CN113472573A - High-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling - Google Patents

High-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling Download PDF

Info

Publication number
CN113472573A
CN113472573A CN202110733366.XA CN202110733366A CN113472573A CN 113472573 A CN113472573 A CN 113472573A CN 202110733366 A CN202110733366 A CN 202110733366A CN 113472573 A CN113472573 A CN 113472573A
Authority
CN
China
Prior art keywords
node
wireless sensor
establishing
sensor network
wsn
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.)
Granted
Application number
CN202110733366.XA
Other languages
Chinese (zh)
Other versions
CN113472573B (en
Inventor
张磊
唐庭龙
汪方毅
崔文超
龚国强
冉昌艳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Three Gorges University CTGU
Original Assignee
China Three Gorges University CTGU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Three Gorges University CTGU filed Critical China Three Gorges University CTGU
Priority to CN202110733366.XA priority Critical patent/CN113472573B/en
Publication of CN113472573A publication Critical patent/CN113472573A/en
Application granted granted Critical
Publication of CN113472573B publication Critical patent/CN113472573B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a high-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling, which comprises the steps of firstly designing a hybrid wireless sensor network perception model comprising a static node, a mobile node and a relay node; then establishing a wireless sensor network high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like; secondly, providing a high-dimensional multi-target decomposition algorithm based on the co-evolution of the double-ion population; and finally, optimizing the evaluation model by using the proposed algorithm.

