CN112929866A - Unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage - Google Patents
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Abstract
The invention provides an unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage, which comprises the following steps: step 1, simulating urban disaster area scenes and constructing a state set of ground mobile user nodes; step 2, recording the position information of the ground mobile user node at each moment obtained in the step 1; and 3, calculating the optimal coverage of the unmanned aerial vehicle network in the urban disaster area according to the position information of the ground mobile user node at each moment in the step 2 and obtaining an optimal coverage strategy. The method provides emergency network communication for mobile user nodes in key areas of urban disaster areas, in an unmanned aerial vehicle network coverage scene of the urban disaster areas, the probability that the urban areas to be covered by the unmanned aerial vehicle network can be determined by the unmanned aerial vehicle cluster, the unmanned aerial vehicle deployment is optimized, the goal of maximizing the weighted coverage of the mobile user nodes in the key areas of the city is achieved on the premise of ensuring the network connectivity, and the adaptive coverage algorithm is used for improving the weighted coverage of the unmanned aerial vehicle network and ensuring the network connectivity.
Description
Technical Field
The invention belongs to the technical field of smart cities and wireless communication, and particularly relates to an unmanned aerial vehicle deployment method for adaptively optimizing network coverage of urban disaster areas.
Background
The occurrence of natural disasters such as earthquakes, hurricanes, volcanic eruptions, tsunamis, etc. causes urban areas to be extremely severely damaged. The urban ground communication infrastructure is extremely easy to be in a congestion paralytic state or seriously damaged due to the occurrence of an emergency, and further environment information and rescue information of an urban disaster area cannot be effectively transmitted, so that the effective implementation of rescue actions is influenced. The buildings in the urban disaster areas collapse, the ruin areas are wide, vehicles are difficult to enter, and it is difficult to find an area for properly deploying the ground base stations, so that the efficiency of deploying the ground base stations in the disaster areas is low, the consumption is high, and the new base stations can be damaged by aftershocks. Based on this, deploying networks of drones has become an efficient solution to these challenges. Because the urbanization speed of China is faster and faster, urban buildings are dense, the population is large, and the urban ground communication infrastructure is extremely easy to be in a congestion paralyzed state or to be seriously damaged due to the occurrence of disasters, the emergency communication provided for disaster areas by adopting the unmanned aerial vehicle network becomes a key.
Research has shown that 72 hours after disaster is "gold rescue time", and therefore, emergency rescue after disaster is an important life saving process (reference Erdelj M, Kr Lo l M, Natalizio E. Wireless sensor Networks and Multi-UAV systems for natural displacement management [ J ]. Computer Networks, 2017, 124: 72-86.). During this period, communication between rescuers and information about the location of victims in cities is of great importance, and they need to share information to know more disaster areas so as to complete the rescue task more efficiently, save more lives and reduce loss. The unmanned aerial vehicle network formed by the unmanned aerial vehicle cluster can better provide emergency communication for disaster areas, and the ratio of the coverage area to the area of the area to be covered is an important index for measuring the coverage capability of the unmanned aerial vehicle network. With the breakthrough of relevant key technologies of unmanned aerial vehicles, unmanned aerial vehicle networks formed by unmanned aerial vehicle clusters are widely applied to the fields of emergency communication, environment monitoring, military reconnaissance, logistics and the like. The unmanned aerial vehicle network plays an increasingly important role in the field of emergency communication due to the characteristics of flexible mobility, strong autonomy and the like. The problem of drone coverage in urban disaster areas also presents many challenges, such as the distribution of ground user nodes in urban disaster areas and the mobility of ground user nodes after a disaster has occurred. In the urban disaster area, some regional user nodes cannot enter the urban disaster area, so that the coverage of the region can be reduced to reduce coverage redundancy. Secondly, due to the mobility of the user node, the unmanned aerial vehicle network is required to dynamically cover the ground user node so as to prevent the user node from moving out of the coverage area of the network and enabling the node to be incapable of communicating. Therefore, the weighted coverage rate of the unmanned aerial vehicle network in the dynamic coverage process of the major areas of the urban disaster areas can be effectively improved by researching the movement rules of the ground user nodes of the urban disaster areas and dividing the areas of the cities.
For the coverage problem of the urban disaster area unmanned aerial vehicle network, some prior arts propose a two-stage optimization algorithm to provide seamless long-term coverage for the urban area (refer to the documents Ragothaman S, Maaref M, Kassas Z M. multi-path-optical UAV project planning for urban UAV navigation with cellular signals [ C ]//2019 IEEE 90th Vehicular Technology reference (VTC2019-Fall). Some prior arts propose a novel unmanned aerial vehicle assisted routing protocol, which focuses on optimizing the network communication service quality of unmanned aerial vehicles in urban environments (refer to the documents "osubbati O S," Chaib N, "Lakas a," et al. uav-assisted supporting service connectivity in urban VANETs [ J ]. IEEE Transactions on Vehicular Technology, 2019, 68 (4): 3944-. Some prior arts concentrate on the research of urban disaster areas, and provide an intelligent strategy for an unmanned aerial vehicle to perform tactical actions in a disaster scene. Dynamic overlay of terrestrial users combined with Jaccard distance and simulated annealing algorithms (ref S-nchez-Garci a J, Garci a-Campos J M, Toral S L, et al. an interactive strategy for visual effects of UAVs in discaster research [ J ]. International Journal of Distributed Sensor Networks, 2016, 12 (3): 8132812.). This strategy maximizes the number of victims of drone service while guaranteeing network connectivity, but there are also more corner nodes that are uncovered. Therefore, most of the work in the prior art is coverage optimization of the whole target area, coverage redundancy is generated, mobility of ground user nodes is not considered, and the actual urban disaster area scene is not met.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention aims to provide an unmanned aerial vehicle deployment method for adaptively optimizing urban disaster area network coverage, emergency network communication is provided for mobile user nodes in key areas of the urban disaster area, in an unmanned aerial vehicle network coverage scene of the urban disaster area, the probability of coverage of the unmanned aerial vehicle network in the urban area to be covered is jointly determined by an unmanned aerial vehicle cluster, the goal of maximizing the weighted coverage of the mobile user node coverage of the key areas of the city is achieved on the premise of ensuring network connectivity by optimizing the deployment of the unmanned aerial vehicle, and the adaptive coverage algorithm is used for improving the weighted coverage of the unmanned aerial vehicle network and ensuring the network connectivity.
