CN112055322A - Underwater wireless sensor network scheduling optimization model based on interval multi-target - Google Patents

Underwater wireless sensor network scheduling optimization model based on interval multi-target Download PDF

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CN112055322A
CN112055322A CN202010773030.1A CN202010773030A CN112055322A CN 112055322 A CN112055322 A CN 112055322A CN 202010773030 A CN202010773030 A CN 202010773030A CN 112055322 A CN112055322 A CN 112055322A
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CN112055322B (en
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孙靖
周慧邦
巩敦卫
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China University of Mining and Technology CUMT
Jiangsu Ocean University
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Jiangsu Ocean University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • 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

Abstract

The invention discloses an underwater wireless sensor network scheduling optimization model based on interval multi-target, which comprises a sensor interval coverage model, an interval energy consumption model and an interval energy balance model; the interval multi-target cultural gene algorithm comprises a local search activation mechanism, local search initial population establishment and a local search strategy. The invention can be applied to uncertain marine environments, and has the main innovation points that: the coverage rate, the energy consumption and the energy balance degree are used as evaluation indexes of the network performance, and the network performance is comprehensively and comprehensively evaluated; the influence of an uncertain environment on the network is fully considered, a three-dimensional underwater wireless sensor network model containing uncertainty is established by using whether the nodes are dormant and the clustering strategy of the nodes, and the network is optimized by using an interval multi-objective evolutionary optimization algorithm, so that the aim of improving the network performance is fulfilled.

Description

Underwater wireless sensor network scheduling optimization model based on interval multi-target
Technical Field
The invention relates to the technical field of underwater sensor networks, in particular to an underwater wireless sensor network scheduling optimization model based on interval multi-objective.
Background
The underwater wireless sensor network transmits the acquired data such as water pressure, water temperature, salt and the like to the water surface base station in a single-hop or multi-hop mode among the nodes, so that the marine environment is monitored. UWSNs are more and more concerned by scholars at home and abroad because the UWSNs can be effectively applied to the fields of ocean exploration, navigation assistance, environmental pollution prediction and the like. For practical application problems of UWSNs, a large number of sensor nodes need to be arranged, so that the network can reach the required coverage rate, and the environment can be effectively monitored in real time. Since the energy of each node is very limited and in an extremely complex marine environment, it is difficult to replace the node battery, so a clustering strategy is generally adopted to balance the energy consumption of the whole network and prolong the service life of the network as much as possible.
According to the method, a spherical underwater sensor network coverage model is established to optimize network coverage control, and sensing power among nodes is adjusted in a self-adaptive mode according to the positions of the nodes and the energy consumption condition of the nodes in a sensing radius range of the nodes, so that the aims of reducing the energy consumption of the whole network and prolonging the service life of the network are fulfilled. The Huangjunjie and the like provide a virtual potential field coverage optimization algorithm of a three-dimensional sensor network model aiming at an underwater environment, and the algorithm is characterized in that: the underwater sensor node is driven by the virtual force, so that the node moves along the Z axis, the perception overlapping area and the perception blind area can be gradually eliminated, and the coverage enhancement of the underwater sensor network is realized. JoseM et al studied the coverage optimization problem of UWSNs by combining the multi-objective evolutionary optimization algorithm NSGA-II with the fish swarm and particle swarm algorithms. The result shows that the algorithm can effectively optimize the sensing area view angle of the sensor node, thereby greatly improving the coverage rate of UWSNs and improving the network performance. A multi-level heterogeneous underwater wireless sensor network algorithm provided by Hezhongdong and the like uses a clustering strategy, simultaneously considers the residual energy and position factors of each sensor node to select a cluster head, and simultaneously flexibly uses a single-hop and multi-hop self-adaptive routing communication protocol to balance the energy consumption of a network area. Simulation shows that the UWSNs coverage rate is unchanged by the algorithm, and the service life of the network is effectively prolonged. For the research results, the performance of the underwater wireless sensor network is optimized in a deterministic environment, however, in practical application, the influence of a complex marine environment on the network performance must be considered, so that the network performance can better meet the practical requirements. Therefore, the development of an interval multi-objective-based scheduling optimization model for the underwater wireless sensor network is a technical problem to be solved by technical personnel in the field.
