CN106793071B - Node positioning-based toxic gas boundary detection method for industrial sensor network - Google Patents
Node positioning-based toxic gas boundary detection method for industrial sensor network Download PDFInfo
- Publication number
- CN106793071B CN106793071B CN201611100670.6A CN201611100670A CN106793071B CN 106793071 B CN106793071 B CN 106793071B CN 201611100670 A CN201611100670 A CN 201611100670A CN 106793071 B CN106793071 B CN 106793071B
- Authority
- CN
- China
- Prior art keywords
- node
- nodes
- degas
- neighbor
- outer boundary
- 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.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Emergency Alarm Devices (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a node positioning-based toxic gas boundary detection method for an industrial sensor network, which comprises the following steps of 1) broadcasting a message packet of a DeGas _ AnchorNode by { coordinates (x, y), a minimum hop number hop, a node number ID and state indication }, 2) calculating the average hop distance between the DeGas _ AnchorNodes, 3) calculating the coordinates of the DeGas _ UnknownNode, 4) determining inner and outer boundary nodes, 5) planarizing a toxic gas monitoring network topological structure by adopting a GG planarization algorithm, 6) constructing an outer boundary region, and 7) calculating the area of the outer boundary region. The toxic gas boundary detection based on node positioning improves the efficiency of the sensor in industrial application, innovatively combines the node positioning and the toxic gas boundary detection algorithm, reduces the consumption of node cost and increases the accuracy of toxic gas boundary detection.
Description
Technical Field
The invention relates to a node positioning-based toxic gas boundary detection method for an industrial sensor network, and belongs to the technical field of gas detection.
Background
In large petrochemical plants, the monitoring of toxic gases is a critical issue, alerting the personnel concerned to the potential risks of caution and to prevent explosions. Industrial Wireless Sensor Networks (IWSNs) have the advantages of easy deployment, small size, high energy efficiency, and flexible sensing nodes, and have become a promising approach for petrochemical plant designers and manufacturers to solve key problems, i.e., toxic gas monitoring.
Moreover, the monitoring of the extent of the leakage area is difficult due to the invisibility of the toxic gases, the rapid movement, and the change of form over time. In most toxic gas leakage accidents, different types of chemical gases can be easily leaked at the same time, and gas mixing can be caused.
It is important to determine the boundary of the toxic gas leakage more accurately. Wireless network communication positioning is generally divided into Range-based and Range-free, the former is a positioning algorithm based on a ranging technology, distance or angle information between points is measured, a trilateration method, a triangulation method or a maximum likelihood estimation method is used to calculate the position of a node, such as a GPS, the positioning of the node is generally about 10 meters, and when the number of used GPS nodes is large, the cost is high, and under the condition that the environment of toxic gas explosion is very complicated, the information of the GPS node is sent to a satellite far away from the GPS node to determine the position of the GPS node, so that the conditions of node failure, such as poor communication signals, interference on the communication signals and the like, are easy to occur; the latter is a positioning algorithm without distance measurement, without distance and angle information, and is realized only according to information such as network connectivity, and by optimizing the algorithm, positioning more accurate than GPS can be realized, and the cost is lower. The toxic gas boundary detection algorithm based on node positioning not only reduces the communication cost, but also improves the positioning precision.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a toxic gas boundary detection method based on node positioning for an industrial sensor network, which uses the fewest working nodes to maintain the global connectivity of the whole sensor network, reduces the node communication cost and simultaneously ensures the coverage requirement on the basis of toxic gas hazard classification.