The invention aims to provide an unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage, which comprises the following steps:
and 3, calculating the optimal coverage of the unmanned aerial vehicle network in the urban disaster area according to the position information of the ground mobile user node at each moment in the step 2, and obtaining an optimal coverage strategy according to the optimal coverage.
Preferably, the simulation of the urban disaster area scene in step 1 includes: the method comprises the steps of dividing an urban area to be covered into different areas, wherein the different areas comprise a building area, a street, a forbidden area, an open area and building debris, and simulating the movement of a ground user node in an urban disaster area by limiting the movement of the node in the different areas, so that a simulated urban disaster area scene is obtained.
Preferably, the dividing of the urban area to be covered into different areas is performed according to an area division model of the urban disaster area, where the area division model of the urban disaster area is as follows:
A＝{B，R，P，X} (1)
wherein, A is the urban disaster area to be covered, B is the building set and
B1-B8respectively represent residential buildings, public buildings, commercial buildings, business and public hybrid buildings, living and public hybrid buildings, business and public hybrid buildings and the like; r is a set of roads and
R1-R6the road is a quick passing road, a landscape tourism street, a street conforming to a sharing type, a residential living street and a landscape leisure walking path; p is an open area such as a park, and X is a prohibited area, i.e., an area, lake, or river, which humans cannot enter.
Preferably, the set of states of the terrestrial mobile user node comprises time of day, direction, speed, coordinate position and/or movement rules.
Preferably, the set of terrestrial mobile user nodes is G ═ G1，g2，…，gnTherein user node giHas a position coordinate of (x)i，yi0), the mobile rule of the mobile user node is:
wherein, random is that the user node moves randomly in the open area; b-trend means that the user node gradually moves to four edges of a building in the building area and finally reaches an outlet along the edges; linear is that a user node moves linearly on a street; b iscolIndicating that the building is collapsed, and fade is the time when the user node is in the building area and the building is collapsed, the user node is trapped in the building area.
Preferably, the step 3 comprises:
step 31, initializing the drone, including: will each unmanned aerial vehicle si∈S(S＝{s1，s2，…，snSetting the state of the weighted coverage rate as an initial state, setting the maximum iteration times, and defining a utility function for calculating the weighted coverage rate;
step 32, updating the location, including: and (3) according to information interaction between all unmanned aerial vehicle nodes in the unmanned aerial vehicle network, fusing the ground mobile user node information obtained in the step (2) with a position updating equation of the unmanned aerial vehicle, calculating an unmanned aerial vehicle set, and selecting a coverage strategy capable of bringing optimal weighted coverage by adopting the weighted coverage of the network under the position set under the position updating equation and the weighted coverage of the network under the current position.
And step 33, circularly executing the step 32 until the weighted coverage rate of the network under the current position of the unmanned aerial vehicle network does not change any more or reaches the set maximum iteration number, and ending the position updating of the unmanned aerial vehicle to obtain the optimal weighted coverage rate and the coverage strategy.
Preferably, the weighted coverage utility function defined in step 31 is specifically:
discretizing the area to be covered into m × n pixel points, so that the global coverage of the unmanned aerial vehicle network is as follows:
wherein j is any pixel point in the region to be covered,
the total number of covered pixel points in the whole area to be covered is calculated;
preferably, the coverage rate of the unmanned aerial vehicle network to the key area, which is obtained by regarding the ground mobile node as a pixel point, is as follows:
wherein G ∈ G ═ G { (G)1，g2，…，gnIs any one of the ground user nodes,
the number of covered ground user nodes is H, and the total number of pixel points in the key area is H;
preferably, the coverage of the forbidden area is sacrificed to improve the coverage of the key area, and the weighted coverage is introduced to perform normalization calculation on the coverage of the whole target area and the coverage of the key area:
cov(W)＝ω1cov(S)+ω2cov(H) (5)
wherein, ω is1Weighting factor, ω, for the overall coverage of the target area2Weighting factor for the coverage of the key region and1＜ω2(ii) a Thus, the objective function is:
f(X)＝max(cov(W)) (6)。
preferably, the location update policy of the set of drones of step 32 includes:
the initial state of the unmanned aerial vehicle set is randomly distributed, firstly, the weighted coverage rate in the current state is calculated by using a formula (5), and then the unmanned aerial vehicle set updates the target position of the next iteration by using a formula (7):
wherein, X (t +1) and Y (t +1) are respectively an abscissa position vector and an ordinate position vector of the t +1 th iteration unmanned aerial vehicle set;
distance vectors are respectively improved for the horizontal and vertical coordinates of the unmanned aerial vehicle of the virtual force guide,
for unmanned aerial vehicles siReceiving the resultant force of the virtual forces of all the unmanned planes in the unmanned plane set, wherein the calculation formula is a formula (8); v. ofα，vβ，vσIs a weight coefficient, vγ＝να+νβ+νσ，X1，Y1，X2，Y2，X3，Y3The calculation formula of (a) is formula (9) -formula (11);
wherein, thetaijIndicates the direction of the force, thetaij＝tan-1((yi-yj)/(xi-xj))；ωaAnd ωrRespectively representing a gravitational coefficient and a repulsive coefficient; distance threshold value dth2r, wherein r is the communication radius of the unmanned aerial vehicle;
minimum value representing the distance of the drone from other drones in the set, dthRepresenting the distance between the drone and the drone; dijRepresenting the distance between drone i and drone j.