Disclosure of Invention
The invention aims to provide an interval multi-objective-based scheduling optimization model of an underwater wireless sensor network to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: an underwater wireless sensor network scheduling optimization model based on interval multi-objective comprises a UWSNs interval multi-objective scheduling optimization model and an interval multi-objective cultural genetic algorithm; the UWSNs interval multi-target scheduling optimization model comprises a sensor interval coverage model, an interval energy consumption model and an interval energy balance model; the interval multi-target cultural gene algorithm comprises a local search activation mechanism, local search initial population establishment and a local search strategy;
setting a three-dimensional underwater monitoring area as V (L multiplied by L), and setting a target point T (T)1,t2,t3...tm) Where m is the number of target points, the coordinate t of the jth target pointj＝(xtj,ytj,ztj) (ii) a Sensor node S ═ S1,s2,s3...sn) Where n is the number of nodesThe position of the ith node is uncertain due to the displacement of the ith node caused by the influence of uncertain factors such as ocean currents, marine life and the like, and the coordinate of the ith node is expressed by intervals, namely the coordinate of the ith node is recorded as
Figure BDA0002617357430000021
The distance between the ith node and the jth target point is therefore recorded as
Figure BDA0002617357430000022
Wherein the content of the first and second substances,
Figure BDA0002617357430000023
Figure BDA0002617357430000031
in the monitoring area, three nodes, namely a cluster head node, an intra-cluster node and a sink node for summarizing cluster head node data exist; each node has four states, -1 (dead node), 0 (dormant cluster node), 1 (active cluster node) and 2 (cluster head node);
the main process is as follows: randomly generating a certain number of cluster head nodes in a monitoring area, selecting the nearest cluster head by other nodes according to the lower bound of the distance to add the cluster head into the cluster, and sending data to the cluster head of the cluster, dividing the cluster head into different levels by the lower bound of the distance by all the cluster head nodes through calculating the distance between the cluster head nodes and the sink node, sending the data to the cluster head close to the sink by the cluster head far from the sink, and directly sending the data to the sink node by the cluster head close to the sink, namely sending the data received by the cluster head nodes to the sink node by a method of selecting single hop or multiple hops through the distance.
As a preferred technical solution of the present invention, the sensor interval coverage model includes the following: when the distance between the target point and the sensor node is smaller than the sensing radius of the node, the target point is considered to be covered by the network, that is, when the sensing radius of the node s is r, the probability that the target point t is covered by the node s is as follows:
Figure BDA0002617357430000032
wherein d (s, t) is the Euclidean distance between the target point t and the node s,
in the sensor node set S ═ (S)1,s2,s3...sn) From the state of the node, the ith node s can be definediThe working state of (2):
Figure BDA0002617357430000033
wherein a isiE { -1,0,1,2}, i.e. xiThe node is considered to be in an operating state when the node is 1, and otherwise, the node is not operated, i is 1,2, …, n;
the jth target point tjBy the ith node siThe probability of coverage can be defined as
Figure BDA0002617357430000041
Wherein:
Figure BDA0002617357430000042
Figure BDA0002617357430000043
the probability that the jth target point is covered by the network can be defined as
Figure BDA0002617357430000044
Wherein
Figure BDA0002617357430000045
Because of the minimization problem, the problem is translated into minimizing uncovered rate;
network uncovered rate is
Figure BDA0002617357430000046
Wherein
Figure BDA0002617357430000047
When node siIn active state (including cluster and non-cluster nodes), xi1, otherwise xi＝0。
As a preferred technical solution of the present invention, the interval energy consumption model includes the following contents: the energy consumption for transferring data between two nodes can be expressed as:
Ei,j＝A(d)×l
wherein d is a node si,sjThe euclidean distance between the two nodes, l is the size of a transmission data packet and the unit is bit, and a (d) represents the energy attenuation of the data packet when the underwater transmission distance is d, and can be represented as follows:
A(d)＝dηad
wherein η is an energy spread factor, and η ═ 2,
Figure BDA0002617357430000051
a (f) is the absorption coefficient, which can be expressed as:
Figure BDA0002617357430000052
wherein f is the carrier frequency;
each node needs to send data to other nodes, needs to calculate the euclidean distance between itself and other nodes, where this distance is also an interval value in an uncertain complex environment, and the upper and lower bounds of energy consumption for transmitting data by all the nodes can be expressed as:
iE＝A(dmin)×l
Figure BDA0002617357430000053
wherein d ismin、dmaxThe lower bound and the upper bound of the distance between the node and the data receiving node are respectively; therefore, the energy consumption of the whole network
