CN109216812B - Charging method of wireless chargeable sensor network based on energy consumption classification - Google Patents
Charging method of wireless chargeable sensor network based on energy consumption classification Download PDFInfo
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Abstract
本发明公开了一种基于能耗分级的无线可充电传感器网络的充电方法。基站式充电方式充电效率较差，扇形分级方式充电有效性不够。本发明如下：一、建立平面直角坐标系。二、根据生命周期的长短，对n个节点进行分类。三、对步骤二所得类别进行二次分类，并计算优化路径总长度。四、筛选出在二次分类总点集中仅出现一次的一次分类点集。五、对各转移点集内的节点进行转移。六、进行一轮充电。本发明通过能耗分级来实现无线可充电传感器的分类，进行实现无线可充电传感器的分批充电，进而减少充电小车的移动能量损耗。本发明通过对生命周期较长的无线可充电传感器充电批次的变化，实现对充电小车路径的进一步优化。
The invention discloses a charging method for a wireless rechargeable sensor network based on energy consumption classification. The charging efficiency of the base station charging method is poor, and the charging efficiency of the sector classification method is not enough. The present invention is as follows: 1. Establishing a plane rectangular coordinate system. 2. According to the length of the life cycle, the n nodes are classified. 3. Perform secondary classification on the categories obtained in step 2, and calculate the total length of the optimized path. 4. Screen out the primary classification point set that appears only once in the secondary classification total point set. 5. Transfer the nodes in each transfer point set. 6. Carry out a round of charging. The invention realizes the classification of the wireless rechargeable sensors through energy consumption classification, and realizes the batch charging of the wireless rechargeable sensors, thereby reducing the mobile energy loss of the charging trolley. The present invention further optimizes the path of the charging trolley by changing the charging batch of the wireless rechargeable sensor with a long life cycle.
Description
Technical Field
The invention belongs to the technical field of energy supply of a wireless chargeable sensor network, and particularly relates to a charging method of the wireless chargeable sensor network based on energy consumption classification.
Background
Because the wireless sensor nodes in a general wireless sensor network are small in size and limited in battery energy, the working time of the sensor nodes is limited. In order to solve the problem of insufficient energy of the Sensor, a Wireless Sensor Network (WRSN), which is a Wireless Rechargeable Sensor Network with an energy collection technology, is developed. For a deployed sensor network, a reasonable and efficient charging mode is found for charging the sensor nodes, and the method is a reasonable mode for enabling the sensor network to continuously survive. Xu cheng hua et al in the patent "directional charging base station deployment method of wireless chargeable sensor network" (patent number: 201610279938.0) propose a method of establishing a directional charging base station in a sensor network, charging the sensor by rotating an angle to cover the entire sensor network, but the charging efficiency of this remote charging method is low.
A movable charging trolley is placed in a wireless rechargeable sensor network, node sets with different residual life cycles are determined according to different energy consumption of sensors, the sensor nodes are charged according to requirements, and each sensor can be guaranteed to be supplemented with energy in a certain period, so that the WRSN can work normally. In related research, royal jade and the like in patent "a charging control method for wireless sensor network nodes" (patent number: 201611042178.8) divide the whole network into fan-shaped areas with different priorities according to the residual energy of the sensor nodes, and charge according to the charging emergency degree of the sensor nodes, but the classification strategy of the method cannot ensure that each sensor node is respectively in different fan-shaped areas according to different requirements, so that the moving path of a charging trolley cannot be reasonably planned.
Disclosure of Invention
The invention provides a charging method of a wireless chargeable sensor network based on energy consumption classification.
The method comprises the following specific steps:
step 1, establishing a planar rectangular coordinate system, wherein a base point in the planar rectangular coordinate system corresponds to the position of a charging base station, and n nodes in the planar rectangular coordinate system correspond to the positions of n wireless chargeable sensors respectively.
And 2, classifying the n nodes according to the life cycle.
2-1, calculating the life cycle T of n nodesiI is 1, 2, …, n, and a life cycle set T is { T ═ T1，T2,…,Tn}。
Ti＝(ERi-ETHi)/Ei。
Wherein E isRiIs the remaining energy in the ith node; eTHiIs an energy threshold in the ith node, ETHi＝10％·ESi；ESiIs the initial energy in the ith node, EiIs the power in the ith node.
2-2, calculating the grading number
TmaxIs the maximum value within the set of life cycles T; t isminIs the minimum value in the life cycle set T;
is (T)max-Tmin)/TminRounding the resulting value upward.
