CN110248377B - Connected target covering method based on adjustable perception radius probability sensor model - Google Patents

Connected target covering method based on adjustable perception radius probability sensor model Download PDF

Info

Publication number
CN110248377B
CN110248377B CN201910421433.7A CN201910421433A CN110248377B CN 110248377 B CN110248377 B CN 110248377B CN 201910421433 A CN201910421433 A CN 201910421433A CN 110248377 B CN110248377 B CN 110248377B
Authority
CN
China
Prior art keywords
sensor
monitoring
coverage
target
sensors
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201910421433.7A
Other languages
Chinese (zh)
Other versions
CN110248377A (en
Inventor
徐向华
陈凛
王然
程宗毛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN201910421433.7A priority Critical patent/CN110248377B/en
Publication of CN110248377A publication Critical patent/CN110248377A/en
Application granted granted Critical
Publication of CN110248377B publication Critical patent/CN110248377B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)

Abstract

The invention discloses a connected target K covering method based on an adjustable perception radius model. The invention comprises the following steps: 1: constructing a network graph according to the position information of the sensors in the network; 2: finding out the shortest path from any sensor to Sink and calculating the communication cost; 3: calculating a set of target points which can be monitored by each sensor under each monitoring radius and do not meet a coverage threshold; 4: calculating a set of candidate sensors for each target point; 5: selecting a target point with the fewest candidate sensors; 6: selecting a coverage set for the target; 7: and (5) repeating the steps 5-6 until a connected coverage set is selected, finishing the algorithm and giving a state scheduling strategy of the sensor. According to the invention, through a probability coverage mode, the monitoring mode of the sensor on the target in an actual scene is better simulated; the coverage method based on the adjustable sensing radius model reduces the energy cost of the network working in unit time and improves the network operation efficiency.

