CN113163332B - Road sign graph coloring unmanned aerial vehicle energy-saving endurance data collection method based on metric learning - Google Patents
Road sign graph coloring unmanned aerial vehicle energy-saving endurance data collection method based on metric learning Download PDFInfo
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Abstract
The invention discloses a road sign graph coloring unmanned aerial vehicle energy-saving endurance data collection method based on metric learning. Mainly solve throughput and energy consumption problem in the unmanned aerial vehicle auxiliary sensor network. The method comprises the following steps: the model establishment is carried out on the unmanned aerial vehicle auxiliary sensor network, a homogeneous network model is provided, and nodes in the network have moving rows and have the characteristics of group mobility and group behaviors. The probabilistic road signs are then applied to construct a global map. And (3) continuously optimizing the trajectory of the unmanned aerial vehicle by using a metric learning method, training a metric matrix off line, and constructing the metric matrix by using an LMNN algorithm in an off-line training stage. And in order to update the node information, a broadcast Hello packet mechanism is adopted, and the Hello packet is broadcast hop by hop from the base station. In order to ensure higher transmission success rate and throughput, a graph coloring mode is adopted to reduce the collision of multi-hop transmission, and as the transmission of a data packet may overlap with a broadcast information collecting node, the problem of multi-hop channel allocation exists in the text, so that the network can be represented by a graph, each point in the graph represents a node in the network, and an edge connecting the two points represents packet sending communication of the two points, so that the problem of channel allocation of the multi-hop network can be converted into the problem of graph coloring. In the unmanned aerial vehicle auxiliary network supporting fast movement, the flight speed of the unmanned aerial vehicle is high, and the ground node can be a mobile robot with mobility, so that a contact time prioritized channel access method is adopted.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle auxiliary sensor networks, and particularly relates to a road sign map coloring unmanned aerial vehicle energy-saving endurance data collection method based on metric learning.
Background
With the rapid development of electronics, sensors and communication counting, unmanned aerial vehicles are widely used in various military and civilian fields. The unmanned aerial vehicle is applied to the wireless sensor network, so that sensor data can be effectively acquired, the service life of the network is prolonged, and the energy consumption of the network is reduced. In unmanned aerial vehicle assisted sensor networks (UWSNs), channel access algorithms are very important, which not only affect system performance, but also affect the energy efficiency of sensor nodes. Therefore, designing an efficient channel access algorithm for UWSNs is a problem worthy of research.
In unmanned aerial vehicle auxiliary sensor network, because unmanned aerial vehicle all has other tasks usually, so unmanned aerial vehicle all flies according to fixed flight path, accomplishes data acquisition's task in the in-process of flying by the way. With the development of unmanned aerial vehicle technology, a flight base station with an unmanned aerial vehicle as a data mule is developed, and the unmanned aerial vehicle can be specially used for data collection, so that how to effectively utilize the high-speed mobility of the unmanned aerial vehicle to plan reasonable flight trajectory to collect data and improve throughput becomes a problem to be solved, the unmanned aerial vehicle is required to collect information of nodes of a whole network for planning a reasonable flight path, and the network is expanded from a single-hop star network to a multi-hop network, so that higher requirements are provided for a channel access algorithm of an unmanned aerial vehicle auxiliary network.
In past research, researchers have proposed a series of wireless channel access algorithms, which can be roughly classified into two categories, contention-based channel access algorithms and assignment-based channel access algorithms. Since there is no dependency on any topology or synchronization information, the contention-based channel access algorithm (e.g., CSMA) is robust to topology dynamics, but due to collisions caused by contention, the performance of the protocol may be greatly degraded in a high contention environment. In contrast, an allocation-based channel access algorithm (e.g., TDMA) utilizes synchronicity between neighboring nodes to achieve collision-free transmission by allocating transmission time slots to each node, and although an allocation-based protocol can generally guarantee better network performance in a high-contention environment, the protocol performance is negatively affected by the inability to reuse time slots and the overhead associated with synchronization in a low-contention environment. How to dynamically balance CSMA and TDMA is therefore a considerable problem.