2-3.i ═ 1, 2, …, n, steps 2-4 are performed in sequence. Obtaining a primary classification total point set CC＝{CC1，CC2,…,CCh}. And entering the step 2-5 after the i reaches n and the step 2-4 is executed.
2-4. if
Adding the ith node into the jth primary classification point set CCj。
2-5, assign 1 to j and 1 to k.
2-6, if j is a divisor of k, classifying the j first time into a point set CCjAdding the kth secondary classification point set Ck. And entering the step 2-7.
2-7, if j is less than h, increasing j by 1; if j is h and k is less than h, then 1 is assigned to j and k is incremented by 1 and steps 2-6 are performed; if j is h and k is h; then the quadratic classification total point set C ═ C has been obtained1，C2，C3，…，ChAnd F, entering the step 3.
And 3, calculating the total length of the optimized path.
3-1.k is 1, 2, …, h, and step 3-2 is performed sequentially.
3-2, determining the kth classification point set C of the charging trolley pair through the ant colony algorithmkOptimized path A for chargingk(ii) a Path AkUsing base point as starting point and end point and passing through k-th classification point set CkAll nodes in the network; obtaining a k classification point set C of the charging trolleykOptimized path A for chargingkLength L ofk。
3-3, calculating the total length of the optimized path
And 4, screening out a primary classification point set which only appears once in the secondary classification total point set C.
4-1. assign 1 to M and j.
4-2. if the j first classification point set CCjIf the point set C appears only once in the secondary classification total point set C, the j-th primary classification point set C isCjAs the M-th transfer point set C'MThen, increasing M by 1 and proceeding to step 4-3; otherwise, directly entering the step 4-3.
4-3, if j is less than h, increasing j by 1, and turning to the step 4-2; if j is h, then a sorted total point set C 'is { C'1,C′2,...,C′MAnd F, entering the step 5.
And 5, transferring the nodes in each transfer point set.
5-1. assign M to r and 1 to s.
5-2. transfer the r to point set C'rS nodes with minimum middle life cycle are transferred to the r-1 transfer point set C'r-1。
5-3.k is 1, 2, …, h, and steps 5-4 are performed sequentially.
5-4, determining the kth classification point set C of the charging trolley pair through the ant colony algorithmkAlternate route A 'for charging'k(ii) a Route A'kUsing base point as starting point and end point and passing through k-th classification point set CkAll nodes in the network; obtaining a k classification point set C of the charging trolleykAlternate route A 'for charging'kLength of L'k。
5-5, calculating the total length of the undetermined path
5-6. if L'TSP<LTSPThen the total length of the pending path L'TSPAs a new optimized total path length LTSP，L′1，L′2，...，L′hRespectively as a new L1，L2，...，LhAnd proceeds to step 5-8. Otherwise, transfer to the r-1 transfer Point set C'r-1S nodes of (C) are transferred back to the r-th set of transfer points C'rAnd proceeds to step 5-7.
5-7, if s is less than r transition point set C'rIncreasing s by 1 according to the number of the internal nodes, and repeating the steps from 5-2 to 5-6; otherwise, go to step 5-9.
5-8, if the r is transferred to the point set C'rIf nodes exist in the node, assigning 1 to s, and repeating the steps 5-2 to 5-6; otherwise, go to step 5-9.
5-9, if r > 2, decreasing r by 1 and repeating steps 5-2 to 5-8, otherwise, entering step 6.
Step 6, z is 1, 2, …, h, and step 7 is performed in sequence.
Step 7, waiting for TminAfter the time, the charging trolley collects C according to the z-th classification pointzOptimized path A for chargingzThe wireless chargeable sensor moves and charges the passing wireless chargeable sensor.
And 8, repeatedly executing the steps 2 to 7.
The invention has the beneficial effects that:
1. according to the wireless chargeable sensor, the classification of the wireless chargeable sensors is realized through energy consumption classification, the batch charging of the wireless chargeable sensors is realized, and the moving energy loss of the charging trolley is further reduced.
2. The charging trolley path is further optimized by changing the charging batch of the wireless chargeable sensor with a longer life cycle.
