Method and system for reducing device energy loss operating time in rechargeable sensor network

Abstract

The application discloses a method and system for reducing energy loss working time of a rechargeable sensor network device, which comprises the following steps: firstly, clustering and fusing the sensor nodes in the wireless rechargeable sensor network by using a cluster fusion algorithm, with the minimum working time of the charging device as the target and the charging device being able to charge all the sensor nodes in the cluster at least at one residence point as the constraint condition; secondly, adjusting the residence point position of each cluster with the minimum energy loss of the charging device as the target; thirdly, setting the charging output power for each sensor node in the cluster with the minimum residence time of the charging device at the residence point as the target; and finally, constructing a charging path by the base station, and charging the sensor nodes in each cluster by the charging device at the optimal residence point according to the power setting table in sequence. The application can optimize the energy loss and working time of the charging device simultaneously, and has stronger applicability.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of rechargeable sensor networks, and particularly relates to a method and system for reducing energy loss and working time of devices in a rechargeable sensor network. BACKGROUND

[0002] With the development of the Internet of Things, rechargeable sensor networks have derived a wide range of application scenarios, such as environmental monitoring, medical care, military reconnaissance, etc. Compared with general sensor networks, sensor nodes in rechargeable sensor networks have higher reliability because they can be continuously supplied with energy by mobile charging devices. Therefore, the design of the charging scheduling method of the charging device will significantly affect the performance of the rechargeable sensor network.

[0003] The wireless charging technology based on magnetic coupling resonance can achieve simultaneous charging of the charging device to sensor nodes within a certain range, i.e. one-to-many charging mode. This mode can effectively improve the charging efficiency and save the moving time and energy consumption of the charging device.

[0004] Most of the existing charging scheduling schemes based on one-to-many charging mode in rechargeable sensor networks only consider the mobile energy loss of the charging device as the optimization target, and almost no consideration is given to the energy loss caused during the charging process. In addition, the working time of the charging device is also ignored in many studies. However, minimizing the working time on the premise of completing the charging task is beneficial to the charging device to have time to return to the base station to supplement enough energy for the next charging scheduling. In addition, there is currently no efficient charging scheduling scheme that simultaneously considers optimizing the energy loss and working time of the charging device. SUMMARY

[0005] The application aims to overcome the deficiencies in the prior art and provides a method for reducing the energy loss and working time of devices in a rechargeable sensor network.

[0006] Technical solution: To achieve the above-mentioned purpose, the technical solution adopted by the application is as follows:

[0007] A method for reducing the energy loss and working time of devices in a rechargeable sensor network, the device being a mobile charging device that provides energy supply to sensor nodes in a rechargeable sensor network in a wireless form, comprising the following steps:

[0008] Step S1, collect rechargeable sensor network information and charging device information. According to the charging device information and the rechargeable sensor network information, minimize the working time of the charging device as the target, and at least enable the charging device to charge all sensor nodes in the cluster at a residence point as the constraint condition, use the cluster fusion algorithm to cluster and fuse the sensor nodes in the wireless rechargeable sensor network, and obtain the fused cluster set and the residence point set.

[0009] Step S2, based on the fused cluster set and the set of residence points, adjusting the residence point position of each cluster to minimize the energy loss of the charging device, with the charging demand of the sensor nodes as the constraint condition, to obtain the optimal set of residence points.

[0010] Step S3, based on the optimal set of residence points, setting the charging output power for each sensor node in the cluster to minimize the residence time of the charging device at the residence point, with the maximum charging output power of the charging device as the constraint condition, to obtain the set of power setting tables.

[0011] Step S4, based on the optimal set of residence points and the set of power setting tables, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster according to the power setting table at the optimal residence point in the order of the charging path.

[0012] Preferably, the method for obtaining the fused cluster set and the set of residence points in step S1:

[0013] Initialization, C s ={s1, s2, …, s N} is the initial set of sensor nodes, s i represents sensor node i; the cluster set C B ={B1, B2, …, B N}, B i represents cluster i, B i ={s i}, i∈[1, N], N is the total number of sensor nodes;

[0014] Step S101, let the sensor node set C′ s =C s ;

[0015] Step S102, form a cluster with all sensor nodes in C′ s , and the position of the residence point r s is the geometric center of the polygon formed by all sensor nodes in C′ s , calculate the charging time of the charging demand E i of each sensor node s i in the cluster , and let the residence time of the charging device be s i ∈C′ s .