Unmanned Aerial Vehicle (UAV) wireless communications has received a great deal of attention in military and civilian applications due to its high mobility, low cost, on-demand deployment and inherent line-of-sight air-to-ground access, and integration with fourth generation mobile communications system (5G) networks. Communication between unmanned aerial vehicle and the ground equipment has constituted ground-to-air communication system, can roughly divide into two types according to the effect difference: the FANET formed by the unmanned aerial vehicle cluster transmits data to the ground base station and the unmanned aerial vehicle auxiliary network, and the unmanned aerial vehicle auxiliary network uses the unmanned aerial vehicle as the aerial base station to collect the data of the ground equipment. The channel model of the ground-air communication system can use a free space propagation model, which means that the traditional channel access algorithm can be simply applied in the ground-air communication system. Line of sight (LOS) communication components dominate air-to-ground channels in many practical scenarios, particularly in rural areas or at moderate altitude drone altitudes. Such channel characteristics allow the channel state information to be determined directly from the location of each node, facilitating the design of high-speed communication systems.
Disclosure of Invention
In order to solve the problems of low throughput performance of an auxiliary network of a fast moving unmanned aerial vehicle and insufficient flight energy consumption of the unmanned aerial vehicle, the embodiment of the invention adopts a metric learning method to plan a path and optimize the throughput, the method obtains a metric matrix through off-line training, and predicts the optimal information of the throughput and the energy consumption performance by using the metric matrix on line. The metric learning method is used for planning the flight path of the unmanned aerial vehicle, the graph coloring method is used for channel access optimization, and the method comprises the following steps:
and establishing a model according to the unmanned aerial vehicle auxiliary sensor network, and applying a network model, an energy consumption model and a movement model to the unmanned aerial vehicle auxiliary sensor network model.
Specifically, ground robot groups move in a region, and unmanned aerial vehicle base stations are unique and move freely in the region. The robot in unmanned aerial vehicle basic station coverage area R is the robot that needs carry out the channel access with unmanned aerial vehicle, and the robot of the outer R of unmanned aerial vehicle coverage area transmits to the basic station through jumping, consequently need not carry out the channel access with unmanned aerial vehicle. Due to the fast mobility of the robot, and the fast mobility of the drone, nodes within the drone coverage R will typically come out of coverage, while nodes outside coverage will enter the drone coverage. The coverage area R of the drone is related to the flying height h of the drone. In the network model, the node and the base station are mobile, the base station is in a constantly moving state in the area, and the node can move in the fixed area to detect the nearby environment information. In this model, energy is required for both the nodes to transmit and receive data. The energy consumption of the transmitting end is related to the data size, the transmission distance and the energy consumption of the power amplifier, and the energy consumption of the receiving end is related to the received data and the transmission distance. If the distance between the transmitter and the receiver is less than a threshold value d0Then the free space model is used. If not, a multipath fading channel model is adopted.