Description

Connected target covering method based on adjustable perception radius probability sensor model
Technical Field
The invention relates to the field of wireless sensor networks, in particular to a connected target covering method based on an adjustable perception radius probability sensor model.
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 complex 2D-like terrain target monitoring applications, such as forests, deserts and the like, wireless sensor nodes need to be deployed in a random throwing mode, and monitoring of all target positions in a monitoring area is met. The random deployment reduces the deployment cost of the network, but also increases the difficulty of scheduling the working state of the sensor. Therefore, the target coverage problem of the wireless sensor network is a very important problem in the monitoring application of the wireless sensor network.
Energy is a major concern in target coverage solutions because sensors are difficult to accept energy replenishment and are only battery powered. In a solution for a Target Coverage problem in a Wireless Sensor network, various Target Coverage scheduling methods are proposed for research of sensors with fixed sensing radius, for example, Yu et al in "On Connected Target K-Coverage in Heterogeneous Wireless sensors Networks" research how to select as few sensors as possible in a Heterogeneous Wireless Sensor network to maintain K Coverage and connectivity of the network, and two methods, a centralized Target K Coverage algorithm and a distributed connectivity K Coverage algorithm, are proposed to solve the problem. Shann et al, in A Max-Flow Based Algorithm for Connected Target Coverage with Probalistic Sensors, studied how to select as few Sensors as possible in a heterogeneous Probabilistic sensor network to maintain network connectivity and Coverage, and proposed a minimum vertex maximum Flow Algorithm Based on network Flow concepts to solve the problem. These methods based on fixed sensing radius models are no longer applicable in probabilistic sensor networks with adjustable sensing radius. Therefore, the invention provides a connected target covering method for a probability sensor network with an adjustable sensing radius. The invention researches how to schedule the working state and the working radius of the sensor, so that the probability of monitoring each target point in the network is not lower than epsilon (0, 1), the network connectivity is maintained, the energy cost of the network in unit time is reduced as much as possible, and the network operation efficiency is improved.
Disclosure of Invention
The invention provides a connected target covering method based on an adjustable perception radius probability sensor model, which ensures that the monitored probability of each target point in a network is not lower than epsilon (0, 1) and the network connectivity is maintained by scheduling the working state and the working radius of a sensor, reduces the energy cost of the network in unit time as far as possible and improves the network operation efficiency. Firstly, constructing a network graph according to collected position information of a sensor; secondly, calculating the shortest path between any sensor node and a sink according to the edge information in the network graph, thereby obtaining the communication route from each sensor to the sink and the communication energy cost in unit time; then, collecting the distribution condition of target points around each sensor, namely a target point set which can be monitored by the sensor under monitoring radiuses corresponding to different working powers and the current monitored state information of each target point; then, according to the collected information, obtaining a candidate sensor set of each target point which does not meet the coverage probability requirement; and thirdly, selecting target points one by one, and selecting a group of sensors and working radii in the candidate sensor set of the target points to enable the target points to meet the coverage probability requirement until all the target points can meet the coverage probability requirement. And finally, obtaining a group of sensor sets meeting the network coverage and communication requirements, thereby obtaining a scheduling scheme of the working state of each sensor in the network.
The technical scheme adopted for solving the technical problem comprises the following steps:
a connected target covering method based on an adjustable perception radius probability sensor model adopts a wireless sensor network as follows: in a planar region of interest, there are M target points O ═ O whose positions are known1，o2，…，oMAnd a Sink. The coverage probability threshold that needs to be met for each target point is epsilon. N omnidirectional probability sensors S ═ S are randomly deployed in the area1，s2，…，sNK working powers exist in each sensor, each power corresponds to a monitoring radius, and therefore each sensor has an adjustable sensing radius set R ═ { R ═ R }1，r2，…，rK}. Each sensor has a fixed communication radius Rtra. All sensors have the same data acquisition rate, assuming that one unit of data is acquired per unit of time. The method comprises the following specific steps:
step 1: constructing a network graph according to the position information of the sensors in the network;
step 2: finding out the shortest path from any sensor to Sink from the network diagram and calculating the communication cost spent on transmitting data of one unit according to the current path;
and step 3: calculating a set of target points which can be monitored by each sensor under each monitoring radius and do not meet a coverage threshold;
and 4, step 4: calculating a set of candidate sensors for each target point;
and 5: a target point with the fewest candidate sensors is selected.
Step 6: and calculating the coverage utility of each sensor in the candidate sensor set of the target point, selecting a sensor with the maximum coverage utility and the radius, updating the coverage condition of the target covered by the sensor under the current radius, and updating the candidate sensor set of each target point. This step is repeated until the current target point meets the coverage requirement.
And 7: and repeating the steps 5-6 until a connected coverage set is selected. And (5) finishing the algorithm and giving a state scheduling strategy of the sensor.
Constructing a network graph in the step 1, and constructing an undirected weight graph G (V, E, W), wherein a vertex V of the graph is a set of all sensor nodes and Sink nodes in a scene; the edge E represents whether the vertexes are communicated or not; the weight W represents the communication cost between two vertices. If two vertices siAnd sjAdjacent (two vertex distance d(s)i，sj)≤Rtra) Then, add one side E ═ E-i，sj) The weight of the edge is omega(s)i，sj)＝eTr(si，sj)+eReWherein the transmission energy cost e of unit dataTr(si，sj)＝a+b·d(si，sj)βA, b and beta are constants which can be set according to the physical characteristics of the sensor, and the received energy cost e of unit dataReIs constant and can be set according to the physical characteristics of the sensor; if the vertex siAnd sjNot adjacent, then the edge e(s) is consideredi，sj) Absence, i.e. letting the side weight ω(s)i，sj) Infinity, +,; if any vertex siAdjacent to Sink, the edge E ═ E-iSink), the weight of the edge is ω(s)i，Sink)＝eTr(si，Sink)+eRe。
Step 2, calculating the shortest path and the energy cost, and calculating any vertex s in the undirected weight value graph G by using a Dijkstra algorithm for solving the shortest pathiAnd Sink and calculating the length of the path, which represents the length from siThe energy cost spent on transferring one unit of data to Sink is denoted as e (path(s)i). If siThere is no communication path with Sink, will siThe sensor is set to be unavailable.
Step 3, calculating a monitoring target point set, calculating target points of the available sensors in sensing areas corresponding to different powers according to the position information, and setting the sensors siThe set of target points sensed at the kth power is denoted as OCov(i，k)。
Step 4, calculating the candidate sensor set of the target point, and according to the information in step 3, monitoring the target point o at the maximum powerjIs not involved in scheduling sensor placement set
Figure BDA0002066109360000041
In (1).
Selecting the target Point o with the fewest candidate Sensors as described in step 5CriThat is:
Figure BDA0002066109360000042
step 6 the slave target point oCriThe step of selecting a coverage set from the candidate sensor set of (1) is as follows:
6-1 calculating the energy consumption of the sensor per unit time under each power, and expressing the formula as eSe(k)＝δ·rk 2。
Where δ is a constant related to the physical characteristics of the sensor.
And 6-2, calculating the monitoring capacity of the sensor to the target point at each power. The monitoring capability can be expressed by the following formula:
Figure BDA0002066109360000043
Figure BDA0002066109360000044
wherein p isi，j，kIndicating sensor siTo the target point o at the k powerjThe monitoring probability of (2); α (k) represents a physical characteristic parameter of the sensor at the kth power, pminRepresents the minimum effective monitoring probability of the sensor, i.e. the monitoring probability at the edge of the monitoring range.
6-3, monitoring probability p of sensor to target pointi，j，kConverted into a monitor gain phii，j，kWherein phii，j，k＝-ln(1-pi，j，k) The monitoring threshold e for the target point may be converted to Φ ═ ln (1-e).
6-4, calculating the minimum openable monitoring radius r of each sensor in the setmin(S). From the resulting set of steps 3 and 4,
Figure BDA0002066109360000045
wherein the content of the first and second substances,
Figure BDA0002066109360000046
represents ojCurrently required monitor gain, SjIndicating the ability to participate in monitoring o in scheduled sensorsjThe monitoring sensor set of (1).
6-5 calculating the coverage utility of each sensor in the set from the minimum power to the maximum power that can be switched on, at each power, and connecting the sensors siThe coverage utility at the kth power is set to CW (i, k), which is expressed by the formula:
CW(i，k)＝CPG(i，k)·(CSG(i，k)+CEG(i，k))
Figure BDA0002066109360000051
Figure BDA0002066109360000052
Figure BDA0002066109360000053
wherein, OucRepresenting a set of target points that do not satisfy the probability of monitoring,
Figure BDA0002066109360000054
indicating the maximum monitoring cost of the sensor,
Figure BDA0002066109360000055
representing the maximum communication path cost of the sensor.
6-6 selects the sensor and power with the greatest utility value, updates and adds the monitoring gain of all the target points monitored by the sensor at that power to the set of monitored sensors for those target points, and removes the sensor from the candidate sensors for all target points.
6-7, judging phineed(oj) If the result is less than or equal to 0. If not, repeatedly executing the step 6-1 to the step 6-6; if true, end step 6.
Repeating the steps 5-6 in the step 7 until a connected coverage set is selected, wherein the detailed steps are as follows:
7-1, judging whether phi exists or notneed(o) > 0. If yes, repeatedly executing the step 5 and the step 6; if not, executing the next step;
and 7-2, combining the currently selected sensor set with the communication path of each sensor to form a connected coverage set. And finishing the algorithm, and giving a state scheduling strategy of the sensor, wherein the state scheduling strategy comprises whether the sensor starts the monitoring or communication unit, and the power of the sensor starts the monitoring unit, and the communication unit should send information to which sensor.
The invention has the beneficial effects that:
1. the invention provides a connected target covering method based on an adjustable perception radius probability sensor model aiming at a two-dimensional terrain application scene, and a target monitoring form of a sensor in an actual scene is better simulated through a probability covering form.
2. The coverage method based on the adjustable sensing radius model reduces the energy cost of the network working in unit time and improves the network operation efficiency.
Drawings
FIG. 1 is a schematic diagram of a wireless sensor network employed in the present invention;
FIG. 2 is a flow chart embodying the present invention;
FIG. 3 is a schematic diagram of a sensor model;
FIG. 4 illustrates the operation of the sensor of FIG. 1 after deployment;
FIG. 5 is a diagram of the scheduled coverage and communication scenario of FIG. 1;
Detailed Description
The invention will be further explained with reference to the drawings.
The invention mainly provides a connected target covering method based on an adjustable perception radius probability sensor model. According to the network schematic diagram of fig. 1, the wireless sensor network adopted by the invention is as follows: in a 2D area scene with a size of L × M, M target points, N sensors, and a Sink have been randomly deployed in advance. The probability threshold of identical coverage for each target point is epsilon. The operating states of the N sensors need to be scheduled, so that the network can reduce the energy cost in unit time as much as possible while ensuring the coverage threshold of the target and the network connectivity. Here 200 sensors and 20 target points are deployed within a 100 x 100 region.
As shown in fig. 2, the present invention uses an omni-directionally tunable perceptual radius probability sensor model operating in a 2D scenario. K working powers exist in each sensor, each power corresponds to one monitoring radius, and therefore each sensor has an adjustable sensing radius set R ═ R-1，r2，…，rK}. One for each sensorWith a fixed communication radius Rtra. All sensors have the same data acquisition rate, assuming that one unit of data is acquired per unit of time. The monitoring probability of the sensor at the edge of the monitoring range is pmin。
As shown in fig. 3, the specific steps of the present invention are described as follows:
step 1: constructing a network graph according to the position information of the sensors in the network;
constructing an undirected weight graph G which is (V, E, W), wherein a vertex V of the graph is a set of all sensor nodes and Sink in the scene; the edge E represents whether the vertexes are communicated or not; the weight W represents the communication cost between two vertices. If two vertices siAnd sjAdjacent (two vertex distance d(s)i，sj)≤Rtra) Then, add one side E ═ E-i，sj) The weight of the edge is omega(s)i，sj)＝eTr(si，sj)+eReWherein the transmission energy cost e of unit dataTr(si，sj)＝a+b·d(si，sj)βA, b and beta are constants which can be set according to the physical characteristics of the sensor, and the received energy cost e of unit dataReIs constant and can be set according to the physical characteristics of the sensor; if the vertex siAnd sjNot adjacent, then the edge e(s) is consideredi，sj) Absence, i.e. letting the side weight ω(s)i，sj) Infinity, +,; if any vertex siAdjacent to Sink, the edge E ═ E-iSink), the weight of the edge is ω(s)i，Sink)＝eTr(si，Sink)+eRe。
Step 2: finding out the shortest path from any sensor to Sink from the network diagram and calculating the communication cost spent on transmitting data of one unit according to the current path;