Wherein, Xα，Yα，Xβ，Yβ，Xσ，YσRespectively selecting alpha, beta and sigma unmanned aerial vehicle position coordinate vectors guided by the unmanned aerial vehicle through an algorithm; a. the1＝A2＝A3＝2αr1- α is a co-operative coefficient vector, α ═ 2-2 ((e)t/T-1)/(e-1)) as convergence factor, T and T being the current and maximum number of iterations, r, respectively1Is [0, 1 ]]And:
wherein, C1＝C2＝C3＝2r2As a vector of co-operative coefficients, r2Is [0, 1 ]]Random vector of (1), XLevy，YLevyAnd (3) updating the unmanned plane position vector of the current unmanned plane set through the Levier flight rule:
wherein X and Y are position vectors of the unmanned aerial vehicle in the initial state; alpha is alpha0Beta is a constant; mu and
are all subject to Gaussian distribution, and
satisfies the following conditions:
in the formula, xd and yd respectively represent improved distance vectors of horizontal and vertical coordinates of the unmanned aerial vehicle set, DmaxIs the maximum step size of the move, in the equation,
delta x and delta y are coordinate difference vectors of ground user nodes closest to the unmanned aerial vehicle set in horizontal distance, and a position coordinate set with the minimum horizontal distance to the unmanned aerial vehicle set is obtained by calculating the minimum value of each column of a matrix (16) and the number of rows where the minimum value is located, wherein n is the number of the unmanned aerial vehicles, and p is the number of ground mobile nodes; the elements in the matrix represent the horizontal distances between the n drones and the p ground user nodes respectively.
The invention has the beneficial effects that:
compared with the prior art, the technical scheme provided by the invention has the following remarkable effects:
(1) the invention discloses an unmanned aerial vehicle deployment method for adaptively optimizing network coverage of an urban disaster area, which aims at the field of urban disaster area rescue and emergency communication and solves the problem of optimizing coverage of ground user nodes moving in key areas of the urban disaster area. The acquired information is merged into the deployment method of the unmanned aerial vehicle, the unmanned aerial vehicle network is not limited by fixed ground user nodes, and the positions of the unmanned aerial vehicle set can be adjusted according to the requirements.
(2) The unmanned aerial vehicle deployment method for self-adaptively optimizing the network coverage of the urban disaster area is reasonably designed according to the disaster area of the urban disaster area, the distribution of buildings and roads in the urban disaster area, the population number of user nodes in each area, the movement rule and the like, a weighting coefficient is introduced to adjust a coverage utility function, the urban disaster area is divided into a key area and a non-key area, the coverage rate of the non-key area is sacrificed to a certain extent, and the coverage rate of the unmanned aerial vehicle network to the key area is further improved.
(3) According to the unmanned aerial vehicle deployment method for adaptively optimizing the urban disaster area network coverage, a dynamic adjustment rule is designed, so that the position of an unmanned aerial vehicle can be dynamically adjusted by the unmanned aerial vehicle network according to the movement change of ground user nodes in the process that the unmanned aerial vehicle network provides network services for urban disaster area ground mobile users, and the network services can be more stably provided for the urban disaster area.
(4) The unmanned aerial vehicle deployment method for self-adaptively optimizing the urban disaster area network coverage maintains the connectivity of the unmanned aerial vehicle network by designing a position adjustment rule, so that the situation that the unmanned aerial vehicle network is not connected due to the fact that the unmanned aerial vehicle adapts to the movement of ground user nodes is avoided. The invention improves the coverage rate of the unmanned aerial vehicle network on the premise of ensuring the network connectivity.
Drawings
Fig. 1 is an emergency network structure diagram of an unmanned aerial vehicle deployment model for adaptively optimizing urban disaster area network coverage according to an embodiment of the invention.
Fig. 2 is a schematic structural diagram of an unmanned aerial vehicle deployment model for adaptively optimizing urban disaster area network coverage according to an embodiment of the invention.
Fig. 3 is a diagram of an urban area allocation according to an embodiment of the present invention.
Fig. 4 is an initial distribution diagram of 298 ground user nodes in an urban disaster area according to an embodiment of the present invention.
Fig. 5 is a node distribution diagram after 298 ground user nodes in urban disaster area move for 298 seconds according to the movement rule according to the embodiment of the invention.
Fig. 6 is a graph showing the convergence of global coverage and weighted coverage for 20 drones under the CS-IGWO algorithm in 10 experiments according to an embodiment of the present invention.
Fig. 7 is a deployment diagram of 20 drones maximizing weighted coverage when a ground user node moves 500s according to an embodiment of the present invention.
