CN106211189B - A kind of isomery multimedia sensor network dispositions method and device - Google Patents
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Abstract
The embodiment of the invention provides a kind of isomery multimedia sensor network dispositions method and devices, belong to wireless sensor network technology field.This method comprises: calculating the quantity of sensor node according to the monitoring area size and sensing range of sensor node；Using around distance model, calculates and be connected to characteristic requirements with whether sensing range inner sensor network meets in the monitoring area size, to adjust the quantity of the sensor node；Probability sensor model is established according to aggregation node position and the sensor node position, and calculates each sensor node to the perception probability of the aggregation node；Adjacent sensors node is ranked up, selection arranges the sensor node of forward preset quantity and the aggregation node establishes connection；Link between adjacent sensors node is ranked up, the link of the preset quantity of arrangement rearward is deleted according to perception probability.The present invention can effectively reduce the energy consumption of node and improve the connectivity of sensor network.
Description
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a heterogeneous multimedia sensor network deployment method and device.
Background
Heterogeneity is an essential feature of wireless multimedia sensor networks. The heterogeneous multimedia sensor network integrates the sensing, collecting, processing and transmitting functions of multimedia data and single scalar data, and is widely applied to various fields, such as: environmental monitoring, video and safety monitoring, traffic management, industrial control and the like.
Sensor node deployment is a fundamental problem for heterogeneous multimedia sensor network applications. The existing sensor node deployment schemes can be divided into static deployment schemes and dynamic deployment schemes according to application time. The static deployment scheme is determined by the network starting time, and the node position is calculated once during the network initialization, without considering the situation that the node moves or the network state changes dynamically. Dynamic deployment schemes require periodic detection of network state and performance and analysis of conditions that may occur around the nodes, which exacerbates the energy consumption of the nodes. Designing a network deployment scheme that is energy efficient and has optimal connectivity performance is therefore a key issue for heterogeneous multimedia sensor network applications.
Disclosure of Invention
The invention provides a heterogeneous multimedia sensor network deployment method and device, aiming at effectively reducing the energy consumption of nodes and improving the connectivity of a sensor network.
In a first aspect, a method for deploying a heterogeneous multimedia sensor network provided in an embodiment of the present invention includes:
calculating the number of the sensor nodes according to the size of the monitoring area and the sensing range of the sensor nodes;
calculating whether the sensor network meets the requirement of the communication characteristic in the size of the monitoring area and the sensing range by using a surrounding distance model, and if not, adjusting the number of the sensor nodes;
establishing a probability perception model according to the positions of the sink nodes and the positions of the sensor nodes, and calculating the perception probability of each sensor node to the sink nodes according to the probability perception model;
sequencing adjacent sensor nodes, and selecting a preset number of sensor nodes arranged in front to establish connection with the sink node;
and sequencing the links between the adjacent sensor nodes, and deleting the links of the preset number arranged behind according to the perception probability.
Preferably, the step of ordering the neighboring sensor nodes comprises:
calculating the bidding price of the sensor nodes according to a calculation model, and then sequencing the adjacent sensor nodes according to the bidding price of each sensor node, wherein the calculation model of the bidding price is as follows:
wherein i is a neighbor node of a newly added node, α is more than or equal to 0, β is more than or equal to 0, lambda is more than or equal to 0, η is more than or equal to 0, α + β + lambda + η is equal to 1, and EriIs the residual energy of node i, EaviAverage residual energy for all neighboring nodes; diThe distance between the newly added node and the node i is obtained;the rate of energy replenishment for node i,the average energy supplement rate of all the adjacent nodes is obtained;is the perceived probability of the node i,is the average perceptual probability of all neighboring nodes.
Preferably, the probability perception model is:
wherein r is the sensing range of the sensor node; r iseIs the measurement of the uncertain monitoring capability of the sensor node, and the parameter β is d (v, x) - (r-r)e) (ii) a Mu and phi are used to measure the probability of node v monitoring the occurrence of an event at target point x when the distance between target point x and node v falls within a certain range.
Preferably, the number of sensor nodes is calculated by the following calculation model:
P(dmin≥1)＝exp(-n·P)
wherein d isminRepresenting the minimum node degree.