Where k is the packet size in bits, d is the distance between two nodes,
and
is the energy dissipation of the transmitter and receiver circuits that each node operates individually. EpsilonfsIs the signal amplification factor, ε, of a free space channel modelmpIs the signal amplification factor of the multipath fading channel model. d0The boundary condition threshold for distinguishing the two models is:
in the movement model, the group movement model we use is a reference point group movement model. In the reference point group movement model, the network is divided into a plurality of groups. For each group, there is one target in the group, and the nodes in the group move according to their targets and maintain certain constraints. The network is divided into several groups according to the requirement, the speed of the nodes in the group is controlled between the minimum speed and the maximum speed, and the direction of the nodes is controlled between 0 and 2 pi. This allows the nodes within the group to maintain restricted random motion due to the presence of the target point within the group. The set of reference points is moved in the model. There is a reference point within each group and each time the reference point of a group member moves to a new location, the group member also moves to a randomly selected location within a circular neighborhood of radius R around its new reference point location. At the same time, at the center of each panel coverage area is a logical guide point whose motion defines the motion of the entire panel, including velocity, direction and acceleration. The logical bootstrap point is based on a specific entity mobility model. The reference points follow around the logical guide points, each guiding one or more reference points, and maintain a constant distance and direction from the logical guide points. The velocity magnitude and direction update formula of the node is as follows:
v∈(vmin,vmax) (4)
θ∈(0,2π) (5)
assume the initial position of the base station is (x)0,y0) And the coordinates after the time t are as follows:
the throughput and the energy prediction are carried out by applying the metric learning, and the data acquisition throughput of the unmanned aerial vehicle and the charging energy consumption of the unmanned aerial vehicle can be optimized.
It is assumed herein that the drone cannot know global node information and therefore cannot obtain global path planning gain information in path planning. In route planning, a probability map is not a global map constructed from specific information, but a map known by local information is constructed by using a certain probability method, and route planning is performed by using the local information. The probability map is a directed-cycle graph and is represented by G ═ Gn (Ge), wherein Gn represents a node set in a space, and Ge represents a local path edge set formed between nodes.
The specific measurement matrix is a matrix structure, and the objective of measurement learning is to calculate the mahalanobis distance measurement between samples by finding a suitable measurement matrix M:
DM(di,dj)2＝(di-dj)TM(di-dj) (7)
where M is a semi-positive definite symmetric matrix, it can be expressed as M ═ LLTThis is equivalent to finding an L matrix as the mapping matrix to map the original data d to a new class space L, so the Euclidean distance can be obtainedTake the special case when looking at the matrix L as an identity matrix.
And establishing a measurement matrix by using the distributed information as an input characteristic attribute, wherein the specifically considered attribute is as follows. 1. Threas is real-time throughput performance, the throughput prediction performance of an unmanned aerial vehicle at the next moment of data collection is related to the current throughput performance, 2, Pos is position information of a ground mobile robot node, the unmanned aerial vehicle data collection throughput performance is related to the distribution of the ground mobile robots, 3, Speed is the Speed of the ground mobile robots, the unmanned aerial vehicle data collection throughput performance is related to the Speed of the ground mobile robots, 4, Degree is the density of the neighbor nodes of the ground mobile robot node, the higher the density is, the more mobile robots exist around the robots, and the throughput can be improved when the unmanned aerial vehicle flies to the direction. 5. Energy is the residual Energy of the charging battery of the ground mobile robot, and the more the residual Energy is, the stronger the charging capability of the unmanned aerial vehicle is.
The label of metric learning is traction force, the traction force represents the willingness of the unmanned aerial vehicle base station to move towards the direction of a certain mobile robot, and the flight track of the unmanned aerial vehicle finally selects the direction with the maximum traction force to fly.
The LMNN algorithm is adopted to construct a metric matrix, and the core of the LMNN algorithm is to lead K input neighbors to belong to the same category in a new conversion space through learning a distance metric, while samples of different categories keep a certain distance. The LMNN algorithm generates the metric matrix as follows.
Firstly, an LMNN-based training network is operated in a test set to train a characteristic metric matrix off line. Suppose, an arbitrary input sample xiWith class label ciIn its K neighbors there is a class label of clOf the intrusion sample xlDefining noise points as for any input sample xiHas c ofl≠ciSatisfies the following conditions:
||L(xi-xl)||2≤||L(xi-xl)||2+1 (8)
where L is a distance metric matrix, xiFor arbitrary input samples, xlFor the intrusion sample, according to the constraint condition, firstly defining a non-equivalent constraint:
wherein D isLMeans for calculating the inter-sample mahalanobis distance measure, DL(xi,xj)＝||L(xi-xj)||2Representing input samples xiAnd homogeneous samples x in the same domainjA distance measure of (d); KNN represents that K samples are in the same category domain; l represents a set of intrusion samples; x is the number oflIs represented by and is at xiIntrusion patterns within the maximum bounds but not identical to the test pattern class labels, clIs xlClass labels of (1); y isilFor sample class label consistency parameter, when xiClass label c ofi＝clWhen y isil1, otherwise 0; equivalent constraints are defined as:
the final combination of non-equivalence and equivalence constraints can construct the following loss function:
ε(L)＝(1-u)εpull(L)+uεpush(L) (11)
wherein u is a weight coefficient generally 0.5.
