CN113163429B - Mobile wireless ad hoc network coverage communication method - Google Patents

Mobile wireless ad hoc network coverage communication method Download PDF

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CN113163429B
CN113163429B CN202110556350.6A CN202110556350A CN113163429B CN 113163429 B CN113163429 B CN 113163429B CN 202110556350 A CN202110556350 A CN 202110556350A CN 113163429 B CN113163429 B CN 113163429B
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张磊
孙水发
唐庭龙
崔文超
龚国强
汪方毅
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China Three Gorges University CTGU
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Abstract

The invention provides a coverage communication method of a mobile wireless ad hoc network, which comprises the steps of firstly establishing a coverage communication model of the mobile wireless ad hoc network and designing a new network connectivity method; then, an improved self-adaptive ion motion algorithm is provided, and a liquid ion updating method based on metamorphism cooperative optimal guidance and a solid ion updating method based on ranking classification evolution are designed; and finally, optimizing the coverage communication model by using the proposed ion motion algorithm. The method can simultaneously optimize the network coverage rate and connectivity, and effectively improve the overall performance of the mobile wireless autonomous network.

Description

Mobile wireless ad hoc network coverage communication method
Technical Field
The invention belongs to the field of wireless sensor networks, and particularly relates to a coverage communication method for a mobile wireless ad hoc network.
Background
The mobile wireless ad hoc network is a distributed, centerless and multi-hop ad hoc network and is widely applied to the fields of environment monitoring, rescue and relief, military communication and the like. Because the mobile wireless ad hoc network has the characteristics of multi-hop transmission, dynamic topology, limited node resources and the like, the reliability of the network structure is relatively weak and the network structure is easily interfered by various kinds of interference, and the resource management technology of the mobile wireless ad hoc network is particularly important in the trend. Aiming at the complex multi-objective optimization problem, the excellent algorithm in the field of computational intelligence at present is used for resource management problem research of the mobile wireless ad hoc network, and the method has very important practical significance.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problem that the existing algorithm can only optimize a single target and is difficult to use in practice, the invention provides a coverage communication method of a mobile wireless ad hoc network, and aims at establishing a new connectivity guarantee mechanism and designing a coverage communication model of the mobile wireless ad hoc network, wherein the coverage communication model comprises coverage rate and connectivity; aiming at the problems that an ion motion algorithm is easy to fall into local optimum and has low convergence precision, a liquid ion updating strategy based on same-anisotropy collaborative optimum guidance and a solid ion updating strategy based on ranking classification evolution are provided, and a new self-adaptive ion motion algorithm is designed; the new algorithm is used for optimizing the coverage rate and connectivity of the mobile wireless ad hoc network simultaneously, and therefore the network coverage performance is improved.
The invention specifically provides a coverage communication method of a mobile wireless ad hoc network, which comprises the following steps:
step 1: establishing a mobile wireless ad hoc network node perception model;
step 2: establishing a mobile wireless ad hoc network coverage communication model;
and step 3: coverage and connectivity of the mobile wireless ad hoc network are optimized.
The step 1 comprises the following steps: the mobile wireless ad hoc network node perception model comprises a mobile node and a relay node, and the communication radius of the mobile node is set to be R, and the communication radius of the relay node is set to be R; the sensing areas of the mobile node and the relay node are circular areas with the respective positions as centers and communication radii as radii.
The step 2 comprises the following steps:
step 2.1: establishing a coverage function of the mobile wireless ad hoc network;
step 2.2: establishing a mobile wireless ad hoc network coverage function connectivity function;
step 2.3: and establishing a mobile wireless ad hoc network coverage connection model F (X).
Step 2.1 comprises: discretely gridding the monitoring area; calculate allNode-aware grid population in an operational state
Figure GDA0003553128300000021
Wherein i is 1,2, …, N is the number of working nodes,
Figure GDA0003553128300000022
calculating the total number G of grids in the whole monitoring area for the grid number perceived by each mobile nodetotalEstablishing a coverage rate function CR of the mobile wireless ad hoc network as follows:
Figure GDA0003553128300000023
step 2.2 comprises: calculating the distances among all the working nodes and recording the distances as a distance matrix D; judging the size relationship between each element in the distance matrix D and the node communication radius r, and establishing the communication state between the nodes in the working state, namely a communication matrix L and a communication matrix DijIs the distance between node i and node j, lijAs shown in formula (2):