Fig. 8 is a graph showing the time-dependent change of the weighted coverage, the network connectivity and the average path loss under three city environment parameters in 1 experiment according to the embodiment of the present invention.
Fig. 9 is a graph showing a change of the weighted coverage and the network connectivity in the suburban environment after adjusting the path loss threshold in 10 experiments according to the embodiment of the present invention.
Fig. 10 is a graph showing the time-dependent changes in global coverage, network connectivity and average pathloss in a suburban environment after adjusting the pathloss threshold in 1 experiment according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings, but the present invention is not limited thereto.
Referring to fig. 1, the unmanned aerial vehicle deployment method for adaptively optimizing the network coverage of the urban disaster area of the embodiment provides an emergency network deployment method capable of effectively improving the global coverage and the coverage of the key area (weighted coverage) for the coverage optimization of the unmanned aerial vehicle network in the emergency rescue field of the urban disaster area and the mobility problem of the ground user nodes, and can adjust the position of the unmanned aerial vehicle according to the movement rule of the ground user nodes so as to serve more ground user nodes in the whole process, thereby rapidly providing a reliable emergency network for the urban disaster area. The distribution of the unmanned aerial vehicle network can be designed reasonably according to the area of a disaster-stricken city, the distribution of buildings, roads and the like, the population number and the density. Compared with the prior art, the coverage area of the unmanned aerial vehicle network to a key area can be effectively enlarged, deployment can be adaptively adjusted according to movement of the ground user nodes, and connectivity and stability of the network are enhanced.
Referring to fig. 1, the emergency network of the embodiment mainly includes an unmanned aerial vehicle, a wireless device, a GPS positioning device, and a remote base station in terms of hardware, and aims to quickly establish the emergency network, provide a reliable and stable communication for disaster areas, and help to implement rescue actions.
Referring to fig. 2, the unmanned aerial vehicle deployment model for adaptively optimizing the network coverage of the urban disaster area is characterized as follows: in a coverage scene of an unmanned aerial vehicle network, for any area to be covered, the probability of being covered by the unmanned aerial vehicle network is jointly determined by unmanned aerial vehicles which can communicate with ground user nodes in the area; by unmanned aerial vehicle deployment and coverage rate control, the aim of maximizing the weighted coverage rate is achieved on the premise of meeting communication requirements and maintaining network connectivity.
The unmanned aerial vehicle deployment method for adaptively optimizing the network coverage of the urban disaster area comprises the following steps:
And 2, recording the position information of the ground user node at each moment through the step 1.
And 3, calculating the optimal coverage of the urban disaster area unmanned aerial vehicle network by combining the position information of the ground user nodes obtained in the step 2 to obtain an optimal coverage strategy, which is as follows:
step 31, initialization: each unmanned aerial vehicle si∈S(S＝{s1，s2，…，sn}) is set as an initial state, the maximum number of iterations is set, and a utility function is defined that calculates the weighted coverage.
Step 32, position updating: and (3) information interaction can be carried out among all unmanned aerial vehicle nodes in the unmanned aerial vehicle network, the ground user node information obtained in the step (2) is fused with the position updating equation of the unmanned aerial vehicle, the network coverage rate of the unmanned aerial vehicle set at the next position set by adopting the position updating equation and the network coverage rate of the unmanned aerial vehicle at the current position are calculated, and a coverage strategy capable of bringing the optimal weighted coverage rate is selected.
Step 33, convergence: and (4) circulating the step 32 until the weighted coverage rate of the unmanned aerial vehicle network does not change any more or the set maximum iteration number is reached, ending the position updating of the unmanned aerial vehicle, and obtaining the optimal weighted coverage rate and the coverage strategy.
The embodiment is implemented as follows:
in step 1, the region division model of the urban disaster area is as follows:
A＝{B，R，P，X} (1)
wherein, A is the urban disaster area to be covered, B is the building set and
B1-B8respectively represent residential buildings, public buildings, commercial buildings, business and public hybrid buildings, living and public hybrid buildings, business and public hybrid buildings and the like; r is a set of roads and
R1-R6the road is a quick passing road, a landscape tourism street, a street conforming to a sharing type, a residential living street and a landscape leisure walking path; p is an open area such as a park, and X is a prohibited area (an area where humans cannot enter, a lake, a river, etc.).
In step 2, the set of ground user nodes is G ═ G1，g2，…，gnTherein user node giHas a position coordinate of (x)i，yi0), the movement rule is:
wherein, random is that the user node moves randomly in the open area; b-trend means that the user node gradually moves to four edges of a building in the building area and finally reaches an outlet along the edges; linear is that a user node moves linearly on a street; b iscolIndicating that the building is collapsed, and fade is the time when the user node is in the building area and the building is collapsed, the user node is trapped in the building area.