The essence of the metric learning offline training is to generalize a set of scoring rules from the data set, each set of input can be given a definite score, and the method has a decisive effect on the unmanned aerial vehicle path planning.
And updating the information of the whole network mobile robot by adopting a mode of periodically broadcasting Hello packets (H _ Pkt) hop by the unmanned aerial vehicle base station. The Hello packet includes the ID of the broadcast packet, the ID of the broadcast transmitting node, the IP address, the node speed, the node position, the broadcast transmission time, and hop count information. The broadcast packet is transmitted to mobile robot nodes of the whole network in a hop-by-hop mode, the mobile robot receiving the broadcast packet regenerates the broadcast packet with the same packet ID by utilizing the information of the mobile robot and transmits the broadcast packet back to the unmanned aerial vehicle base station through the same path, and the unmanned aerial vehicle base station utilizes the information as the input of metric learning to carry out path planning.
After network initialization, periodically broadcasting Beacon packets by the unmanned aerial vehicle to collect basic information of ground sensor nodes, broadcasting information of a flight path to the sensor nodes in a coverage range by the unmanned aerial vehicle, collecting sensor information in the coverage range of the next flight path by the sensor nodes through multiple hops, and sending the sensor information to the unmanned aerial vehicle, wherein the unmanned aerial vehicle adjusts the flight path by using the information and carries out communication scheduling planning. The unmanned aerial vehicle path planning is carried out after the first periodic broadcasting information collection is finished, but due to the mobility of the ground sensor nodes, the planned flight path point is not the optimal flight path, so the unmanned aerial vehicle carries out the optimization adjustment of the flight path by using a metric learning method according to the position and speed information of the sensor nodes on the path point, the next adjustment path point is output periodically, and the next adjustment path point can not be effectively adjusted every time.
After the next flight path point is determined, the fact that the information of the unmanned aerial vehicle flying in the future period is known means that the unmanned aerial vehicle carries out scheduling distribution of channel access through the flight track of the unmanned aerial vehicle and the ground sensor information in the coverage area, the unmanned aerial vehicle actively establishes connection with the ground sensor nodes according to a channel access strategy to collect sensor data, and the process can be effectively achieved by a graph coloring theory.
Since the transmission of the data packet may overlap with the broadcast information collecting node, there is a multi-hop channel allocation problem, so that the network can be represented by a graph, each point in the graph represents a node in the network, and an edge connecting two points represents packet transmission communication of two points, so that the channel allocation problem of the multi-hop network can be converted into a graph coloring problem.
Given an undirected graph G ═ V, E, where V is the set of vertices and E is the set of edges. The graph coloring problem translates into dividing the set V into K independent subsets that do not contain any edges. The shading map problem is mainly used to calculate the minimum K value of color set, and the number of channels is used herein to represent the number of color sets K in the problem. It is known that K channels utilize graph coloring theory in reverse to build a collision-free undirected graph for time slot assignment channel access. The basic theory of channel access is that when a node is in a transmit mode, it cannot receive a data packet, and in a receive mode, it can receive data packets of multiple nodes at the same time, so the color represents that the node occupies a channel to transmit data, and in the time slot represented by the color, the node cannot receive the data packet. In the shading algorithm used herein, the following parameters need to be defined: 1. and each node coloring number n, 2, a node neighbor coloring number table and 3, coloring expiration time.
