CN110278567B - K-fence construction and charger optimized deployment method of wireless chargeable sensor network - Google Patents

K-fence construction and charger optimized deployment method of wireless chargeable sensor network Download PDF

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CN110278567B
CN110278567B CN201910421434.1A CN201910421434A CN110278567B CN 110278567 B CN110278567 B CN 110278567B CN 201910421434 A CN201910421434 A CN 201910421434A CN 110278567 B CN110278567 B CN 110278567B
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徐向华
李腾龙
王然
程宗毛
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Hangzhou Dianzi University
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Abstract

The invention discloses a k-fence construction and charger optimized deployment method of a wireless chargeable sensor network. The method comprises the following specific steps: step 1: constructing a fence graph according to the monitoring area information; step 2: and solving the fence network structure by using a minimum cost maximum flow algorithm. And step 3: and finding each sensor node forming the fence according to the solving information of the minimum cost maximum flow algorithm. And 4, step 4: dividing the whole monitoring area into grid points, and calculating the utility of arranging the chargers at each grid point. And 5: one charger position is selected according to the charging utility of each grid point. Judging whether all the sensor nodes meet the charging requirement or not, and ending when the charging requirement is met; otherwise, returning to step 4 to recalculate the charging utility of the remaining grids. The invention combines the fence coverage with the arrangement of the chargers, and reduces the arrangement quantity of the chargers on the premise of ensuring the fence coverage requirement.