calculating any vertex s in the undirected weight value graph G by using Dijkstra algorithm for solving shortest pathiAnd Sink and calculating the length of the path, which represents the length from siThe energy cost spent on transferring one unit of data to Sink is denoted as e (path(s)i). If siThere is no communication path with Sink, will siThe sensor is set to be unavailable.
And step 3: calculating a set of target points which can be monitored by each sensor under each monitoring radius and do not meet the coverage requirement;
calculating target points of the available sensors in sensing areas corresponding to different powers according to the position information, and arranging the sensors siThe set of target points sensed at the kth power is denoted as OCov(i，k)。
And 4, step 4: calculating a set of candidate sensors for each target point;
according to the information in step 3, the target point o can be monitored under the maximum powerjIs not involved in scheduling sensor placement set
Figure BDA0002066109360000071
In
And 5: a target point with the fewest candidate sensors is selected.
Selecting the target point o with the fewest candidate sensorsCriThat is:
Figure BDA0002066109360000072
Figure BDA0002066109360000073
step 6: and calculating the coverage utility of each sensor in the candidate sensor set of the target point, selecting a sensor with the maximum coverage utility and the radius, updating the coverage condition of the target covered by the sensor under the current radius, and updating the candidate sensor set of each target point. This step is repeated until the current target point meets the coverage requirement.
6-1 calculating the energy consumption of the sensor per unit time under each power, and expressing the formula as eSe(k)＝δ·rk 2。
Where δ is a constant related to the physical characteristics of the sensor.
6-2 calculate the sensor's ability to monitor the target point at each power. The monitoring capability can be expressed by the following formula:
Figure BDA0002066109360000081
Figure BDA0002066109360000082
wherein p isi，j，kIndicating sensor siTo the target point o at the k powerjThe monitoring probability of (2); α (k) represents a physical characteristic parameter of the sensor at the kth power, pminRepresents the minimum effective monitoring probability of the sensor, i.e. the monitoring probability at the edge of the monitoring range.
6-3 monitoring probability p of sensor to target pointi，j，kConverted into a monitor gain phii，j，kWherein phii，j，k＝-ln(1-pi，j，k) The monitoring threshold e for the target point may be converted to Φ ═ ln (1-e).
6-4 calculating the minimum monitoring radius r that each sensor in the set can turn onmin(s). From the resulting set of steps 3 and 4,
Figure BDA0002066109360000083
Figure BDA0002066109360000084
wherein the content of the first and second substances,
Figure BDA0002066109360000085
Figure BDA0002066109360000086
represents ojCurrently required monitor gain, SjIndicating the ability to participate in monitoring o in scheduled sensorsjThe monitoring sensor set of (1).
6-5 calculate the coverage of each sensor in the set at minimum to maximum power to turn on, at each powerCover effect, sensor siThe coverage utility at the kth power is set to CW (i, k), which is expressed by the formula:
CW(i，k)＝CPG(i，k)·(CSG(i，k)+CEG(i，k))
Figure BDA0002066109360000087
Figure BDA0002066109360000091
Figure BDA0002066109360000092
wherein, OucRepresenting a set of target points that do not satisfy the probability of monitoring,
Figure BDA0002066109360000093
indicating the maximum monitoring cost of the sensor,
Figure BDA0002066109360000094
representing the maximum communication path cost of the sensor.
6-6 selects the sensor and power with the greatest utility value, updates and adds the monitoring gain of all the target points monitored by the sensor at that power to the set of monitored sensors for those target points, and removes the sensor from the candidate sensors for all target points.
6-7 judgment of phineed(oj) If the result is less than or equal to 0. If not, repeatedly executing the step 6-1 to the step 6-6; if true, end step 6
And 7: and repeating the steps 5-6 until a connected coverage set is selected. And (5) finishing the algorithm and giving a state scheduling strategy of the sensor.
7-1 determining whether phi existsneed(o) > 0. If yes, repeatedly executing the step 5 and the step 6; if not, executing the next step;
7-2 combining the currently selected sensor set with the communication path of each sensor to form a connected coverage set. And finishing the algorithm, and giving a state scheduling strategy of the sensor, wherein the state scheduling strategy comprises whether the sensor starts the monitoring or communication unit, and the power of the sensor starts the monitoring unit, and the communication unit should send information to which sensor. As shown in fig. 4 and 5, fig. 4 shows a set of coverage sets selected by the network in fig. 1, and fig. 5 shows a scheduling policy for the connected coverage sets and the communication paths.