Description

High-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling
Technical Field
The invention belongs to the field of wireless sensor networks, and particularly relates to a high-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling.
Background
The wireless sensor network is a multi-hop autonomous network and is widely applied to the fields of environmental monitoring, rescue and disaster relief, military communication and the like. The wireless sensor network has the characteristics of multi-hop transmission, dynamic topology, limited node resources and the like, so that the resource management technology of the wireless sensor network is particularly important. Aiming at the complex high-dimensional multi-objective optimization problem, the excellent algorithm in the field of computational intelligence at present is used for resource scheduling research of the wireless sensor network, and the method has very important practical significance.
Disclosure of Invention
In order to overcome the defects of the prior art,
the invention provides a high-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling, which comprises the following steps: establishing a hybrid network perception model comprising static nodes, mobile nodes and relay nodes, designing a novel WSN high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like, and finally providing a dual-ion population co-evolution high-dimensional multi-target optimization algorithm for optimization of the evaluation model. The method specifically comprises the following steps:
step 1, establishing a WSN (wireless sensor network) perception model of a hybrid wireless sensor network;
step 2, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network;
and 3, optimizing the WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network.
In step 1, the WSN perception model of the hybrid wireless sensor network comprises a static node, a mobile node and a relay node, and the communication radius of the static node is set to be rsThe communication radius of the mobile node is rmThe communication radius of the relay node is R.
The step 2 comprises the following steps:
step 2-1, establishing a coverage rate evaluation function;
step 2-2, establishing a connectivity evaluation function;
step 2-3, establishing an energy consumption evaluation function;
step 2-4, establishing a life cycle evaluation function;
step 2-5, establishing a time delay evaluation function;
2-6, establishing an economic evaluation function;
and 2-7, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network.
Step 2-1 comprises: discretizing a two-dimensional plane area to be monitored into a grid shape, and calculating the total number N of the gridstotal(ii) a Calculating the total number of grids covered by all wireless sensor nodes in the working state of the WSN (wireless sensor network)
Figure BDA0003140527850000021
i is 1,2, …, N, N is the number of working nodes,
Figure BDA0003140527850000022
the number of grids covered by the ith node;
establishing a coverage rate evaluation function CrComprises the following steps:
Figure BDA0003140527850000023
step 2-2 comprises: calculating the distances between all working wireless sensor nodes and recording the distances as a distance matrix D; then, the magnitude relation between each element in the distance matrix D and the node perception radius r is judged, so that the communication state between nodes in the working state, namely a communication matrix L is established, wherein DijIs the distance between node i and node j, lijThe relationship between the node i and the node j is shown in formula (2):
Figure BDA0003140527850000024
Figure BDA0003140527850000025
the established connection matrix L is a symmetric matrix, and elements in the symmetric matrix are not 0, namely 1; the WSN formed by all the working nodes has connectivity which must satisfy the following conditions:
condition 1: rank (l) is equal to N,
condition 2: sum (L) is more than or equal to 2(N-1),
wherein rank (L) is the rank of the matrix L, and sum (L) is the sum of all elements of the matrix L;
the condition 1 indicates that the rank of the connectivity matrix L is N, and any node is ensured to have a node capable of communicating with the node; the condition 2 represents that the sum of all elements of the connectivity matrix is more than or equal to 2(N-1), and at least one other node capable of communicating with any node is ensured to exist in any node;
establishing a connectivity evaluation function C as follows:
max C＝max Rank(L) (3)
Figure BDA0003140527850000031
the step 2-3 comprises the following steps: establishing an energy consumption evaluation function E as follows:
Figure BDA0003140527850000032
Figure BDA0003140527850000033
Figure BDA0003140527850000034
in the formula, EtEnergy consumption for transmitting data per unit time, ErEnergy consumption for receiving data per unit time, EiIs the energy consumption of the ith node per unit time, tiAmount of data sent for the ith node, EecIs an electricityEnergy consumption, AecTo amplify the energy consumption, diIs the communication distance between the ith node and the next hop node, lambda is the data loss coefficient, riThe amount of data received for the ith node.
The steps 2-4 comprise: lifetime L of the ith nodeiComprises the following steps:
Figure BDA0003140527850000035
Es＝Eini-t×Ei (9)
in the formula, EsResidual energy for wireless sensor nodes, EiniIs the initial energy of the wireless sensor node, t is the working time of the node, EiEnergy consumption of the ith node in unit time;