3. The invention can ensure the stable and continuous work of each wireless chargeable sensor.
Drawings
Figure 1 is a schematic view of a distribution of wireless chargeable sensors, charging base stations in one example of the present invention;
FIG. 2(a), FIG. 2(b), FIG. 2(c) and FIG. 2(d) are diagrams of the paths of four trolleys after step 2 is performed according to an embodiment of the present invention;
fig. 3(a), 3(b), 3(c) and 3(d) are four trolley path diagrams obtained after step 5 is executed according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention relates to a charging method of a wireless chargeable sensor network based on energy consumption classification, which aims at the condition that all wireless chargeable sensors in the wireless sensor network are arranged on the same plane and the specifications of the wireless chargeable sensors are the same except for different power consumption. The charging mode is that the charging trolleys are driven out from the charging base station and move to the position where the wireless chargeable sensor is needed to carry out near-field magnetic coupling resonance wireless charging one by one, and the charging efficiency is high.
The charging method of the wireless chargeable sensor network based on energy consumption classification comprises the following specific steps:
step 1, as shown in fig. 1, a WRSN model, i.e., a planar rectangular coordinate system, is established, so that the coordinates of the charging base station are (R, R), and all the n wireless chargeable sensors are located in the charging square in the first quadrant of the planar rectangular coordinate system. Coordinate points (0,0), (0,2R), (2R,0), (2R,2R) are the four vertices of the charging square, respectively. The position of the charging base station corresponds to a base point, and the positions of the n wireless chargeable sensors correspond to n nodes. The coordinates of the n nodes in the rectangular plane coordinate system are respectively (x)i,yi) I is 1, 2, …, n. The n nodes are sorted.
And 2, classifying the n nodes according to the life cycle.
2-1, calculating the life cycle T of n nodesi，i＝1，2，…，n，TiIs given in days, resulting in a set of life cycles T ═ T1，T2,…,Tn}。
Ti＝(ERi-ETHi)/Ei。
Wherein E isRiThe remaining energy (i.e. the current remaining capacity of the battery) in the ith node; e, ETHiIs an energy threshold in the ith node, ETHi＝10％·ESi；ESiIs the initial energy (i.e. the full charge of the battery) E in the ith nodeiIs the power in the ith node.
When the energy of one node is less than the energy threshold ETHiAnd if so, the wireless chargeable sensor corresponding to the node is considered to be dead (incapable of working).
2-2, calculating the grading number
TmaxIs the maximum value within the set of life cycles T; t isminIs the minimum value in the life cycle set T;
is (T)max-Tmin)/TminRounding the resulting value upward.
2-3.i ═ 1, 2, …, n, steps 2-4 are performed in sequence. Obtaining a primary classification total point set CC＝{CC1，CC2,…,CCh}. And entering the step 2-5 after the i reaches n and the step 2-4 is executed.
2-4. if
Adding the ith node into the jth primary classification point set CCj。
2-5, assign 1 to j and 1 to k.
2-6, if j is a divisor of k (i.e., j is divided by k), the j-th primary classification point set CCjAdding the kth secondary classification point set Ck. And entering the step 2-7.
2-7, if j is less than h, increasing j by 1; if j is h and k is less than h, then 1 is assigned to j and k is incremented by 1 and steps 2-6 are performed; if j is h and k is h; step 3 is entered, where a secondary classification total point set C ═ C is obtained1，C2，C3，…，Ch}。
And 3, solving the size of the path traversed by the charging trolley each time.
3-1.k is 1, 2, …, h, and step 3-2 is performed sequentially.
3-2, determining the kth classification point set C of the charging trolley pair through the ant colony algorithmkOptimization of chargingPath Ak(ii) a Path AkUsing the base point as the starting point and the end point, and passing through the k-th classification point set CkAll nodes within. Obtaining a k classification point set C of the charging trolleykLength L of optimized path for chargingk＝ACATSP(Ck)。ACATSP(Ck) Calculated by ant colony algorithm, the charging base station is taken as a starting point and an end point, and passes through a k-th secondary classification point set CkPath length of all nodes within. The ant colony algorithm adopts an algorithm which is licensed to be put forward in 'TSP problem research based on the improved ant colony algorithm' published in software guide.
3-3, calculating the total length of the optimized path
And 4, screening out a primary classification point set which only appears once in the secondary classification total point set C.
4-1. assign 1 to M and j.
4-2. if the j first classification point set CCjOnly appears once in the quadratic classification total point set C (i.e. the jth quadratic classification point set CCjBelong to and only belong to C1，C2，C3,…,ChOne of them), the j-th primary classification point set C is sortedCjAs the M-th transfer point set C'M(at this time, the M-th transition point set C'MAnd j first classification point set CCjChanging Mth transfer point set C 'for different expressions of the same set'MI.e. change the jth classification point set CCj) Then, increasing M by 1 and proceeding to step 4-3; otherwise, directly entering the step 4-3.