The mechanism for establishing the collision-free coloring graph is that in the process of forwarding a data packet by a node i, coloring information of the node is added into the data packet, and a color-collision-free distribution mode is kept between the coloring information and neighbors in a one-hop range, namely, each node needs to ensure that the color of the node is different from the colors of all the neighbor nodes.
The unmanned aerial vehicle basic station carries out wireless charging when collecting ground mobile robot data, and ground mobile robot is equipped with wireless battery charging outfit, can long-range energy of providing give the unmanned aerial vehicle basic station, keeps the equipment energy consumption of unmanned aerial vehicle basic station sufficient.
And the unmanned aerial vehicle base station performs time division multiple access time slot allocation on the nodes in each group, and allocates time slots to the nodes in the coverage range of the unmanned aerial vehicle according to the priority sequence of the contact time to transmit data packets. And the real-time throughput result is returned to the unmanned aerial vehicle track optimization module, so that the flight track of the unmanned aerial vehicle is optimized. And reconstructing the transmission priority based on the position distribution and the change of the contact time caused by the mobility of the sensor node, wherein the transmission priority is used as a measurement index of the channel access priority. In a traditional ground-air communication network, data are transmitted to a ground sensor priority access channel according to the ground coverage and the flight direction of an unmanned aerial vehicle. In the unmanned aerial vehicle auxiliary network supporting fast movement, the flight speed of the unmanned aerial vehicle is high, and the ground node can be a mobile robot with mobility, so that a contact time prioritized channel access method is adopted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a traffic map coloring unmanned aerial vehicle energy-saving cruising data collection method based on metric learning according to an embodiment of the present invention;
FIG. 2 provides an overall block diagram for an embodiment of the present invention;
FIG. 3 is a schematic diagram of the broadcast Hello packet collecting map information according to the present invention;
FIG. 4 is a schematic diagram of a graph coloring method according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical scheme of the invention is specifically explained according to the attached drawings.
The method for collecting the energy-saving endurance data of the road map coloring unmanned aerial vehicle based on metric learning comprises the following steps:
s101, model establishment is carried out according to the unmanned aerial vehicle auxiliary sensor network, and a network model, an energy consumption model and a movement model are applied to the unmanned aerial vehicle auxiliary sensor network model.
The network model defines the data transmission process, the nodes in the cluster transmit the collected information to the cluster heads, and the routing established between the cluster heads collects the informationThe information is transmitted to the base station, and the member nodes can also be used as relay routes so as to establish connection. The energy consumption model describes the energy consumption during data transmission if the distance between the transmitter and the receiver is less than a threshold value d0Then the free space model is used. If not, a multipath fading channel model is adopted. The movement model describes the unstable movement of the node and sets simulation boundaries.
And S102, constructing a global map by applying the probability signposts. It is assumed herein that the drone cannot know global node information and therefore cannot obtain global path planning gain information in path planning. In route planning, a probability map is not a global map constructed from specific information, but a map known by local information is constructed by using a certain probability method, and route planning is performed by using the local information. The probability map is a directed-cycle graph and is represented by G ═ Gn (Ge), wherein Gn represents a node set in a space, and Ge represents a local path edge set formed between nodes.
And S103, training a metric matrix off line. The specific measurement matrix is a matrix structure, and the objective of measurement learning is to calculate the mahalanobis distance measurement between samples by finding a suitable measurement matrix M.
And S104, constructing a measurement matrix by using an LMNN algorithm. The LMNN algorithm is adopted to construct a metric matrix, and the core of the LMNN algorithm is to lead K input neighbors to belong to the same category in a new conversion space through learning a distance metric, while samples of different categories keep a certain distance.