In order to solve the technical problems, the invention adopts the technical scheme that:
a node positioning-based toxic gas boundary detection method for an industrial sensor network comprises the following steps:
1): the method comprises the steps that a DeGas _ AnchorNode broadcasts an information packet, the DeGas _ AnchorNode broadcasts a data packet containing self coordinate information to neighbor nodes in a flooding mode in a sensor network, and the data packet comprises { coordinates (x, y), a minimum hop number hop, a node number ID and state indication }, wherein the minimum hop number hop is initialized to be 0; the receiving node records each arrived beacon node group, only stores the group with the minimum hop count, then adds 1 to the minimum hop count and forwards the group to the own neighbor node, and through the stage, all nodes in the sensor network can record the minimum hop count group information of each DeGas _ AnchorNode; the DeGas _ AnchorNode refers to an anchor node for toxic gas detection;
2) firstly, all the DeGas _ AnchorNodes broadcast the DPH of the DeGas _ AnchorNodes to non-DeGas _ AnchorNode neighbors, and the DeGas _ UnknownNode neighbor nodes of the DeGas _ AnchorNodes transmit DPH packets in the whole network; the DeGas _ UnknownNode refers to an unknown node for toxic gas detection;
3) calculating the coordinate of the DeGas _ UnknownNode;
4) determining inner and outer boundary nodes, the process is as follows:
firstly, dividing the nodes into nodes inside the gas and nodes outside the gas, then obtaining the neighbor nodes of each node inside the gas, if the neighbor nodes have the nodes outside the gas, the node is an outer boundary node, and similarly, obtaining the neighbor nodes of the nodes outside the gas, if the neighbor nodes have the nodes inside the gas, the node is an inner boundary node;
5) adopting GG planarization algorithm to planarize the toxic gas monitoring network topology structure;
6) constructing an outer boundary region;
7) the area of the outer boundary region is calculated.
The foregoing step 2) calculates the average hop distance between the DeGas _ anchors, specifically as follows:
each DeGas _ AnchorNode node calculates the average per-hop distance DPH of the node in the sensor network according to the coordinate information and the minimum hop count of other DeGas _ AnchorNode nodes collected in the step 1), and the calculation method is as follows:
let the coordinates of any two DeGas _ AnchorNodes be (x)i,yi)，(xj,yj) Then, the average per-hop distance between the DeGas _ anchors is:
where hoss represents the sum of the hop counts.
The method for calculating the coordinates of the DeGas _ unknown node in the step 3) includes that after the unknown node receives the DPH, the value is used as the distance of each hop in the sensor network, the distance to each beacon node can be calculated by combining the minimum hop count to each DeGas _ unknown node recorded in the step 1), and after the unknown node obtains the coordinates of three beacon nodes, the coordinates of the unknown node are calculated through a Tricalibration Trilateration algorithm.
The principle of monitoring the planarization of the network topology structure by toxic gas in the step 5) is as follows: the neighbor nodes of a certain node must meet the circle formed by the distance from the node to the neighbor nodes and the circumference does not contain other points.
The aforementioned step 6) of constructing the outer boundary region comprises the following steps:
(6-1) taking an outer boundary node A and an inner boundary node B from the inner and outer boundary nodes determined in the step 4), wherein the outer boundary node and the inner boundary node are neighbors;
(6-2) obtaining all neighbor nodes C of the outer boundary node A1C2C3C4…CnExcluding the neighboring nodes which are the outer boundary node and the inner boundary node selected in the step (1) and do not belong to the inner and outer boundary nodes;
(6-3) taking the outer boundary node A as a vertex angle of an angle, and enabling the vertex angle to be matched with the inner boundary node B and any one neighbor node C extracted in the step (6-1)i，Ci∈C1C2C3C4…CnThe edges of the composition are two sides of the angle, and the cross product of the vectors of the two sides is calculated firstly
If the obtained cross product is larger than zero, calculating a cos ∠ BAC value, wherein the neighbor node with the largest cos ∠ BAC value is the found node;
(6-4) taking the neighbor node found in the step (6-3) as a central node C, and acquiring all neighbor nodes D of the neighbor node1D2D3D4…DnHandle D1D2D3D4…DnIn the step (6-1), the selected outer boundary node and inner boundary node and the conditions which do not belong to the inner boundary node and the outer boundary node are all excluded;
(6-5) taking the central node C as a vertex angle of one corner, wherein the vertex angle is matched with any one neighbor node D of the inner boundary node B and the central node C extracted in the step (6-1)i，Di∈D1D2D3D4…DnThe edges of the composition are two sides of the corner, and the cross product of the vectors of the two sides is calculated firstly
If the cross product is less than zero, calculating a cos ∠ BCD value, and if the cross product is less than zero, determining the neighbor node with the smallest cos ∠ BCD value as the found neighbor node;
and (6-6) repeating the steps (6-4) and (6-5) until all nodes meeting the conditions are found out, and connecting the found nodes in sequence to obtain an area, namely the outer boundary area.
