CN109451556B - Method for charging wireless sensor network based on UAV - Google Patents
Method for charging wireless sensor network based on UAV Download PDFInfo
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- CN109451556B CN109451556B CN201811430560.5A CN201811430560A CN109451556B CN 109451556 B CN109451556 B CN 109451556B CN 201811430560 A CN201811430560 A CN 201811430560A CN 109451556 B CN109451556 B CN 109451556B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention relates to a wireless sensor network charging technology, in particular to a method for charging a wireless sensor network based on a UAV (unmanned aerial vehicle), which comprises the following steps: collecting ID, position information, residual energy and working state of each sensor network node; determining member nodes and a clustering scheme, and solving the communication energy consumption of the member nodes and cluster head nodes; determining an energy transmission power model of the UAV; calculating the time required by charging each node i by the UAV in the mth random periodic clustering scheme through an optimization algorithm; searching the time required by the UAV to fully charge all nodes at any position by using an optimization algorithm from N possible clustering schemes, and selecting the maximum time to be regarded as the time required by the UAV to fully charge the whole wireless sensing network; and finding out the minimum time cost value and the corresponding optimal UAV charging position or path from the maximum time corresponding to the N possible clustering schemes. The invention can fully charge the wireless sensor network at the minimum time cost after multiple rounds of energy consumption, so that the wireless sensor network can stably run for a long time.
Description
Technical Field
The invention relates to a wireless energy transmission technology of a wireless sensor network, in particular to a method for charging the wireless sensor network based on a UAV (unmanned aerial vehicle).
Background
With the increasing development and maturation of embedded microcomputer systems, wireless communication technologies and sensor technologies, the ability of non-contact interaction between people and various conditions in the real world is continuously enhanced by the wireless sensor network, and the wireless sensor network is gradually becoming an emerging networking application mode comparable to the internet.
The wireless sensor network is a distributed wireless sensor network consisting of a plurality of wireless sensor nodes. Because the wireless sensor network is basically composed of a wireless sensor which can sense and collect external environment data, the data related to the monitored and detected object can be collected by the wireless sensor network terminal, namely the wireless sensor, and a unique multi-hop self-organizing network is formed through a wireless communication mode.
Since the birth of wireless sensor network technology, how to prolong the life cycle of the wireless sensor network and ensure the stability of the wireless sensor network is an important problem to be solved urgently by domestic and foreign scholars. Since the wireless sensor network nodes are generally powered by batteries and carry very limited energy, how to enable the wireless sensor network to perform data collection, fusion and transmission under the limited energy can still keep a long network operation life cycle, so that the wireless sensor network can operate as stably as possible, which is one of the hotspots of the current research. From the perspective of the structure and the operation mode of the wireless sensor network, the energy consumption in the wireless sensor network is divided into sensor calculation energy consumption, sensor communication energy consumption, sensor data acquisition energy consumption and the like, wherein the sensor communication energy consumption is the most important, and the influence of the clustering mode in the wireless sensor network on the sensor communication energy consumption is very large. Therefore, selecting the optimal clustering principle in the wireless sensor network area is an important strategy for prolonging the life cycle of the wireless sensor network.
With the development of new unmanned aerial vehicle technology, the unmanned aerial vehicle path planning strategy and design draws wide attention of scholars at home and abroad. The method for prolonging the life cycle of the wireless sensor network by combining the unmanned aerial vehicle path planning strategy with the wireless energy transmission technology also becomes a research hotspot at present.
Disclosure of Invention
The invention aims to solve the problems of poor stability and short service life of the existing wireless sensor network, and provides a method for charging the wireless sensor network based on a UAV (unmanned aerial vehicle). the method can search the position of the optimal chargeable UAV and the optimal cluster head selection scheme through optimization algorithms such as an exhaustive search algorithm or a genetic algorithm, so that the wireless sensor network can be fully charged with electricity at the minimum time cost after multiple rounds of energy consumption, and the wireless sensor network can stably run for a long time.