Preferably, the surrounding distance model is:
wherein,namely the Euclidean distance between two nodes; x is the number ofmax、ymaxThe maximum values of the horizontal and vertical coordinates of the boundary of the region when the origin of the rectangular coordinate system is at the center of the region are respectively.
In a second aspect, an apparatus for deploying a heterogeneous multimedia sensor network provided in an embodiment of the present invention is applied to a computing terminal, and the apparatus includes:
the quantity calculation unit is used for calculating the quantity of the sensor nodes according to the size of the monitoring area and the sensing range of the sensor nodes;
the quantity adjusting unit is used for calculating whether the sensor network meets the requirement of the communication characteristic in the size and the sensing range of the monitoring area by using a surrounding distance model, and if the sensor network does not meet the requirement of the communication characteristic, the quantity of the sensor nodes is adjusted;
the perception probability calculation unit is used for establishing a probability perception model according to the positions of the sink nodes and the positions of the sensor nodes and calculating the perception probability of each sensor node to the sink nodes according to the probability perception model;
the link establishing unit is used for sequencing adjacent sensor nodes and selecting a preset number of sensor nodes arranged in front to establish connection with the sink node;
and the link deleting unit is used for sequencing the links between the adjacent sensor nodes and deleting the links of the preset number arranged behind according to the perception probability.
Preferably, the link establishing unit calculates bidding prices of the sensor nodes according to a calculation model, and then sorts the neighboring sensor nodes according to the bidding prices of the sensor nodes, wherein the calculation model of the bidding price is as follows:
wherein i is a neighbor node of a newly added node, α is more than or equal to 0, β is more than or equal to 0, lambda is more than or equal to 0, η is more than or equal to 0, α + β + lambda + η is equal to 1, and EriIs the residual energy of node i, EaviAverage residual energy for all neighboring nodes; diThe distance between the newly added node and the node i is obtained;the rate of energy replenishment for node i,the average energy supplement rate of all the adjacent nodes is obtained;is the perceived probability of the node i,is the average perceptual probability of all neighboring nodes.
Preferably, the probability perception model is:
wherein r is the sensing range of the sensor node; r iseIs the measurement of the uncertain monitoring capability of the sensor node, and the parameter β is d (v, x) - (r-r)e) (ii) a Mu and phi are used to measure the probability of node v monitoring the occurrence of an event at target point x when the distance between target point x and node v falls within a certain range.
Preferably, the number of sensor nodes is calculated by the following calculation model:
P(dmin≥1)＝exp(-n·P)
wherein d isminRepresenting the minimum node degree.
Preferably, the surrounding distance model is:
wherein,namely the Euclidean distance between two nodes; x is the number ofmax、ymaxThe maximum values of the horizontal and vertical coordinates of the boundary of the region when the origin of the rectangular coordinate system is at the center of the region are respectively.
According to the heterogeneous multimedia sensor network deployment method and device provided by the embodiment of the invention, whether the sensor network meets the requirement of the communication characteristic is calculated by using the surrounding distance model so as to adjust the number of the sensor nodes; establishing a probability perception model to calculate the perception probability of each sensor node to the sink node; and sequencing links between adjacent sensor nodes and sensors, selecting a preset number of sensor nodes arranged in front to establish connection with the sink node, and deleting a preset number of links arranged in back according to the perception probability. Therefore, the energy consumption of the nodes can be effectively reduced, and the connectivity of the sensor network can be improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of a heterogeneous multimedia sensor network according to an embodiment of the present invention.
Fig. 2 is a block diagram of a sensor node according to an embodiment of the present invention.
Fig. 3 is a flowchart of a heterogeneous multimedia sensor network deployment method according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating a boundary effect of a heterogeneous multimedia sensor network according to an embodiment of the present invention.
Fig. 5 is a simulation diagram for calculating connectivity characteristics of a heterogeneous multimedia sensor network according to an embodiment of the present invention.
Fig. 6 is a block diagram of a heterogeneous multimedia sensor network deployment device according to an embodiment of the present invention.