The foregoing step 7) calculates the area of the outer boundary region, and the process is as follows:
the outer boundary area obtained in the step 6) is a polygon, the polygon is provided with n sides, and the coordinates of two points on any one side are (X)i,Yi)，(Xi+1,Yi+1) Then, the area S of the polygon is:
the invention has the beneficial effects that:
by adopting the method, the global connectivity of the whole sensor network is kept by using the minimum working nodes, the node communication cost is reduced, and the requirement of coverage degree is ensured on the basis of toxic gas hazard classification. The algorithm provided by the invention is suitable for monitoring the toxic gas leakage boundary and the toxic gas diffusion direction in a special toxic gas leakage area, and the method greatly improves the traditional toxic gas boundary detection precision in the whole sensing field. The toxic gas boundary detection based on node positioning improves the efficiency of the sensor in industrial application, innovatively combines the node positioning and the toxic gas boundary detection algorithm, reduces the consumption of node cost and increases the accuracy of toxic gas boundary detection.
Drawings
FIG. 1 is a diagram of a Trilateration Trilateration location algorithm;
FIG. 2 is a diagram of a Trilateration Trilateration location solution process;
FIG. 3 is a schematic diagram of three spherical intersections of a Trilateration Trilateration location algorithm;
figure 4 is a schematic diagram of inner and outer border nodes;
FIG. 5 is an example of a planarization algorithm;
FIG. 6 is a schematic diagram of a neighbor node of B;
FIG. 7 is the neighbor node of B after the planarization algorithm;
FIG. 8 illustrates selected outer boundary nodes and inner boundary nodes in an embodiment;
FIG. 9 is a schematic diagram of a neighboring node of the outer border node A in FIG. 8;
FIG. 10 is a diagram illustrating neighboring nodes of neighboring node C determined in an embodiment;
fig. 11 shows the final determined outer boundary region.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention discloses a toxic gas boundary detection algorithm based on node positioning for an industrial sensor network, which comprises the following steps:
the method comprises the following steps: the DeGas _ anchor node broadcasts a packet (coordinates, ID, minimum number of hops, status indication), and refers to an anchor node for poison detection:
the DeGas _ anchor node broadcasts a data packet containing self coordinate information to a neighbor node thereof in a flooding manner in the sensor network, wherein the data packet comprises { coordinate (x, y), minimum hop count hop, node number ID and state indication }, and the minimum hop count hop is initialized to 0. The receiving node records each arrived beacon node packet, only stores the packet with the minimum hop count, then adds 1 to the minimum hop count and forwards the packet to the own neighbor node, and in the process, the receiving node ignores the packet with larger hop count from the same DeGas _ AnchorNode. Through the stage, all nodes in the network can record the minimum hop count grouping information of each DeGas _ AnchorNode;
and step two, calculating the average hop distance between the DeGas _ AnchorNodes.
Each DeGas _ Anchors node calculates the average distance per hop in the network, namely the average distance per hop DPH, according to the coordinate information and the minimum hop count of other DeGas _ Anchors nodes collected in the step one, and the calculation method is as follows:
let the coordinates of any two DeGas _ AnchorNodes be (x)i,yi),(xj,yj) Then, the average per-hop distance between the DeGas _ anchors is:
DPH denotes the average distance per hop and hops denotes the sum of the number of hops.
Firstly, all the DeGas _ AnchorNode broadcast the DPH to non-DeGas _ AnchorNode neighbors, and the DeGas _ UnknownNode neighbors of the DeGas _ AnchorNode transmit DPH packets in the whole network; the DeGas _ unknown node refers to an unknown node for toxic gas detection.
Step three: calculating the coordinates of the DeGas _ UnknownNode:
and when the unknown node receives the average hop distance, the value is used as the hop distance of the unknown node in the network, and the distance to each beacon node can be calculated by combining the minimum hop number to each DeGas _ AnchorNode recorded in the step one. After the unknown node obtains the coordinates of the three beacon nodes, the coordinates of the unknown node can be calculated through a trilateration method.