S105, broadcasting a Hello packet to collect map information. And updating the information of the whole network mobile robot by adopting a mode of periodically broadcasting Hello packets (H _ Pkt) hop by the unmanned aerial vehicle base station. The Hello packet includes the ID of the broadcast packet, the ID of the broadcast transmitting node, the IP address, the node speed, the node position, the broadcast transmission time, and hop count information. The broadcast packet is transmitted to mobile robot nodes of the whole network in a hop-by-hop mode, the mobile robot receiving the broadcast packet regenerates the broadcast packet with the same packet ID by utilizing the information of the mobile robot and transmits the broadcast packet back to the unmanned aerial vehicle base station through the same path, and the unmanned aerial vehicle base station utilizes the information as the input of metric learning to carry out path planning. See in particular fig. 3.
And S106, performing channel access based on the graph coloring method. The mechanism for establishing the collision-free coloring graph is that in the process of forwarding a data packet by a node i, coloring information of the node is added into the data packet, and a color-collision-free distribution mode is kept between the coloring information and neighbors in a one-hop range, namely, each node needs to ensure that the color of the node is different from the colors of all the neighbor nodes. See in particular fig. 4.
And S107, measuring the access priority based on the contact time, and defining the access priority according to the length of the contact time. And the unmanned aerial vehicle base station performs time division multiple access time slot allocation on the nodes in each group, and allocates time slots to the nodes in the coverage range of the unmanned aerial vehicle according to the priority sequence of the contact time to transmit data packets. And the real-time throughput result is returned to the unmanned aerial vehicle track optimization module, so that the flight track of the unmanned aerial vehicle is optimized. And reconstructing the transmission priority based on the change of the contact time caused by the position distribution and the mobility of the sensor node, wherein the transmission priority is used as the most important measurement index of the channel access priority.
The invention assumes that the nodes are randomly distributed according to the group reference point group movement model, the unmanned aerial vehicle group performs group movement in the region, and the base station is unique and can freely move in the region. The unmanned aerial vehicles in the group can communicate with each other, and the communication between the unmanned aerial vehicles in the group is intermittently connected, so that the network has the intermittent connection characteristic of the opportunity network. The unmanned aerial vehicle is provided with a memory space which can cache data packets, the data packets are placed into the cache when the network connection is disconnected, and data are transmitted when the network is connected. After deployment of the sensor nodes in the field, the nodes may move within a fixed area to detect nearby environmental information. The invention considers how to ensure the effective transmission of data under the group moving scene. Considering the actual situation, the following assumptions are made:
(1) the nodes have equal initial energy and computing power and equal positions;
(2) the nodes are randomly deployed in the region and accord with the group movement model initialization characteristics;
(3) all nodes in the network are mobile, including base stations and other nodes;
(4) the nodes know their own properties (e.g., remaining energy, speed and direction, etc.);
(5) the nodes adjust the transmission power according to the received signal strength and the communication link between the nodes is symmetrical.
(6) The drone remains flying at the same altitude.
(7) The drone may be controlled in flight.