Claims (8)

1. A connected target covering method based on an adjustable perception radius probability sensor model is characterized in that the adopted wireless sensor network is as follows: in a planar region of interest, there are M target points O ═ O whose positions are known1，o2，...，oMAnd a Sink; the coverage probability threshold value required to be met by each target point is epsilon; n omnidirectional probability sensors S ═ S are randomly deployed in the area1，s2，...，sNK working powers exist in each sensor, each power corresponds to a monitoring radius, and therefore each sensor has an adjustable sensing radius set R ═ { R ═ R }1，r2，...，rK}; each sensor has a fixed communication radius Rtra(ii) a All sensors have the same data acquisition rate, assuming that one unit of data is acquired per unit time; the method comprises the following specific steps:
step 1: constructing a network graph according to the position information of the sensors in the network;
step 2: finding out the shortest path from any sensor to Sink from the network diagram and calculating the communication cost spent on transmitting data of one unit according to the current path;
and step 3: calculating a set of target points which can be monitored by each sensor under each monitoring radius and do not meet a coverage threshold;
and 4, step 4: calculating a set of candidate sensors for each target point;
and 5: selecting a target point with the fewest candidate sensors;
step 6: calculating the coverage utility of each sensor in the candidate sensor set of the target point, selecting a sensor with the maximum coverage utility and the radius, updating the coverage condition of a target covered by the sensor under the current radius, and updating the candidate sensor set of each target point; repeating the steps until the current target point meets the coverage requirement;
and 7: repeating the steps 5-6 until a connected coverage set is selected; and (5) finishing the algorithm and giving a state scheduling strategy of the sensor.
2. The connected target covering method based on the adjustable perception radius probability sensor model as claimed in claim 1, wherein the network graph is constructed in step 1, an undirected weight graph G is constructed, (V, E, W), and a vertex V of the graph is a set of all sensor nodes and Sink in a scene; the edge E represents whether the vertexes are communicated or not; the weight W represents the communication cost between two vertexes; if two vertices siAnd sjAdjacent, specifically two vertex distance d(s)i，sj)≤RtraThen, add one side E ═ E-i，sj) The weight of the edge is omega(s)i，sj)＝eTr(si，sj)+eReWherein the transmission energy cost e of unit dataTr(si，sj)＝a+b·d(si，sj)βA, b and beta are constants which can be set according to the physical characteristics of the sensor, and the received energy cost e of unit dataReIs constant and can be set according to the physical characteristics of the sensor; if the vertex siAnd sjNot adjacent, then the edge e(s) is consideredi，sj) Absence, i.e. letting the side weight ω(s)i，sj) Infinity, +,; if any vertex siAdjacent to Sink, the edge E ═ E-iSink), the weight of the edge is ω(s)i，Sink)＝eTr(si，Sink)+eRe。
3. The connected target covering method based on the adjustable perception radius probability sensor model according to claim 1The method is characterized in that the shortest path and the energy cost are calculated in the step 2, and any vertex s in the undirected weight value graph G is calculated by using a Dijkstra algorithm for solving the shortest pathiAnd Sink and calculating the length of the path, which represents the length from siThe energy cost spent on transferring one unit of data to Sink is denoted as e (path(s)i) (ii) a If siThere is no communication path with Sink, will siThe sensor is set to be unavailable.
4. The connected target covering method based on the probability sensor model with adjustable sensing radius as claimed in claim 1, wherein step 3 calculates a set of monitoring target points, calculates target points of available sensors in sensing areas corresponding to different powers according to the position information, and connects the sensors siThe set of target points sensed at the kth power is denoted as OCov(i，k)。
5. The method according to claim 1, wherein the set of candidate sensors for calculating the target point in step 4 can monitor the target point o at maximum power according to the information in step 3jIs not involved in scheduling sensor placement set