Figure GDA0003553128300000024
Figure GDA0003553128300000025
parameter lijUsed for judging whether the node i is communicated with the node j, if so, lijIs 1, otherwise lijIs 0;
all nodes of a mobile wireless ad hoc network have connectivity that must satisfy the following two conditions:
condition 1: rank (l) ═ N;
condition 2: sum (L) is not less than 2 (N-1);
sum (L) represents the sum of all elements in the connectivity matrix L;
the condition 1 indicates that the rank of the connection matrix L is N, and the existence of nodes communicated with any node is ensured; 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;
therefore, a connectivity function C of a coverage rate function of the mobile wireless ad hoc network is established as follows:
max C＝max Rank(L) (3)
Figure GDA0003553128300000031
in the formula, Rank is a Rank function.
Step 2.3 comprises: establishing a coverage connectivity model F (X) of the mobile wireless ad hoc network, wherein the model F comprises the following steps:
F(X)＝max[CR(X),C(X)] (5)
wherein X is ═ X1,x2,…,xN]Is an N-dimensional variable, xi＝[lxi,lyi]Is a two-dimensional planar position coordinate, lx, of the ith mobile nodei,lyiRespectively representing the abscissa and ordinate of the two-dimensional plane of the ith mobile node.
The step 3 comprises the following steps:
step 3.1: setting initial parameters including the size P of the ion population and the maximum evolution iteration number Gmax；
Step 3.2: randomly generating an initialization ion population X1,…,Xi,…,XP，Xi＝[x1,…,xj,…,xN]，xj＝[lxj,lyj]Is the position of the jth node, XPRepresents the P-th ion in the ion population; dividing the ion population into two, namely an anion population and a cation population; calculating an objective function value F (X) for each ioni)＝[CR(Xi),C(Xi)]Converting the objective function value into an fitness value:
E＝w1×CR+w2×C (6)
wherein E is the ion fitness value, w1And w2Is a weight parameter;
step 3.3: updating the liquid state of the ion population based on the same-anisotropy collaborative optimal guidance strategy, and calculating the objective function value of anions and cations in the new population:
Ai(t+1)＝Ai(t)+z×AFi t×(Cbest(t)-Ai(t))+(1-z)×AFi t×(Abest(t)-Ai(t))
Ci(t+1)＝Ci(t)+z×CFi t×(Abest(t)-Ci(t))+(1-z)×CFi t×(Cbest(t)-Ci(t)) (7)
where t is the number of evolutionary iterations, Ai(t) represents the position of the ith anion, AFi tRepresents the coefficient of attraction of the i-th anion, Ci(t) represents the position of the ith cation, CFi tRepresents the attraction coefficient of the ith cation, and z is a random number between 0 and 1; abest (t) is the anion with the optimal fitness in the anion population at the t iteration, and Cbest (t) represents the cation with the optimal fitness in the cation population at the t iteration;
step 3.4: and performing solid state updating of the ion population based on the ranking classification evolution strategy.
Step 3 is to optimize equation (6) using an ion motion algorithm, i.e., to optimize both coverage and connectivity objectives so that they are optimal at the same time.
Step 3.4 comprises: new anions and cations are sorted according to the target function value, namely, the anion population is divided into two, the cation population is divided into two:
Figure GDA0003553128300000041
Figure GDA0003553128300000042
wherein, FitCi(t) represents a cation CiFitness of (1), FitAi(t) is an anion AiNUM is the ion number of the anion and cation population，EAi(t) fitness value of the ith anion, ECi(t) is the fitness value of the ith cation;
the updating mode of the positive ions and the negative ions with the front fitness values is as follows:
Figure GDA0003553128300000043
Figure GDA0003553128300000044
wherein rand ([ a, b ]) is a random number between a and b;
the updating mode of the negative and positive ions with the latter fitness value is as follows:
Ai(t+1)＝Ai(t)+rand()×(Abest(t)-Ai(t))+rand()×(Abest-Ai(t)) (12)
Ci(t+1)＝Ci(t)+rand()×(Cbest(t)-Ci(t))+rand()×(Cbest-Ci(t)) (13)
step 3.5: judging a termination condition: if t is GmaxAnd outputting the solution with the optimal fitness value in the ion population as a result, otherwise, returning to the step 3.3 when t is t + 1.
Has the advantages that: the invention designs a new coverage connectivity mechanism of a wireless ad hoc network, provides an adaptive ion motion algorithm, combines coverage rate and connectivity into a fitness function, and finally optimizes a coverage connectivity model by using the provided algorithm. The coverage rate and the connectivity of the wireless ad hoc network can be optimized simultaneously, so that the overall performance and the service quality of the wireless ad hoc network are improved; the improved algorithm improves the convergence speed, ensures that the algorithm can quickly provide a coverage communication scheme, and adjusts the operation condition of the wireless ad hoc network in real time, thereby ensuring the practical application of the wireless ad hoc network.
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The foregoing and/or other advantages of the invention will become further 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 node awareness model of a mobile wireless ad hoc network.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The invention provides a coverage communication method of a mobile wireless ad hoc network, which comprises the following steps:
step 1: and establishing a node perception model of the mobile wireless ad hoc network. The mobile wireless ad hoc network mainly comprises a mobile node and a relay node, wherein the communication radius of the mobile node is set to be R, and the communication radius of the relay node is set to be R, so that the mobile wireless ad hoc network node perception model is shown in figure 1.