Claims (7)
1. The method for collecting the energy-saving cruising data of the road map coloring unmanned aerial vehicle based on metric learning is characterized by comprising the following steps of:
firstly, establishing a model according to an unmanned aerial vehicle auxiliary sensor network, and applying a network model, an energy consumption model and a movement model to the unmanned aerial vehicle auxiliary sensor network model;
secondly, constructing a global map by using local information by using probability road signs, wherein the probability map is not constructed by determined information, but is constructed by using a method of a certain probability, and the local information is used for path planning, the probability map is a undirected cyclic map and is represented by G ═ Gn, Ge, wherein Gn represents a node set in a space, and Ge represents a local path edge set formed between nodes;
thirdly, constructing a measurement matrix, wherein the specific measurement matrix is a matrix structure, and the purpose of measurement learning is to calculate the mahalanobis distance measurement between samples by searching a proper measurement matrix M;
fourthly, using an LMNN algorithm to realize off-line training of the characteristic metric matrix, wherein the core of the LMNN algorithm is that a distance metric is learned to enable K input neighbors to belong to the same class in a new conversion space, and samples of different classes keep a certain distance;
fifthly, the broadcasting Hello packet collects map information, the broadcasting packet is transmitted to mobile robot nodes of the whole network in a hop-by-hop mode, the mobile robot receiving the broadcasting packet regenerates the broadcasting packet with the same packet ID by utilizing the information of the mobile robot and transmits the broadcasting packet back to the unmanned aerial vehicle base station through the same path, and the unmanned aerial vehicle base station utilizes the information as input of metric learning to carry out path planning;
sixthly, accessing a channel based on a graph coloring method, wherein a mechanism for establishing a collision-free coloring graph is to maintain a color-collision-free distribution mode with neighbors in a one-hop range by adding coloring information of the node into the data packet in the process of forwarding the data packet by the node i, namely, each node needs to ensure that the color of the node is different from the colors of all the neighbor nodes;
and seventhly, based on the contact time measurement access priority, the unmanned aerial vehicle base station performs time division multiple access time slot allocation on the nodes in each group, allocates time slot transmission data packets to the nodes in the coverage range of the unmanned aerial vehicle according to the priority sequence of the contact time, and returns the real-time throughput result to the unmanned aerial vehicle track optimization module to optimize the flight track of the unmanned aerial vehicle.
2. The method for collecting energy-saving cruising data of unmanned aerial vehicle based on roadmap of metric learning of claim 1, wherein said unmanned aerial vehicle assists the establishment of sensor network model, in particular, the network model defines the moving state of the node and the base station, defines the data transmission mode of the node, determines the channel access mechanism,
the energy consumption model defines a boundary condition threshold d0：
εfsIs the signal amplification factor, ε, of a free space channel modelmpIs the signal amplification factor of the multipath fading channel model,
if the distance between the transmitter and the receiver is less than a threshold value d0Then a free space model is used, if not, a multipath fading channel model is used,
the mobile model describes the unstable motion of the nodes, the nodes randomly select the traveling direction and speed, the new speed and direction are selected in a preset range, the selected group mobile model is a reference point group mobile model, in the reference point group mobile model, the network is divided into a plurality of groups, for each group, a target exists in the group, the nodes in the group move according to the targets, and certain constraint is kept, the network is divided into several specific groups according to needs, because the target exists in the group, the nodes in the group keep limited random motion, and the random direction and the random speed in the reference point group mobile model are selected as follows:
v∈(vmin,vmax) (2)
θ∈(0,2π) (3)
wherein the speed v of the nodes in the group is controlled at a minimum speed vminTo a maximum velocity vmaxAnd the moving direction theta of the node is controlled to be between 0 and 2 pi.
3. The method for collecting the energy-saving cruising data of the road map coloring unmanned aerial vehicle based on metric learning as claimed in claim 1, wherein the metric matrix is trained offline, and the metric matrix is established by using distributed information as input characteristic attributes, specifically considering the following attributes, wherein 1, threads is real-time throughput performance, and the throughput prediction performance of the unmanned aerial vehicle at the next moment of data collection is related to the current throughput performance; 2. pos is position information of a ground mobile robot node, and unmanned aerial vehicle data collection throughput performance is related to distribution of the ground mobile robot; 3. speed is the Speed of the ground mobile robot, and unmanned aerial vehicle data collection throughput performance is related to the Speed of the ground mobile robot; 4. the Degree is the density of the nodes adjacent to the ground mobile robot nodes, the higher the density is, the more mobile robots exist around the robot, and the throughput can be improved when the robot flies to the direction; 5. energy is the residual Energy of the charging battery of the ground mobile robot, the more the residual Energy is, the stronger the charging capability of the unmanned aerial vehicle is,
the label of metric learning is traction force, the traction force represents the willingness of the unmanned aerial vehicle base station to move towards the direction of a certain mobile robot, and the flight track of the unmanned aerial vehicle finally selects the direction with the maximum traction force to fly.