4-3, if j is less than h, increasing j by 1, and turning to the step 4-2; if j is h, the process proceeds to step 5, where a total classification point set C 'is obtained as { C'1,C′2,...,C′M}。
And 5, transferring the nodes in each transfer point set.
5-1. assign M to r and 1 to s.
5-2. transfer the r to point set C'rTransferring the s nodes with the minimum middle life cycle to the r-1 transfer pointC'r-1Since the transition point set corresponds to one primary classification point set, the change of the transition point set is the change of the corresponding primary classification point set.
5-3.k ═ 1, 2, …, h, and steps 5-4 are performed sequentially
5-4, determining the kth classification point set C of the charging trolley pair through the ant colony algorithmkAlternate route A 'for charging'k(ii) a Route A'kUsing the base point as the starting point and the end point, and passing through the k-th classification point set CkAll nodes within. Obtaining a k classification point set C of the charging trolleykLength L 'of alternate path for charging'k＝ACATSP(Ck)。ACATSP(Ck) Calculated by ant colony algorithm, the charging base station is taken as a starting point and an end point, and passes through a k-th secondary classification point set CkPath length of all nodes within.
5-5, calculating the total length of the undetermined path
5-6. if L'TSP<LTSPThen the total length of the pending path L'TSPAs a new optimized total path length LTSP，L′1，L′2，...，L′hRespectively as a new L1，L2，...，LhAnd proceeds to step 5-8. Otherwise, transfer to the r-1 st transfer point set C 'in step 5-2'r-1S nodes of (C) are transferred back to the r-th set of transfer points C'rAnd proceeds to step 5-7.
5-7, if s is less than r transition point set C'rIncreasing s by 1 according to the number of the internal nodes, and repeating the steps from 5-2 to 5-6; otherwise, go to step 5-9.
5-8, if the r is transferred to the point set C'rIf nodes exist in the node, assigning 1 to s, and repeating the steps 5-2 to 5-6; otherwise, go to step 5-9.
5-9, if r is more than 2, reducing r by 1, and repeatedly executing the steps 5-2 to 5-8, otherwise, entering the step 6, wherein the total length L of the optimized path is the sameTSPI.e. the final optimized path length. Charging circuit corresponding to final optimized path lengthThe path is the final charging path. Proceed to step 6.
Step 6, z is 1, 2, …, h, and step 7 is performed in sequence.
Step 7, waiting for TminAfter time, the charging trolley is according to AzAnd moving the corresponding path and charging the passing wireless chargeable sensor.
And 8, repeatedly executing the steps 2 to 7.
The calculation is performed by taking 47300J as an example of n being 10, R being 50m, the initial energy of each wireless chargeable sensor, and the energy threshold being 4730J.
The coordinates of the corresponding nodes of each wireless chargeable sensor are as follows:
after the classification of step 2, a first primary classification point set CC1Including node S1And node S2. Set of second-order classification points CC2Including node S3Node S4And node S5. Third-order classification point set CC3Including node S6. Fourth primary classification point set CC4Including node S7Node S8Node S9And node S10。
L obtained in step 31、L2、L3、L4The corresponding paths are shown in fig. 2(a), fig. 2(b), fig. 2(c), fig. 2(d), respectively. L obtained in step five1、L2、L3、L4The corresponding paths are shown in fig. 3(a), 3(b), 3(c), and 3(d), respectively.
When the traditional node full traversal algorithm is applied, the charging trolley needs to travel 1098.2m when completing charging in one period. When the charging trolley is used, the charging trolley only needs to travel 732.5m after completing one cycle of charging. Therefore, the efficiency of the charging trolley can be greatly improved, and the loss of the charging trolley is reduced.