Figure BDA0002617357430000054
Equal to the sum of the energy consumptions of all the working nodes, wherein:
Figure BDA0002617357430000055
Figure BDA0002617357430000056
as a preferred technical solution of the present invention, the interval energy balance model includes the following contents: suppose an ith node siThe residual energy of
Figure BDA0002617357430000057
The number of nodes in the u-th area is nuAnd u is 1,2, …, k, and we take the average remaining energy of each region as the average energy of the region, so the average energy of the u-th region is:
Figure BDA0002617357430000058
wherein the content of the first and second substances,
Figure BDA0002617357430000059
Figure BDA00026173574300000510
the maximum value S (n) of the average energy of the regions is found by calculating the average energy of k regionsu)maxAnd a minimum value S (n)u)minThen, the interval span of the network can be obtained by the calculation formula of the interval
Figure BDA0002617357430000061
Wherein the content of the first and second substances,
Figure BDA0002617357430000062
Figure BDA0002617357430000063
the smaller the interval span, the better the balance of the network.
As a preferred technical solution of the present invention, the local search activation mechanism includes the following: for the multi-objective optimization problem, the hyper-volume of the Pareto optimal solution set X obtained by calculation is defined as:
Figure BDA0002617357430000064
wherein x isrefIs a reference point; λ is the Leeberg measure; [ solution ] A method for producing a polymerIPIs the interval Pateto dominance relation;
the algorithm determines whether to activate a local search mechanism by using the change degree of the super-volume of the front generation and the back generation of the population, and the main process of the local search mechanism activation is as follows:
first, the general formula
Figure BDA0002617357430000065
Calculating the hyper-volume of each generation of non-dominated solution set; then, the difference between the two previous generations of super volumes is calculated: deltat＝|Ht-Ht-1|；
Finally, setting a threshold interval for the over-volume change value
Figure BDA0002617357430000066
Wherein the content of the first and second substances,ξ∈(0,1)，
Figure BDA0002617357430000067
when in use
Figure BDA0002617357430000068
When the local search link is activated; the purpose of setting the lower limit of the threshold value is to prevent the local search from being repeatedly activated due to small change of the super volume in the later period of evolution, thereby saving computing resources; because of ΔtThe absolute value of the difference value of the super volumes of the two generations of populations is obtained, so that the purpose of the upper threshold is to prevent the super volume of the next generation from being far smaller than that of the previous generation, thereby influencing the quality of a solution set.
As a preferred technical solution of the present invention, the establishing of the local search initial population includes the following steps:
in addition to the above, for the interval optimization problem of research uncertainty, uncertainty of an individual is also used as an evaluation index of individual quality, and the smaller uncertainty of an individual is, the better the performance of the individual is, in view of this, the algorithm takes the individual ultra-volume contribution and uncertainty as evaluation indexes of individual performance, and the quality of the individual x is defined as:
Figure BDA0002617357430000071
in the formula, CH (x), I (x) respectively represent the interval hyper-volume and uncertainty of an individual x, alpha and beta respectively represent weights for adjusting the influence degree of the hyper-volume and the uncertainty on the performance of the individual, the larger the IP value is, the higher the performance of the individual is, the individual with the highest performance is found out through the formula, then the individual is taken as a population center, N individuals closest to the individual are found out to be used as an initial population of local search, and N is the size of the local search population.
As a preferred technical solution of the present invention, the local search policy includes the following contents: by locally searching the initial population, a tool is providedThe strategy of local search of a body is that firstly, an initial population of local search is used for generating a size N through genetic operations such as crossing, mutation and the likelocalFollowed by comparing the individuals in the population with the individuals in the parent population, respectively, and if the excess volume of an individual present in the parent population is less than the individual and the uncertainty is greater than the individual, then N is calculatedlocalThe individual in the population is reserved in an offspring population, finally, the offspring population and a parent population are combined, the ordering is carried out through the ultravolume contribution of the individual, and the first N is selectedlocalAnd when the local search is terminated, adding the optimal solution set obtained by the local search into the global search population, and continuing to perform the global search.