[0016] Step S103, select the sensor node s k with the longest charging time , judge the distance from s k to the residence point r s ​ Is it less than d? max d max Maximum distance for wireless power transfer to charging devices:

[0017] Step S1031, if Let D s C B Remove C′ from the middle s The remaining cluster set of the middle node is used to construct D. s ∪{C′ s} and {{s k}}∪D s ∪{C′ s \s k The path length L is obtained by traversing the TSP paths of each cluster. s L s&k The movement durations are respectively Where v is the moving speed of the charging device. For C′ s \s k The dwell time t of the charging device is obtained by executing step S102. s\k If satisfied Then proceed to step S104. Otherwise, proceed to step S105.

[0018] Step S1032, if Perform step S104.

[0019] Step S104, let C′ s =C′ s \s k Return to step S102.

[0020] Step S105, merge clusters, let B = ∪B i , where s i ∈B i And s i ∈C′ s Delete cluster set C B B in i The merged cluster B is added to C. B C s =C s -C′ s If C s If not empty, return to step S101. Otherwise, end, obtaining the merged cluster set C. B and the set of dwelling points C r ={r0, r1, ..., r M r M+1}, where r0 = r M+1 M represents the location of the base station, and C represents the cluster set. BThe number of clusters and the number of residence points r in the cluster. p The location is cluster B p The geometric center of the polygon formed by all sensor nodes, p∈[1,M].

[0021] Preferably: In step S102, the charging device is calculated for each sensor node s within the cluster. i Charging demand E i Charging time The formula is:

[0022]

[0023] in, For charging devices, each sensor node within the cluster s i Charging demand E i Charging time, E i For sensor node s i The charging demand, For sensor node s i to the outpost s The distance, |C′ s |For C s The number of sensor nodes in ', P max This refers to the maximum charging output power of the charging device. For charging efficiency, according to the Friesian transport equation, we have

[0024]

[0025] Among them G t G r , η, L P λ and α are constants.

[0026] Preferred method: The method for obtaining the optimal set of dwelling points in step S2 is as follows:

[0027] For the cluster set C B Each cluster B m For m∈[1,M], construct polygon M with all sensor nodes in the cluster as vertices. m .

[0028] For the set of dwelling points C r Each dwelling point r m To determine whether a dwell point is outside the polygon using the ray casting method, the objective function is set as follows:

[0029]

[0030] Where, f(r) m Let τ(r) be the objective function. m) represents the energy loss caused by charging all sensor nodes in cluster B m at the residence point r m , E u is the charging demand of sensor node S m in cluster B u , n m is the number of sensor nodes in cluster B m , and d u is the distance from sensor node s m to residence point r m . m Based on the polygon M m , the boundary constraint Q m is set as:

[0032]

[0033] where (x m , y min ) is the position coordinate of residence point r u , x max = min{x u}, x min = max{x u}, y max = min{y u}, y u = max{y u}, (x u , y u ) is the position coordinate of sensor node S m , and s r ∈ B m .

[0034] For each residence point r m in the residence point set C m , the optimal residence point r′ m is calculated based on the objective function f(r r ) and the boundary constraint Q M using the L-BFGS-B method with boundary constraint, thereby obtaining the optimal residence point set C′ M+1 = {r0, r1′, …, r′ r , r m}.

[0035] Preferably, the method of step 3 obtains the power setting table set as follows:

[0036] For each optimal residence point r′ m in the optimal residence point set C′ u , the charging device charges cluster Bm the charging power allocated to each sensor node s in cluster B u the charging output power set by the charging device is:

[0037]

[0038] The derivation process of the formula is as follows:

[0039] Suppose the charging device is cluster B m the charging power allocated to each sensor node s in cluster B the optimal residence point r' m the distance from the charging device to each sensor node s in cluster B In order to minimize the residence time t of the charging device at the residence point m Therefore, the charging time of the charging device to each sensor node should be as long as possible, that is,

[0040]

[0041] Also,

[0042]

[0043] Therefore, the simultaneous equations can be obtained

[0044]

[0045] Therefore,

[0046]

[0047] Therefore, the power allocation table is obtained Further, the power setting table set C is obtained T ={T1, T2, …, T M}.