Description

K-fence construction and charger optimized deployment method of wireless chargeable sensor network
Technical Field
The invention relates to the field of wireless sensor networks, in particular to a method for constructing a k-fence and deploying a charger.
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, none of these studies can guarantee continuous and permanent operation of the network, and therefore we propose a method of deploying chargers in a k-fence network to guarantee continuous operation of the network and minimize the number of deployed chargers.
Disclosure of Invention
The invention provides a k-fence construction and charger optimized deployment 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 sensor nodes that make up the fence are calculated using a least cost maximum flow algorithm. Then, a charger is deployed in the network to charge the sensor nodes. Meanwhile, the sensor nodes forming the fence are replaced in the charger deploying process, so that the deploying number of the chargers is reduced as little as possible.
The technical scheme adopted for solving the technical problem comprises the following steps:
the system comprises a two-dimensional rectangular narrow-band area, N omnidirectional sensor nodes and a plurality of fixed chargers, wherein the N omnidirectional sensor nodes and the fixed chargers are randomly deployed in the two-dimensional rectangular narrow-band area, the positions of the fixed chargers are selectable, and power can be continuously supplied. 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 each sensor node forming the fence according to the solving information of the minimum cost maximum flow algorithm.
And 4, step 4: dividing the whole monitoring area into grid points, and calculating the utility of arranging the chargers at each grid point.
And 5: one charger position is selected according to the charging utility of each grid point. Judging whether all the sensor nodes meet the charging requirement or not, and ending when the charging requirement is met; otherwise, returning to step 4 to recalculate the charging utility of the remaining grids.
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. 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.
1-2, adding edges in the weight graph G according to the information of the sensor nodes in the monitoring area. If sensor node siAnd sensors sjCoverage areas overlap, then, a directed edge is added<si’,sj>The cost of the edge is 0 and the flow rate 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 0 and 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 0 and the flow rate is 1. For each sensor node s simultaneouslyiAdding a directed edge<si,si’>Edge ofAt a cost of ωi，ωiThe flow rate is 1 for the specific energy consumption of the sensor. Finally, add the directed edge<rslot,rslot’>The cost of the edge is 0 and the flow rate is k.
And 2, calculating k barriers with the minimum cost by using a minimum cost maximum flow algorithm.
Step 3, finding each sensor node forming the fence according to the solving information of the minimum cost maximum flow algorithm:
3-1, from the left boundary lslotBegin looking for each sensor node that makes up k fences if vertex lslotWith a certain vertex siThe initial flow rate 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 that constitutes a barrier is completely found, we record the vertex sequence as: q1＝{lslot,s1,s1’,…,sn1,sn1’,rslot,rslot', we can in turn find k 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,…,sn1And finding the sensor nodes forming the k fences in sequence. Let us remember that the set of sensors forming k bars is S ═ P1∪P2∪...∪Pk＝{s1,…,sn}。
Step 4, dividing the monitoring area into grids with the side length tau (the size can be set to be about 0.1-1 meter):
4-1, calculating each sensor node siIs covered by a distance λ(s)i): when sensor node siWhen covering the left or right border of the monitored area, siThe coverage distance of the sensor is the length of a vertical connecting line between the sensor node and the boundary; when sensor node siWith a certain node s in the same fencejWhen the coverage areas of (a) overlap each other, siHas a coverage distance of node siAnd node sjHalf the distance.
And 4-2, calculating the charging utility of each grid point according to the coverage distance of the sensor nodes. Grid location cjCharge effect of (c) muj): at cjWhen the charger is arranged, the sensor in the charging coverage range of the charger receives the electric quantity of the charger. We define that two sensor nodes in the same fence will only compute the charging utility of the sensor near the right if the energy consumption requirements of the sensor near the left are met. Therefore, the following formula is given:
Figure BDA0002066109050000041
wherein left(s)i) Representing a node siWhether the left node of (1) has satisfied the charging requirement is 1, otherwise is 0.
And 5, selecting a charger position according to the charging utility of each grid point. Judging whether all the sensor nodes meet the charging requirement or not, and ending when the charging requirement is met; otherwise, returning to step 4 to recalculate the charging utility of the remaining grids. The method comprises the following specific steps:
5-1, let set S ═ S, set grid size τ to divide grid points, set Z ═ Z1,z2… represents all grid points. Then, calculating the charging utility of all grid points, ordering the charging utilities of all grid points from large to small, and selecting the grid vertex z with the maximum utilitytopAs a charger location. Also, when the sensors in set S' can be ztopPosition-arranged charger CjWhen charging, the charging power required by the sensor will become smaller, i.e. ωi＝ωi-pr(d(si,cj)). When the perceived energy consumption of a sensor is 0, this sensor is removed from the set S'.
5-2, simultaneously calculating the charge quantity received by the sensor nodes which do not form the fence when a certain sensor sjThe received electric quantity meets the charging effectTime-of-use judgment sensor sjWhether or not it is a certain sensor s constituting a barrieriAn equivalence node of, and siDoes not meet the energy consumption requirements. At this time, using a sensor sjReplacement sensor siAnd (4) nodes. Definition of equivalent nodes: if sensor node siIs a sensor node forming a fence, if we use sensor node siRemoving s from the fencejBy adding the fence, the integrity of the original fence is not affected, so the node sjIs the sensor siThe equivalent node of (2).
5-3, grid points from z to be selected as locations for arranging chargerstopRemove from set Z while leaving grid points ZtopAnd adding the mixture into the set C. When there are sensors in the set S' that meet the charging demand, return to step 4. Otherwise, the output set C is all the charger arrangement positions.