The invention has the beneficial effects that: 1. in the underwater wireless sensor network scheduling optimization model with multiple targets in the interval, three nodes, namely a cluster head node, an intra-cluster node and a sink node for summarizing cluster head node data exist in a three-dimensional underwater environment; each node has four states, -1 (dead node), 0 (dormant cluster node), 1 (active cluster node) and 2 (cluster head node); the main process is as follows: randomly generating a certain number of cluster head nodes in a monitoring area, selecting the nearest cluster head from the other nodes according to the lower bound of the distance, adding the nearest cluster head into the cluster, and sending data to the cluster head of the cluster; all cluster head nodes divide the cluster heads into different levels by a distance lower bound through calculating the distance between the cluster head nodes and the sink node, the cluster head far away from the sink sends data to the cluster head near the sink, and the cluster head near the sink directly sends the data to the sink node; and a reasonable scheduling mode of clustering and combination of single hop and multi-hop is realized. The network coverage rate is improved, the energy consumption of the network is reduced, the network energy balance degree is good, and the service life of the network is prolonged.
2. Interval multi-target cultural gene algorithm:
the algorithm utilizes the existing interval parameter multi-objective evolution optimization algorithm to carry out global search, determines whether to activate a local search mechanism or not through the change rate of the super volume of a population in the search process so as to judge the time of local search, then selects an individual with large super volume and small uncertainty as a center to construct an initial generation population of the local search, and carries out local search by taking the uncertainty and the super volume of the population as evaluation functions, wherein three key technologies of the algorithm are a local search activation mechanism, the initialization of a local search population and a local search strategy.
Drawings
FIG. 1 is a schematic diagram of a mobile sensor network according to the present invention;
FIG. 2 is a line graph of an interval hyper-volume and a determined hyper-volume;
FIG. 3 is a comparison of interval model and deterministic model.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention more readily understood by those skilled in the art, and thus will more clearly and distinctly define the scope of the invention.
Example (b): referring to fig. 1, the present invention provides a technical solution: an underwater wireless sensor network scheduling optimization model based on interval multi-objective comprises a UWSNs interval multi-objective scheduling optimization model and an interval multi-objective cultural genetic algorithm;
the UWSNs interval multi-target scheduling optimization model comprises a sensor interval coverage model, an interval energy consumption model and an interval energy balance model; the interval multi-target cultural gene algorithm comprises a local search activation mechanism, local search initial population establishment and a local search strategy;
setting a three-dimensional underwater monitoring area as V (L multiplied by L), and setting a target point T (T)1,t2,t3...tm) Where m is the number of target points, the coordinate t of the jth target pointj＝(xtj,ytj,ztj) (ii) a Sensor node S ═ S1,s2,s3...sn) Wherein n is the number of nodes, the ith node can shift due to the influence of uncertain factors such as ocean currents, marine life and the like, so that the position of the ith node has uncertainty, the coordinate of the ith node is represented by an interval, namely the coordinate of the ith node is recorded as
Figure BDA0002617357430000091
The distance between the ith node and the jth target point is therefore recorded as
Figure BDA0002617357430000092
Wherein the content of the first and second substances,
Figure BDA0002617357430000093
Figure BDA0002617357430000094
in the monitoring area, three nodes, namely a cluster head node, an intra-cluster node and a sink node for summarizing cluster head node data exist; each node has four states, -1 (dead node), 0 (dormant cluster node), 1 (active cluster node) and 2 (cluster head node);
the main process is as follows: randomly generating a certain number of cluster head nodes in a monitoring area, selecting the nearest cluster head by other nodes according to the lower bound of the distance to add the cluster head into the cluster, and sending data to the cluster head of the cluster, dividing the cluster head into different levels by the lower bound of the distance by all the cluster head nodes through calculating the distance between the cluster head nodes and the sink node, sending the data to the cluster head close to the sink by the cluster head far from the sink, and directly sending the data to the sink node by the cluster head close to the sink, namely sending the data received by the cluster head nodes to the sink node by a method of selecting single hop or multiple hops through the distance.
