time to u' k.I -u k.I .exist Figure 3 A k,i =u k,i -u k,i-1 represents the time interval between the generation of the i-1th packet and the ith packet; T k,i =u' k,i -u k,i Represents the system time of the i-th packet, which includes queue waiting time and packet processing time.

[0139] Given an observation time window t, the time average AoI at k can be obtained as:

[0140]

[0141] Equivalent to Δ k The area between (t) and the t-axis, that is:

[0142]

[0143] The area of ​​the trapezoid is O k,i (i>1) can be further expressed as:

[0144]

[0145] In this embodiment, considering the decoding error, some data packets that fail to decode will cause the AoI curve to grow linearly. Figure 4 As shown, the gray cross indicates that at time stamp u' k,3 The AoI curve will decrease only when the next packet is successfully decoded. Indicates the time interval between the generation of the i-1th successfully decoded packet and the ith successfully decoded packet, represents the system time of the i-th successfully decoded packet. Therefore, from formula (1.36), we can get:

[0146]

[0147] When the observation time τ tends to infinity, the time-averaged AoI at k becomes:

[0148]

[0149] in, Represents the index of the latest successfully decoded packet. According to the above formula, it is obvious that and Therefore, the time average AoI can be written as:

[0150]

[0151] In this embodiment, it is hoped that the information obtained at the Monitor is the latest, while the information obtained at the Eav is outdated. The loss of real-time information means that the Eav cannot make effective interference and sabotage behaviors. If the data packets obtained by the Eav cannot be decoded correctly, while the data packets at the UAV and Monitor can be successfully decoded, this will produce a significant AoI difference, thereby ensuring the security of the system. Figure 5 Give a graphic description of the above situation.

[0152] Depend on Figure 5 Available, and The area difference between them can be expressed as:

[0153]

[0154] Down Figure 6 A simple data packet processing flow is given. The Device generates data packets with an exponentially distributed frequency l. The data packets will first pass through the UAV. Then, if the data packet is successfully decoded at the UAV, it will be re-encoded and transmitted to the Monitor. At this time, the Eav will also receive the data packet; if the data packet fails to be decoded at the UAV, the data packet will be discarded. At this time, the AoI at all receivers will increase linearly.

[0155] The service time at Monitor and Eav follows an exponential distribution with parameter m. In addition, in order to simplify the analysis of time-averaged AoI, this embodiment ignores the waiting time of each data packet in the queue, that is, when a new data packet arrives, the previous data packet has already completed the service. Note that since UAV only performs simple decoding and forwarding tasks, the system time at UAV in this patent is also negligible. It can be found that It will only decrease if decoding is successful at the UAV (Monitor).

[0156] In this embodiment, the temporal average age of information (AOI) at receiver k∈{E,M} can be calculated as:

[0157]

[0158] The temporal average age of information (AOI) derivation process is as follows:

[0159] In formula (1.39) It can be written as:

[0160]

[0161] Taking into account the decoding error at the receiver, the effective arrival rate of k∈{E,M} is defined as That is, the arrival rate of successfully decoded data packets. Obviously, we can get:

[0162]

[0163] Since the device generates data packets in an exponential distribution, The PDF of can be expressed as:

[0164]

[0165] So we get:

[0166]

[0167] And because the waiting time in the system time is negligible, we get:

[0168]

[0169] according to and The distribution of can be obtained:

[0170]

[0171] Substituting equation (1.46) into equation (1.39) yields:

[0172]

[0173] Substituting equation (1.48), equation (1.49), and equation (1.50) into equation (1.52), we get equation (1.44), and the proof is complete.

[0174] In order to more intuitively compare the AoI growth of Eav relative to Monitor, Normalize and determine the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor:

[0175]

[0176] Then substitute equation (1.41) into equation (1.50) to obtain The closed expression of .

[0177] In this embodiment, the goal is to obtain the optimal hovering position of a UAV. Make The maximum value reflects the security of the system. Therefore, according to (1.53), the following optimization problem is obtained:

[0178]

[0179] sP U ≤P max , (1.54a)

[0180] r U,D ∈(0,r),(1.54b)

[0181] l U,D >0, (1.54c)

[0182] Among them, formula (1.54a) indicates that P can be changed U To maintain the average SNR at the monitor constant, so that the decoding error probability remains at a low constant value, the upper limit of which is P max. Equations (1.54b) and (1.54c) limit the deployment range of the UAV. The AoIs of Monitor and Eav are affected by whether the data packet at the UAV is decoded correctly, which corresponds to the deployment location of the UAV. Since it is difficult to obtain a closed-form solution to the optimization problem P, this embodiment proposes an iterative algorithm to solve it.