The Trilateration Trilateration location algorithm is as follows:
(1) referring to fig. 1, it is known that three DeGas _ AnchorNode positions are respectively set to (x)1,y1),(x2,y2),(x3,y3)；
(2) Coordinates (x) of the known DeGas _ UnknownNode0,y0) Distances to three points are respectively d1、d2、d3With d1,d2,d3Making three circles for the radius, and obtaining the intersection point, namely the position calculation formula of the DeGas _ UnknownNode according to the Pythagorean theorem:
(x1-x0)2+(y1-y0)2＝d1 2(2)
(x2-x0)2+(y2-y0)2＝d2 2(3)
(x3-x0)2+(y3-y0)2＝d3 2(4)
the solving process is as follows:
referring to FIG. 2, let DeGas _ UnknownNode position be (x, y), let the first sphere P therein1Has a sphere center coordinate of (0,0), P2On the same ordinate, the coordinates of the center of the sphere are (d,0), P3The coordinates of the center of the sphere are (i, j), and the radii of the three spheres are r1、r2、r3And z is the height of the intersection point of the three spheres and the horizontal plane. Then there are:
r1 2＝x2+y2+z2(5)
r2 2＝(x–d)2+y2+z2(6)
r3 2＝(x-i)2+(y-j)2+z2(7)
when z is 0, that is, three circles intersect at a point on the horizontal plane, the x axis of the DeGas _ unknown node is solved first:
x＝(r1 2-r2 2+d2)/2d (8)
transforming the second formula into the first formula2Substituting the formula II and the formula III to obtain a y-axis calculation formula of the DeGas _ UnknownNode:
y＝(r1 2-r3 2-x2+(x-i)2+j2)/2j (9)
the circles and intersections are plotted as in fig. 3.
However, in actual positioning, a given distance cannot really intersect three circles at a point due to measurement errors, and a rectangle needs to be drawn in an intersection area and the center position of the rectangle needs to be calculated.
Step four: determining inner and outer boundary nodes
The method comprises the steps of firstly dividing nodes into nodes inside gas and nodes outside the gas, secondly acquiring neighbor nodes of each node inside the gas, and if the neighbor nodes have nodes outside the gas, the nodes are outer boundary nodes, otherwise, the neighbor nodes are not outer boundary nodes. And similarly, respectively acquiring neighbor nodes of the nodes outside the gas, wherein if the neighbor nodes have the nodes inside the gas, the nodes are inner boundary nodes, otherwise, the neighbor nodes are not the inner boundary nodes.
As shown in fig. 4, the black circle region in the figure is a range of gas, the node such as the point A, B, C, D, E, F, G, H, I inside the black circle in the figure is a node inside the gas, and all the points outside the black circle region are nodes outside the gas and are in contact with the external nodeThe nodes connected with each other and positioned in the gas are inner boundary nodes, the nodes connected with each other and positioned outside the gas are outer boundary nodes in the same way, for example, the node A in the figure is an inner boundary node, and the node A is an outer boundary node1、A2Being outer boundary nodes, C and E are both nodes within the gas, not inner boundary nodes.
Step five, the toxic gas monitoring network topology structure planarization (using graph planarization algorithm GG as an example, GG is an abbreviation of GabrlelGraph)
To pair
If the edge (u, v) ∈ GG, then there is no w, such that d2(u,w)+d2(v,w)<d2(u, v), the circle and the circumference having the diameter (u, v) as shown in FIG. 5 are defined equivalently, and no other points are included.
As shown in FIG. 6, the neighbors of B may be A, C, D, E, F, G, H, I nodes, and it can be known from GG planarization algorithm that B and A, C, D, E, F, H, I neighbors must satisfy the circle formed by the distance from B to the point and the circumference of the circle does not contain other points to satisfy the requirement, the circle with AB as the diameter shown in FIG. 6 contains C, D points, which is not satisfied, the circle with BC as the diameter contains D, E points, which is not satisfied, while as shown in FIG. 7, the circle formed by B and I does not contain other points, which is satisfied, and the GG planarization algorithm is connected with I as a neighbor node instead of A, C points.
Step six: constructing an outer boundary region
(1) And taking out an outer boundary node A and an inner boundary node B from the inner and outer boundary nodes determined in the fourth step, wherein the outer boundary node and the inner boundary node are neighbors, as shown in FIG. 8.
(2) Obtaining all neighbor nodes C of the outer boundary node A1C2C3C4…CnAnd (3) excluding all the cases that the neighbor nodes are the outer boundary node and the inner boundary node selected in the step (1) and do not belong to the inner boundary node and the outer boundary node, as shown in fig. 9.