establishing life period evaluation function LnetComprises the following steps:
Lnet＝min Li (10)。
the steps 2-5 comprise: establishing a time delay evaluation function RtComprises the following steps:
Figure BDA0003140527850000041
where d is the communication path distance,
Figure BDA0003140527850000042
is the duty cycle of the ith node, CdFor communication time, DPdIs the packet duration.
The steps 2-6 comprise: establishing a WSN economic evaluation function EcComprises the following steps:
Figure BDA0003140527850000043
Figure BDA0003140527850000044
Figure BDA0003140527850000045
in the formula, wfAnd wdRepresents a weight coefficient, LnetFor hybrid wireless sensor network WSN lifetime, Costf(si) For the ith sensor node siFinancial cost of P(s)i) For the ith sensor node siThe probability of normal work in a designated area, phi is a specific constant of the wireless sensor;
Figure BDA0003140527850000046
in order to be at a financial cost,
Figure BDA0003140527850000047
to the deployment cost;
the steps 2-7 comprise: the method comprises the following steps of establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network:
F(X)＝(f1(X),f2(X),f3(X),f4(X),f5(X),f6(X))＝(Cr,C,E,Lnet,Rt,Ec) (16)
wherein F (X) is an objective function, and X is (X)1,…,xi,…,xN) Is a variable, and is a function of,
Figure BDA0003140527850000048
parameters of the ith wireless sensor node comprise position coordinates of the node
Figure BDA0003140527850000049
Node operating time ratio
Figure BDA00031405278500000410
Whether or not it is in working state deltaiNode operating rule deltai1, otherwise δi＝0；f1(X),f2(X),f3(X),f4(X),f5(X),f6(X) corresponds to 6 functions C, respectivelyr,C,E,Lnet,Rt,Ec。
The step 3 comprises the following steps:
step 3-1, randomly generating an initial population with the scale of P, dividing the population into two, namely an anion population A and a cation population C, and setting the maximum evolution generation Gmax：
Figure BDA0003140527850000051
Wherein the content of the first and second substances,
Figure BDA0003140527850000052
the p/2 th row of A is the p/2 th anion individual;
Figure BDA0003140527850000053
the p/2 th row of C is the p/2 th cation individual;
Figure BDA0003140527850000054
is an element of row p/2 and column N of the anion population A;
Figure BDA0003140527850000055
is an element of row p/2 and column N of cation population C;
and 3-2, updating the liquid state of the ion population based on cooperative guidance of the isotropic optimal ions and the anisotropic optimal ions:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t))+rand()×(Abest(t)-Ai(t))， (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t))+rand()×(Cbest(t)-Ci(t))， (18)
where t is the number of evolutionary iterations, Ai(t) represents the ith anion, Ci(t) represents the ith cationCbest (t) represents the optimum cation in the t generation, abest (t) represents the optimum anion in the t generation, and rand () is a random number between 0 and 1;
and 3-3, updating the solid state of the ion population based on heterosexual optimal ion guidance:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t)) (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t)) (18)；
step 3-4: carrying out high-dimensional multi-target individual selection operation, and establishing a fitness evaluation function which is adaptively adjusted, wherein the fitness evaluation function is shown as a formula (19):
Figure BDA0003140527850000056
Figure BDA0003140527850000057
where t is the number of evolutionary iterations, GmaxFor maximum number of evolutionary iterations, X is an individual in the anion population a or the cation population C; fit (X) is a fitness function of the individual X; θ (X) is the angle between individual Y and the smallest angle X;
Figure BDA0003140527850000061
is fiNormalized function of (X), let fiValue of (X) to [0,1](ii) a d (X) is a vector
Figure BDA0003140527850000062
The Euclidean distance of (c);
Figure BDA0003140527850000063
is the normalized target vector for individual X, as shown in equation (21):
Figure BDA0003140527850000064
Figure BDA0003140527850000065
wherein cos (θ (X)) represents an angle between the individual X and the individual Y whose angle is the smallest, and d (X) represents objective function information of the individual X;
step 3-5, judging whether the maximum evolution algebra G is reachedmaxAnd if so, outputting the optimal solution in the population, otherwise, turning to the step 3-2.
Has the advantages that: the invention provides a novel high-dimensional multi-target service quality evaluation model of a wireless sensor network, which comprises the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like, so that the performance requirement of the wireless sensor network can be more comprehensively reflected. The method comprises an ion population liquid state updating mode based on isotropic optimal ion and anisotropic optimal ion cooperative guidance, an ion population solid state updating method based on anisotropic optimal ion guidance and a self-adaptive evaluation function method, and effectively improves the algorithm convergence speed, so that the resource allocation scheme of the wireless sensor network can be quickly adjusted, and the overall performance of the network is enhanced.
Drawings
The foregoing and other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a schematic diagram of a hybrid network-aware model.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
As shown in fig. 2, the present invention provides a high-dimensional multi-objective collaborative optimization method for resource scheduling of a wireless sensor network, including:
step 1: establishing a WSN perception model of the hybrid wireless sensor network,including a large number of static nodes, a modest number of mobile nodes, and relay nodes. Setting the communication radius of a static node as rsThe communication radius of the mobile node is rmAnd if the communication radius of the relay node is R, the hybrid WSN sensing model is as shown in fig. 1.
(step 2, according to the step 1, establishing a WSN high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like.)
Step 2: in order to comprehensively evaluate the performance of the WSN, a WSN high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like is established.
Step 2.1: and establishing a coverage rate evaluation function. Firstly, discretizing a two-dimensional plane area to be monitored into a grid shape, wherein the precision can be determined according to actual needs, such as 1m multiplied by 1 m; then, the total number N of grids in the whole monitoring area is calculatedtotal(ii) a Secondly, the total number of grids covered by all the wireless sensor nodes in the working state of the WSN is calculated
Figure BDA0003140527850000071
i is 1,2, …, N, N is the number of working nodes,
Figure BDA0003140527850000072
the number of grids covered for each node; establishing a WSN coverage evaluation function CrComprises the following steps:
Figure BDA0003140527850000073
the position of the static node is fixed, and the coverage rate of the WSN is only related to the positions of the static node and the mobile node and the number of nodes in the working state.
Step 2.2: and establishing a connectivity evaluation function. Firstly, calculating the distances between all working wireless sensor nodes, and recording the distances as a distance matrix D; then, the size relation between each element in the distance matrix D and the node perception radius r is judged, and the nodes in the working state are establishedThe connected state, i.e., the connected matrix L. Where N is the number of nodes in operation, dijIs the distance between node i and node j, lijAs shown in formula (2).
Figure BDA0003140527850000074
Figure BDA0003140527850000081
The established connectivity matrix L is a symmetric matrix and the elements therein are not 0, i.e. 1. Further analysis can show that the WSN formed by all the working nodes has connectivity which must satisfy the following conditions: 1) rank (l) ═ N; 2) sum (L) ≧ 2 (N-1). The condition 1 indicates that the rank of the connectivity matrix L is N, and it is ensured that any node has a node capable of communicating with the node. Condition 2 indicates that the sum of all elements of the connectivity matrix is greater than or equal to 2(N-1), ensuring that there is at least one other node with which any node can communicate. Establishing a WSN connectivity evaluation function C as follows:
max C＝max Rank(L) (3)
Figure BDA0003140527850000082
the connectivity of a WSN is largely determined by the location of the stationary and mobile nodes in an operational state.
Step 2.3: and establishing an energy consumption evaluation function. The energy consumption of the wireless sensor node is mainly spent in the data transmission and data receiving processes, and therefore, the WSN energy consumption evaluation function E is established as follows:
Figure BDA0003140527850000083
Figure BDA0003140527850000084
Figure BDA0003140527850000085
in the formula, EtEnergy consumption for transmitting data per unit time, ErEnergy consumption for receiving data per unit time, EiIs the energy consumption of the ith node in unit time, N is the number of working nodes, tiAmount of data sent for the ith node, EecFor electrical energy consumption, AecTo amplify the energy consumption, diIs the communication distance between the ith node and the next hop node, lambda is the data loss coefficient, riThe amount of data received for the ith node. The energy consumption of the WSN is mainly related to the node communication distance and the amount of data carried per unit time.
Step 2.4: and establishing a life cycle evaluation function. The WSN life cycle is considered to be terminated when certain nodes of the WSN die prematurely, so that the network cannot be connected and isolated areas appear, and normal communication among the nodes is finally influenced. The lifetime of an individual node is generally determined by the remaining energy E of the nodesAnd the amount of data t carried per unit timei+riOn the other hand, the lifetime of the node is:
Figure BDA0003140527850000091
in the formula, i is 1,2, …, and N is the number of wireless sensor nodes in operation.
Es＝Eini-t×Ei (9)
In the formula, EiniIs the initial energy of the wireless sensor node, t is the working time of the node, EiIs the energy consumption of the ith node in unit time.
Therefore, the life cycle of the WSN is defined as the life cycle of the node which is dead firstly due to the exhaustion of electric quantity, and a life cycle evaluation function L is establishednetComprises the following steps:
Lnet＝min Li,i＝1,2,…,N (10)
the lifetime of a WSN is mainly related to the node communication distance, working time and the amount of data per unit time.
Step 2.5: and establishing a time delay evaluation function. In order to reduce the WSN delay, the node wake-up time and the number of working nodes should be increased appropriately, so that there are more and faster communication paths for information transmission. The WSN delay is mainly focused on multi-hop transmission data, i.e., multi-hop relay delay. For this purpose, a WSN time delay evaluation function R is establishedtComprises the following steps:
Figure BDA0003140527850000092
wherein d is the communication path distance, r is the node communication radius,
Figure BDA0003140527850000093
is the duty cycle of the ith node, CdFor communication time, DPdIs the packet duration. The delay of the WSN is mainly related to the distance between nodes and the working time and duty ratio of the nodes.
Step 2.6: and establishing an economic evaluation function. For a single wireless sensor node, the economic cost of the single wireless sensor node mainly comprises the financial cost EfAnd deployment cost Ed. The financial cost is mainly related to purchase cost, the deployment cost is mainly related to the contribution rate of the wireless sensor nodes to the coverage in the specified area, and the larger the contribution rate is, the lower the average cost is. For this purpose, a WSN economic evaluation function E is establishedcComprises the following steps:
Figure BDA0003140527850000101
Figure BDA0003140527850000102
Figure BDA0003140527850000103
in the formula, wfAnd wdRepresents a weight coefficient, LnetFor hybrid wireless sensor network WSN lifetime, Costf(si) For the ith sensor node siFinancial cost of P(s)i) For the ith sensor node siAnd phi is a specific constant of the wireless sensor in the probability of normal work in the designated area. The economics of a WSN are primarily related to the life-time and operating-time ratio of the nodes.
Step 2.7: the method comprises the following steps of establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network:
F(X)＝(f1(X),f2(X),f3(X),f4(X),f5(X),f6(X))＝(Cr,C,E,Lnet,Rt,Ec) (15)
wherein F (X) is an objective function, and X is (X)1,…,xi,…,xN) Is a variable, N is the number of nodes in the WSN,
Figure BDA0003140527850000104
parameters for the ith wireless sensor node, including node location
Figure BDA0003140527850000105
Node operating time ratio
Figure BDA0003140527850000106
Whether or not it is in working state deltai(node operating rule δi1, otherwise δi＝0)。
(step 3 according to step 2, a high-dimensional multiobjective optimization algorithm for evaluation model optimization is proposed)
And step 3: in order to solve the model established by the formula (15), a dual-ion population co-evolution high-dimensional multi-target optimization algorithm for wireless sensor network resource scheduling is provided, and meanwhile, the coverage rate, the connectivity, the energy consumption, the life cycle, the time delay and the economy are optimized.
Step 3.1: randomly generating an initial population with the scale of P, dividing the population into two, namely an anion population A and a cation population AIon population C. Setting maximum evolution algebra Gmax。
Figure BDA0003140527850000107
Step 3.2: and updating the liquid state of the ion population based on cooperative guidance of the isotropic optimal ions and the anisotropic optimal ions.
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t))+rand()×(Abest(t)-Ai(t)) (16)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t))+rand()×(Cbest(t)-Ci(t)) (17)
Where t is the number of evolutionary iterations, Ai(t) represents the ith anion, Ci(t) represents the ith cation, cbest (t) represents the optimum cation in the t generation, abest (t) represents the optimum anion in the t generation, and rand () is a random number between 0 and 1.
Step 3.3: and updating the solid state of the ion population based on heterosexual optimal ion guidance.
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t)) (18)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t)) (19)
Step 3.4: and (3) carrying out high-dimensional multi-target individual selection operation, and establishing a fitness evaluation function of self-adaptive adjustment, as shown in a formula (20). The function mainly aims at the population distribution in the early stage of population evolution and mainly aims at the population convergence in the later stage of evolution, so that the diversity and the convergence of the algorithm are effectively balanced.
Figure BDA0003140527850000111
Figure BDA0003140527850000112
Where t is the number of evolutionary iterations, GmaxFor maximum number of evolutionary iterations, X is the individual in either the anion population a or the cation population C.
Figure BDA0003140527850000113
Is the normalized target vector for individual X, as shown in equation (22).
Figure BDA0003140527850000114
Figure BDA0003140527850000115
Note that X and Y take values in both a population or C population. Wherein cos (θ (X)) represents the angle between the individual X and the individual Y with the smallest angle, and measures the distribution of the algorithm, and d (X) represents the objective function information of the individual X, and measures the convergence of the algorithm.
Step 3.5: judging whether the maximum evolution algebra G is reachedmaxIf so, outputting the optimal solution in the population, otherwise, turning to the step 3.2.
The invention provides a high-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling, and a plurality of methods and approaches for implementing the technical scheme, and the above description is only a preferred embodiment of the invention, and it should be noted that, for those skilled in the art, a plurality of improvements and modifications can be made without departing from the principle of the invention, and these improvements and modifications should also be regarded as the protection scope of the invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (10)