Figure FDA0003565283320000021
In (1).
6. The method of claim 1, wherein the step 5 is performed by selecting the target point o with the least candidate sensorsCriThat is:
Figure FDA0003565283320000022
7. connected object coverage based on adjustable perceptual radius probability sensor model according to claim 1Method, characterized in that step 6 is carried out from a target point oCriThe step of selecting a coverage set from the candidate sensor set of (1) is as follows:
6-1 calculating the energy consumption of the sensor per unit time under each power, and expressing the formula as eSe(k)＝δ·rk 2；
Wherein δ is a constant related to the physical characteristics of the sensor;
6-2, calculating the monitoring capacity of the sensor to a target point under each power; the monitoring capability can be expressed by the following formula:
Figure FDA0003565283320000031
Figure FDA0003565283320000032
wherein p isi，j，kIndicating sensor siTo the target point o at the k powerjThe monitoring probability of (2); α (k) represents a physical characteristic parameter of the sensor at the kth power, pminRepresents the minimum effective monitoring probability of the sensor, i.e. the monitoring probability at the edge of the monitoring range;
6-3 monitoring probability p of sensor to target pointi，j，kConverted into a monitor gain phii，j，kWherein phii，j，k＝-ln(1-pi，j，k) The monitoring threshold e for the target point may be converted to Φ ═ ln (1-e);
6-4 calculating the minimum monitoring radius r that each sensor in the set can turn onmin(s); from the resulting set of steps 3 and 4,
Figure FDA0003565283320000033
wherein the content of the first and second substances,
Figure FDA0003565283320000034
represents ojCurrently required monitor gain, SjIndicating the ability to participate in monitoring o in scheduled sensorsjThe monitoring sensor set of (1);
6-5 calculating the coverage utility of each sensor in the set from the minimum power to the maximum power that can be switched on, at each power, and connecting the sensors siThe coverage utility at the kth power is set to CW (i, k), which is expressed by the formula:
CW(i，k)＝CPG(i，k)·(CSG(i，k)+CEG(i，k))
Figure FDA0003565283320000041
Figure FDA0003565283320000042
Figure FDA0003565283320000043
wherein, OucRepresenting a set of target points that do not satisfy the probability of monitoring,
Figure FDA0003565283320000044
indicating the maximum monitoring cost of the sensor,
Figure FDA0003565283320000045
representing the maximum communication path cost of the sensor;
6-6 selecting the sensor and power with the maximum utility value, updating and adding the monitoring gain of all target points monitored by the sensor at the power to the monitoring sensor set of the target points, and removing the sensor from the candidate sensors of all target points;
6-7 judgment of phineed(oj) Whether the value is less than or equal to 0 or not; if not, repeating the step 6-1 to the step6-6; if true, end step 6.
8. The method for covering connected objects based on the adjustable perceptual radius probability sensor model as claimed in claim 1, wherein the steps 5-6 are repeated in step 7 until a connected coverage set is selected, and the detailed steps are as follows:
7-1 determining whether phi existsneed(o) > 0; if yes, repeatedly executing the step 5 and the step 6; if not, executing the next step;
7-2, combining the currently selected sensor set with the communication path of each sensor to form a communication coverage set; and finishing the algorithm, and giving a state scheduling strategy of the sensor, wherein the state scheduling strategy comprises whether the sensor starts the monitoring or communication unit, and the power of the sensor starts the monitoring unit, and the communication unit should send information to which sensor.
CN201910421433.7A 2019-05-21 2019-05-21 Connected target covering method based on adjustable perception radius probability sensor model Expired - Fee Related CN110248377B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910421433.7A CN110248377B (en) 2019-05-21 2019-05-21 Connected target covering method based on adjustable perception radius probability sensor model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910421433.7A CN110248377B (en) 2019-05-21 2019-05-21 Connected target covering method based on adjustable perception radius probability sensor model