The labels in the figure are respectively:
the system comprises a sensor node 100, a sink node 200, a monitoring center 300 and a heterogeneous multimedia sensor network deployment device 400;
a sensing unit 101, a processing unit 102 and a wireless transmitting and receiving unit 103;
a number calculation unit 401, a number adjustment unit 402, a perception probability calculation unit 403, a contact establishment unit 404, and a link deletion unit 405.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
As shown in fig. 1, the heterogeneous multimedia sensor network may include a sensor node 100, an aggregation node 200, a monitoring center 300, and the like. The sensor nodes 100 include a multimedia sensor node that generates multimedia data and a scalar sensor node that generates scalar data. The multimedia sensor nodes comprise video nodes, audio nodes, image nodes and the like. The scalar sensor nodes comprise a temperature node, a humidity node, a pressure node and the like. The multimedia sensor nodes and the scalar sensor nodes are dispersedly deployed in a monitoring area and are mainly responsible for completing the collection tasks of multimedia information and scalar information. The multimedia information and scalar information are finally delivered to the sink node 200 and finally reach the monitoring center 300 through the internet or a communication satellite network.
As shown in fig. 2, the sensor node 100 includes a sensing unit 101, a processing unit 102, and a wireless transceiving unit 103. The sensing unit 101 is connected to the processing unit 102, and the processing unit 102 is connected to the wireless transceiver 103. Optionally, the sensing unit 101 may include a sensor and an analog-to-digital converter. The processing unit 102 may include a processor and a memory. The wireless transceiver unit 103 includes a transceiver, a MAC access device, and a network transmitter. The working process is as follows: the sensor collects surrounding multimedia data and scalar data, performs analog-to-digital conversion through the analog-to-digital converter, and sends the data to the processor for processing, the processor sends the processed result to the memory for storage and sends the result to the transceiver, and the transceiver forwards the received result to the sink node 200 through the MAC access device and the network transmitter. And finally to the monitoring center 300 through the internet or a communication satellite network.
The heterogeneous multimedia sensor network deployment method provided by the embodiment of the invention adopts a method combining static deployment and dynamic adjustment, and deploys the positions of the sensor nodes 100 by establishing a probability perception model and a surrounding distance measurement method considering a boundary effect, so as to form a network topology structure with optimal connectivity and robustness. And dynamically adjusting the deployment scheme in the network operation process, and dynamically adjusting the network structure according to the residual energy and the energy supplement rate of the nodes.
As shown in fig. 3, a method for deploying a heterogeneous multimedia sensor network according to an embodiment of the present invention includes the following steps:
s101: the number of the sensor nodes 100 is calculated according to the size of the monitoring area and the sensing range of the sensor nodes 100.
Wherein, as shown in FIG. 4, the n transmission ranges are r0Sensor (2)The nodes 100 are randomly distributed over the area a independently of each other, and the probability density function of the distribution of the sensor node 100 locations is fX(x) And assuming that the distribution area is a circular area with a radius a, where A0(x) Representing the coverage of node v. For any given sensor node 100v in the network, the possible value of the node degree is {1,2, …, n }, which is defined as a sample space SD. Degree S of sensor node 100vDEach value in (a) corresponds to a certain probability. Therefore, the degree of any given sensor node 100v is a discrete random variable, denoted by D, and the discrete probability distribution P (D ═ D) and the expected value e (D) of the sensor node 100v are calculated first; and on the basis, the network deployment scheme of the sensor node 100 is researched.