Discretizing an area to be covered into m multiplied by n pixel points, and defining that the link loss between a user and the unmanned aerial vehicle is smaller than a path loss threshold PLmaxIn time, the user may be covered by the drone. In the target area a, the target area b,there are m ground user nodes, and the node set is G ═ G1，g2，...，gm}; there are n unmanned aerial vehicle nodes, and the node set is S ═ S1，s2，...，sn}. Ith unmanned aerial vehicle node siHas a position coordinate of (x)i，yi，hi) Let the position coordinate of pixel point j be (x)j，yj,0). Then unmanned aerial vehicle node siThe horizontal distance from the pixel point j is as follows:
by adopting an A2G channel model, the probability of a line-of-sight communication (LoS) link between the pixel point j and the unmanned aerial vehicle i is:
where α and β are environmental parameters and the numerical settings are related to the building density of the city. h isiFor unmanned aerial vehicles siThe flying height of (c). Furthermore, the probability of a non line-of-sight (NLoS) communication link is:
due to the complex urban background, there is a lot of noise and interference. Therefore, in urban disaster areas, wireless propagation signals have losses due to building shadowing and scattering, in addition to free space propagation losses. Thus, unmanned aerial vehicle siLoss models of LoS and NLoS links between the local node and the pixel point j are respectively as follows:
wherein f iscIs the carrier frequency; etaLoSAnd ηNLoSRespectively, the extra path loss under the line-of-sight communication link and the non-line-of-sight link; c is the speed of light; dijFor unmanned aerial vehicles siDistance to pixel point j:
thus, under the LoS and NLoS models, the drone siThe average path loss of the link A2G between the pixel j is:
in order to ensure the service quality, the receiving power of the pixel point j must exceed a certain threshold, that is, the pixel point j and the unmanned aerial vehicle siLink loss therebetween is less than or equal to a certain threshold value PLmaxThen, pixel point j is controlled by unmanned aerial vehicle siCovering, namely:
Lij≤PLmax (10)
then pixel point j can be controlled by unmanned aerial vehicle node siThe perceived probability is:
any pixel point can be perceived by a plurality of unmanned aerial vehicle nodes simultaneously, so the joint probability of pixel point j perceived by the unmanned aerial vehicle node set S is expressed as:
then the global coverage of the drone network is:
similarly, the coverage rate of the unmanned aerial vehicle network to the key area, which is obtained by regarding the ground mobile node as a pixel point, is as follows:
sacrificing the coverage of the forbidden area to some extent can increase the coverage of the key area, so that the weighted coverage is introduced to perform normalized calculation on the coverage of the whole target area and the coverage of the key area:
cov(W)＝ω1cov(S)+ω2cov(H) (15)
wherein, ω is1Weighting factor, ω, for the overall coverage of the target area2Weighting factor for the coverage of the key region and1＜ω2. Thus, the objective function is:
f(X)＝max(cov(W)) (16)
the location update policy of the unmanned aerial vehicle set in step 32 is specifically as follows:
the initial state of the unmanned aerial vehicle set is randomly distributed, firstly, the weighted coverage rate in the current state is calculated by using a formula (16), and then the unmanned aerial vehicle set updates the target position of the next iteration by using a formula (17):
wherein, X (t +1) and Y (t +1) are respectively an abscissa position vector and an ordinate position vector of the t +1 th iteration unmanned aerial vehicle set;
distance vectors are respectively improved for the horizontal and vertical coordinates of the unmanned aerial vehicle of the virtual force guide,
for unmanned aerial vehicles siReceiving the resultant force of the virtual forces of all the unmanned planes in the unmanned plane set, wherein the calculation formula is a formula (8); v. ofα，vβ，vσIs a weight coefficient, X1，Y1，X2，Y2，X3，Y3The calculation formula of (2) is formula (19) to formula (21).
Wherein, thetaijIndicates the direction of the force, thetaij＝tan-1((yi-yj)/(xi-xj))；ωaAnd ωrRespectively representing the attraction coefficient and the repulsion coefficient. Distance threshold value dth2r, wherein r is the communication radius of the drone.
Minimum value representing the distance of the drone from other drones in the set, dthRepresenting the distance between the drone and the drone.
Wherein, Xα，Yα，Xβ，Yβ，Xσ，YσRespectively selecting alpha, beta and sigma unmanned aerial vehicles guided by the algorithm to obtain position coordinate vectors of the unmanned aerial vehicles; a. the1＝A2＝A3＝2αr1- α is a co-operative coefficient vector, α ═ 2-2 ((e)t/T-1)/(e-1)) is a convergence factor, T and T are the current iteration number and the maximum iteration number, respectively, r1Is [0, 1 ]]And:
wherein, C1＝C2＝C3＝2r2As a vector of co-operative coefficients, r2Is [0, 1 ]]A random vector of (1). XLevy，YLevyAnd (3) updating the unmanned plane position vector of the current unmanned plane set through the Levier flight rule:
wherein X and Y are position vectors of the unmanned aerial vehicle in the initial state; alpha is alpha0Is a constant;
in the formula, xd and yd respectively represent improved distance vectors of horizontal and vertical coordinates of the unmanned aerial vehicle set, DmaxIs the maximum step size of the move. In the formula (I), the compound is shown in the specification,
and delta x and delta y are coordinate difference vectors of ground user nodes closest to the unmanned aerial vehicle set in horizontal distance, and the position coordinate set with the minimum horizontal distance to the unmanned aerial vehicle set is obtained by calculating the minimum value of each column of the matrix (25) and the row number of the minimum value. Wherein n is the number of unmanned aerial vehicles, and p is the number of ground mobile nodes.