Publications (2)

Publication Number Publication Date
CN110248377A CN110248377A (en) 2019-09-17
CN110248377B true CN110248377B (en) 2022-05-03

Family

ID=67884510

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910421433.7A Expired - Fee Related CN110248377B (en) 2019-05-21 2019-05-21 Connected target covering method based on adjustable perception radius probability sensor model

Country Status (1)

Country Link
CN (1) CN110248377B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102647726A (en) * 2012-02-17 2012-08-22 无锡英臻科技有限公司 Balancing optimizing strategy for energy consumption of coverage of wireless sensor network
CN103002590A (en) * 2012-11-23 2013-03-27 南京邮电大学 Scheduling method of directed nodes in wireless sensor network
CN108601032A (en) * 2018-04-10 2018-09-28 南京大学 A kind of dispositions method being connected to oriented Sensor Task monitoring under constraint

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105764066B (en) * 2016-01-29 2018-10-16 江苏省电力公司南京供电公司 A kind of cooperation covering method towards distribution information Perception

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102647726A (en) * 2012-02-17 2012-08-22 无锡英臻科技有限公司 Balancing optimizing strategy for energy consumption of coverage of wireless sensor network
CN103002590A (en) * 2012-11-23 2013-03-27 南京邮电大学 Scheduling method of directed nodes in wireless sensor network
CN108601032A (en) * 2018-04-10 2018-09-28 南京大学 A kind of dispositions method being connected to oriented Sensor Task monitoring under constraint

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于感知概率的无线传感器网络k重覆盖算法;蒋丽萍等;《计算机应用研究》;20090915(第09期);全文 *

Also Published As

Publication number Publication date
CN110248377A (en) 2019-09-17

Similar Documents

Publication Publication Date Title
Ho et al. Optimization of wireless sensor network and UAV data acquisition
Nazib et al. Energy-efficient and fast data collection in UAV-aided wireless sensor networks for hilly terrains
Beiranvand et al. I-LEACH: An efficient routing algorithm to improve performance & to reduce energy consumption in Wireless Sensor Networks
Popa et al. Robotic deployment of sensor networks using potential fields
Uddin et al. Agriculture internet of things: AG-IoT
CN115243273B (en) Wireless sensor network coverage optimization method, device, equipment and medium
CN103347288B (en) A kind of wireless sensor network does not wait width hierarchical routing protocol method
Ho et al. Heuristic algorithm and cooperative relay for energy efficient data collection with a UAV and WSN
CN103095577B (en) Context-sensitive Uneven Cluster routing algorithm
CN112947548B (en) Unmanned aerial vehicle formation planning method and system based on frequency spectrum map
Tambe et al. Magnetic induction based cluster optimization in non-conventional WSNs: A cross layer approach
CN110267323B (en) Connected target K covering method based on adjustable perception radius model
Chen et al. Mobile wireless charging and sensing by drones
CN110248377B (en) Connected target covering method based on adjustable perception radius probability sensor model
Huang et al. A method for deploying the minimal number of UAV base stations in cellular networks
CN113891329A (en) Frequency spectrum monitoring method and system based on multi-hop forwarding of unmanned aerial vehicle
CN109121178A (en) One kind carrying out energy heterogeneous star networking route designing method based on Artificial Potential Field
Yan et al. Fairness-aware data offloading of IoT applications enabled by heterogeneous UAVs
Joshi et al. A multi-objective metaheuristic approach based adaptive clustering and path selection in iot enabled wireless sensor networks
Kang et al. Energy-efficient data aggregation and collection for multi-UAV-enabled IoT networks
CN114217630B (en) Dynamic time-space unmanned aerial vehicle charging method based on attention mechanism
CN115696190A (en) Method for optimizing positioning of wireless sensor network node
CN114980134A (en) Virtual force-based dynamic coverage method for multiple moving targets
Zhao et al. Fuzzy deployment for wireless sensor networks
Bhakare et al. Distance distribution approach of minimizing energy consumption in grid wireless sensor network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20220503