The invention is realized by the following technical scheme: a method for charging a wireless sensor network based on a UAV (unmanned aerial vehicle) comprises the following steps:
s1, collecting the ID, the position information, the residual energy and the working state of each sensor network node in the wireless sensor network; the sensor network nodes are divided into cluster head nodes and member nodes;
s2, determining member nodes and a clustering scheme in the wireless sensor network area, and solving the corresponding member node communication energy consumption and cluster head node communication energy consumption;
s3, determining an energy transmission power model of an energy transmission module UAV;
s4, calculating the time required by the energy transmission module UAV to fully charge each sensor network node i in the wireless sensor network in the mth random periodic clustering scheme through an optimization algorithm;
s5, searching the time required by the energy transmission module UAV to fully charge all sensor network nodes at any position in the whole wireless sensor network area by using an optimization algorithm from N possible clustering schemes, selecting the maximum time, and determining the maximum time as the time required by the energy transmission module UAV to fully charge the whole wireless sensor network;
and S6, finding out the minimum time cost value and the corresponding optimal UAV charging position or path from the maximum time corresponding to the N possible clustering schemes.
According to the technical scheme, in the whole wireless sensor network area, the random selection of the cluster head and the member configuration scheme are carried out in each block of wireless sensor network area, the clustering schemes of each block of wireless sensor network area are not influenced mutually, and then the clustering schemes of each area are combined with each other to form the cluster head and member configuration scheme (i.e. the clustering scheme), and compared with the prior art, the beneficial effects obtained by the invention comprise that:
comparing the cluster head of the whole wireless sensor network with the optimal time required by filling the wireless sensor network corresponding to the member configuration scheme, and selecting the clustering scheme corresponding to the minimum time and the corresponding UAV optimal charging position; namely, under the condition of energy consumption corresponding to a cluster head and a member configuration scheme of the wireless sensor network, the position or the path corresponding to the UAV time optimal cost is obtained. According to the invention, the optimal time cost position is searched in each cluster head and member configuration scheme, and finally, each optimal time cost is compared, the UAV position and the cluster head member configuration scheme corresponding to the minimum optimal time cost are selected to charge the wireless sensing network, so that the wireless sensing network can be fully charged with the minimum time cost after multiple rounds of energy consumption. The invention researches a related optimization algorithm for finding out the optimal UAV charging position and path under the condition of least time cost, and simultaneously selects an optimal cluster head selection scheme.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a wireless sensor network employed in one embodiment;
FIG. 3 is a network clustering of the wireless sensor network in one embodiment;
FIG. 4 is a diagram illustrating the results of a simulation performed across a wireless sensor network in one embodiment;
FIG. 5 is a graph of objective function values versus algebra of the algorithm in one embodiment;
FIG. 6 is a simulation of optimal UAV position at a fixed communications base station position in one embodiment;
fig. 7 is a diagram of objective function convergence when the position of the communication base station is fixed in one embodiment.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.
Examples
In the invention, a sensor network node selects a rechargeable wireless sensor network node; the UAV is an energy sending module for wireless energy transmission, and the UAV flies at a fixed height and transmits electric energy to wireless sensor nodes within an energy transmission range.
The scene of the wireless chargeable sensor network is divided into four areas, and the sensor network nodes in each area are divided into cluster head nodes and member nodes, wherein the member nodes comprise dormant nodes, working nodes and inactive nodes, and the cluster head nodes are generated by equal probability and periodic selection. In a wireless chargeable sensor network, each working node transmits respective position information and ID information to a periodically and randomly generated cluster head node through a single hop, and the cluster head node integrates and fuses the received various information into a data packet and transmits the data packet to a communication base station through the form of the single hop. The principle of clustering the wireless sensor network nodes is a random periodic clustering principle, and the type of each cluster head member configuration scheme (i.e. clustering scheme) is the multiplication of the number of nodes in each area, namely:
N＝N1*N2*N3*N4……Nn。
as shown in fig. 1, the implementation process and steps of the method of the present invention are as follows:
first, after the wireless sensor network L turns around the cluster head, the ID, the location information, the remaining energy, the operating state, and the like of each sensor network node at that time are collected.
Herein, assuming that the remaining energy of each working node is E _ min (i) (the cluster head node needs to integrate the received data for forwarding and other operations, the remaining energy is small, but the lower limit value of the minimum remaining energy is E _ min, when the remaining energy of the working node reaches E _ min, the working node is converted into a dormant node), and the communication energy consumption is E-E _ min (E is the initial energy of each node).