Claims (1)
1. A charging method of a wireless chargeable sensor network based on energy consumption classification is characterized in that:
step 1, establishing a planar rectangular coordinate system, wherein a base point in the planar rectangular coordinate system corresponds to the position of a charging base station, and n nodes in the planar rectangular coordinate system respectively correspond to the positions of n wireless chargeable sensors;
step 2, classifying the n nodes according to the life cycle;
2-1, calculating the life cycle T of n nodesiI is 1, 2, …, n, and a life cycle set T is { T ═ T1，T2,…,Tn}；
Ti＝(ERi-ETHi)/Ei；
Wherein E isRiIs the remaining energy in the ith node; eTHiIs an energy threshold in the ith node, ETHi＝10％·ESi；ESiIs the initial energy in the ith node, EiIs the power in the ith node;
2-2, calculating the grading number
TmaxIs the maximum value within the set of life cycles T; t isminIs the minimum value in the life cycle set T;
is (T)max-Tmin)/TminRounding up the value;
2-3.i ═ 1, 2, …, n, steps 2-4 are performed sequentially; obtaining a primary classification total point set CC＝{CC1，CC2,…,CCh}; when i reaches n and the step 2-4 is executed, entering the step 2-5;
2-4. if
Adding the ith node into the jth primary classification point set CCj；
2-5, assigning 1 to j and 1 to k;
2-6, if j is a divisor of k, classifying the j first time into a point set CCjAdding the kth secondary classification point set Ck(ii) a Entering the step 2-7;
2-7, if j is less than h, increasing j by 1; if j is h and k is less than h, then 1 is assigned to j and k is incremented by 1 and steps 2-6 are performed; if j is h and k is h; then the quadratic classification total point set C ═ C has been obtained1，C2，C3，…，ChFourthly, entering the step 3;
step 3, calculating the total length of the optimized path;
3-1.k ═ 1, 2, …, h, performing step 3-2 in sequence;
3-2, determining the kth classification point set C of the charging trolley pair through the ant colony algorithmkOptimized path A for chargingk(ii) a Path AkUsing base point as starting point and end point and passing through k-th classification point set CkAll nodes in the network; obtaining a k classification point set C of the charging trolleykOptimized path A for chargingkLength L ofk；
3-3, calculating the total length of the optimized path
Step 4, screening out a primary classification point set which only appears once in the secondary classification total point set C;
4-1, assigning 1 to M and j;
4-2. if the j first classification point set CCjIf the point set C appears only once in the secondary classification total point set C, the j-th primary classification point set C isCjAs the M-th transfer point set C'MThen, increasing M by 1 and proceeding to step 4-3; otherwise, directly entering the step 4-3;
4-3, if j is less than h, increasing j by 1, and turning to the step 4-2; if j is h, then a sorted total point set C 'is { C'1,C′2,...,C′MStep 5 is entered;
step 5, transferring the nodes in each transfer point set;
5-1, assigning M to r and 1 to s;
5-2. transfer the r to point set C'rS nodes with minimum middle life cycle are transferred to the r-1 transfer point set C'r-1；
5-3.k ═ 1, 2, …, h, sequentially performing steps 5-4;
5-4, determining the kth classification point set C of the charging trolley pair through the ant colony algorithmkAlternate route A 'for charging'k(ii) a Route A'kUsing base point as starting point and end point and passing through k-th classification point set CkAll nodes in the network; obtaining a k classification point set C of the charging trolleykAlternate route A 'for charging'kLength of L'k；
5-5, calculating the total length of the undetermined path
5-6. if L'TSP<LTSPThen the total length of the pending path L'TSPAs a new optimized total path length LTSP，L′1，L′2，...，L′hRespectively as a new L1，L2，...，LhAnd proceeding to step 5-8; otherwise, transfer to the r-1 st transfer point set C 'in step 5-2'r-1S nodes of (C) are transferred back to the r-th set of transfer points C'rAnd proceeding to step 5-7;
5-7, if s is less than r transition point set C'rIncreasing s by 1 according to the number of the internal nodes, and repeating the steps from 5-2 to 5-6; otherwise, entering the step 5-9;
5-8, if the r is transferred to the point set C'rIf nodes exist in the node, assigning 1 to s, and repeating the steps 5-2 to 5-6; otherwise, entering the step 5-9;
5-9, if r is more than 2, reducing r by 1, and repeatedly executing the steps 5-2 to 5-8, otherwise, entering the step 6;
step 6, changing z to 1, 2, …, h, and executing step 7 in sequence;
step 7, waiting for TminAfter the time, the charging trolley collects C according to the z-th classification pointzOptimized path A for chargingzMove and aim atCharging by the wireless chargeable sensor;
and 8, repeatedly executing the steps 2 to 7.
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| CN107635238A (en) * | 2017-08-28 | 2018-01-26 | 昆明理工大学 | A multi-hop wireless rechargeable sensor network repeater layout method based on clustering idea |
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| CN107635238A (en) * | 2017-08-28 | 2018-01-26 | 昆明理工大学 | A multi-hop wireless rechargeable sensor network repeater layout method based on clustering idea |
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