When the sensor nodes 100 are deployed in an arbitrary distribution, it is assumed that a given sensor node 100v is located at x, and another sensor node 100v' has a probability density function of fX(x') an arbitrary probability distribution randomly falls within the region a. If the sensor node 100v' falls within r around x0In a circular area of radius, the sensor node 100v' is a neighbor node of v. The probability that the sensor node 100v' is a neighbor of the sensor node 100v, i.e., it falls within the area A0(x) The probability of being in is then
For each sensor node 100 except v, there are two possibilities, namely "neighboring node of v" and "neighboring node of not v", which are respectively represented by 1 and 0, and then the sample space is Sv0,1, and P (S)v＝1)＝P1(x)，P(Sv＝0)＝1-P1(x) In that respect For a network with n sensor nodes 100, the probability that the degree of the sensor node 100v is d is A0(x) The probability of having d sensor nodes 100 in a region can be represented by a binomial distribution:
the expected value is
If P1(x) Smaller and n larger, the binomial distribution can be approximated as a poisson distribution:
wherein:
the probability of the sensor node 100v being isolated is region A0(x) Probability of no other nodes within:
approximating a binomial distribution as a Poisson distribution, i.e., P1(x) Smaller and larger n, the probability that the sensor node 100v has d neighbors at most can be expressed as
Since x may be located at any point in the region a, the probability density function of its distribution is fX(x) The mathematical expectation of D is then the weighted sum of the above conditional probability values at all possible locations:
generally, each sensor node 100 in the network is required to have at least k neighbor nodes (d (v) ≧ k,) I.e. minimum node degree d of all sensor nodes 100minIs required to satisfy dminK is more than or equal to k. Assuming that the node degrees are statistically independent, there is dminThe probability of k being equal to or greater than k (k being equal to or greater than 1)
If P (D.ltoreq.k-1) is small and n is large, the above formula can be approximately expressed as a Poisson distribution
Further, it is possible to obtain:
P(dminnot less than 1) ═ exp (-n.P (node isolated)
I.e. the required network connectivity is satisfied with dminAnd (3) a network deployment scheme under the condition of being more than or equal to k.
When the sensor nodes 100 are deployed according to a uniform distribution, the probability density function of the node distribution at this time is
Wherein A | | | | pi a2The length of a one-dimensional finite region or the area of a two-dimensional finite region.
Then there is
At this time the probability P1(x) Depending only on area A and node coverage A0(x) The area of the intersection region of (1) can be represented by A'0(x)＝||A0(x) ∩ A | the distance of the position from the boundary of the area A is not less than r0Are called intermediate nodes, their transmission coverage areaThe expected value of the node degree isThe distance between the position and the boundary of the area A is less than r0Are called edge nodes, their transmission coverage area is equal to the area A 'of the intersection area of the area A'0(x) Is less thanResulting in a desired value of its node degree being less thanThus, the expectation of the degree of a randomly chosen node is, taking into account the boundary effect, that
For a circular area a with a radius a, the origin of the coordinate system is set at the center position of the area a, and the node position x is expressed by polar coordinates (r, Φ), and dx is replaced by the polar coordinate system integral rdrd θ, without loss of generality. If r is less than or equal to a-r0I.e. v is an intermediate node; if a-r0< r.ltoreq.a, in which case A 'needs to be determined'0(x)。
It can be derived that:
according to the cosine theorem
Thus, can obtain
Further obtain the
Comprehensively obtaining:
then there is
The average value of the degrees of the randomly selected nodes can be obtained as follows:
normalized transmission distance using nodesAlternatively, the above equation can be transformed into:
obtaining the probability of existence of isolated nodes in the network as
Further, in the static deployment scheme and the dynamic deployment scheme, studies on network coverage and connectivity are more. Most of the existing connectivity problems are to analyze the connectivity characteristics of the network by taking the euclidean distance between the sensor nodes 100 as a measure under the assumption that a certain specific relationship exists between the sensing range Sr and the communication range Tr of the sensor nodes 100. Early connectivity studies assumed that Tr was much larger than Sr. The existing network connectivity research usually assumes that Sr and Tr are equal, and analyzes the deployment scheme of the sensor nodes 100 and the network connectivity, and the research focuses on making the network form k connectivity, which means that there are k independent paths between each pair of sensor nodes 100. When k > 1, the network can tolerate some sensor node 100 or link failure. The deployment schemes under the above two conditions may cause a network connectivity problem when the communication range of the sensor node 100 is limited, and the euclidean distance measurement method does not consider the influence of the boundedness of an actual monitoring area on the connectivity of the sensor node 100 close to the boundary so as to reach the overall connectivity of the network.
For this purpose, a surrounding distance model is introduced. Alternatively, the sensor node is assumedAnd v2Are represented by coordinates of (x)1,y1)、(x2,y2) Then the surrounding distance between them is:
wherein,namely the Euclidean distance between two nodes; x is the number ofmax、ymaxThe maximum values of the horizontal and vertical coordinates of the boundary of the region when the origin of the rectangular coordinate system is at the center of the region are respectively.