In an application scene, a working node communication energy consumption model (namely an energy consumption model) of the wireless sensor network adopts a first-order radio model,that is when the inter-node communication distance diiWhen the energy consumption model is closer, a free space channel model is adopted; when the inter-node communication distance diiAnd when the distance is far, the energy consumption model adopts a multi-path attenuation model. Due to different node types, the algorithm scene energy consumption model can be divided into a member node energy consumption model and a cluster head node energy consumption model. Assume that the distance between nodes known to communicate with each other is diiAnd when 1bit data is transmitted, calculating a related energy consumption model:
1. when the sending node is a member node, the energy consumed by the sending node is as follows:
2. when the sending node is a member node, and the comparison of the transmission distances is not considered, the energy consumption of the sending node is as follows:
3. when the sending node is a cluster head node, the energy consumed for sending the information data is as follows:
4. the energy consumed for receiving the information data (cluster head nodes and member nodes) is as follows:
ERX(K)＝KEelec
the above equations are the energy consumed by the node in sending and receiving 1bit data, EDAEnergy consumption for data integration, namely energy consumed for integrating 1bit data; d0The distance threshold value of the node energy consumption is a fixed value; k is the amount of data received and sent; eelecFor the energy consumption of receiving or sending 1bit data by a node, the invention only considers the information receiving of the cluster head node;fsandmpattribute parameters of the sensor network nodes; the symbol b is a Boolean variable, the value b is zero when the data transmission mode between two nodes is single hop, and the number between the nodes isAnd b is 1 when the transmission mode is multi-hop.
In the algorithm model, the energy transmission mathematical model of the mobile UAV to the wireless sensor network node is a linear energy transmission model, that is:
wherein Q isi(x (t), y (t)) represents the energy receiving power of the ith sensor network node at the point (x (t), y (t)) in the wireless sensor network area, hk(t) is the power magnitude related to distance, β0Representing the energy received by a node at a unit distance of 1m on the transmission channel. P represents a constant in transmission power. (x)k,yk) The coordinates of the UAV flight in a two-dimensional plane are shown, and H represents the flight height in a three-dimensional coordinate.
From the above, since the cluster head node has more energy consumption for receiving and transmitting, the energy consumption of the cluster head node is much larger than that of the member node.
Secondly, determining member nodes and a clustering scheme in a wireless sensor network area, and solving communication energy consumption e of the corresponding member nodesiEnergy consumption E for communication with cluster head nodesiAnd the energy consumption of cluster head node communication is the sum of the energy consumption of information receiving and the energy consumption of information sending.
And thirdly, determining an energy transmission power model of the energy transmission module UAV.
Fourthly, the time required by the energy transmission module UAV to fully charge each sensor network node i in the wireless sensor network in the mth random periodic clustering scheme is obtained through an optimized search algorithm
(including the time required to fully charge the cluster head nodes),
fifthly, searching any position UAV _ loc (x, y) of the UAV in the whole wireless sensor network area for all transmissions by using an optimization algorithm from N possible clustering schemesTime required for sensor network node to be fully charged
Wherein
Selecting the maximum time Ti_max_UAV_loc(x(t),y(t)) mIt is considered to be the time required for the energy transmission module UAV to fully charge the entire wireless sensor network.
Sixthly, maximum time T corresponding to N possible clustering schemesi_max_UAV_loc(x(t),y(t)) mIn the method, the minimum time cost value is found
And corresponding optimal UAV charging locations or paths.
The convergence degree, the convergence speed and the accuracy of different optimization algorithms on the problem objective function in the patent application scene of the invention are different. In the embodiment, a basic differential evolution algorithm is used for carrying out optimization search on the position of the UAV in a sensor network area, and the algorithm is a population-based adaptive global optimization algorithm and belongs to one of evolution algorithms. The method has the characteristics of simple structure, easy realization, quick convergence, strong robustness and the like, so the method is widely applied to various fields of data mining, pattern recognition, digital filter design, artificial neural networks, electromagnetism and the like. In this embodiment, a differential evolution algorithm is used to perform variation, intersection operation, competition operation, and the like on position data of a communication base station and a Unmanned Aerial Vehicle (UAV) in a UAV time cost function of an auxiliary wireless sensor network, and then data after the variation, intersection, competition, and the like are substituted into a target time cost function to obtain a shortest optimal time cost function value.