The sensor zone coverage model includes the following:
when the distance between the target point and the sensor node is smaller than the sensing radius of the node, the target point is considered to be covered by the network, that is, when the sensing radius of the node s is r, the probability that the target point t is covered by the node s is as follows:
Figure BDA0002617357430000101
wherein d (s, t) is the Euclidean distance between the target point t and the node s,
in the sensor node set S ═ (S)1,s2,s3...sn) From the state of the node, the ith node s can be definediThe working state of (2):
Figure BDA0002617357430000102
wherein a isiE { -1,0,1,2}, i.e. xiThe node is considered to be in an operating state when the node is 1, and otherwise, the node is not operated, i is 1,2, …, n;
the jth target point tjBy the ith node siThe probability of coverage can be defined as
Figure BDA0002617357430000103
Wherein:
Figure BDA0002617357430000104
Figure BDA0002617357430000105
the probability that the jth target point is covered by the network can be defined as
Figure BDA0002617357430000106
Wherein
Figure BDA0002617357430000107
Because of the minimization problem, the problem is translated into minimizing uncovered rate;
network uncovered rate is
Figure BDA0002617357430000108
Wherein
Figure BDA0002617357430000111
Figure BDA0002617357430000112
The interval energy consumption model comprises the following contents: the energy consumption for transferring data between two nodes can be expressed as:
Ei,j＝A(d)×l
wherein d is a node si,sjThe euclidean distance between the two nodes, l is the size of a transmission data packet and the unit is bit, and a (d) represents the energy attenuation of the data packet when the underwater transmission distance is d, and can be represented as follows:
A(d)＝dηad
wherein η is an energy spread factor, and η ═ 2,
Figure BDA0002617357430000113
a (f) is the absorption coefficient, which can be expressed as:
Figure BDA0002617357430000114
wherein f is the carrier frequency;
each node needs to send data to other nodes, needs to calculate the euclidean distance between itself and other nodes, where this distance is also an interval value in an uncertain complex environment, and the upper and lower bounds of energy consumption for transmitting data by all the nodes can be expressed as:
iE＝A(dmin)×l
Figure BDA0002617357430000115
wherein d ismin、dmaxRespectively, the lower bound of the distance between the node and the node receiving the dataAnd an upper bound; therefore, the energy consumption of the whole network
Figure BDA0002617357430000116
Equal to the sum of the energy consumptions of all the working nodes, wherein:
Figure BDA0002617357430000121
Figure BDA0002617357430000122
when node siIn active state (including cluster and non-cluster nodes), xi1, otherwise xi＝0。
The interval energy balance model comprises the following contents: suppose an ith node siThe residual energy of
Figure BDA0002617357430000123
The number of nodes in the u-th area is nuAnd u is 1,2, …, k, and we take the average remaining energy of each region as the average energy of the region, so the average energy of the u-th region is:
Figure BDA0002617357430000124
wherein the content of the first and second substances,
Figure BDA0002617357430000125
Figure BDA0002617357430000126
the maximum value S (n) of the average energy of the regions is found by calculating the average energy of k regionsu)maxAnd a minimum value S (n)u)minThen, the interval span of the network can be obtained by the calculation formula of the interval
Figure BDA0002617357430000127
Wherein the content of the first and second substances,
Figure BDA0002617357430000128
Figure BDA0002617357430000129
the smaller the interval span, the better the balance of the network.
The local search activation mechanism includes the following: for the multi-objective optimization problem, the hyper-volume of the Pareto optimal solution set X obtained by calculation is defined as:
Figure BDA0002617357430000131
wherein x isrefIs a reference point; λ is the Leeberg measure;
Figure BDA0002617357430000137
is the interval Pateto dominance relation;
the algorithm determines whether to activate a local search mechanism by using the change degree of the super-volume of the front generation and the back generation of the population, and the main process of the local search mechanism activation is as follows:
first, the general formula
Figure BDA0002617357430000132
Calculating the hyper-volume of each generation of non-dominated solution set; then, the difference between the two previous generations of super volumes is calculated: deltat＝|Ht-Ht-1|；
Finally, setting a threshold interval for the over-volume change value
Figure BDA0002617357430000133
Wherein the content of the first and second substances,ξ∈(0,1)，
Figure BDA0002617357430000134
when in use
Figure BDA0002617357430000135
When the local search link is activated; the purpose of setting the lower limit of the threshold value is to prevent the local search from being repeatedly activated due to small change of the super volume in the later period of evolution, thereby saving computing resources; because of ΔtThe absolute value of the difference value of the super volumes of the two generations of populations is obtained, so that the purpose of the upper threshold is to prevent the super volume of the next generation from being far smaller than that of the previous generation, thereby influencing the quality of a solution set.