Step 2: and establishing a coverage communication model of the mobile wireless ad hoc network, including coverage rate and connectivity, and designing a new communication guarantee mechanism.
Step 2.1: and establishing a coverage function of the mobile wireless ad hoc network. Discretely gridding the monitoring area; computing total number of grids perceived by all worker nodes
Figure GDA0003553128300000051
Wherein i is 1,2, …, N is the number of working nodes,
Figure GDA0003553128300000052
a number of grids perceived for each mobile node; calculating the total number G of grids in the whole monitoring areatotal(ii) a The coverage function CR of the mobile wireless ad hoc network is established as follows:
Figure GDA0003553128300000053
step 2.2: and establishing a mobile wireless ad hoc network coverage function connectivity function. Calculating the distances among all the working nodes and recording the distances as a distance matrix D; and judging the size relationship between each element in the distance matrix D and the node communication radius r so as to establish a communication state between nodes in a working state, namely a communication 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 GDA0003553128300000054
Figure GDA0003553128300000055
Since the connected matrix L is a symmetric matrix, and the elements in the connected matrix L are not 0 or 1. For this reason, all nodes of a mobile wireless ad hoc network have connectivity that must satisfy the following two 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 communicating with it. 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.
For this purpose, the connectivity function C for establishing the mobile wireless ad hoc network is:
max C＝max Rank(L) (3)
Figure GDA0003553128300000061
in the formula, L is a communication matrix between nodes, Rank is a Rank solving function, and N is the number of working nodes of the mobile wireless ad hoc network.
Step 2.3: establishing a coverage connectivity model F (X) of the mobile wireless ad hoc network, wherein the model F comprises the following steps:
F(X)＝max[CR(X),C(X)] (5)
wherein X is ═ X1,x2,…,xN]Is N dimension variable, N is the number of working nodes of the mobile wireless ad hoc network, xi＝[lxi,lyi]I is the two-dimensional plane position coordinate of the ith mobile node, i is 1,2, …, N.
(step 3 an adaptive ion mobility algorithm for simultaneous coverage and connectivity optimization was proposed according to step 2)
And step 3: and providing an adaptive ion motion algorithm based on an isogeny collaborative optimal guiding strategy and a ranking classification evolution strategy, and simultaneously optimizing the coverage rate and connectivity of the mobile wireless ad hoc network.
Step 3.1: setting initial parameters including the scale N of the ion population and the maximum evolution iteration number Gmax。
Step 3.2: randomly generating an initialization ion population X1,…,Xi,…,XN，Xi＝[x1,…,xj,…,xN]，xj＝[lxj,lyj]Is the node position; dividing the ion population into two, namely an anion population and a cation population; calculating an objective function value F (X) for each ioni)＝[CR(Xi),C(Xi)]And converting the objective function value into an adaptability value.
E＝w1×CR+w2×C (6)
Wherein E is the ion fitness value, w1＝w2＝0.5，
Step 3.3: and updating the liquid state of the ion population based on the same-anisotropy collaborative optimal guidance strategy, and calculating the objective function value of anions and cations in the new population.
Ai(t+1)＝Ai(t)+z×AFi t×(Cbest(t)-Ai(t))+(1-z)×AFi t×(Abest(t)-Ai(t))
Ci(t+1)＝Ci(t)+z×CFi t×(Abest(t)-Ci(t))+(1-z)×CFi t×(Cbest(t)-Ci(t)) (7)
Where t is the number of evolutionary iterations, Ai(t) represents the position of the ith anion, AFi tRepresents the coefficient of attraction of the i-th anion, Ci(t) represents the position of the ith cation, CFi tRepresents the attraction coefficient of the ith cation, and z is a random number between 0 and 1.
Step 3.4: and performing solid state updating of the ion population based on the ranking classification evolution strategy.
And respectively sorting the new anions and cations according to the target function values, namely dividing the anion population into two and dividing the cation population into two.
Figure GDA0003553128300000071
Figure GDA0003553128300000072
Wherein NUM is the ion number of the anion and cation population, EAi(t) fitness value of the ith anion, ECi(t) is the fitness value of the ith cation.
The updating mode of the positive ions and the negative ions with the front fitness values is as follows:
Figure GDA0003553128300000073
Figure GDA0003553128300000074
where t is the number of evolutionary iterations and rand ([ a, b ]) is a random number between a and b.
The updating mode of the negative and positive ions with the latter fitness value is as follows:
Ai(t+1)＝Ai(t)+rand()×(Abest(t)-Ai(t))+rand()×(Abest-Ai(t)) (12)
Ci(t+1)＝Ci(t)+rand()×(Cbest(t)-Ci(t))+rand()×(Cbest-Ci(t)) (13)
step 3.5: and judging a termination condition. If t is GmaxIf not, t is t +1, and the step returns to step 3.3.
The present invention provides a method for coverage connectivity of a mobile wireless ad hoc network, and a plurality of methods and approaches for implementing the technical solution, where the above description is only a preferred embodiment of the present invention, it should be noted that, for those skilled in the art, a plurality of improvements and modifications may be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (1)