S102: and calculating whether the sensor network meets the requirement of the communication characteristic in the size and the sensing range of the monitoring area by using a surrounding distance model, and if not, adjusting the number of the sensor nodes 100.
In a wireless sensor network, the sensor nodes 100 may fail or may not normally communicate due to factors such as interference, and it is necessary to ensure that the network does not become disconnected due to the failure of some sensor nodes 100 or links to normally operate. Therefore, the wireless sensor network partThe deployment scheme can ensure that a plurality of alternative paths exist among the sensor nodes 100, so that the network has certain fault tolerance, and the more paths without common edges (or common vertexes), the better the fault tolerance. The k-connected characteristic of the network means that k-1 nodes are deleted arbitrarily, and the network formed by the rest nodes is still connected. To meet the requirements of fault tolerant design, the designed network need not be 1-connected, i.e., the network is connected and has better connectivity, e.g., 2-connected, 3-connected, or k-connectedGeneral case of (1).
Considering the influence of the boundary effect of the monitoring area, constructing a network topology structure based on a surrounding distance model, and calculating P (k-connectivity) and P (d) of the network by adopting a Monte Carlo simulation methodminK) or more. And verifying whether the sensor network meets the requirement of the communication characteristic in the size and the sensing range of the monitoring area, and if not, adjusting the number of the sensor nodes 100. As shown in fig. 5, the simulation result indicates that, when there are many nodes, the result calculated by the formula can be directly used as the theoretical value of P (k-connected), and the nodes are deployed accordingly.
S103: establishing a probability perception model according to the positions of the sink nodes 200 and the positions of the sensor nodes 100, and calculating the perception probability of each sensor node 100 to the sink nodes 200 according to the probability perception model.
The probability perception model is as follows:
wherein r is the sensing range of the sensor node 100; r iseIs a measure of the uncertainty monitoring capability of the sensor node 100, and the parameter β is d (v, x) - (r-r)e) (ii) a Mu and phi are used to measure the distance between the target point x and the node v within a certain range,node v monitors the probability of an event occurring at target point x.
S104: and sequencing adjacent sensor nodes, and selecting a preset number of sensor nodes arranged in front to establish connection with the sink node.
Alternatively, assuming that there are N sensor nodes 100 and M links initially, the network is in a dynamic adjustment scheme that can win or lose the advantages as follows. A new sensor node 100 is added and connected to N (0 ≦ N ≦ N) existing old sensor nodes 100. When a newly added sensor node 100 selects a link with an existing old node, bidding price B according to the following formula and the like according to the factors of residual energy, energy supplement rate, perception probability and the like of each sensor node 100iAnd sequencing all the adjacent sensor nodes 100, and selecting the first k +1 sensor nodes 100 to be connected according to the requirement of the k-connectivity characteristic of the network.
Wherein i is a neighbor node of a newly added node, α is more than or equal to 0, β is more than or equal to 0, lambda is more than or equal to 0, η is more than or equal to 0, α + β + lambda + η is equal to 1, and EriIs the residual energy of node i, EaviAverage residual energy for all neighboring nodes; diThe distance between the newly added node and the node i is obtained;the rate of energy replenishment for node i,the average energy supplement rate of all the adjacent nodes is obtained;is the perceived probability of the node i,for all that isAverage perceived probability of neighboring nodes.
S105: and sequencing the links between the adjacent sensor nodes 100, and deleting the links of the preset number arranged at the back according to the perception probability.
According to the factors of the residual energy, the energy supplement rate, the perception probability and the like of each node, all the edges are sequenced according to the competitive bidding price of the formula, and according to the probability pxDeleting M (M is more than or equal to 0 and less than or equal to M) links, and finally mpxThe bar link is deleted.
Further, as shown in fig. 6, an embodiment of the invention provides a heterogeneous multimedia sensor network deployment apparatus 400, which is applied to a computing terminal with data processing capability. The apparatus may include a number calculation unit 401, a number adjustment unit 402, a perceptual probability calculation unit 403, a link establishment unit 404, a link deletion unit, and the like.