In this embodiment, the actual application and simulation scenario of the algorithm is a wireless sensor network as shown in fig. 2, the wireless sensor network nodes are divided into two sensor network node areas, and the small dots are UAV energy transmission positions corresponding to the optimal scheme.
As shown in fig. 3, according to a periodic random clustering principle and an information flow direction, a network clustering situation of one round of wireless sensor networks is described, a working node transmits respective node ID information, position information and the like to a cluster head node in a single-hop transmission manner, the cluster head node integrates and fuses received information of each sensor node, and then sends a fused data packet to a communication base station in a single-hop transmission manner.
As shown in fig. 4, under the variable condition of optimizing the UAV location, by comparing the time required for charging the whole wireless sensor network area by using various clustering schemes, the UAV location at the time is recorded, where the time cost is the minimum (i.e., the time required for charging the mobile flying UAV to fully charge the whole wireless sensor network is the minimum), and the location is the optimal location for the rechargeable UAV to perform energy transmission with the minimum cost, and the clustering scheme is the optimal clustering scheme. At this time, under the condition of fixing the position of the communication base station, the variable UAV position is optimized by using a differential evolution algorithm, and the simulation is performed on the whole wireless sensor network, and the result is shown in fig. 4.
Fig. 5 shows a relationship diagram between objective function values and algorithm algebra after optimization search is performed on flight positions of Unmanned Aerial Vehicles (UAVs) in a wireless sensor network region by using a differential evolution algorithm, and evolution, competition, variation and other operations are performed on position variables of the Unmanned Aerial Vehicles (UAVs) in a constructed time cost objective function under a wireless sensor network clustering condition and a fixed communication base station, wherein a time cost objective function value is shown on a vertical coordinate, an operation algebra of the algorithm is shown on a horizontal coordinate, and the time cost objective function value tends to converge as the operation algebra increases and the time cost objective function value decreases with the increase of the operation algebra.
Fig. 6 shows simulation of optimal UAV charging positions in a wireless sensor network with an expanded number of nodes, where the network scale is slightly expanded and divided into three independent clustered regions. Theoretically, the data to be processed in the wireless sensor network is in order of magnitude increase as the network scale is increased. Fig. 7 illustrates the convergence of the objective function when the position of the communication base station is fixed.
As can be seen from the above, as the scale of the wireless sensor network is increased, the data that the algorithm needs to process in the wireless sensor network is increased as the scale of the wireless sensor network is increased, and the time for the algorithm to process the data is also increased.
Therefore, for a large-scale wireless sensor network in practical application, an efficient energy compensation strategy for the unmanned aerial vehicle is very necessary. According to the large-scale wireless sensing network in practical application, because the range of energy transmission and communication of the unmanned aerial vehicle is limited, the large-scale wireless sensing network is divided into countless small areas, and the small areas have the optimal positions (such as the conditions studied above) of the UAVs for energy transmission theoretically, namely the large-scale wireless sensing network in practical application has the optimal UAV energy transmission positions in a plurality of different areas. In order to better meet the requirements of practical application, the key points of the simulation scenario of the invention are as follows: 1. the scale of the wireless sensor network is increased in the original scene; 2. simultaneously using an Unmanned Aerial Vehicle (UAV) as a mobile communication relay and a wireless energy transmitter; 3. the original scene is used as a small area divided in a large-scale area, wherein the optimal position point of the UAV for energy transmission and information collection is recorded as a UAV anchor point.