(3) The outer boundary node A is a vertex angle of an angle, the vertex angle and the firstInner boundary node B and any neighbor node C taken out at one timei，Ci∈C1C2C3C4…CnThe edges of the composition are two sides of the angle, and the cross product of the vectors of the two sides is calculated firstly
If the cross product is larger than zero, calculating cos ∠ BAC value, the neighbor node with the larger cos ∠ BAC value is the found node, otherwise, if the cross product is smaller than zero, calculating cos ∠ BAC value of two vectors, the neighbor node with the smaller cos ∠ BAC value is the found neighbor node, and the found point is C4。
(4) And (4) taking the neighbor node C found in the step (3) as a central node C to obtain all neighbors D of the neighbor node1D2D3D4…Dn. All the conditions that the neighbor nodes are an outer boundary node and an inner boundary node extracted in the step (1) and do not belong to the inner boundary node and the outer boundary node are excluded, in this case, D is excluded1As shown in fig. 10.
(5) Taking the neighbor node C found in the step (3) as a vertex angle of a corner, the vertex angle, the inner boundary node B taken out for the first time and any neighbor node D of the neighbor node C found in any step (3)i，Di∈D1D2D3D4…DnThe edges of the composition are two sides of the corner, and the cross product of the vectors of the two sides is calculated firstly
If the cross product is larger than zero, obtaining a cos ∠ BCD value, wherein the neighbor node with the larger cos ∠ BCD value is the found node, otherwise, if the cross product is smaller than zero, calculating a cos ∠ BCD value, and the neighbor node with the smaller cos ∠ BCD value is the found neighbor node D4。
(6) According to the method, the found neighbor nodes are taken as the center nodes, the steps (4) and (5) are repeated until all the nodes meeting the conditions are found out, and the found nodes A, C, D, E, F, G … are connected in sequence, and the obtained area is the outer boundary area, as shown in fig. 11.
Step seven: calculating the area of the outer boundary region:
the resulting outer boundary region is a polygon having n sides, and the coordinates of two points on any one side are (X)i,Yi) And (X)i+1,Yi+1) Then, the area of the polygon is:
the above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (3)
1. A toxic gas boundary detection method based on node positioning for an industrial sensor network is characterized by comprising the following steps:
1): the method comprises the steps that a DeGas _ AnchorNode broadcasts an information packet, the DeGas _ AnchorNode broadcasts a data packet containing self coordinate information to neighbor nodes in a flooding mode in a sensor network, and the data packet comprises { coordinates (x, y), a minimum hop number hop, a node number ID and state indication }, wherein the minimum hop number hop is initialized to be 0; the receiving node records each arrived beacon node group, only stores the group with the minimum hop count, then adds 1 to the minimum hop count and forwards the group to the own neighbor node, and through the stage, all nodes in the sensor network can record the minimum hop count group information of each DeGas _ AnchorNode; the DeGas _ AnchorNode refers to a beacon node for toxic gas detection;
2) firstly, all the DeGas _ AnchorNodes broadcast the DPH of the DeGas _ AnchorNodes to non-DeGas _ AnchorNode neighbors, and the DeGas _ UnknownNode neighbor nodes of the DeGas _ AnchorNodes transmit DPH packets in the whole network; the DeGas _ UnknownNode refers to an unknown node for toxic gas detection;
calculating the average distance per hop DPH between the DeGas _ AnchorNodes comprises the following steps:
each DeGas _ AnchorNode node calculates the average per-hop distance DPH of the node in the sensor network according to the coordinate information and the minimum hop count of other DeGas _ AnchorNode nodes collected in the step 1), and the calculation method is as follows:
let the coordinates of any two DeGas _ AnchorNodes be (x)i,yi)，(xj,yj) Then, the average per-hop distance between the DeGas _ anchors is:
wherein hoss represents the sum of hop counts;
3) calculating the coordinates of the DeGas _ UnknownNode, comprising the following steps: after the unknown node receives the DPH, the value is used as the distance of each hop in the sensor network, the distance to each beacon node can be calculated by combining the minimum hop number to each DeGasAnchorNode recorded in the step 1), and after the unknown node obtains the coordinates of three beacon nodes, the coordinates of the unknown node are calculated through a Tricalibration Trilateration algorithm;
4) determining inner and outer boundary nodes, the process is as follows:
firstly, dividing the nodes into nodes inside the gas and nodes outside the gas, then obtaining the neighbor nodes of each node inside the gas, if the neighbor nodes have the nodes outside the gas, the node is an outer boundary node, and similarly, obtaining the neighbor nodes of the nodes outside the gas, if the neighbor nodes have the nodes inside the gas, the node is an inner boundary node;
5) adopting GG planarization algorithm to planarize the toxic gas monitoring network topology structure;
6) constructing an outer boundary region; the method comprises the following steps:
(6-1) taking an outer boundary node A and an inner boundary node B from the inner and outer boundary nodes determined in the step 4), wherein the outer boundary node and the inner boundary node are neighbors;
(6-2) obtaining all neighbor nodes C of the outer boundary node A1C2C3C4…CnExcluding the neighboring nodes which are the outer boundary node and the inner boundary node selected in the step (6-1) and do not belong to the inner and outer boundary nodes;
(6-3) taking the outer boundary node A as a vertex angle of an angle, and enabling the vertex angle to be matched with the inner boundary node B and any one neighbor node C extracted in the step (6-1)i，Ci∈C1C2C3C4…CnThe edges of the composition are two sides of the angle, and the cross product of the vectors of the two sides is calculated firstly
If the obtained cross product is larger than zero, calculating a cos ∠ BAC value, wherein the neighbor node with the largest cos ∠ BAC value is the found node;
(6-4) taking the neighbor node found in the step (6-3) as a central node C, and acquiring all neighbor nodes D of the neighbor node1D2D3D4…DnHandle D1D2D3D4…DnIn the step (6-1), the selected outer boundary node and inner boundary node and the conditions which do not belong to the inner boundary node and the outer boundary node are all excluded;
(6-5) taking the central node C as a vertex angle of one corner, wherein the vertex angle is matched with any one neighbor node D of the inner boundary node B and the central node C extracted in the step (6-1)i，Di∈D1D2D3D4…DnThe edges of the composition are two sides of the corner, and the cross product of the vectors of the two sides is calculated firstly
If the cross product is larger than zero, obtaining cos ∠ BCD value, the neighbor node with the largest cos ∠ BCD value is the found node, otherwise, if the cross product is smallCalculating a cos ∠ BCD value when the value is zero, wherein the neighbor node with the smallest cos ∠ BCD value is the found neighbor node;
(6-6) repeating the steps (6-4) and (6-5) until all nodes meeting the conditions are found out, and connecting the found nodes in sequence to obtain an area which is an outer boundary area;
7) the area of the outer boundary region is calculated.
2. The method for detecting the toxic gas boundary of the industrial sensor network based on the node location as claimed in claim 1, wherein the principle of step 5) for monitoring the topology planarization of the network for the toxic gas is as follows: the neighbor nodes of a certain node must meet the circle formed by the distance from the node to the neighbor nodes and the circumference does not contain other points.
