CN110263228B - K-weak fence construction and mobile charging scheduling method for wireless chargeable sensor network - Google Patents
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Abstract
The invention discloses a method for constructing a k-weak fence and scheduling mobile charging of a wireless chargeable sensor network. The method comprises the following specific steps: the system comprises a two-dimensional rectangular narrow-band area, N omnidirectional sensor nodes and a movable charging car, wherein the N omnidirectional sensor nodes and the movable charging car are randomly deployed, and related parameters can be set according to the network scale. The method comprises the following specific steps: step 1: constructing a fence graph according to the monitoring area information; and acquiring the coverage radius, coverage energy consumption and position information of the sensor nodes from the region, constructing a barrier graph, and setting the side rights, the flow and the like. Step 2: and solving the fence network structure by using a minimum cost maximum flow algorithm. And step 3: and finding the sensor nodes forming each fence according to the information of the minimum cost maximum flow algorithm. And 4, step 4: and calculating various parameters of the charging vehicle according to the solved fence nodes. And 5: and calculating the label of each fence, and determining the charging sequence. According to the invention, the requirements of fence coverage and charging are combined, the charging efficiency of the charging vehicle is improved, and the requirement of fence coverage is ensured.
Description
Technical Field
The invention relates to the field of wireless sensor networks, in particular to a k-weak fence construction and mobile charging scheduling method.
Background
With the development of society and the progress of science and technology, wireless sensor networks are more and more widely applied to many fields such as military security and protection, environmental monitoring and the like. In the application of boundary intrusion monitoring, the wireless sensor nodes need to be deployed according to the shape and the topographic characteristics of a monitoring area to meet the requirement of seamless fence coverage for preventing intrusion, so that the fence coverage problem of the wireless sensor network is an important problem in the application of wireless sensor network monitoring.
Studies on fence construction methods in fence coverage are various, and studies considering fence coverage and wireless charging at the same time are almost none. The strategy for sleep scheduling is proposed by Kumar in the article maximum the Lifetime of a Barrier of Wireless Sensors. The author respectively provides two fence construction algorithms aiming at whether the types of the sensor nodes are the same or not, and the service life of the network is maximized. Fang xinggang et al in a directed K-fence construction method based on a selection box (patent No. CN201510240143.4) constructed K-fence coverage by movable sensor nodes with the objective of minimizing mobile energy consumption as an optimization goal. However, these studies cannot guarantee the continuous and permanent operation of the network, so we propose a k-fence construction method combining the point-to-point wireless charging technology and a scheduling strategy of the charging vehicle.
Disclosure of Invention
The invention provides a k-weak fence construction and mobile charging scheduling method of a wireless chargeable sensor network. Firstly, a network flow graph is constructed according to the requirement of a monitoring area and node parameter information. Next, the required barrier nodes are calculated using a least cost maximum flow algorithm. Then, each fence forming the fence is found according to the minimum cost maximum flow algorithm information, and each parameter setting requirement of the charging car for maintaining the fence network is calculated. And finally, numbering the barriers, and determining the charging sequence of the barriers in each period.
The technical scheme adopted for solving the technical problem comprises the following steps:
the wireless sensing network adopted by the invention is as follows: n omnidirectional static sensor nodes are randomly deployed in a two-dimensional rectangular narrow-band area, a movable charging car is arranged in a boundary area, and related parameters can be set according to the network scale. The method comprises the following specific steps:
step 1: constructing a k fence graph according to the monitoring area information;
and acquiring the coverage radius, coverage energy consumption and position information of the sensor nodes from the region, constructing a barrier graph, and setting the side rights, the flow and the like.
Step 2: and solving the fence network structure by using a minimum cost maximum flow algorithm.
And step 3: and finding the sensor nodes forming each fence according to the information of the minimum cost maximum flow algorithm.
And 4, step 4: and calculating various parameters of the charging vehicle according to the solved fence nodes.
And 5: the charging sequence is determined for the fence number.