Preferably, the network adaptive deployment algorithm (CS-IGWO) of the unmanned aerial vehicle based on the livin improved grey wolf update mechanism in step 3 is executed, and the unmanned aerial vehicle performs exploration and policy selection according to the local coverage fitness value until the position selection of all the unmanned aerial vehicles reaches the best or the maximum convergence number, which is specifically as follows:
(1) initializing algorithm parameters and inputting position information of ground user nodes;
(2) calculating a fitness function value by using a formula (6), and reserving three groups of unmanned aerial vehicle positions with the best fitness values;
(3) updating the position of the current unmanned aerial vehicle set according to formula (7);
(4) calculating a fitness function value according to the position of the new unmanned aerial vehicle set, and reserving the position of the unmanned aerial vehicle set with the maximum fitness value to the next generation;
(5) updating alpha, A1，A2，A3And C1，C2，C3；
(6) Calculating the fitness of all unmanned aerial vehicle sets;
(7) updating the positions of the three groups of unmanned aerial vehicles with the best fitness;
(8) and circularly executing the formula (3) to the formula (7) until the selection of the coverage rate converges or the set iteration number is reached.
Example 1
The specific embodiment is described below: the system simulation adopts matlab software, and the setting of parameters does not influence the generality; consider unmanned aerial vehicle communication network under the city disaster area environment. The area to be covered is two-dimensional, with a plan view approximating a 1000m rectangular area. FIG. 3 is an example of dividing the urban disaster area into areas according to an actual map, for convenience of observation, the areas except the forbidden area are distinguished by different lines, and the divided buildings are combined into a whole
The road set is
Wherein the content of the first and second substances,
each movable area is randomly distributed with a certain number of ground user nodes, the total number of the user nodes is 298, and fig. 4 is a distribution diagram of the ground user nodes at the beginning of the example. The moving speed of the ground user node is 0.5-3m/s, and the node speed in each divided area is randomly changed in the whole target area. The maximum speed represents that the ground user node is escaping from the danger zone, or seeking help. The node which is stationary or moves at the minimum speed represents that the node is difficult to move due to injury or bad surrounding environment, and fig. 5 is a distribution situation of the nodes when the urban disaster area user node movement model program runs for 500 s. The number of the nodes of the unmanned aerial vehicle is 20, the nodes are initially randomly distributed in the whole target area, and the maximum moving step length is 125 m; all drones hover over the same altitude 125m and move at a constant speed of 5 m/s; all unmanned aerial vehicle wireless transmission scope are 125m, can cover and use wireless transmission scope 125m as the circular region in ground of radius, and in communication range, can carry out the information interaction between the unmanned aerial vehicle. The carrier frequency of the unmanned aerial vehicle is set to 2000MHz, the environment parameters are set to alpha and 0.3, beta and 110, and other parameters are set to etaLoS＝1dB，ηNLoS＝20dB，PL max85 dB. The maximum number of iterations of the algorithm is 400. Considering the network-adaptive deployment algorithm (CS-IGWO) of the drone based on the neyvin improved grey wolf update mechanism, the specific operation is as the above specific implementation process until the weighted coverage rate converges or the set number of iterations is reached. At each moment, the set of drones flies to their respective positions according to the result of step convergence in the specific implementation process.
And (3) simulation result analysis:
fig. 6 is a convergence curve of global coverage and weighted coverage and a change curve of network connectivity for a network of 20 drones under the CS-IGWO algorithm considered (10 experiments). The multiple simulation experiment results avoid the contingency of the algorithm results, the weighted coverage rate of the algorithm is converged to 93.42% on average, the connectivity of the network is stable, and the graph shows that the weighted coverage rate is maximized on the premise of ensuring the connectivity of the network.
Fig. 7 shows the deployment result of maximizing weighted coverage of 20 drones when the ground user node in the urban disaster area with the environmental parameters α being 0.3 and β being 110 moves for 500s in example 1. The figure shows that the unmanned aerial vehicle network maintains network connectivity, and the coverage rate of non-key areas is low, namely, the coverage rate of key areas in the urban disaster area is increased in key points.
Fig. 8 is a graph of weighted coverage, network connectivity, and average path loss over time under the CS-IGWO algorithm under consideration of different environmental parameters (1 experiment). The graph shows that the weighted coverage rate, the connectivity and the average path loss of the unmanned aerial vehicle network are stable in change at different environmental parameters and different moments, which proves that the CS-IGWO algorithm can stably carry out self-adaptive coverage on urban disaster areas and achieves the maximization of the weighted coverage rate under the condition of maintaining the network connectivity. However, under the environment parameter α ═ 0.5 and β ═ 300, the path loss is slightly higher than the weighted coverage rate under the environment parameter α ═ 0.3 and β ═ 110, because the environment parameter α ═ 0.5 and β ═ 300 represents a dense city, and the path loss is increased due to shielding and scattering of buildings in the dense city; the environment parameter α is 0.1, and β is 750, which represents a suburban area, and β is 750, where there are few user nodes in the suburban area and the distribution is sparse, so that the unmanned aerial vehicle needs to fly to a higher height to cover the ground user nodes, and at this time, more path loss is generated, which results in reduction of the weighted coverage.
Fig. 9 and 10 show the change of coverage and network connectivity in a suburban environment (10 experiments) and the change of global coverage, network connectivity and path loss at different times in 1 experiment after the path loss threshold is adjusted. Since the CS-IGWO algorithm sets the path loss threshold value and is autonomously controllable, when the CS-IGWO algorithm is applied to suburban environments, the optimal coverage can be met by properly increasing the threshold value (100dB) of the path loss. However, because the suburban environment user nodes are randomly distributed in the area to be covered, no key area exists, that is, no weighted coverage exists, and therefore the threshold parameter is analyzed and adjusted and then the threshold parameter is applied to the global coverage and the network connectivity change in 10 experiments in the suburban environment. In 10 experiments of the unmanned aerial vehicle, the global coverage and the network connectivity of the network are stable, and at different moments, the global coverage, the connectivity and the path loss of the network are stable. Fig. 9 and 10 demonstrate the validity and reasonableness of the proposed method by adapting the CS-IGWO algorithm of the present invention to be equally applicable in suburban environments.