[0048] Preferably, the specific method of step 4 is: based on the optimal residence point set C' r and the power setting table set C T , the base station constructs a TSP path traversing all optimal residence points, and the charging device charges the sensor nodes in cluster B according to the power setting table T at each optimal residence point r' m in the charging path order. m m

[0049] Preferably, the position of the residence point r s is the geometric center of the polygon formed by all sensor nodes in C' s and the position of the residence point r p is the geometric center of the polygon formed by all sensor nodes in cluster B p The calculation method of the geometric center of the polygon is:​​

[0050] For any sensor node set C with sensor node number H, H>0, the position coordinate (x, y) of the residence point r is calculated as where (x h , y h ) is the position coordinate of the sensor node s h , s h ∈C.

[0051] Preferably: in S1, initially, each sensor node in the wireless rechargeable sensor network forms a cluster, there is only one sensor node in the cluster, and the position of the residence point is the position of the sensor node.

[0052] Another object of the present application is to provide a system for reducing the working time length of the energy loss of the equipment in the rechargeable sensor network, for realizing the method for reducing the working time length of the energy loss of the equipment in the rechargeable sensor network, comprising a collection unit, a fused cluster set and residence point set obtaining unit, an optimal residence point set obtaining unit, a power setting table set obtaining unit, and a charging control unit, wherein:

[0053] The collection unit is used to collect the rechargeable sensor network information and the charging equipment information.

[0054] The fused cluster set and residence point set obtaining unit is used to, according to the charging equipment information and the rechargeable sensor network information, take minimizing the working time length of the charging equipment as the target, take the constraint condition that the charging equipment can simultaneously charge all the sensor nodes in the cluster at least at one residence point, utilize the cluster fusion algorithm to cluster and fuse the sensor nodes in the wireless rechargeable sensor network, and obtain the fused cluster set and residence point set.

[0055] The optimal residence point set obtaining unit is used to, based on the fused cluster set and residence point set, take minimizing the energy loss of the charging equipment as the target, take the charging demand amount of the sensor nodes as the constraint condition, adjust the residence point position of each cluster, and obtain the optimal residence point set.

[0056] The power setting table set obtaining unit is used to, based on the optimal residence point set, take minimizing the residence time length of the charging equipment at the residence point as the target, take the maximum charging output power of the charging equipment as the constraint condition, set the charging output power for each sensor node in the cluster, and obtain the power setting table set.

[0057] The charging control unit is used to, based on the optimal residence point set and the power setting table set, construct a charging path by the base station, and make the charging equipment charge the sensor nodes in each cluster according to the power setting table at the optimal residence point in the charging path order.

[0058] Another object of the present application is to provide a computer system comprising a memory for storing computer programs / instructions and a processor for executing the computer programs / instructions to implement the method for reducing the energy loss and working time of a charging device in a rechargeable sensor network.

[0059] Compared with the prior art, the present application has the following beneficial effects:

[0060] Firstly, the sensor nodes in the wireless rechargeable sensor network are clustered and fused by using a cluster fusion algorithm, with the objective of minimizing the working time of the charging device and the constraint condition that the charging device can simultaneously charge all the sensor nodes in the cluster at at least one residence point, to obtain a fused cluster set and a residence point set. Then, based on the fused cluster set and the residence point set, the residence point position of each cluster is adjusted, with the objective of minimizing the energy loss of the charging device and the constraint condition of the charging demand of the sensor nodes, to obtain an optimal residence point set. Subsequently, the charging output power of each sensor node in the cluster is set, with the objective of minimizing the residence time of the charging device at the residence point and the constraint condition of the maximum charging output power of the charging device, to obtain a power setting table set. Finally, based on the optimal residence point set and the power setting table set, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster according to the power setting table at the optimal residence point in the order of the charging path. By using the method of the present application, the problem of simultaneously minimizing the energy loss and working time of the charging device is split, the mobile time is optimized first, the energy loss is optimized second, and the residence time is optimized last, thereby reducing the problem solving complexity. Secondly, according to the greedy strategy, the cluster fusion algorithm can ensure that there is at least one residence point in the fused cluster to enable the charging device to charge all the nodes in the cluster under the condition of meeting the optimization objective. In addition, the L-BFGS-B method with boundary constraint is used to adjust the residence point position, which realizes the minimization of the energy loss of the charging device and has high efficient convergence and robustness, thereby reducing the search time. Finally, according to the charging characteristics, the charging power setting method can reduce the residence time of the charging device to the maximum extent. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 is the execution flowchart of the method for reducing the energy loss and working time of a charging device in the rechargeable sensor network.