And (3) constructing the k-fence graph in the step 1 according to the coverage range and the coverage energy consumption of the sensor nodes. When the number of the fences is required to be k, k +1 fences need to be solved, and the charging strategy is to open the k fences and simultaneously charge the other fence. The detailed steps are as follows:
1-1, constructing a directed weight graph G ═ (V)G,EG,WG,FG) (ii) a Vertex V of the graphGIs a collection of points in a scene, where each sensor node siIs split into a set s of vertex pairsiAnd si' while adding a virtual vertex l to the left boundaryslotAdding vertex pair set r for right boundaryslotAnd rslot'; edge EGAn edge representing a vertex; weight WGRepresents the cost of the edge; weight FGRepresenting the traffic of the edge, determining the maximum period of the network
Namely, the minimum service life of all the sensor nodes is the maximum operation period of the network. The time segment for simultaneously setting the charging vehicle to serve each fence is
1-2, adding edges in the weight graph G according to the information of the sensor nodes in the monitoring area. Constructed weak fence coverage, so as to work as sensor node siIn the vertical region with the sensor sjCoverage areas overlap, then, a directed edge is added<si,sj>The cost of the edge is
Epsilon is the mobile energy consumption unit of the charging vehicle is J/m, dis(s)i,sj) Is the distance between the two sensors and the flow is 1. When sensor node siWhen the left boundary can be covered, the directed edge is added<lslot,si>The cost of the edge is
The flow rate was 1. When sensor node siWhen the right boundary can be covered, the directed edge is added<si,rslot>The cost of the edge is
The flow rate was 1. For each sensor node s simultaneouslyiAdding a directed edge<si,si′>The cost of the edge is
Alpha is the energy conversion rate of the output power of the charging vehicle, and the flow rate is 1. Finally, add the directed edge<rslot,rslot′>The cost of the edge is 0 and the flow rate is k + 1.
Step 2, calculating k +1 barriers with the minimum cost by using a minimum cost maximum flow algorithm, such as a classical (EK) algorithm.
And 3, finding out nodes forming the fence according to the information of the data structure after the minimum cost maximum flow algorithm is operated:
3-1, from the left boundary lslotBegin looking for each sensor node that forms k +1 fences if vertex lslotWith a certain vertex siThe flow rate of the original network graph is 1, after the minimum cost maximum flow algorithm is operated, the flow rate is changed into 0, and then the vertex siThe corresponding sensor node is a node that forms the fence. We look sequentially down until r is reachedslot' when the sensor node constituting a barrier is completely found, the vertex sequence is recorded as:
Q1＝{lslot,s1,s1’,…,sn,sn’,rslot,rslot', one can find k +1 such sets of sequences in turn.
And 3-2, extracting the sensor nodes forming the fence for each sequence. Such as Q1Removing left and right boundaries and the virtual vertex to obtain C1＝{s1,…,snFinding out the nodes of the sensors forming a fence in sequence to form k +1 fencesThe sensor node of (1).
And 4, setting parameters of the charging vehicle according to the sensors forming the fence:
4-1, P for the fence sequence1＝{s1,…,snCalculate the length of each barrier | μi|＝dist(lslot,s1)+dist(s1,s2)+...+dist(sn,rslot) The amount of electricity consumed per fence cycle
4-2, calculating the length of each fence and the electric quantity consumed in one period according to the calculation method in the step 4-1. Then, for each fence, the total time that the charging vehicle serves each fence is as follows:
wherein v is the moving speed of the charging vehicle, c is the output power of the charging vehicle, and alpha is the energy conversion rate of the output power of the charging vehicle. Setting the speed v and the power c of the charging car for each fence so that tiTau is less than or equal to the time requirement.
And 5, numbering the k +1 fences from top to bottom according to the left boundary nodes, and determining the charging dormancy sequence of each fence. Meanwhile, the charging capacity of each sensor node is the energy consumed by the sensor in one period, namely, the sensor node is charged to a full-charge state.
The invention has the beneficial effects that:
1. the invention combines the fence coverage and charging problems and provides a k-weak fence construction and mobile charging scheduling method of a wireless chargeable sensor network, and compared with the traditional fence coverage, the method can ensure the lasting operation of the network.
2. According to the invention, the energy consumption of the maintenance network of the charging vehicle can be effectively reduced by considering the mobile energy consumption of the charging vehicle while constructing the fence.
Drawings
FIG. 1 is a conversion of a fence sensor node to a network minimum cost maximum flow algorithm graph in accordance with the present invention;
FIG. 2 is a diagram of the present invention segmenting sensor nodes into two vertices;
FIG. 3 is a diagram of replacing original sensor nodes with segmented vertices;
FIG. 4 is a schematic diagram of the charging vehicle;
FIG. 5 is a flow chart of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
The invention mainly provides a method for constructing a k-weak fence and scheduling mobile charging of a wireless chargeable sensor network. All the sensors are omnidirectional sensors with the same specification, have the same monitoring coverage radius and can carry out omnidirectional monitoring. In a 2D area scene with the size of L x H, N sensors are randomly deployed in advance, but barrier coverage is not formed, sensor nodes are required to be scheduled to form a k barrier, and a monitoring network can run persistently.