In practical large-scale wireless sensor network application, the unmanned aerial vehicle has a limited energy transmission and communication range, and the wireless sensor network in practical application has an overlarge size, so that the UAV cannot supplement energy in a large range within a certain time. Based on the reasons and the described scenes, the invention also provides a priority energy supplement algorithm based on the large-scale wireless sensor network and Unmanned Aerial Vehicle (UAV) technology, the algorithm is a moving track strategy of the UAV on the optimal UAV position point in each small area, and the purpose is to improve the energy transmission efficiency of the UAV and optimize the energy consumption mode of the large-scale wireless sensor network through the optimized moving track strategy. Under the condition that the communication base station is fixed, each wireless sensor network node needs to transmit each acquired data information to the cluster head node and then forwards the data information to the communication base station through the cluster head node, and the data flow transmitted beside the communication base station is large, so that the wireless sensor beside has huge energy consumption burden; the UAV has less time cost for energy transmission to the wireless sensing network of the unfixed communication base station, and the wireless sensing networkThe energy consumption of the net is less. Therefore, the unmanned aerial vehicle is used as a mobile communication relay for energy transmission to perform energy compensation on the large-scale wireless sensor network, and the flight mode of the unmanned aerial vehicle in the wireless sensor network is to perform real-time energy compensation and information collection decision according to real-time direction probability. When the unmanned aerial vehicle carries out energy transmission and data collection tasks, the unmanned aerial vehicle takes the optimal information collection energy transmission position in each area as the theoretical optimal anchor point of the UAV, so that the optimal information collection and energy transmission tasks are carried out. Unmanned Aerial Vehicle (UAV) carries out energy data optimal time cost t in each area under considerationeTime cost t of flying to optimal anchor point of UAV theory in small area in each directionfAnd the dormancy rate of sensor network nodes in a small area (i.e. the ratio B of the number of energy-depleted nodes to the number of summary points)i_empty＝nempty/Nz) And then, judging and planning the next flight direction and track of the unmanned aerial vehicle. After the unmanned aerial vehicle completes the previous cycle energy transmission task, the unmanned aerial vehicle returns to the service station for energy supplement (or waits for the next cycle task on the spot when the UAV has sufficient energy), and waits for the energy transmission and information collection task in the next cycle.
As described above, the present invention can be preferably realized.
Claims (6)
1. A method for charging a wireless sensor network based on a UAV (unmanned aerial vehicle) is characterized by comprising the following steps:
s1, collecting IDs, position information, residual energy and working states of all sensor network nodes in the wireless sensor network after L rounds of cluster head rotation, wherein the sensor network nodes are divided into cluster head nodes and member nodes;
s2, determining member nodes and a clustering scheme in the wireless sensor network area, and solving the corresponding member node communication energy consumption and cluster head node communication energy consumption;
s3, determining an energy transmission power model of an energy transmission module UAV;
s4, calculating the time required by the energy transmission module UAV to fully charge each sensor network node i in the wireless sensor network in the mth random periodic clustering scheme through an optimized search algorithm;
s5, searching the time required by the energy transmission module UAV to fully charge all sensor network nodes at any position in the whole wireless sensor network area by using an optimization algorithm from N possible clustering schemes, selecting the maximum time, and determining the maximum time as the time required by the energy transmission module UAV to fully charge the whole wireless sensor network;
and S6, finding out the minimum time cost value and the corresponding optimal UAV charging position or path from the maximum time corresponding to the N possible clustering schemes.
2. The UAV-based method of charging a wireless sensor network of claim 1 wherein the cluster head node communication energy consumption is a sum of information receiving energy consumption and information transmitting energy consumption.
3. The UAV-based method of charging a wireless sensor network of claim 1 wherein the member nodes comprise a dormant node, a working node, and an inactive node, and wherein the cluster head nodes are generated based on an equiprobable, periodic selection.
4. The UAV-based method of charging a wireless sensor network of claim 1 wherein the optimization algorithm is a basic differential evolution algorithm.
5. The method of claim 3, wherein the energy consumption model of the working nodes is a first-order radio model, when the communication distance d between nodes isiiWhen the energy consumption model is closer, a free space channel model is adopted; when the inter-node communication distance diiAnd when the distance is far, the energy consumption model adopts a multi-path attenuation model.
6. The UAV-based method of charging a wireless sensor network of claim 1, wherein: the energy transmission mathematical model of the energy transmission module UAV to the sensor network node is a linear energy transmission model:
wherein Q isi(x (t), y (t)) represents the energy receiving power of the ith sensor network node at the point (x (t), y (t)) in the wireless sensor network area, hk(t) is the power magnitude related to distance, β0Representing the energy received by a node at a unit distance of 1m on the transmission channel, P represents a constant in the transmission power, (x)k,yk) The coordinates of the energy transmission module UAV flying in a two-dimensional plane are shown, and H represents the flying height in a three-dimensional coordinate.
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