[0062] Figure 2 is the cluster distribution diagram in the network before and after the execution of the cluster fusion algorithm, (a) is that each sensor node forms a cluster before the execution of the cluster fusion algorithm, and (b) is that part of the clusters are fused into one cluster after the execution of the cluster fusion algorithm.

[0063] Figure 3 is the residence point adjustment diagram, and Table 1 is an example of the information of the sensor nodes in the cluster and the residence point.

[0064] Figure 4 is a schematic diagram of setting charging output power for each sensor node in a cluster by the charging device at the optimal residence point.

[0065] Figure 5 is a schematic diagram of constructing a charging path by the base station to traverse all the optimal residence points. DETAILED DESCRIPTION

[0066] The present application will be further clarified by the following examples, which should be considered as merely illustrative of the present application and not in limitation of the scope of the application, which is set forth in the claims. Various modifications of the application in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description, and it is understood that such modifications are within the scope of the present application as defined by the appended claims.

[0067] The embodiment provides a method for reducing energy loss working time of a device in a chargeable sensor network, the device being a movable charging device for providing energy supply for sensor nodes in the chargeable sensor network in a wireless form, and the method comprising the following steps:

[0068] In step S1, information of the chargeable sensor network and information of the charging device are collected, the information of the charging device including working time, and the information of the chargeable sensor network including sensor nodes. According to the information of the charging device and the information of the chargeable sensor network, clustering and fusion of the sensor nodes in the wireless chargeable sensor network are performed by using a cluster fusion algorithm, with the objective of minimizing the working time of the charging device and the constraint condition that the charging device can simultaneously charge all the sensor nodes in a cluster at least at one residence point, to obtain a fused cluster set and a residence point set.

[0069] In step S2, based on the fused cluster set and the residence point set, the residence point position of each cluster is adjusted, with the objective of minimizing energy loss of the charging device and the constraint condition of charging demand of the sensor nodes, to obtain an optimal residence point set.

[0070] In step S3, based on the optimal residence point set, charging output power is set for each sensor node in a cluster, with the objective of minimizing residence time of the charging device at the residence point and the constraint condition of the maximum charging output power of the charging device, to obtain a power setting table set.

[0071] In step S4, based on the optimal residence point set and the power setting table set, a charging path is constructed by a base station, and the charging device charges the sensor nodes in each cluster according to the power setting table at the optimal residence point in the order of the charging path.

[0072] In some embodiments, the execution flow of the method is as shown in Figure 1As shown, the process includes: Initially, each sensor node in the wireless rechargeable sensor network forms a cluster, with only one sensor node within each cluster, and the dwell point location is the sensor node's location. With the objective of minimizing the charging device's operating time and the constraint that the charging device can simultaneously charge all sensor nodes within the cluster at at least one dwell point, a cluster fusion algorithm is executed to obtain the fused cluster set and dwell point set. Next, the L-BFGS-B method (a quasi-Newton method with boundary constraints) is used to adjust each dwell point to minimize the dwell time of the charging device at each dwell point. Then, based on the optimal dwell point set, the charging output power is set for each sensor node within the cluster to minimize the dwell time of the charging device. Finally, the base station constructs a TSP path that traverses all optimal dwell points, and the charging device, according to the charging path order, sequentially charges the sensor nodes in the cluster at each optimal dwell point according to the power setting table.

[0073] In some specific embodiments, the method for obtaining the fused cluster set and the dwell point set in step S1 is as follows:

[0074] s i Let i represent sensor node and C represent the set of sensor nodes. s ={s1, s2, ..., s N}, B i Let i be a cluster and C be the set of clusters. B ={B1, S2, ..., B N}, initially B i ={s i Let}, i∈[1,N], where N is the total number of sensor nodes. Let the sensor nodes s i The charging demand is E i The maximum charging output power of the charging device is P. max .

[0075] Step S101, let the sensor node set C′ s =C s C s This is the initial set of sensor nodes.