According to the model schematic diagram of fig. 1, the wireless sensor network adopted by the invention is as follows: in a two-dimensional narrowband region of interest, N omnidirectional sensors are randomly deployed. Initially, the location, coverage and coverage energy consumption of all sensors are known. The invention needs to design a strategy for constructing a k fence of the sensor, design a charging vehicle moving route and the like.
As shown in fig. 1, 2 and 5, the specific steps of the present invention are described as follows:
step 1: constructing a fence graph according to the monitoring area information;
1-1, constructing a directed weight graph G ═ (V)G,EG,WG,FG) (ii) a As shown in FIG. 2, all sensor nodes are split into a vertex pair set siAnd si'; as can be seen in FIG. 3, a virtual vertex/is added to the left boundaryslotAdding vertex pair set r for right boundaryslotAnd rslot'; edge EGAn edge representing a vertex; weight WGRepresents the cost of the edge; weight FGRepresenting the traffic of the edge. Determining a maximum period of a network
Namely, the minimum service life of all the sensor nodes is the maximum operation period of the network. Meanwhile, the charging vehicle is set to serve each fence in time segments of
1-2, adding edges in the weight graph G according to the information of the sensor nodes in the monitoring area. As in fig. 1, the sensing nodes s overlapping each other in the vertical direction in the coverage rangei,sjAdding a directed edge<si’,sj>The cost of the edge is
Epsilon is the mobile energy consumption unit of the charging vehicle is J/m, dis(s)i,sj) Is the distance between the two sensors and the flow is 1. When sensor node siCan be covered to the left border (s in fig. 1)1Covering the left border), add a directed edge<lslot,si>The cost of the edge is
The flow rate was 1. When sensor node siCan be covered to the right boundary (s in FIG. 1)5) Adding a directed edge<si,rslot>The cost of the edge is
The flow rate was 1. For each sensor node s simultaneouslyiAdding a directed edge<si,si’>The cost of the edge is
Alpha is the energy conversion rate of the output power of the charging vehicle, and the flow rate is 1. Finally, add the directed edge<rslot,rslot’>The cost of the edge is 0 and the flow rate is k + 1.
And 2, calculating k +1 barriers with the minimum cost by using a minimum cost maximum flow algorithm, such as an EK algorithm.
And 3, finding out nodes forming the fence according to the information of the data structure after the minimum cost maximum flow algorithm is operated:
3-1, from the left boundary lslotBegin looking for each sensor node that forms k +1 fences if vertex lslotWith a certain vertex siThe flow rate of the original network graph is 1, after the minimum cost maximum flow algorithm is operated, the flow rate is changed into 0, and then siIs a node that constitutes the fence. We look sequentially down until r is reachedslot' when the sensor node that constitutes a barrier is completely found, we record the vertex sequence as: q1＝{lslot,s1,s1’,…,sn,sn’,rslot,rslot', we can in turn find k +1 such sets of sequences.
And 3-2, extracting the sensor nodes forming the fence for each sequence. Such as Q1Removing left and right boundaries and the virtual vertex to obtain P1＝{s1,…,snAnd finding the sensor nodes forming the k +1 barriers in sequence.
And 4, setting parameters of the charging vehicle according to the sensors forming the fence:
4-1, as in FIG. 4, the gray line represents a fence P1＝{s1,s9,s3,s7,s6}, then | μ1|＝dist(lslot,s1)+dist(s1,s9)+dist(s9,s3)+dist(s3,s7)+dist(s7,s6)+dist(s6,rslot) The amount of electricity E consumed by the fence for one period1＝T×(ω1+ω9+ω3+ω7+ω6)。
4-2, for this bar, the total time of charge car service is:
wherein v is the moving speed of the charging vehicle, c is the output power of the charging vehicle, and alpha is the energy conversion rate of the output power of the charging vehicle. We set the charging car speed v, the power c for each fence so that t1Tau is less than or equal to the time requirement. And calculating the rest fences according to the method.
And 5, numbering k +1 fences in sequence, and charging each fence in sequence from small to large. Such as P1＝{s1,s9,s3,s7,s6}，P2＝{s10,s2,s8,s4,s5}. Meanwhile, the charging capacity of each sensor node is the energy consumed by the sensor in one period, namely, the sensor node is charged to a full-charge state.