4. The method of claim 1, wherein the LMNN algorithm is used to train the feature metric matrix, and the LMNN-based training network is first run in the testing set to train the feature metric matrix off-line, assuming any input sample xiWith class label ciIn its K neighbors there is a class label of clOf the intrusion sample xlDefining noise points as for any input sample xiIs provided with cl≠ciAnd satisfies the following conditions:
||L(xi-xl)||2≤||L(xi-xl)||2+1 (4)
where L is a distance metric matrix, xiFor arbitrary input samples, xlFor the intrusion sample, according to the constraint condition, firstly defining a non-equivalent constraint:
wherein D isLMeans for calculating the inter-sample mahalanobis distance measure, DL(xi,xj)＝||L(xi-xj)||2Representing input samples xiAnd homogeneous samples x in the same domainjA distance measure of (a); KNN represents that K samples are in the same category domain; l represents a set of intrusion samples; x is the number oflIs represented by and is at xiIntrusion patterns within the maximum boundary but not identical to the test pattern class labels, clIs xlClass labels of (1); y isilFor the sample class consistency parameter, when xiClass label c ofi＝clWhen y isil1, otherwise 0; equivalent constraints are defined as:
the final combination of non-equivalence and equivalence constraints can construct the following loss function:
ε(L)＝(1-u)εpull(L)+uεpush(L) (7)
wherein u is a weight coefficient which is generally 0.5;
the essence of the metric learning offline training is to generalize a set of scoring rules from the data set, each set of inputs can be given a definite score, and the method has a decisive effect on unmanned aerial vehicle path planning.
5. The method for collecting energy-saving cruising data of a road map coloring unmanned aerial vehicle based on metric learning according to claim 1, wherein a Hello packet is broadcasted to collect map information, the information of the mobile robots in the whole network is updated in a mode that a base station of the unmanned aerial vehicle periodically broadcasts the Hello packet (H _ Pkt) hop by hop, the Hello packet includes an ID of a broadcast packet, an ID of a broadcast node, an IP address, a node speed, a node position, a broadcast transmission time and hop count information, the broadcast packet is transmitted to the mobile robot nodes in the whole network in a hop-by-hop mode, the mobile robots receiving the broadcast packet reproduce broadcast packets with the same packet IDs by using their own information and transmit the broadcast packets back to the base station of the unmanned aerial vehicle through the same path, and the base station of the unmanned aerial vehicle uses the information as input of the metric learning to perform path planning.
6. The method of claim 1, wherein the energy-saving cruising data of the road map coloring unmanned aerial vehicle is accessed by a channel based coloring method, and an undirected graph G is given (V, E), wherein V is a vertex set and E is an edge set, the graph coloring problem is converted into a way of dividing the set V into K independent subsets without any edges, the coloring graph problem is mainly used for calculating a minimum color group number K value, the color group number K in the problem is represented by the number of channels, the K channels are known to utilize graph coloring theory reversely to establish a collision-free undirected graph for time slot allocation channel access, the basic theory of channel access is that when a node is in a transmitting mode, a data packet cannot be received, and when the node is in a receiving mode, a data packet of a plurality of nodes can be received simultaneously, so that the colors represent occupied channels for transmitting data, the node cannot receive the data packet in the time slot represented by the color, and in the coloring algorithm used herein, the following parameters need to be defined: 1. and each node coloring number n, 2, a node neighbor coloring number table and 3, coloring expiration time.
7. The method of claim 1, wherein the priority of access is measured based on contact time, and since the flight speed of the drone is fast and the contact time with the ground mobile robot is short, it is likely that the drone will fly out of the contact area with the sensor by the allocated access time, and therefore a priority measurement of contact time is required, the priority measurement being based on the contact time, and the ground mobile robot with shorter contact time will be placed in the earlier time to access the channel for data transmission, so as to ensure higher throughput performance.
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