1. A method for connecting coverage of a mobile wireless ad hoc network is characterized by comprising the following steps:
step 1: establishing a mobile wireless ad hoc network node perception model;
step 2: establishing a mobile wireless ad hoc network coverage communication model;
and step 3: optimizing the coverage and connectivity of the mobile wireless ad hoc network;
the step 1 comprises the following steps: the mobile wireless ad hoc network node perception model comprises a mobile node and a relay node, and the communication radius of the mobile node is set to be R, and the communication radius of the relay node is set to be R; the sensing areas of the mobile node and the relay node are circular areas with the respective positions as centers and the communication radius as the radius;
the step 2 comprises the following steps:
step 2.1: establishing a coverage function of the mobile wireless ad hoc network;
step 2.2: establishing a mobile wireless ad hoc network coverage function connectivity function;
step 2.3: establishing a mobile wireless ad hoc network coverage communication model F (X);
step 2.1 comprises: discretely gridding the monitoring area; calculating the total number of grids sensed by all nodes in working state
Figure FDA0003553128290000011
Wherein i is 1,2, …, N is the number of working nodes,
Figure FDA0003553128290000012
calculating the total number G of grids in the whole monitoring area for the grid number perceived by each mobile nodetotalEstablishing a coverage rate function CR of the mobile wireless ad hoc network as follows:
Figure FDA0003553128290000013
step 2.2 comprises: calculating the distances among all the working nodes and recording the distances as a distance matrix D; judging the size relationship between each element in the distance matrix D and the node communication radius r, and establishing the communication state between the nodes in the working state, namely a communication matrix L and a communication matrix DijIs the distance between node i and node j, lijAs shown in formula (2):
Figure FDA0003553128290000014
Figure FDA0003553128290000015
parameter lijUsed for judging whether the node i is communicated with the node j, if so, lijIs 1, otherwise lijIs 0;
all nodes of a mobile wireless ad hoc network have connectivity that must satisfy the following two conditions:
condition 1: rank (l) ═ N;
condition 2: sum (L) is not less than 2 (N-1);
sum (L) represents the sum of all elements in the connectivity matrix L;
the condition 1 indicates that the rank of the connection matrix L is N, and the existence of nodes communicated with any node is ensured; 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;
therefore, a connectivity function C of a coverage rate function of the mobile wireless ad hoc network is established as follows:
max C＝max Rank(L) (3)
Figure FDA0003553128290000021
in the formula, Rank is a Rank function;
step 2.3 comprises: establishing a coverage connectivity model F (X) of the mobile wireless ad hoc network, wherein the model F comprises the following steps:
F(X)＝max[CR(X),C(X)] (5)
wherein X is ═ X1,x2,…,xN]Is an N-dimensional variable, xi＝[lxi,lyi]Is a two-dimensional planar position coordinate, lx, of the ith mobile nodei,lyiRespectively representing the abscissa and the ordinate of the two-dimensional plane of the ith mobile node;
the step 3 comprises the following steps:
step 3.1: setting initial parameters including the size P of the ion population and the maximum evolution iteration number Gmax；
Step 3.2: randomly generating an initialization ion population X1,…,Xi,…,XP，Xi＝[x1,…,xj,…,xN]，xj＝[lxj,lyj]Is the position of the jth node, XPRepresents the P-th ion in the ion population; dividing the ion population into two, namely an anion population and a cation population; calculating an objective function value F (X) for each ioni)＝[CR(Xi),C(Xi)]Converting the objective function value into an fitness value:
E＝w1×CR+w2×C (6)
wherein E is the ion fitness value, w1And w2Is a weight parameter;
step 3.3: updating the liquid state of the ion population based on the same-anisotropy collaborative optimal guidance strategy, and calculating the objective function value of anions and cations in the new population:
Ai(t+1)＝Ai(t)+z×AFi t×(Cbest(t)-Ai(t))+(1-z)×AFi t×(Abest(t)-Ai(t))
Ci(t+1)＝Ci(t)+z×CFi t×(Abest(t)-Ci(t))+(1-z)×CFi t×(Cbest(t)-Ci(t)) (7)
where t is the number of evolutionary iterations, Ai(t) represents the position of the ith anion, AFi tRepresents the coefficient of attraction of the i-th anion, Ci(t) represents the position of the ith cation, CFi tRepresents the attraction coefficient of the ith cation, and z is a random number between 0 and 1; abest (t) is the anion with the optimal fitness in the anion population at the t iteration, and Cbest (t) represents the cation with the optimal fitness in the cation population at the t iteration;
step 3.4: updating the solid state of the ion population based on a ranking classification evolution strategy;
step 3.4 comprises: new anions and cations are sorted according to the target function value, namely, the anion population is divided into two, the cation population is divided into two:
Figure FDA0003553128290000031
Figure FDA0003553128290000032
wherein, FitCi(t) represents a cation CiFitness of (1), FitAi(t) is an anion AiNUM is the ion number of the anion and cation population, EAi(t) fitness value of the ith anion, ECi(t) is the fitness value of the ith cation;
the updating mode of the positive ions and the negative ions with the front fitness values is as follows:
Figure FDA0003553128290000033
Figure FDA0003553128290000034
wherein rand ([ a, b ]) is a random number between a and b;
the updating mode of the negative and positive ions with the latter fitness value is as follows:
Ai(t+1)＝Ai(t)+rand()×(Abest(t)-Ai(t))+rand()×(Abest-Ai(t)) (12)
Ci(t+1)＝Ci(t)+rand()×(Cbest(t)-Ci(t))+rand()×(Cbest-Ci(t)) (13)
step 3.5: judging a termination condition: if t is GmaxAnd outputting the solution with the optimal fitness value in the ion population as a result, otherwise, returning to the step 3.3 when t is t + 1.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106254155A (en) * 2016-09-20 2016-12-21 北京农业信息技术研究中心 The reparation Enhancement Method of the covering performance of a kind of wireless sensor network and device
CN111093216A (en) * 2019-12-25 2020-05-01 北京石油化工学院 Wireless sensor network node scheduling method based on improved binary particle swarm 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
CN112073897A (en) * 2020-07-31 2020-12-11 深圳市贝贝特科技实业有限公司 Method for rapidly establishing ad hoc network by using unmanned aerial vehicle projection

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI524792B (en) * 2013-09-27 2016-03-01 財團法人資訊工業策進會 Transmission control method and communication apparatus using the same for vehicular ad hoc network system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106254155A (en) * 2016-09-20 2016-12-21 北京农业信息技术研究中心 The reparation Enhancement Method of the covering performance of a kind of wireless sensor network and device
CN111093216A (en) * 2019-12-25 2020-05-01 北京石油化工学院 Wireless sensor network node scheduling method based on improved binary particle swarm 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
CN112073897A (en) * 2020-07-31 2020-12-11 深圳市贝贝特科技实业有限公司 Method for rapidly establishing ad hoc network by using unmanned aerial vehicle projection

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
一种无线传感器网络的概率覆盖增强算法;范兴刚等;《软件学报》;20160215(第02期);全文 *
一种移动自组网的区域覆盖策略;任孝平等;《计算机工程》;20080120(第02期);全文 *
无线传感器网络部分覆盖算法及连通性研究;胡劲文等;《计算机工程与应用》;20080121(第03期);全文 *

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