The invention has the beneficial effects that:
the invention combines the problems of fence coverage and charger deployment, provides a charger position selection method integrating fence structural characteristics, and reduces the deployment number of chargers on the premise of ensuring the permanent operation of a network.
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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 graphical representation of sensor coverage and charger charging utility calculations in accordance with the present invention;
FIG. 3 is a diagram of an equivalence node;
FIG. 4 is a diagram illustrating the effect of the complete charger deployment;
FIG. 5 is a flow chart of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1-5, the present invention mainly provides a method for constructing a k-fence of a wireless chargeable sensor network and optimally deploying a charger. 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.
As shown in fig. 1, 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. 1, all sensor nodes are split into a vertex pair set siAnd si'; adding a virtual vertex s for the left boundary and adding a vertex pair set t and t' for the right boundary; edge EGAn edge representing a vertex; weight WGRepresents the cost of the edge; weight FGRepresenting the traffic of the edge.
1-2, adding edges in the weight graph G according to the information of the sensor nodes in the monitoring area. As shown in FIG. 1, the coverage areas of the sensing nodes s overlapi,sjAdding a directed edge<si’,sj>The cost of the edge is 0 and the flow rate is 1. When sensor node siCan be covered to the left border (s in fig. 1)1Covering the left border), add a directed edge<s,s1>The cost of the edge is 0 and the flow rate is 1. When sensor node siCan be covered to the right boundary (s in FIG. 1)5) Adding a directed edge<s5,t>The cost of the edge is 0 and the flow rate is 1. For each sensor node s simultaneouslyiAdding a directed edge<si,si’>The cost of the edge is omegai，ωiThe flow rate is 1 for the specific energy consumption of the sensor. Finally, add the directed edge<rslot,rslot’>The cost of the edge is 0 and the flow rate is k.
And 2, solving the fence network structure by using a minimum cost maximum flow algorithm.
Step 3, finding each sensor node forming the fence according to the solving information of the minimum cost maximum flow algorithm:
3-1, starting from the left border s, looking for each sensor node constituting k bars, if a vertex s is associated with a vertex siThe initial flow rate 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 for it in turn until t' is reached, at which point the sensor nodes that constitute a fence are completely found, and we record the vertex sequence as: q1＝{s,s1,s1’,…,sn1,sn1', t, t' }, we can find k 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 P1＝{s1,…,sn1And finding the sensor nodes forming the k fences in sequence. Let us remember that the set of sensors forming k bars is S ═ P1∪P2∪...∪Pk＝{s1,…,sn}。
Step 4, dividing the monitoring area into grids with the side length tau (the size can be set to be about 0.1-1 meter):
4-1, calculating each sensor node siIs covered by a distance λ(s)i): when sensor node siWhen covering the left or right border of the monitored area, siThe coverage distance of the sensor is the length of a vertical connecting line between the sensor node and the boundary; when sensor node siWith a certain node s in the same fencejWhen the coverage areas of (a) overlap each other, siHas a coverage distance of node siAnd node sjHalf the distance. As shown in FIG. 2, the sensor node s1 has a coverage distance λ(s)1)＝L1+L2/2。
And 4-2, calculating the charging utility of each grid point according to the coverage distance of the sensor nodes. Grid location cjCharge effect of (c) muj): at cjWhen the charger is arranged, the sensor in the charging coverage range of the charger receives the electric quantity of the charger. We define simultaneously that when two sensor nodes in the same fence can be served by a charger, the charging utility of the sensor near the right will be calculated only when the sensor energy consumption demand near the left is met. Therefore, the following formula is given:
Figure BDA0002066109050000071
wherein left(s)i) Representing a node siWhether the left node of (1) has satisfied the charging requirement is 1, otherwise is 0. As shown in fig. 2, charger C1Simultaneous coverage of sensor s1And s2In the computing charger C1While charging is effective, but only the sensor s1When the charging demand of (C) is satisfied, the charger C is calculated1For sensor s2The charging utility of (1).
And 5, selecting a charger position according to the charging utility of each grid point. Judging whether all the sensor nodes meet the charging requirement or not, and ending when the charging requirement is met; otherwise, returning to step 4 to recalculate the charging utility of the remaining grids. The method comprises the following specific steps:
5-1, setting the grid size to be tau, dividing the grid points by the set Z { Z ═ S ═ Z-1,z2… shows all grid points, calculates the charging effect of all grid points, orders the charging effects of all grid points from large to small, selects the grid vertex z with the maximum effecttopAs a charger location. Also, when the sensors in set S' can be ztopPosition-arranged charger CjWhen charging, the charging power required by the sensor will become smaller, i.e. ωi＝ωi-pr(d(si,cj)). When the perceived energy consumption of a sensor is 0, this sensor is removed from the set S'.
5-2, at the same time, calculating the transmission of non-formed fenceThe amount of charge received by a sensor node when a certain sensor s isjJudging the sensor s when the received electric quantity satisfies the charging effectjWhether or not a sensor s forms a barrieriAn equivalence node of, and siIs not in demand, at which time sensor s is usedjReplacement sensor siAnd (4) nodes. Definition of equivalent nodes: if sensor node siIs a sensor node constituting a fence, and if we use sensor node siRemoving s from the fencejBy adding the fence, the integrity of the original fence is not affected, so the node sjIs the sensor siThe equivalent node of (2). As shown in fig. 3, the sensor s1Is s2。
5-3, grid points from z to be selected as locations for arranging chargerstopRemove from set Z while leaving grid points ZtopAnd adding the mixture into the set C. When there are sensors in the set S' that meet the charging demand, return to step 4. Otherwise, the output set C is all the charger arrangement positions. Fig. 4, one possible charger arrangement.