The local search initial population establishment comprises the following contents: in addition to the above, for the interval optimization problem of research uncertainty, uncertainty of an individual is also used as an evaluation index of individual quality, and the smaller uncertainty of an individual is, the better the performance of the individual is, in view of this, the algorithm takes the individual ultra-volume contribution and uncertainty as evaluation indexes of individual performance, and the quality of the individual x is defined as:
Figure BDA0002617357430000136
in the formula, CH (x), I (x) respectively represent the interval hyper-volume and uncertainty of an individual x, alpha and beta respectively represent weights for adjusting the influence degree of the hyper-volume and the uncertainty on the performance of the individual, the larger the IP value is, the higher the performance of the individual is, the individual with the highest performance is found out through the formula, then the individual is taken as a population center, N individuals closest to the individual are found out to be used as an initial population of local search, and N is the size of the local search population.
The local search strategy includes the following: the initial population is searched locally to give a specific local search strategy, and the initial population searched locally is first used to generate N size through genetic operations such as crossing, mutation and the likelocalFollowed by comparing the individuals in the population with the individuals in the parent population, respectively, if there is a super population of individuals in the parent populationVolume less than the individual and uncertainty greater than the individual, then N will belocalThe individual in the population is reserved in an offspring population, finally, the offspring population and a parent population are combined, the ordering is carried out through the ultravolume contribution of the individual, and the first N is selectedlocalAnd when the local search is terminated, adding the optimal solution set obtained by the local search into the global search population, and continuing to perform the global search.
The invention also provides a simulation experiment, which comprises the following contents:
the model environment is realized by adopting MatlabR2016b programming on an Intel (R) core (TM) i5-5200HCPU @2.2GHZ computer and a Windows10 operating system, and the feasibility and the effectiveness of the model provided by the text are verified by adopting the ultra-volume and the optimal solution set as evaluation indexes to be compared with a deterministic model.
The simulation environment and algorithm parameters are shown in table 1.
TABLE 1 simulation Environment and Algorithm parameters
Figure BDA0002617357430000141
Figure BDA0002617357430000151
And (3) analyzing an ultra-volume result: as can be seen from fig. 2, the hyper-volume obtained by the multi-objective optimization model based on intervals studied herein is also an interval value, and the interval hyper-volume gradually increases as the evolution algebra increases. In the comparison with the conventional deterministic model, it can be seen that the range of the deterministic model is between the upper and lower bounds of the range of the deterministic model. The experiment shows that the interval model studied in the text effectively optimizes the network performance, and the model can be applied to uncertain environments more widely than the traditional deterministic model.