[0183] In the (s+1)th iteration, the horizontal and vertical coordinates of the UAV can be calculated as follows:

[0184]

[0185] The convergence condition of the coordinate descent method is is a certain tolerance. It can be seen that the objective function increases with iteration. In practice, if the decoding error probability at Eav is not 100%, the objective function is finite, which ensures the convergence of the proposed algorithm. Finally, the optimal deployment position of the UAV can be obtained from the optimization problem P. For the specific algorithm pseudo code, see Figure 7 .

[0186] The embodiment of the present invention proposes a drone security relay method for ensuring the timeliness of Internet of Things information, and utilizes the air-to-ground (A2G) line-of-sight channel characteristics of the drone to enhance the transmission reliability. In this method, the age of information (AoI) is used as an indicator of signal transmission, which highlights the reliability of signal transmission while also emphasizing the real-time nature of the signal; the method provided in this embodiment jointly analyzes the position and transmission power of the drone in the presence of a hidden eavesdropper, and proposes an optimization problem based on the AoI model, aiming to use the timeliness of information to reflect the security performance of the network, and finally solves the optimization problem by using an iterative method. By optimizing the hovering position and transmission power of the drone in the air, a reliable transmission solution is provided for remote devices in the Internet of Things that lack infrastructure for communication coverage, and it is ensured that the hidden eavesdropper on the ground has a high decoding error rate, thereby ensuring the reliability, timeliness and security of information transmission, and the method proposed in this embodiment has a small number of iterative calculations.

[0187] The experimental data graph comparing the simulation results is shown in Figure 2. Figure 8 As shown, it can be seen that the method proposed in this embodiment has an obvious curve difference compared with the traditional method. Therefore, compared with the transmission scheme with fixed position and fixed power, the drone-assisted safe transmission method proposed in this embodiment is effective in achieving reliable communication and ensuring safety.

[0188] The present application also provides an electronic device, such as Fig. 9As shown, a processor 501 and a memory 502, wherein the processor 501 and the memory 502 may be connected via a bus or other means.

[0189] The processor 501 may be a central processing unit (CPU). The processor 501 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0190] The memory 502 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the drone security relay method for ensuring the timeliness of IoT information in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory.

[0191] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0192] The one or more modules are stored in the memory 502, and when executed by the processor 501, the following is performed: Figure 1 The drone security relay method for ensuring the timeliness of IoT information in the illustrated embodiment.

[0193] For details of the above electronic equipment, please refer to Figure 1 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0194] This embodiment also provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the drone security relay method for ensuring the timeliness of IoT information in any of the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0195] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A drone security relay method for ensuring the timeliness of Internet of Things information, characterized in that: Applicable to systems consisting of IoT devices, drones, surveillance cameras, and eavesdropping devices, including: Determine the average decoding error probability of the drone at the current location based on the signal transmission power of the IoT device, the location of the IoT device, and the current location of the drone; Determine the average decoding error probability of the eavesdropping device when the UAV sends the target communication signal at the current location based on a preset threshold of the average decoding error probability of the monitor, the location of the eavesdropping device, and the current location of the UAV; Determine the time average information age at the monitor based on the average decoding error probability of the drone at the current position and the average decoding error probability of the monitor, and determine the time average information age at the eavesdropping device based on the average decoding error probability of the drone at the current position and the average decoding error probability of the eavesdropping device; Determine the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor according to the time average information age at the eavesdropping device and the time average information age at the monitor; Determine whether the influence degree obtained by the UAV at the current position meets the maximization convergence condition. If not, continue to maximize the influence degree as the goal. The optimal transmission power of the UAV is less than the maximum power and the position of the UAV and the position of the IoT device meet the preset conditions. Perform the next iterative optimization on the position of the UAV until the optimal position of the UAV is determined.

2. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 1, characterized in that: The target communication signal is a signal sent by the UAV at the current position with the optimal transmission power. Based on the preset threshold of the average decoding error probability of the monitor, the position of the eavesdropping device and the current position of the UAV, the average decoding error probability of the eavesdropping device when the UAV sends the target communication signal at the current position is determined, including: Taking the average decoding error probability of the monitor as the preset threshold, the optimal transmission power of the UAV at the current position is solved; Determine the average signal-to-noise ratio of the eavesdropping device based on the optimal transmission power of the drone at the current location, the location of the eavesdropping device, and the current location of the drone; The average signal-to-noise ratio of the eavesdropping device is used as a parameter of the signal-to-noise ratio probability density function to determine the average decoding error probability of the eavesdropping device.

3. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 1, characterized in that: According to the signal transmission power of the IoT device, the location of the IoT device and the current location of the drone, the average decoding error probability of the drone at the current location is determined, including: Determine the average signal-to-noise ratio of the drone at its current location based on the signal transmission power of the IoT device, the location of the IoT device, and the current location of the drone; The average signal-to-noise ratio of the drone at the current position is used as the parameter of the signal-to-noise ratio probability density function to determine the average decoding error probability of the drone at the current position.

4. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 2 or 3, characterized in that: The signal-to-noise ratio probability density function is: in, They represent the average signal-to-noise ratios of drones, monitors, and eavesdropping devices in LoS and NLoS scenarios, respectively. U stands for drone, M stands for monitor, E stands for eavesdropping device, P stands for D represents the signal transmission power of IoT devices, a U,D represents the large-scale fading between drones and IoT devices, κ represents the Rice coefficient, and σ 2 represents the distribution parameter of additive white Gaussian noise, P U represents the signal transmission power of the drone, a U,M represents the large-scale fading between the drone and the monitor, a U,E Represents the large-scale fading between the drone and the eavesdropping device.

5. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 4, characterized in that: Taking the average signal-to-noise ratio of the drone at the current location as the signal-to-noise ratio probability density function parameter, determining the average decoding error probability of the drone at the current location includes: in, The average decoding error probability of device k, where k is any one of the drones, eavesdropping devices, and surveillance cameras, (a) is the first-order Riemann integral approximation, m represents the number of bits of information contained in each update sent by the IoT device, and n represents the number of bits encoded into the block length.

6. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 5, characterized in that: Determine the time average information age at the monitor according to the average decoding error probability of the drone at the current position and the average decoding error probability of the monitor, and determine the time average information age at the eavesdropping device according to the average decoding error probability of the drone at the current position and the average decoding error probability of the eavesdropping device, including: in, represents the time average information age of device k, where k is any of the eavesdropping devices and monitors. represents the average decoding error probability of the drone at its current location, The average decoding error probability of device k, where k is any of the eavesdropping devices and the monitor, λ represents the frequency of data packets generated by the IoT device, and μ represents the distribution range parameter of the service time at the monitor and the eavesdropping device that follows the exponential distribution.

7. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 6, characterized in that: According to the time average information age at the eavesdropping device and the time average information age at the monitor, determining the influence of the time average information age change at the eavesdropping device on the time average information age at the monitor, including: in, represents the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor, represents the time-averaged information age at the eavesdropping device, Represents the time-averaged information age at the monitor.

8. A drone secure relay method for ensuring the timeliness of Internet of Things information according to any one of claims 1-3, characterized in that: With the goal of maximizing the impact, the optimal transmission power of the drone is less than the maximum power and the drone position and the IoT device position meet the preset conditions. The position of the drone is iteratively optimized until the optimal position of the drone is determined, including: Among them, P U Represents the transmitting power of the UAV, P max Indicates the maximum transmit power limit of the drone. represents the influence of the change of the time average information age at the eavesdropping device on the time average information age at the monitor, (r U,D ,l U,D ) represents the relative position coordinates of the drone and the IoT device, and r represents the maximum limit range of the horizontal coordinate in the relative position coordinates of the drone and the IoT device.

9. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 2, characterized in that: Taking the average decoding error probability of the monitor as the preset threshold, the optimal transmission power of the drone at the current position is solved, including: According to the monitor average decoding error probability function and the preset threshold, a function is constructed to subtract the two as the function to be solved; Use the secant method to find the zero point of the function to be solved. When the function to be solved meets the convergence condition, the zero point is obtained. The optimal transmission power of the UAV at its current position is obtained based on the zero point.

10. A drone secure relay method for ensuring the timeliness of Internet of Things information according to claim 9, characterized in that: According to the zero point, the optimal transmission power of the drone at the current position is: in, Indicates the optimal transmission power of the drone at its current location, represents the zero point, κ represents the Rice coefficient, σ 2 represents the distribution parameter of additive white Gaussian noise, a U,M Represents the large-scale fading between the drone and the monitor.

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