Claims (1)

1. The k-fence construction and charger optimized deployment method of the wireless chargeable sensor network is characterized in that the adopted wireless sensor network is as follows: in a two-dimensional narrow-band area, N omnidirectional sensors are randomly deployed; 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 the 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 each sensor node forming the fence according to the solving information of the minimum cost maximum flow algorithm;
and 4, step 4: dividing the whole monitoring area into grid points, and calculating the utility of arranging a charger at each grid point;
and 5: selecting a charger position according to the charging utility of each grid point; judging whether all the sensor nodes meet the charging requirement or not, and ending when the charging requirement is met; otherwise, returning to the step 4 to recalculate the charging utility of the rest grids;
the k fence graph constructed in the step 1 is determined according to the coverage range and the coverage energy consumption of the sensor nodes; 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 FGTraffic representing edges;
1-2, adding edges in a weighted graph G according to the information of the sensor nodes in the monitoring area; if sensor node siAnd sensors sjCoverage areas overlap, then, a directed edge is added<si’,sj>The cost of the edge is 0 and the flow rate 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 0 and 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 0 and the flow rate is 1; for each sensor node s simultaneouslyiAdding a directed edge<si,si’>The cost of the edge is omegai，ωiThe flow rate is 1 for the unit energy consumption of the sensor; finally, add the directed edge<rslot,rslot’>The cost of the edge is 0, and the flow rate is k;
the step 3 is realized as follows:
3-1, from the left boundary lslotBegin looking for each sensor node that makes up k fences if vertex lslotWith a certain vertex siThe initial flow rate 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; 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’,…,sn1,sn1’,rslot,rslot', we can find k such sets of sequences in turn;
3-2, extracting sensor nodes forming a fence for each sequence; such as Q1Removing left and right boundaries and the virtual vertex to obtain P1＝{s1,…,sn1Finding the sensor nodes forming k barriers in sequence for the sensor nodes forming one barrier; let us remember that the set of sensors forming k bars is S ═ P1∪P2∪...∪Pk＝{s1,…,sn}；
Step 4, dividing the monitoring area into grids with the side length of tau, and concretely realizing the following steps:
4-1, calculating each sensor node siIs covered by a distance λ(s)i): when sensor node siWhen covering the left or right border of the monitored area, siThe coverage distance of the sensor is the length of a vertical connecting line between the sensor node and the boundary; when sensor node siWith a certain node s in the same fencejWhen the coverage areas of (a) overlap each other, siHas a coverage distance of node siAnd node sjHalf the distance;
4-2, calculating the charging utility of each grid point according to the coverage distance of the sensor nodes; grid location cjCharge effect of (c) muj): at cjWhen the charger is arranged, the sensor in the charging coverage range of the charger receives the electric quantity of the charger; we define simultaneously that when two sensor nodes in the same fence can be served by a charger, only when the sensor near the left consumes energyWhen the requirement is met, the charging utility close to the right sensor is calculated; therefore, the following formula is given:
Figure FDA0003318845380000021
wherein left(s)i) Representing a node siWhether the left node of (1) meets the charging requirement is 1, otherwise, the left node of (0) is 0;
the step 5 is realized as follows:
5-1, let set S ═ S, set grid size τ to divide grid points, set Z ═ Z1,z2… denotes all grid points; then, calculating the charging utility of all grid points, ordering the charging utilities of all grid points from large to small, and selecting the grid vertex z with the maximum utilitytopAs a charger location; also, when the sensors in set S' can be ztopPosition-arranged charger CjWhen charging, the charging power required by the sensor will become smaller, i.e. ωi＝ωi-pr(d(si,cj) ); removing one sensor from the set S' when its perceived energy consumption is 0;
5-2, simultaneously calculating the charge quantity received by the sensor nodes which do not form the fence when a certain sensor sjJudging the sensor s when the received electric quantity satisfies the charging effectjWhether or not a sensor s forms a barrieriAn equivalence node of, and siIs not in demand, at which time sensor s is usedjReplacement sensor siA node; definition of equivalent nodes: if sensor node siIs a sensor node constituting a fence, and if we use sensor node siRemoving s from the fencejBy adding the fence, the integrity of the original fence is not affected, so the node sjIs the sensor siThe equivalent node of (2);
5-3, grid points from z to be selected as locations for arranging chargerstopRemove from set Z while leaving grid points ZtopAdding the mixture into the set C; when present in the set S' to satisfy the charging requirementReturning to the step 4 when the sensor is detected; otherwise, the output set C is all the charger arrangement positions.
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