Compared with the prior art, the technical scheme provided by the embodiment has the following remarkable effects:
(1) according to the unmanned aerial vehicle deployment method for adaptively optimizing the network coverage of the urban disaster area, aiming at the fields of urban disaster area rescue and emergency communication, the problem of optimizing the coverage of the ground user nodes moving in the key areas of the urban disaster area is solved. The acquired information is merged into the deployment method of the unmanned aerial vehicle, the unmanned aerial vehicle network is not limited by fixed ground user nodes, and the positions of the unmanned aerial vehicle set can be adjusted according to the requirements.
(2) The unmanned aerial vehicle deployment method for adaptively optimizing the network coverage of the urban disaster area is reasonably designed according to the disaster area of the urban disaster area, the distribution of buildings and roads in the urban disaster area, the population number of user nodes in each area, the movement rules and other conditions, a weighting coefficient is introduced to adjust a coverage utility function, the urban disaster area is divided into a key area and a non-key area, the coverage rate of the non-key area is sacrificed to a certain extent, and the coverage rate of the unmanned aerial vehicle network to the key area is further improved.
(3) According to the unmanned aerial vehicle deployment method for adaptively optimizing the network coverage of the urban disaster area, by designing a dynamic adjustment rule, in the process that the unmanned aerial vehicle network provides network services for the ground mobile users of the urban disaster area, the unmanned aerial vehicle network can dynamically adjust the position of the unmanned aerial vehicle according to the movement change of the ground user nodes, and then the network services can be more stably provided for the urban disaster area.
(4) According to the unmanned aerial vehicle deployment method for self-adaptive optimization of urban disaster area network coverage, the connectivity of the unmanned aerial vehicle network is maintained by designing a position adjustment rule, so that the situation that the unmanned aerial vehicle network is not connected due to the fact that the unmanned aerial vehicle adapts to the movement of ground user nodes is avoided. The invention improves the coverage rate of the unmanned aerial vehicle network on the premise of ensuring the network connectivity.
The technical solutions provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific examples, and the descriptions of the embodiments are only used to help understanding the principles of the embodiments of the present invention; meanwhile, the detailed description and the application scope of the embodiments according to the present invention may be changed by those skilled in the art, and in summary, the present disclosure should not be construed as limiting the present invention.
Claims (10)
1. An unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage is characterized by comprising the following steps:
step 1, simulating urban disaster area scenes and constructing a state set of ground mobile user nodes;
step 2, recording the position information of the ground mobile user node at each moment obtained in the step 1;
and 3, calculating the optimal coverage of the unmanned aerial vehicle network in the urban disaster area according to the position information of the ground mobile user node at each moment in the step 2, and obtaining an optimal coverage strategy according to the optimal coverage.
2. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 1, characterized in that: the simulation city disaster area scene of the step 1 comprises: the method comprises the steps of dividing an urban area to be covered into different areas, wherein the different areas comprise a building area, a street, a forbidden area, an open area and building debris, and simulating the movement of a ground user node in an urban disaster area by limiting the movement of the node in the different areas, so that a simulated urban disaster area scene is obtained.
3. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 2, characterized in that: the method is characterized in that the urban area to be covered is divided into different areas according to an area division model of the urban disaster area, wherein the area division model of the urban disaster area is as follows:
A＝{B，R，P，X} (1)
wherein, A is the urban disaster area to be covered, B is the building set and
B1-B8respectively represent residential buildings, public buildings, commercial buildings, business and public hybrid buildings, living and public hybrid buildings, business and public hybrid buildings and the like; r is a set of roads and
R1-R6the road is a quick passing road, a landscape tourism street, a street conforming to a sharing type, a residential living street and a landscape leisure walking path; p is an open area such as a park, and X is a prohibited area, i.e., an area, lake, or river, which humans cannot enter.
4. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 1, characterized in that: the set of states for constructing the ground mobile user node includes time of day, direction, speed, coordinate location and/or movement rules.
5. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 4, characterized in that: the set of the ground mobile user nodes is G ═ G1，g2，…，gnTherein user node giHas a position coordinate of (x)i，yi0), the mobile rule of the mobile user node is:
wherein, random is that the user node moves randomly in the open area; b-trend means that the user node gradually moves to four edges of a building in the building area and finally reaches an outlet along the edges; linear is that a user node moves linearly on a street; b iscolIndicating that the building is collapsed, and fade is the time when the user node is in the building area and the building is collapsed, the user node is trapped in the building area.
6. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 1, characterized in that: the step 3 comprises the following steps:
step 31, initializing the drone, including: will each unmanned aerial vehicle si∈S(S＝{s1，s2，…，snSetting the state of the weighted coverage rate as an initial state, setting the maximum iteration times, and defining a utility function for calculating the weighted coverage rate;
step 32, updating the location, including: and (3) according to information interaction between all unmanned aerial vehicle nodes in the unmanned aerial vehicle network, fusing the ground mobile user node information obtained in the step (2) with a position updating equation of the unmanned aerial vehicle, calculating an unmanned aerial vehicle set, and selecting a coverage strategy capable of bringing optimal weighted coverage by adopting the weighted coverage of the network under the position set under the position updating equation and the weighted coverage of the network under the current position.