[0076] Step s102, view C′ s All sensor nodes in the cluster form a cluster, with a residence point r s The position is C′ s The geometric center of the polygon formed by all sensor nodes in the cluster is calculated, and the charging device is the s of each sensor node within the cluster. i Charging demand E i Charging time This results in the charging device remaining in the charging station for a certain period of time. s i ∈C′ s .

[0077] Step S103: Select charging duration sensor nodes k , determine s k to the outpost s distance Is it less than d? max d max Maximum distance for wireless power transfer to charging devices:

[0078] Step S1031, if Let D s C B Remove C′ from the middle s The remaining cluster set of the middle node is used to construct D. s ∪{C′ s} and {{s k}}∪D s ∪{C′ s \s k The path length L is obtained by traversing the TSP paths of each cluster. s L s&k The movement durations are respectively Where v is the moving speed of the charging device. For C′ s \s k The dwell time t of the charging device is obtained by executing step S102. s\k If satisfied Then proceed to step S104. Otherwise, proceed to step S105.

[0079] Step S1032, if Perform step S104.

[0080] Step S104, let C′ s =C′ s \s k Return to step S102.

[0081] Step S105, merge clusters, let B = UB i , where s i ∈B i And s i ∈C′ s Delete cluster set C B B in i The merged cluster B is added to C. B C s =C s -C′ s If C s If not empty, return to step S101. Otherwise, end, obtaining the merged cluster set C. B and the set of dwelling points Cr ={r0, r1, ..., r M r M+1}, where r0 = r M+1 M represents the location of the base station, and C represents the cluster set. B The number of clusters and the number of residence points r in the cluster. p The location is cluster B p The geometric center of the polygon formed by all sensor nodes, p∈[1,M].

[0082] Preferably: In step S102, the charging device is calculated for each sensor node s within the cluster. i Charging demand E i Charging time The formula is:

[0083]

[0084] in, For charging devices, each sensor node within the cluster s i Charging demand E i Charging time, E i For sensor node s i The charging demand, For charging energy efficiency, For sensor node s i to the outpost s The distance, |C′ s |For C s The number of sensor nodes in ', P max This refers to the maximum charging output power of the charging device.

[0085] by Figure 2 For example, 24 sensor nodes are deployed within a network region of arbitrary shape and size. Before executing the cluster fusion algorithm, each sensor node forms a separate cluster, resulting in 24 clusters, such as... Figure 2 As shown in (a). After executing the cluster fusion algorithm, some clusters contain multiple sensor nodes, and the dwell point is the geometric center of the node within the cluster, as shown in (a). Figure 2 As shown in (b), there are 15 clusters at this time.

[0086] In another embodiment, the method for obtaining the optimal set of dwell points in step S2 is as follows:

[0087] For the cluster set C B Each cluster B m For m∈[1,M], construct polygon M with all sensor nodes in the cluster as vertices. m .

[0088] For the set of dwelling points C r Each dwelling point rm , the target function is set as:

[0089]

[0090] wherein f(r m ) is the target function, τ(r m ) represents the energy loss caused by the charging device at the residence point r m for charging all sensor nodes in the cluster B m , E u is the charging demand of the sensor node s m in the cluster B u , n m is the number of sensor nodes in the cluster B m , is the distance from the sensor node s u to the residence point r m .

[0091] Based on the polygon M m , the boundary constraint Q m is set as:

[0092]

[0093] wherein (x m , y m ) is the position coordinate of the residence point r m , x min = min{x u}, x max = max{x u}, y min = min{y u}, y max = max{y u}, (x u , y u ) is the position coordinate of the sensor node S u , s u ∈ B m .

[0094] For each residence point r r in the residence point set C m , based on the target function f(r m ) and the boundary constraint Q m , the optimal residence point r′ m is calculated by using the quasi-Newton method with boundary constraint L-BFGS-B method, thereby obtaining the optimal residence point set C′ r = {r0, r1′, …, r′ M , rM+1}

[0095] by Figure 3 For example, suppose cluster B m There are four sensor nodes: s1, s2, s3, and s4, with a dwell point r. m Their coordinates are shown in Table 1. According to the adjustment method described, the objective function is f(r). m and boundary constraints Qm: Finally, the adjusted optimal residence point r′ was obtained. m The location coordinates are (4.1, 4.2). Charging all nodes in the cluster at this residence point can minimize the energy loss of the charging equipment.