The number calculating unit 401 is configured to calculate the number of the sensor nodes 100 according to the size of the monitoring area and the sensing range of the sensor nodes 100. The number of the sensor nodes 100 is calculated by the following calculation model:
P(dmin≥1)＝exp(-n·P)
wherein d isminRepresenting the minimum node degree.
In this embodiment, the number calculating unit 401 is configured to execute the step S101 shown in fig. 3, and details about the number calculating unit 401 may refer to the description of the step S101, which is not repeated herein.
The number adjusting unit 402 is configured to calculate whether the sensor network meets the requirement of the connectivity characteristic in the size of the monitoring area and the sensing range by using a surrounding distance model, and if not, adjust the number of the sensor nodes 100. The surrounding distance model is as follows:
wherein,namely the Euclidean distance between two nodes; x is the number ofmax、ymaxThe maximum values of the horizontal and vertical coordinates of the boundary of the region when the origin of the rectangular coordinate system is at the center of the region are respectively.
In this embodiment, the number adjusting unit 402 is configured to execute the step S102 shown in fig. 3, and details of the number adjusting unit 402 may refer to the description of the step S102, which is not repeated herein.
The sensing probability calculating unit 403 is configured to establish a probability sensing model according to the positions of the sink nodes 200 and the positions of the sensor nodes 100, and calculate the sensing probability of each sensor node 100 for the sink node 200 according to the probability sensing model. Wherein the probability perception model is:
wherein r is the sensing range of the sensor node 100; r iseIs a measure of the uncertainty monitoring capability of the sensor node 100, and the parameter β is d (v, x) - (r-r)e) (ii) a Mu and phi are used to measure the probability of node v monitoring the occurrence of an event at target point x when the distance between target point x and node v falls within a certain range.
In this embodiment, the sensing probability calculating unit 403 is configured to execute step S103 shown in fig. 3, and details of the sensing probability calculating unit 403 may refer to the description of step S103, which is not repeated herein.
The link establishing unit 404 is configured to sequence the adjacent sensor nodes 100, and select a preset number of sensor nodes 100 arranged in front to establish a connection with the sink node 200. In this embodiment, the link establishing unit 404 is configured to execute the step S104 shown in fig. 3, and for a detailed description of the link establishing unit 404, reference may be made to the description of the step S104, which is not described herein again.
The link establishing unit 404 calculates the bidding price of the sensor nodes 100 according to a calculation model, and then sorts the neighboring sensor nodes 100 according to the bidding price of each sensor node 100, where the calculation model of the bidding price is:
wherein i is a neighbor node of a newly added node, α is more than or equal to 0, β is more than or equal to 0, lambda is more than or equal to 0, η is more than or equal to 0, α + β + lambda + η is equal to 1, and EriIs the residual energy of node i, EaviAverage residual energy for all neighboring nodes; diThe distance between the newly added node and the node i is obtained;the rate of energy replenishment for node i,the average energy supplement rate of all the adjacent nodes is obtained;is the perceived probability of the node i,is the average perceptual probability of all neighboring nodes.
The link deleting unit 405 is configured to sort links between adjacent sensor nodes 100, and delete a preset number of links arranged in the back according to the sensing probability. In this embodiment, the link deleting unit 405 is configured to execute the step S105 described in fig. 3, and for a detailed description of the link deleting unit 405, reference may be made to the description of the step S105, which is not described herein again.
According to the method and the device for deploying the heterogeneous multimedia sensor network, provided by the embodiment of the invention, whether the sensor network meets the requirement of the communication characteristic is calculated by using the surrounding distance model so as to adjust the number of the sensor nodes 100; establishing a probability perception model to calculate the perception probability of each sensor node 100 to the sink node 200; and sequencing links between adjacent sensor nodes 100 and sensors, selecting a preset number of sensor nodes 100 arranged in front to establish connection with the sink node 200, and deleting a preset number of links arranged in back according to the perception probability. Therefore, the energy consumption of the nodes can be effectively reduced, and the connectivity of the sensor network can be improved.