And step 33, circularly executing the step 32 until the weighted coverage rate of the network under the current position of the unmanned aerial vehicle network does not change any more or reaches the set maximum iteration number, and ending the position updating of the unmanned aerial vehicle to obtain the optimal weighted coverage rate and the coverage strategy.
7. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 6, characterized in that: the weighted coverage utility function defined in step 31 is specifically:
discretizing the area to be covered into m × n pixel points, so that the global coverage of the unmanned aerial vehicle network is as follows:
wherein j is any pixel point in the region to be covered,
the total number of covered pixel points in the whole area to be covered.
8. The unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 7, characterized in that: the coverage rate of the unmanned aerial vehicle network to the key area, which can be obtained by regarding the ground mobile node as a pixel point, is as follows:
wherein G ∈ G ═ G { (G)1，g2，…，gnIs any one of the ground user nodes,
the number of covered ground user nodes is H, and the total number of pixel points in the key area is H;
9. the unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 8, characterized in that: and sacrificing the coverage rate of the forbidden area so as to improve the coverage rate of the key area, and introducing the weighted coverage rate to carry out normalized calculation on the coverage rate of the whole target area and the coverage rate of the key area:
cov(W)＝ω1cov(S)+ω2cov(H) (5)
wherein, ω is1Weighting factor, ω, for the overall coverage of the target area2Weighting factor for the coverage of the key region and1＜ω2(ii) a Thus, the objective function is:
f(X)＝max(cov(W)) (6)。
10. the unmanned aerial vehicle deployment method for self-adaptively optimizing urban disaster area network coverage according to claim 9, characterized in that: the step 32 of updating the location policy of the set of drones includes:
the initial state of the unmanned aerial vehicle set is randomly distributed, firstly, the weighted coverage rate in the current state is calculated by using a formula (5), and then the unmanned aerial vehicle set updates the target position of the next iteration by using a formula (7):
wherein, X (t +1) and Y (t +1) are respectively an abscissa position vector and an ordinate position vector of the t +1 th iteration unmanned aerial vehicle set;
distance vectors are respectively improved for the horizontal and vertical coordinates of the unmanned aerial vehicle of the virtual force guide,
for unmanned aerial vehicles siReceiving the resultant force of the virtual forces of all the unmanned planes in the unmanned plane set, wherein the calculation formula is a formula (8); v. ofα，vβ，vσIs a weight coefficient, vγ＝vα+vβ+vσ，X1，Y1，X2，Y2，X3，Y3The calculation formula of (a) is formula (9) -formula (11);
wherein, thetaijIndicates the direction of the force, thetaij＝tan-1((yi-yj)/(xi-xj))；ωaAnd ωrRespectively representing a gravitational coefficient and a repulsive coefficient; distance threshold value dth2r, wherein r is the communication radius of the unmanned aerial vehicle;
minimum value representing the distance of the drone from other drones in the set, dthRepresenting the distance between the drone and the drone; dijRepresenting the distance between drone i and drone j.
Wherein, Xα，Yα，Xβ，Yβ，Xσ，YσRespectively selecting alpha, beta and sigma unmanned aerial vehicle position coordinate vectors guided by the unmanned aerial vehicle through an algorithm; a. the1＝A2＝A3＝2αr1-αAs a vector of cooperative coefficients, α ═ 2-2 ((e)t/T-1)/(e-1)) as convergence factor, T and T being the current and maximum number of iterations, r, respectively1Is [0, 1 ]]And:
wherein, C1＝C2＝C3＝2r2As a vector of co-operative coefficients, r2Is [0, 1 ]]Random vector of (1), XLevy，YLevyAnd (3) updating the unmanned plane position vector of the current unmanned plane set through the Levier flight rule:
wherein X and Y are position vectors of the unmanned aerial vehicle in the initial state; alpha is alpha0Beta is a constant; mu and
are all subject to Gaussian distribution, and
satisfies the following conditions:
in the formula, xd and yd respectively represent improved distance vectors of horizontal and vertical coordinates of the unmanned aerial vehicle set, DmaxIs the maximum moving step length, wherein，
Delta x and delta y are coordinate difference vectors of ground user nodes closest to the unmanned aerial vehicle set in horizontal distance, and a position coordinate set with the minimum horizontal distance to the unmanned aerial vehicle set is obtained by calculating the minimum value of each column of a matrix (16) and the number of rows where the minimum value is located, wherein n is the number of the unmanned aerial vehicles, and p is the number of ground mobile nodes; the elements in the matrix represent the horizontal distances between the n drones and the p ground user nodes respectively.
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| CN114374951B (en) * | 2022-01-12 | 2024-04-30 | 重庆邮电大学 | Dynamic pre-deployment method for multiple unmanned aerial vehicles |
| CN115278698A (en) * | 2022-06-13 | 2022-11-01 | 北京邮电大学 | Unmanned aerial vehicle base station dynamic deployment method and device based on dynamic user distribution prediction |
| CN115278698B (en) * | 2022-06-13 | 2024-02-02 | 北京邮电大学 | Unmanned aerial vehicle base station dynamic deployment method and device based on dynamic user distribution prediction |
| CN115562334A (en) * | 2022-09-13 | 2023-01-03 | 中国地震应急搜救中心 | Multi-source disaster data fusion-oriented unmanned aerial vehicle cluster task cooperation method and system |
| CN116567572B (en) * | 2023-04-10 | 2024-03-22 | 武汉大学 | Unmanned aerial vehicle self-organizing communication network space-time evolution deployment method, device, equipment and medium |
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