3. The method for detecting toxic gas boundary based on node location in industrial sensor network according to claim 1, wherein the step 7) calculates the area of the outer boundary region by the following process:
the outer boundary area obtained in the step 6) is a polygon, the polygon is provided with n sides, and the coordinates of two points on any one side are (X)i,Yi)，(Xi+1,Yi+1) Then, the area S of the polygon is:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611100670.6A CN106793071B (en) | 2016-12-05 | 2016-12-05 | Node positioning-based toxic gas boundary detection method for industrial sensor network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611100670.6A CN106793071B (en) | 2016-12-05 | 2016-12-05 | Node positioning-based toxic gas boundary detection method for industrial sensor network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106793071A CN106793071A (en) | 2017-05-31 |
| CN106793071B true CN106793071B (en) | 2020-04-03 |
Family
ID=58883185
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611100670.6A Active CN106793071B (en) | 2016-12-05 | 2016-12-05 | Node positioning-based toxic gas boundary detection method for industrial sensor network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106793071B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116347377B (en) * | 2023-02-28 | 2024-02-02 | 南京农业大学 | Pest dense area positioning method and system based on Internet of things of solar insecticidal lamps |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314748A (en) * | 2011-07-05 | 2012-01-11 | 万达信息股份有限公司 | Poisonous gas leakage source-locating method based on wireless sensor network |
| CN104320822A (en) * | 2014-10-27 | 2015-01-28 | 广东石油化工学院 | Method for positioning boundary region of poisonous gas in industrial factory district |
| CN104931202A (en) * | 2015-06-03 | 2015-09-23 | 广东石油化工学院 | Gas leakage internal boundary positioning algorithm |
| CN105554835A (en) * | 2015-12-09 | 2016-05-04 | 河海大学常州校区 | Toxic gas tracking method based on virtual node migration in wireless sensor network |
-
2016
- 2016-12-05 CN CN201611100670.6A patent/CN106793071B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314748A (en) * | 2011-07-05 | 2012-01-11 | 万达信息股份有限公司 | Poisonous gas leakage source-locating method based on wireless sensor network |
| CN104320822A (en) * | 2014-10-27 | 2015-01-28 | 广东石油化工学院 | Method for positioning boundary region of poisonous gas in industrial factory district |
| CN104931202A (en) * | 2015-06-03 | 2015-09-23 | 广东石油化工学院 | Gas leakage internal boundary positioning algorithm |
| CN105554835A (en) * | 2015-12-09 | 2016-05-04 | 河海大学常州校区 | Toxic gas tracking method based on virtual node migration in wireless sensor network |
Non-Patent Citations (2)
| Title |
|---|
| Poster Abstract:DeGas:Toxic Gas Boundary Area Detection in Industrial Wireless Sensor Networks;Lei Shu 等;《ACM/IEEE International Conference on Information Proceeding in Sensor Networks IEEE,2016》;20160428;全文 * |
| Toxic Gas Boundary Area Detection in Large-Scale Petrochemical Plants with Industrial Wireless Sensor Networks;Lei Shu 等;《IEEE Communications Magazine》;20161014;第54卷(第10期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106793071A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9329257B2 (en) | Cooperative localization for wireless networks | |
| Peng et al. | Angle of arrival localization for wireless sensor networks | |
| Niculescu et al. | Position and orientation in ad hoc networks | |
| EP1617601B1 (en) | Distributed precision based localization algorithm for ad-hoc wireless networks | |
| CN102291818B (en) | Pseudo-three-dimensional positioning method for wireless sensor network based on terrain information | |
| CN103513229A (en) | Positioning method based on WIFI signal | |
| Chen et al. | A cooperative localization scheme for Zigbee-based wireless sensor networks | |
| CN106793071B (en) | Node positioning-based toxic gas boundary detection method for industrial sensor network | |
| Huang et al. | AoA assisted localization for underwater Ad-Hoc sensor networks | |
| Mishra et al. | Enhanced path planning model for anchor-free distributed localization in wireless sensor networks | |
| Yi et al. | An Enhanced Hybrid 3D Localization Algorithm Based on APIT and DV-Hop. | |
| CN108414977A (en) | The method for realizing localization for Mobile Robot based on wireless sensor network | |
| Yang et al. | 3D surface localization with terrain model | |
| Oliva et al. | Network localization by shadow edges | |
| Zhao et al. | Cut-and-sew: A distributed autonomous localization algorithm for 3D surface wireless sensor networks | |
| Jiang et al. | An iterative boundary node localization algorithm based on DV-hop scheme in WSN | |
| CN106937298B (en) | A kind of improved wireless sensor network 3-D positioning method | |
| Shioda et al. | Cooperative localization revisited: Error bound, scaling, and convergence | |
| Tabassum et al. | Comparative node selection‐based localization technique for wireless sensor networks: A bilateration approach | |
| Tondwalkar et al. | Terrestrial localization by using angle of arrival measurements in wireless sensor network | |
| Das et al. | Sensor localization and obstacle boundary detection algorithm in WSN | |
| Akshay et al. | A comparative analysis of localization techniques in wireless sensor network | |
| Mishra et al. | An enhanced path planning model for anchor-free localization in wireless sensor networks | |
| Tabassum et al. | Localization techniques in wireless sensor networks: a comprehensive survey | |
| Benbadis et al. | Exploring landmark placement strategies for topology-based localization in wireless sensor networks |
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 |