It should be noted that the apparatus provided in the embodiment of the present invention has the same implementation principle and the same technical effect as the foregoing method embodiment. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A heterogeneous multimedia sensor network deployment method, the method comprising:
calculating the number of the sensor nodes according to the size of the monitoring area and the sensing range of the sensor nodes;
calculating whether the sensor network meets the requirement of the communication characteristic in the size of the monitoring area and the sensing range by using a surrounding distance model, and if not, adjusting the number of the sensor nodes; the surrounding distance model is as follows:
wherein x is1、x2Respectively representing the abscissas of two nodes, y1、y2Respectively representing the vertical coordinates of the two nodes; euclidean distance between two nodesxmax,ymaxRespectively is the maximum value of the horizontal coordinate and the vertical coordinate of the boundary of the region when the origin of the rectangular coordinate system is at the center of the region;
establishing a probability perception model according to the positions of the sink nodes and the positions of the sensor nodes, and calculating the perception probability of each sensor node to the sink nodes according to the probability perception model;
sequencing adjacent sensor nodes, and selecting a preset number of sensor nodes arranged in front to establish connection with the sink node; the step of ordering the neighboring sensor nodes comprises:
calculating the bidding price of the sensor nodes according to a calculation model, and then sequencing the adjacent sensor nodes according to the bidding price of each sensor node, wherein the calculation model of the bidding price is as follows:
wherein i is a neighbor node of a newly added node, α is more than or equal to 0, β is more than or equal to 0, lambda is more than or equal to 0, η is more than or equal to 0, α + β + lambda + η is equal to 1, and EriIs the residual energy of node i, EaviAverage residual energy for all neighboring nodes; diThe distance between the newly added node and the node i is obtained;the rate of energy replenishment for node i,the average energy supplement rate of all the adjacent nodes is obtained;is the perceived probability of the node i,the average perception probability of all adjacent nodes is obtained;
sequencing links between adjacent sensor nodes, and deleting the links of the preset number arranged behind according to the perception probability; the step of ordering links between adjacent sensor nodes comprises:
and sequencing all links according to the residual energy, the energy supplement rate, the perception probability and the bidding price of each node.
2. A heterogeneous multimedia sensor network deployment device applied to a computing terminal is characterized by comprising:
the quantity calculation unit is used for calculating the quantity of the sensor nodes according to the size of the monitoring area and the sensing range of the sensor nodes;
the quantity adjusting unit is used for calculating whether the sensor network meets the requirement of the communication characteristic in the size and the sensing range of the monitoring area by using a surrounding distance model, and if the sensor network does not meet the requirement of the communication characteristic, the quantity of the sensor nodes is adjusted; the surrounding distance model is as follows:
wherein x is1、x2Respectively representing the abscissas of two nodes, y1、y2Respectively representing the vertical coordinates of the two nodes; euclidean distance between two nodesxmax,ymaxAre respectively provided withThe maximum value of the horizontal and vertical coordinates of the boundary of the region when the origin of the rectangular coordinate system is at the center of the region;
the perception probability calculation unit is used for establishing a probability perception model according to the positions of the sink nodes and the positions of the sensor nodes and calculating the perception probability of each sensor node to the sink nodes according to the probability perception model;
the link establishing unit is used for sequencing adjacent sensor nodes and selecting a preset number of sensor nodes arranged in front to establish connection with the sink node; the link establishing unit is specifically configured to:
calculating the bidding price of the sensor nodes according to a calculation model, and then sequencing the adjacent sensor nodes according to the bidding price of each sensor node, wherein the calculation model of the bidding price is as follows:
wherein i is a neighbor node of a newly added node, α is more than or equal to 0, β is more than or equal to 0, lambda is more than or equal to 0, η is more than or equal to 0, α + β + lambda + η is equal to 1, and EriIs the residual energy of node i, EaviAverage residual energy for all neighboring nodes; diThe distance between the newly added node and the node i is obtained;the rate of energy replenishment for node i,the average energy supplement rate of all the adjacent nodes is obtained;is the perceived probability of the node i,the average perception probability of all adjacent nodes is obtained;
the link deleting unit is used for sequencing links between adjacent sensor nodes and deleting the links of the preset number behind the links according to the perception probability; the link deletion unit is specifically configured to:
and sequencing all links according to the residual energy, the energy supplement rate, the sensing probability and the bidding price of each node, and deleting the links with the preset number arranged later according to the sensing probability.
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