Claims (1)
1. The k-weak fence construction and mobile charging scheduling method of the wireless chargeable sensor network is characterized in that the adopted wireless sensor network is as follows: the method comprises the following steps that N omnidirectional static sensor nodes are randomly deployed in a two-dimensional rectangular narrow-band area, a movable charging car is arranged in a boundary area, relevant parameters are set according to the network scale and requirements, and the method comprises the following specific steps:
step 1: constructing a k fence graph according to the monitoring area information;
acquiring the coverage radius, coverage energy consumption and position information of the sensor nodes from a two-dimensional rectangular narrow-band area, constructing a fence graph, and setting the side weight and the flow;
step 2: solving the fence network structure by using a minimum cost maximum flow algorithm;
and step 3: finding out the sensor nodes forming each fence according to the information of the minimum cost maximum flow algorithm;
and 4, step 4: calculating various parameters of the charging vehicle according to the solved fence nodes;
and 5: determining a charging sequence for the fence marks, numbering k +1 fences according to the sequence of left boundary nodes from top to bottom, and determining a charging dormancy sequence of each fence; meanwhile, the charging electric quantity of each sensor node is the energy consumed by the sensor in one period, namely the sensor node is charged to a full-charge state;
the step 1 is specifically realized as follows:
the k fence graph is constructed according to the coverage radius and the coverage energy consumption of the sensor nodes; when the number of the fences is required to be k, k +1 fences need to be solved, and the charging strategy is to open the k fences and simultaneously charge the other fence; the detailed steps are as follows:
1-1, constructing a directed weight graph G ═ (V)G,EG,WG,FG) (ii) a Vertex V of the graphGIs a collection of points in a scene, where each sensor node siIs split into a set s of vertex pairsiAnd a virtual vertex si' while adding vertex for the left boundaryslotAdding vertex pair set r for right boundaryslotAnd rslot'; edge EGAn edge representing a vertex; weight WGRepresents the cost of the edge; weight FGRepresenting the traffic of the edge, determining the maximum period of the network
Namely, the minimum service life of all the sensor nodes is the maximum operation period of the network; the time segment for simultaneously setting the charging vehicle to serve each fence is
1-2, adding edges in a weighted graph G according to the information of the sensor nodes in the monitoring area; constructed weak fence coverage, so as to work as sensor node siIn the vertical region with the sensor sjCoverage areas overlap, then, a directed edge is added<si,sj>The cost of the edge is
Epsilon is the unit of mobile energy consumption of the charging vehicle, and the specific unit is J/m, dis(s)i,sj) Is the distance between the two sensors, the flow is 1; when sensor node siWhen the left boundary can be covered, the directed edge is added<lslot,si>The cost of the edge is
The flow rate is 1; when sensor node siWhen the right boundary can be covered, the directed edge is added<si,rslot>The cost of the edge is
The flow rate is 1; for each sensor node s simultaneouslyiAdding a directed edge<si,si′>The cost of the edge is
Alpha is the energy conversion rate of the output power of the charging vehicle, and the flow is 1; finally, add the directed edge<rslot,rslot′>The cost of the edge is 0, and the flow rate is k + 1;
calculating k +1 fences with the minimum cost by using a minimum cost maximum flow algorithm and a classical EK algorithm;
step 3, finding out nodes forming the fence according to the information of the data structure after the minimum cost maximum flow algorithm is operated, and concretely realizing the following steps:
3-1, from the left boundary lslotBegin looking for each sensor node that forms k +1 fences if vertex lslotWith a certain vertex siThe flow rate of the original network graph is 1, after the minimum cost maximum flow algorithm is operated, the flow rate is changed into 0, and then the vertex siThe corresponding sensor node is a node forming the fence; looking for it successively until r is reachedslot' when the sensor node constituting a barrier is completely found, the vertex sequence is recorded as: q1＝{lslot,s1,s1’,…,sn,sn’,rslot,rslot' }, finding k +1 such sequence sets in turn;
3-2, extracting sensor nodes forming a fence for each sequence; for sequence Q1Removing left and right boundaries and virtual vertices to obtain C1＝{s1,…,snFinding the sensor nodes forming k +1 fences in sequence;
the step 4 is realized as follows:
4-1, C for fence sequencei＝{S1....SnCalculate the length of each barrier | μi|＝dist(lslot,S1)+dist(S1,S2)+...+dist(Sn,rslot) The amount of electricity consumed per fence cycle
Sj∈Ci；
4-2, calculating the length of each fence and the electric quantity consumed in one period according to the calculation method in the step 4-1; then, for each fence, the total time that the charging vehicle serves each fence is as follows:
wherein v is the moving speed of the charging vehicle, c is the output power of the charging vehicle, and alpha is the energy conversion rate of the output power of the charging vehicle; setting the speed v and the power c of the charging car for each fence so that tiTau is less than or equal to the time requirement.
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