And (4) analyzing the result of the optimal solution set: as can be seen in FIG. 3, whether or not it is presented hereinThe interval multi-objective optimization model is also a traditional deterministic model, and can realize the optimization of network performance and the normalization of processing results in the evolution process(C+E+G)/3The reduction proves that the uncovered rate, the energy consumption and the energy balance degree of the network are reduced to a certain extent along with the increase of evolution algebra, so that the performance of the network is improved. The comparison between the interval model and the deterministic model also shows that the interval multi-objective optimization model provided by the invention has certain improvement on the performance of the network, and compared with the traditional deterministic model, the interval model is more suitable for practical application.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

Claims (7)

1. An underwater wireless sensor network scheduling optimization model based on interval multi-objective is characterized in that: the method comprises a UWSNs interval multi-target scheduling optimization model and an interval multi-target cultural gene algorithm;
the UWSNs interval multi-target scheduling optimization model comprises a sensor interval coverage model, an interval energy consumption model and an interval energy balance model; the interval multi-target cultural gene algorithm comprises a local search activation mechanism, local search initial population establishment and a local search strategy;
setting a three-dimensional underwater monitoring area as V (L multiplied by L), and setting a target point T (T)1,t2,t3...tm) Where m is the number of target points, the coordinate t of the jth target pointj＝(xtj,ytj,ztj) (ii) a Sensor node S ═ S1,s2,s3...sn) Wherein n is the number of nodes, the ith node can generate offset due to the influence of uncertain factors such as ocean current, marine life and the like, so that the position of the ith node has uncertainty, and the coordinate of the ith node is represented by an interval, namely the ith nodeIs noted as
Figure FDA0002617357420000011
The distance between the ith node and the jth target point is therefore recorded as
Figure FDA0002617357420000012
Wherein the content of the first and second substances,
Figure FDA0002617357420000013
Figure FDA0002617357420000014
in the monitoring area, three nodes, namely a cluster head node, an intra-cluster node and a sink node for summarizing cluster head node data exist; each node has four states, -1 (dead node), 0 (dormant cluster node), 1 (active cluster node) and 2 (cluster head node);
the main process is as follows: randomly generating a certain number of cluster head nodes in a monitoring area, selecting the nearest cluster head by other nodes according to the lower bound of the distance to add the cluster head into the cluster, and sending data to the cluster head of the cluster, dividing the cluster head into different levels by the lower bound of the distance by all the cluster head nodes through calculating the distance between the cluster head nodes and the sink node, sending the data to the cluster head close to the sink by the cluster head far from the sink, and directly sending the data to the sink node by the cluster head close to the sink, namely sending the data received by the cluster head nodes to the sink node by a method of selecting single hop or multiple hops through the distance.
2. The UWSNs interval multi-objective scheduling optimization model of claim 1, wherein the sensor interval coverage model comprises the following:
when the distance between the target point and the sensor node is smaller than the sensing radius of the node, the target point is considered to be covered by the network, that is, when the sensing radius of the node s is r, the probability that the target point t is covered by the node s is as follows:
Figure FDA0002617357420000021
wherein d (s, t) is the Euclidean distance between the target point t and the node s,
in the sensor node set S ═ (S)1,s2,s3...sn) From the state of the node, the ith node s can be definediThe working state of (2):
Figure FDA0002617357420000022
wherein a isiE { -1,0,1,2}, i.e. xiThe node is considered to be in an operating state when the node is 1, and otherwise, the node is not operated, i is 1,2, …, n;
the jth target point tjBy the ith node siThe probability of coverage can be defined as
Figure FDA0002617357420000023
Wherein:
Figure FDA0002617357420000024
Figure FDA0002617357420000025
the probability that the jth target point is covered by the network can be defined as
Figure FDA0002617357420000026
Wherein
Figure FDA0002617357420000031
Because of the minimization problem, the problem is translated into minimizing uncovered rate;
network uncovered rate is
Figure FDA0002617357420000032
Wherein
Figure FDA0002617357420000033
Figure FDA0002617357420000034
3. The UWSNs interval multi-objective scheduling optimization model according to claim 1, wherein the interval energy consumption model comprises the following:
the energy consumption for transferring data between two nodes can be expressed as:
Ei,j＝A(d)×l
wherein d is a node si,sjThe euclidean distance between the two nodes, l is the size of a transmission data packet and the unit is bit, and a (d) represents the energy attenuation of the data packet when the underwater transmission distance is d, and can be represented as follows:
A(d)＝dηad
wherein η is an energy spread factor, and η ═ 2,
Figure FDA0002617357420000035
a (f) is the absorption coefficient, which can be expressed as:
Figure FDA0002617357420000036
wherein f is the carrier frequency;
each node needs to send data to other nodes, needs to calculate the euclidean distance between itself and other nodes, where this distance is also an interval value in an uncertain complex environment, and the upper and lower bounds of energy consumption for transmitting data by all the nodes can be expressed as:
iE＝A(dmin)×l
Figure FDA0002617357420000041
wherein d ismin、dmaxThe lower bound and the upper bound of the distance between the node and the data receiving node are respectively; therefore, the energy consumption of the whole network
Figure FDA0002617357420000042
Equal to the sum of the energy consumptions of all the working nodes, wherein:
Figure FDA0002617357420000043
Figure FDA0002617357420000044
when node siIn active state (including cluster and non-cluster nodes), xi1, otherwise xi＝0。
4. The UWSNs interval multi-objective scheduling optimization model of claim 1, wherein the interval energy scale model comprises the following:
suppose an ith node siThe residual energy of
Figure FDA0002617357420000045
The number of nodes in the u-th area is nuAnd u is 1,2, …, k, and we take the average remaining energy of each region as the average energy of the region, so the average energy of the u-th region is:
Figure FDA0002617357420000046
wherein the content of the first and second substances,
Figure FDA0002617357420000047
Figure FDA0002617357420000048
the maximum value S (n) of the average energy of the regions is found by calculating the average energy of k regionsu)maxAnd a minimum value S (n)u)minThen, the interval span of the network can be obtained by the calculation formula of the interval
Figure FDA0002617357420000049
Wherein the content of the first and second substances,
Figure FDA00026173574200000410
Figure FDA0002617357420000051
the smaller the interval span, the better the balance of the network.