[0096] Table 1

[0097] Position coordinates Energy demand E i ]]>

[00100] S1 (1.0，4.5) 100 Joule [S2] (4.5，6.0) 170 Joule [S3] (6.0，4.0) 180 Joule [CD AT S4] (3.0，1.0) 50 Joule r m ]]> (3.4，3.6) -

[0098] In another embodiment, step 3 is a method for obtaining the power setting table set:

[0099] For the optimal set of dwelling points C′ r Each optimal residence point r′ m The charging equipment is cluster B. m Each sensor node s u Set charging output power for:

[0100]

[0101] Power setting table This leads to the power setting table set C. T ={T1, T2, ..., T M}

[0102] by Figure 4 Cluster B m Taking four sensor nodes s1, s2, s3, and s4 as an example, based on the distance from the optimal dwell point to each sensor node and the energy requirement of each sensor node, the calculation method described above is used to obtain the charging output power set by the charging device for each sensor node.

[0103] In another embodiment, the dwell point r s The position is C′ s The geometric center and dwell point r of the polygon formed by all sensor nodes. p The location is cluster B p The method for calculating the geometric center of the polygon formed by all sensor nodes is as follows:

[0104] For any set of sensor nodes C with number H, where H > 0, the position coordinates (x, y) of the dwell point r are calculated as follows: Where (x) h y h ) represents sensor node s h Position coordinates, s h ∈C.

[0105] In another embodiment, step 4 is specifically implemented as follows: based on the optimal set of dwelling points C′ r and power setting table set C T The base station constructs a TSP path that traverses all optimal camping points. The charging equipment, in the order of the charging path, sequentially camps at each optimal camping point r′. m According to the power setting table T m For cluster B m The sensor nodes in the system are being charged.

[0106] by Figure 5 For example, the constructed TSP path starts from the base station, passes through the camping points of 15 clusters in the network, charges the sensor nodes in the cluster according to the power setting table and energy demand at each camping point, and finally returns to the base station.

[0107] In another embodiment, a system for reducing the operating time of devices with energy loss in a rechargeable sensor network is provided, which is used to implement the method for reducing the operating time of devices with energy loss in the rechargeable sensor network. The system includes a data acquisition unit, a unit for obtaining a fused cluster set and a dwell point set, a unit for obtaining an optimal dwell point set, a power setting table set, and a charging control unit, wherein:

[0108] The acquisition unit is used to acquire information from rechargeable sensor networks and charging devices.

[0109] The unit for obtaining the fused cluster set and dwell point set is used to cluster and fuse the sensor nodes in the wireless rechargeable sensor network based on the charging device information and the rechargeable sensor network information, with the goal of minimizing the working time of the charging device and the constraint that the charging device can charge all sensor nodes in the cluster at least at one dwell point at the same time, to obtain the fused cluster set and dwell point set.

[0110] The optimal dwell point set acquisition unit is used to adjust the dwell point position of each cluster based on the fused cluster set and dwell point set, with the goal of minimizing the energy loss of the charging equipment and the charging demand of the sensor nodes as a constraint, to obtain the optimal dwell point set.

[0111] The power setting table set obtaining unit is configured to set a charging output power for each sensor node in a cluster based on the optimal residence point set, with the minimum residence duration of the charging device at the residence point as the target and the maximum charging output power of the charging device as the constraint condition, to obtain a power setting table set.

[0112] The charging control unit is configured to construct a charging path based on the optimal residence point set and the power setting table set, and the charging device charges the sensor nodes in each cluster at the optimal residence point according to the power setting table in the charging path order.

[0113] In another embodiment, a computer system is provided, comprising a memory and a processor, the memory is configured to store computer programs / instructions, and the processor is configured to execute the computer programs / instructions to implement the method for reducing the working duration of the energy loss of the device in the rechargeable sensor network.