1. The high-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling is characterized by comprising the following steps of:
step 1, establishing a WSN (wireless sensor network) perception model of a hybrid wireless sensor network;
step 2, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network;
and 3, optimizing the WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network.
2. The method according to claim 1, wherein in step 1, the WSN perception model of the hybrid wireless sensor network comprises a static node, a mobile node and a relay node, and the communication radius of the static node is set to be rsThe communication radius of the mobile node is rmThe communication radius of the relay node is R.
3. The method of claim 2, wherein step 2 comprises:
step 2-1, establishing a coverage rate evaluation function;
step 2-2, establishing a connectivity evaluation function;
step 2-3, establishing an energy consumption evaluation function;
step 2-4, establishing a life cycle evaluation function;
step 2-5, establishing a time delay evaluation function;
2-6, establishing an economic evaluation function;
and 2-7, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network.
4. The method of claim 3, wherein step 2-1 comprises: discretizing a two-dimensional plane area to be monitored into a grid shape, and calculating the total number N of the gridstotal(ii) a Calculating the total number of grids covered by all wireless sensor nodes in the working state of the WSN (wireless sensor network)
Figure FDA0003140527840000011
i is 1,2, …, N, N is the number of working nodes,
Figure FDA0003140527840000012
the number of grids covered by the ith node;
establishing a coverage rate evaluation function CrComprises the following steps:
Figure FDA0003140527840000013
5. the method of claim 4, wherein step 2-2 comprises: calculating the distances between all working wireless sensor nodes and recording the distances as a distance matrix D; then, the magnitude relation between each element in the distance matrix D and the node perception radius r is judged, so that the communication state between nodes in the working state, namely a communication matrix L is established, wherein DijIs the distance between node i and node j, lijThe relationship between the node i and the node j is shown in formula (2):
Figure FDA0003140527840000021
Figure FDA0003140527840000022
the established connection matrix L is a symmetric matrix, and elements in the symmetric matrix are not 0, namely 1; the WSN formed by all the working nodes has connectivity which must satisfy the following conditions:
condition 1: rank (l) is equal to N,
condition 2: sum (L) is more than or equal to 2(N-1),
wherein rank (L) is the rank of the matrix L, and sum (L) is the sum of all elements of the matrix L;
the condition 1 indicates that the rank of the connectivity matrix L is N, and any node is ensured to have a node capable of communicating with the node; the condition 2 represents that the sum of all elements of the connectivity matrix is more than or equal to 2(N-1), and at least one other node capable of communicating with any node is ensured to exist in any node;
establishing a connectivity evaluation function C as follows:
max C＝max Rank(L) (3)
Figure FDA0003140527840000023
6. the method of claim 5, wherein steps 2-3 comprise: establishing an energy consumption evaluation function E as follows:
Figure FDA0003140527840000024
Figure FDA0003140527840000025
Figure FDA0003140527840000026
in the formula, EtEnergy consumption for transmitting data per unit time, ErEnergy consumption for receiving data per unit time, EiIs the energy consumption of the ith node per unit time, tiAmount of data sent for the ith node, EecFor electrical energy consumption, AecTo amplify the energy consumption, diIs the communication distance between the ith node and the next hop node, lambda is the data loss coefficient, riThe amount of data received for the ith node.
7. The method of claim 6, wherein steps 2-4 comprise: lifetime L of the ith nodeiComprises the following steps:
Figure FDA0003140527840000031
Es＝Eini-t×Ei (9)
in the formula, EsResidual energy for wireless sensor nodes, EiniIs the initial energy of the wireless sensor node, t is the working time of the node, EiEnergy consumption of the ith node in unit time;
establishing life period evaluation function LnetComprises the following steps:
Lnet＝min Li (10)。
8. the method of claim 7, wherein steps 2-5 comprise: establishing a time delay evaluation function RtComprises the following steps:
Figure FDA0003140527840000032
where d is the communication path distance,
Figure FDA0003140527840000033
is the duty cycle of the ith node, CdFor communication time, DPdIs the packet duration.
9. The method of claim 8, wherein steps 2-6 comprise: establishing a WSN economic evaluation function EcComprises the following steps:
Figure FDA0003140527840000034
Figure FDA0003140527840000035
Figure FDA0003140527840000036
in the formula, wfAnd wdRepresenting weight coefficients，LnetFor hybrid wireless sensor network WSN lifetime, Costf(si) For the ith sensor node siFinancial cost of P(s)i) For the ith sensor node siThe probability of normal work in a designated area, phi is a specific constant of the wireless sensor;
Figure FDA0003140527840000041
in order to be at a financial cost,
Figure FDA0003140527840000042
to the deployment cost;
the steps 2-7 comprise: the method comprises the following steps of establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network:
F(X)＝(f1(X),f2(X),f3(X),f4(X),f5(X),f6(X))＝(Cr,C,E,Lnet,Rt,Ec) (16)
wherein F (X) is an objective function, and X is (X)1,…,xi,…,xN) Is a variable, and is a function of,
Figure FDA0003140527840000043
parameters of the ith wireless sensor node comprise position coordinates of the node
Figure FDA0003140527840000044
Node operating time ratio
Figure FDA0003140527840000045
Whether or not it is in working state deltaiNode operating rule deltai1, otherwise δi＝0；f1(X),f2(X),f3(X),f4(X),f5(X),f6(X) corresponds to 6 functions C, respectivelyr,C,E,Lnet,Rt,Ec。
10. The method of claim 9, wherein step 3 comprises:
step 3-1, randomly generating an initial population with the scale of P, dividing the population into two, namely an anion population A and a cation population C, and setting the maximum evolution generation Gmax：
Figure FDA0003140527840000046
Wherein the content of the first and second substances,
Figure FDA0003140527840000047
the p/2 th row of A is the p/2 th anion individual;
Figure FDA0003140527840000048
the p/2 th row of C is the p/2 th cation individual;
Figure FDA0003140527840000049
is an element of row p/2 and column N of the anion population A;
Figure FDA00031405278400000410
is an element of row p/2 and column N of cation population C;
and 3-2, updating the liquid state of the ion population based on cooperative guidance of the isotropic optimal ions and the anisotropic optimal ions:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t))+rand()×(Abest(t)-Ai(t))， (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t))+rand()×(Cbest(t)-Ci(t))， (18)
where t is the number of evolutionary iterations, Ai(t) represents the ith anion, Ci(t) represents the ith cation, Cbest (t) represents the optimum cation in the t generation, abest (t) represents the optimum cation in the t generationMost preferred anion, rand () is a random number between 0 and 1;
and 3-3, updating the solid state of the ion population based on heterosexual optimal ion guidance:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t)) (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t)) (18)；
step 3-4: carrying out high-dimensional multi-target individual selection operation, and establishing a fitness evaluation function which is adaptively adjusted, wherein the fitness evaluation function is shown as a formula (19):
Figure FDA0003140527840000051
Figure FDA0003140527840000052
where t is the number of evolutionary iterations, GmaxFor maximum number of evolutionary iterations, X is an individual in the anion population a or the cation population C; fit (X) is a fitness function of the individual X; θ (X) is the angle between individual Y and the smallest angle X;
Figure FDA0003140527840000053
is fiNormalized function of (X), let fiValue of (X) to [0,1](ii) a d (X) is a vector
Figure FDA0003140527840000054
The Euclidean distance of (c);
Figure FDA0003140527840000055
is the normalized target vector for individual X, as shown in equation (21):
Figure FDA0003140527840000056
Figure FDA0003140527840000057
wherein cos (θ (X)) represents an angle between the individual X and the individual Y whose angle is the smallest, and d (X) represents objective function information of the individual X;
step 3-5, judging whether the maximum evolution algebra G is reachedmaxAnd if so, outputting the optimal solution in the population, otherwise, turning to the step 3-2.
CN202110733366.XA 2021-06-30 2021-06-30 High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling Active CN113472573B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110733366.XA CN113472573B (en) 2021-06-30 2021-06-30 High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110733366.XA CN113472573B (en) 2021-06-30 2021-06-30 High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling

Publications (2)

Publication Number Publication Date
CN113472573A true CN113472573A (en) 2021-10-01
CN113472573B CN113472573B (en) 2022-06-28

Family

ID=77874213

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110733366.XA Active CN113472573B (en) 2021-06-30 2021-06-30 High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling

Country Status (1)

Country Link
CN (1) CN113472573B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114139459A (en) * 2021-12-30 2022-03-04 中国地质大学（武汉） Wireless sensor configuration optimization method based on constrained multi-objective optimization algorithm
CN114666880A (en) * 2022-03-16 2022-06-24 中南大学 Method for reducing end-to-end delay in delay-sensitive wireless sensor network

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102014398A (en) * 2010-09-21 2011-04-13 上海大学 Optimal deployment method of large-scale industrial wireless sensor network based on differential evolution algorithm
US20110261738A1 (en) * 2010-04-27 2011-10-27 International Business Machines Corporation Adaptive wireless sensor network and method of routing data in a wireless sensor network
CN102625324A (en) * 2012-03-08 2012-08-01 上海大学 Wireless optical fiber sensor network deployment method based on particle swarm optimization
CN102984731A (en) * 2012-12-06 2013-03-20 重庆工商大学 Adjustment method of heterogeneous wireless sensor network nodes based on multiple covering
WO2017035853A1 (en) * 2015-09-02 2017-03-09 武汉大学 Method for constructing and maintaining energy-saving wireless sensor network
CN108235347A (en) * 2017-12-21 2018-06-29 北京科技大学 A kind of wireless sensor network consumption control method
CN108650030A (en) * 2018-05-07 2018-10-12 上海电力学院 The multiple convergence node dispositions methods of the water surface of underwater wireless sensor network
CN109831792A (en) * 2019-03-11 2019-05-31 中国科学院上海微系统与信息技术研究所 A kind of wireless sensor network topology control method based on multiple-objection optimization
CN111988786A (en) * 2020-06-08 2020-11-24 长江大学 Sensor network covering method and system based on high-dimensional multi-target decomposition algorithm
CN112654050A (en) * 2020-12-21 2021-04-13 江西理工大学 Wireless sensor network optimized coverage scheme of enhanced sparrow search algorithm
CN112711906A (en) * 2020-12-26 2021-04-27 北京工业大学 Optimization method for energy balance problem of wireless sensor network