5. The UWSNs interval multi-objective scheduling optimization model according to claim 1, wherein the local search activation mechanism comprises the following:
for the multi-objective optimization problem, the hyper-volume of the Pareto optimal solution set X obtained by calculation is defined as:
Figure FDA0002617357420000052
wherein x isrefIs prepared from radix GinsengExamination points; λ is the Leeberg measure; [ solution ] A method for producing a polymerIPIs the interval Pateto dominance relation;
the algorithm determines whether to activate a local search mechanism by using the change degree of the super-volume of the front generation and the back generation of the population, and the main process of the local search mechanism activation is as follows:
first, the general formula
Figure FDA0002617357420000053
Calculating the hyper-volume of each generation of non-dominated solution set; then, the difference between the two previous generations of super volumes is calculated: deltat＝|Ht-Ht-1|；
Finally, setting a threshold interval for the over-volume change value
Figure FDA0002617357420000054
Wherein the content of the first and second substances,ξ∈(0,1)，
Figure FDA0002617357420000055
when in use
Figure FDA0002617357420000056
When the local search link is activated; the purpose of setting the lower limit of the threshold value is to prevent the local search from being repeatedly activated due to small change of the super volume in the later period of evolution, thereby saving computing resources; because of ΔtThe absolute value of the difference value of the super volumes of the two generations of populations is obtained, so that the purpose of the upper threshold is to prevent the super volume of the next generation from being far smaller than that of the previous generation, thereby influencing the quality of a solution set.
6. The UWSNs interval multi-objective scheduling optimization model according to claim 1, wherein the local search initiation population establishment comprises the following:
in addition to the above, for the interval optimization problem of research uncertainty, uncertainty of an individual is also used as an evaluation index of individual quality, and the smaller uncertainty of an individual is, the better the performance of the individual is, in view of this, the algorithm takes the individual ultra-volume contribution and uncertainty as evaluation indexes of individual performance, and the quality of the individual x is defined as:
Figure FDA0002617357420000061
in the formula, CH (x), I (x) respectively represent the interval hyper-volume and uncertainty of an individual x, alpha and beta respectively represent weights for adjusting the influence degree of the hyper-volume and the uncertainty on the performance of the individual, the larger the IP value is, the higher the performance of the individual is, the individual with the highest performance is found out through the formula, then the individual is taken as a population center, N individuals closest to the individual are found out to be used as an initial population of local search, and N is the size of the local search population.
7. The UWSNs interval multi-objective scheduling optimization model according to claim 1, wherein the local search strategies include the following: the initial population is searched locally to give a specific local search strategy, and the initial population searched locally is first used to generate N size through genetic operations such as crossing, mutation and the likelocalFollowed by comparing the individuals in the population with the individuals in the parent population, respectively, and if the excess volume of an individual present in the parent population is less than the individual and the uncertainty is greater than the individual, then N is calculatedlocalThe individual in the population is reserved in an offspring population, finally, the offspring population and a parent population are combined, the ordering is carried out through the ultravolume contribution of the individual, and the first N is selectedlocalAnd when the local search is terminated, adding the optimal solution set obtained by the local search into the global search population, and continuing to perform the global search.
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