[0114] The present application minimizes the working duration of the charging device as the target, and at least simultaneously charges all sensor nodes in the cluster at one residence point as the constraint condition, and uses the cluster fusion algorithm to cluster and fuse the sensor nodes in the wireless rechargeable sensor network; secondly, the residence point position of each cluster is adjusted to minimize the energy loss of the charging device; then, the charging output power for each sensor node in the cluster is set to minimize the residence duration of the charging device at the residence point; finally, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster at the optimal residence point according to the power setting table in the charging path order. The present application is an efficient charging scheduling scheme, which can optimize the energy loss and working duration of the charging device at the same time, and has stronger applicability.

[0115] The above only describes the preferred embodiments of the present application, and it should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for reducing the length of time a device is operational with reduced energy loss in a rechargeable sensor network, the device being a mobile charging device that provides energy replenishment to sensor nodes in a rechargeable sensor network in a wireless form, characterized in that, The method comprises the following steps: Step S1, collecting chargeable sensor network information and charging device information; According to the charging device information and the chargeable sensor network information, the sensor nodes in the wireless chargeable sensor network are clustered and fused by using a cluster fusion algorithm, so as to obtain a fused cluster set and a residence point set, with the minimum working time of the charging device as the target and with the charging device being able to simultaneously charge all sensor nodes in a cluster at one residence point as the constraint condition; Step S2, based on the fused cluster set and the residence point set, adjusting the residence point position of each cluster to obtain an optimal residence point set, with the minimum energy loss of the charging device as the target and with the charging demand of the sensor nodes as the constraint condition; Step S3, based on the optimal residence point set, setting the charging output power for each sensor node in the cluster to obtain a power setting table set, with the minimum residence time of the charging device at the residence point as the target and with the maximum charging output power of the charging device as the constraint condition; Step S4, based on the optimal residence point set and the power setting table set, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster at the optimal residence point according to the power setting table in sequence according to the charging path.

2. The method of claim 1, wherein the method further comprises: The method for obtaining the fused cluster set and the residence point set in step S1 is as follows: Initialization, C s = {s1, s2,..., s N} is the initial sensor node set, s i represents sensor node i; Cluster set C B = {B1, B2,..., B N}, B i represents cluster i, B i = {s i}, i ∈ [1, N], N is the total number of sensor nodes; Step S101, let the sensor node set C' s = C s ; Step S102, if C' s All sensor nodes form a cluster, the residence point r s The position of C' s The geometric center of the polygon formed by all sensor nodes in C', calculate the charging demand E i of each sensor node s i in the cluster by the charging device And the residence time of the charging device is s i ∈C' s ; Step S103: Select charging duration sensor nodes k , determine s k to the outpost s distance Is it less than d? max d max Maximum distance for wireless power transfer to charging devices: Step S1031, if Let D s C B Remove C′ from the middle s The remaining cluster set of the middle node is used to construct D. s ∪{C′ s } and {{s k }}∪D s ∪{C′ s \s k The path length L is obtained by traversing the TSP paths of each cluster. s L s&k The movement durations are respectively Where v is the moving speed of the charging device; for C′ s \s k The dwell time t of the charging device is obtained by executing step S102. s\k If the conditions are met Then proceed to step S104; otherwise, proceed to step S105. Step S1032, if Step S104 is executed. Step S104, let C' s = C' s \ k , return to step S102; Step S105, merge the cluster, let B=∪B i , where s i ∈B i and s i ∈C′ s , delete B B in the cluster set C i , add the merged cluster B to C B ; C s =C s -C′ s , if C s is not empty, return to step S101; otherwise end, get the merged cluster set C B and the set of residence points C r ={r0, r1, …, r M , r M+1}, where r0=r M+1 represents the base station position, M is the number of clusters in the cluster set C B , and the position of the residence point r p is the geometric center of the polygon formed by all sensor nodes in the cluster B p , p∈[1, M].

3. The method of claim 2, wherein the method further comprises: determining a time period for each device to operate in the low energy mode; and transmitting the time period to each device. In step S102, the charging device is calculated for each sensor node s within the cluster. i Charging demand E i Charging time The formula is: wherein, is the charging demand of each sensor node s i in the cluster, i is the charging duration of each sensor node s i in the cluster, i is the charging demand of sensor node s is the charging energy efficiency, is the distance from sensor node s i to the residing point r s , s is the number of sensor nodes in C s ′, max is the maximum charging output power of the charging device.