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110261738A1 (en) * 2010-04-27 2011-10-27 International Business Machines Corporation Adaptive wireless sensor network and method of routing data in a wireless sensor network
CN102014398A (en) * 2010-09-21 2011-04-13 上海大学 Optimal deployment method of large-scale industrial wireless sensor network based on differential evolution algorithm
CN102625324A (en) * 2012-03-08 2012-08-01 上海大学 Wireless optical fiber sensor network deployment method based on particle swarm optimization
CN102984731A (en) * 2012-12-06 2013-03-20 重庆工商大学 Adjustment method of heterogeneous wireless sensor network nodes based on multiple covering
WO2017035853A1 (en) * 2015-09-02 2017-03-09 武汉大学 Method for constructing and maintaining energy-saving wireless sensor network
CN108235347A (en) * 2017-12-21 2018-06-29 北京科技大学 A kind of wireless sensor network consumption control method
CN108650030A (en) * 2018-05-07 2018-10-12 上海电力学院 The multiple convergence node dispositions methods of the water surface of underwater wireless sensor network
CN109831792A (en) * 2019-03-11 2019-05-31 中国科学院上海微系统与信息技术研究所 A kind of wireless sensor network topology control method based on multiple-objection optimization
CN111988786A (en) * 2020-06-08 2020-11-24 长江大学 Sensor network covering method and system based on high-dimensional multi-target decomposition algorithm
CN112654050A (en) * 2020-12-21 2021-04-13 江西理工大学 Wireless sensor network optimized coverage scheme of enhanced sparrow search algorithm
CN112711906A (en) * 2020-12-26 2021-04-27 北京工业大学 Optimization method for energy balance problem of wireless sensor network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
支子聪等: "分层分簇无线传感器网络汇聚层的多目标优化部署", 《传感技术学报》 *
祁育仙等: "混合WSNs中基于多目标优化的覆盖控制算法", 《传感器与微系统》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114139459A (en) * 2021-12-30 2022-03-04 中国地质大学（武汉） Wireless sensor configuration optimization method based on constrained multi-objective optimization algorithm
CN114139459B (en) * 2021-12-30 2024-04-12 中国地质大学（武汉） Wireless sensor configuration optimization method based on constraint multi-objective optimization algorithm
CN114666880A (en) * 2022-03-16 2022-06-24 中南大学 Method for reducing end-to-end delay in delay-sensitive wireless sensor network
CN114666880B (en) * 2022-03-16 2024-04-26 中南大学 Method for reducing end-to-end delay in delay-sensitive wireless sensor network

Also Published As

Publication number Publication date
CN113472573B (en) 2022-06-28

Similar Documents

Publication Publication Date Title
Alazab et al. Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities
CN113472573B (en) High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling
Morsy et al. Proposed energy efficient algorithm for clustering and routing in WSN
CN103052147B (en) A kind of method of energy efficiency multi-stage annular networking based on wireless sense network
Wan et al. AHP based relay selection strategy for energy harvesting wireless sensor networks
Hassanzadeh et al. Towards optimal monitoring in cooperative ids for resource constrained wireless networks
Tukisi et al. Multi-hop PSO based routing protocol for Wireless Sensor Networks with Energy Harvesting
Xiang et al. K-terminal reliability of ad hoc networks considering the impacts of node failures and interference
Choudhary et al. Multi-objective optimization framework complying IEEE 802.15. 6 communication standards for wireless body area networks
He et al. Resource allocation strategy for UAV-assisted non-linear energy harvesting MEC system
CN113163429B (en) Mobile wireless ad hoc network coverage communication method
Song et al. Maximizing packets collection in wireless powered IoT networks with charge-or-data time slots
Jaradat et al. Genetic Algorithm Energy Optimization in 3D WSNs with Different Node Distributions.
Zhu et al. SyRoC: Symbiotic robotics for QoS-aware heterogeneous applications in IoT-edge-cloud computing paradigm
Wibisono et al. A node density-based approach for energy-efficient data gathering protocol in wireless sensor network environments
Ren et al. An opportunistic routing for energy-harvesting wireless sensor networks with dynamic transmission power and duty cycle
Prakash et al. [Retracted] Mixed Linear Programming for Charging Vehicle Scheduling in Large‐Scale Rechargeable WSNs
Liu et al. Learning-based multi-UAV assisted data acquisition and computation for information freshness in WPT enabled space-air-ground PIoT
CN113056029B (en) Energy-limited terminal random access method suitable for low-earth-orbit satellite network
CN109561494B (en) Resource allocation method, device and equipment for hybrid power supply Internet of things
Sun et al. Confidence interval based model predictive control of transmit power with reliability constraint
You et al. Distributed Deep Learning for RIS Aided UAV-D2D Communications in Space-Air-Ground Networks
Ajofoyinbo et al. Directional antennas MAC protocol for energy‐efficient wireless sensor networks with mobile sink
Shehab et al. Self-organizing single-hop wireless sensor network using a genetic algorithm: Longer lifetimes and maximal throughputs
Kori et al. Resource Allocation Scheme for Wireless Sensor Networks: Machine Learning Approach

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
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20211001

Assignee: Hubei Zhigan Space Information Technology Co.,Ltd.

Assignor: CHINA THREE GORGES University

Contract record no.: X2023980051109

Denomination of invention: A high-dimensional multi-objective collaborative optimization method for resource scheduling in wireless sensor networks

Granted publication date: 20220628

License type: Common License

Record date: 20231213