4. The method of claim 3, wherein the method further comprises: The method for obtaining the optimal residence point set in step S2 is as follows: For each cluster B B of the cluster set C m , m e [1, M], a polygon M m is constructed with all sensor nodes in the cluster as vertices. For the set of dwelling points C r Each dwelling point r m To determine whether a dwell point is outside the polygon using the ray casting method, the objective function is set as follows: wherein f(r m ) is an objective function, τ(r m ) represents the energy loss caused by the charging device charging all sensor nodes in cluster B m at the residence point r m , E u is the charging demand of sensor node s m in cluster B u , n m is the number of sensor nodes in cluster B m , is the distance from sensor node s u to residence point r m ; Based on the polygon M m , the boundary constraint Q m is set as: wherein (x m , y m ) is the position coordinate of the residence point r m , x min = min{x u}, x max = max{x u}, y min = min{y u}, y max = max{y u}, (x u , y u ) is the position coordinate of the sensor node S u , s u ∈ B m ; For the set of dwelling points C r Each dwelling point r m Based on the objective function f(r) m ) and boundary constraints Q m The optimal dwell point r′ is calculated using the quasi-Newton method with boundary constraints, the L-BFGS-B method. m Thus, the optimal set of dwelling points C′ is obtained. r ={r0, r1′, ..., r′ M r M+1 } 5. The method of claim 4, wherein the method further comprises: The method for obtaining the power setting table set in step 3 is as follows: for each optimal residence point r' of the optimal residence point set C' s m , the charging device sets the charging output power for each sensor node s of the cluster B m u :​​​ Power setting table Further, a power setting table set C is obtained T = {T1, T2,..., T M}.

6. The method of claim 5, wherein the method further comprises: The specific method of step 4 is: based on the optimal residence point set C' r and the power setting table set C T , the base station constructs a TSP path traversing all the optimal residence points, and the charging device sequentially charges the sensor nodes in the cluster B m at each optimal residence point r' m according to the power setting table T m in the order of the charging path.

7. The method of claim 6, wherein the method further comprises: The position of the resident point r s The position of the resident point r s The geometric center of the polygon formed by all sensor nodes in the cluster B p The position of the resident point r p The geometric center of the polygon formed by all sensor nodes in the cluster B For any sensor node set C with number of sensor nodes H, H > 0, the position coordinate (x, y) of the residence point r is calculated as where (x h ,y h ) is the position coordinate of sensor node s h , s h ∈ C.

8. The method of claim 7, wherein the method further comprises: In step S1, each sensor node in the wireless chargeable sensor network forms a cluster, there is only one sensor node in the cluster, and the residence point position is the sensor node position.

9. A system for reducing the operating time of devices with energy loss in a rechargeable sensor network, characterized in that: The method for reducing the working time of the device with energy loss in the chargeable sensor network according to any one of claims 1-8 comprises a collection unit, a fused cluster set and residence point set obtaining unit, an optimal residence point set obtaining unit, a power setting table set obtaining unit, and a charging control unit, wherein: The collection unit is used to collect chargeable sensor network information and charging device information; The fused cluster set and residence point set obtaining unit is used to cluster and fuse the sensor nodes in the wireless chargeable sensor network by using a cluster fusion algorithm, so as to obtain a fused cluster set and a residence point set, with the minimum working time of the charging device as the target and with the charging device being able to simultaneously charge all sensor nodes in a cluster at one residence point as the constraint condition according to the charging device information and the chargeable sensor network information; The optimal residence point set obtaining unit is used to adjust the residence point position of each cluster to obtain an optimal residence point set, with the minimum energy loss of the charging device as the target and with the charging demand of the sensor nodes as the constraint condition based on the fused cluster set and the residence point set; The power setting table set obtaining unit is used to set the charging output power for each sensor node in the cluster to obtain a power setting table set, with the minimum residence time of the charging device at the residence point as the target and with the maximum charging output power of the charging device as the constraint condition based on the optimal residence point set. The charging control unit is configured to construct a charging path based on the optimal set of residence points and the set of power setting tables, and the charging device sequentially charges the sensor nodes in each cluster at the optimal residence points according to the power setting tables along the charging path.

10. A computer system, characterized by The memory is configured to store computer programs / instructions; and the processor is configured to execute the computer programs / instructions to implement the method for reducing the working time length of the device energy loss in the rechargeable sensor network according to any one of claims 1-8.

Images