CN115696494A - Large-scale ad hoc network multipoint relay selection method based on ant colony optimization - Google Patents
Large-scale ad hoc network multipoint relay selection method based on ant colony optimization Download PDFInfo
- Publication number
- CN115696494A CN115696494A CN202210934581.0A CN202210934581A CN115696494A CN 115696494 A CN115696494 A CN 115696494A CN 202210934581 A CN202210934581 A CN 202210934581A CN 115696494 A CN115696494 A CN 115696494A
- Authority
- CN
- China
- Prior art keywords
- node
- ant
- current
- pheromone
- hop
- 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.)
- Pending
Links
Classifications
-
- 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
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a large-scale ad hoc network multipoint relay selection method based on ant colony optimization, which can effectively reduce the size of a selected MPR set, further reduce the packet loss rate of a network and improve the throughput of the network. Firstly, an ant colony algorithm is introduced into the selection process of an MPR set, secondly, a local three-hop neighbor database and speed measurement are added into a path selection decision of a node, and then, for the updating of pheromones, a global pheromone updating rule is adopted to update the pheromones on the path so as to prevent the algorithm from converging to a local optimal solution. The method provided by the invention overcomes the problems that the traditional MPR selection algorithm is easy to generate redundancy and cannot adapt to the difficulty of the current large-scale ad hoc network.
Description
Technical Field
The invention relates to the technical field of optimization technology in the field of computers and ad hoc networks in the field of mobile communication, in particular to an ant colony optimization-based large-scale ad hoc network multipoint relay selection method.
Background
In recent years, the development of mobile ad hoc networks has become more and more attractive in the civil field and the military field, wherein an Unmanned Aerial Vehicle (UAV) is widely applied in the aspects of real-time monitoring, search and rescue, relay transmission, strategic combat and the like by virtue of its small size, low cost, convenient deployment and the like. However, the unmanned aerial vehicle ad hoc network has the characteristics of strong node mobility, fast network topology change, frequent data interaction, high energy consumption and the like, and the traditional routing algorithm cannot meet the requirements on network transmission delay, packet loss rate, routing overhead and the like.
The Optimized Link State Routing (OLSR) protocol is a classic Link State protocol that accomplishes Link probing, neighbor discovery, and selection of Multi-Point Relay (MPR) nodes by broadcasting Hello packets between nodes. And establishing and maintaining the whole Topology of the network after acquiring MPR node information by using a Topology Control (TC) group, and finally calculating a path by using a Dijkrastra algorithm to generate a route. The selection of the MPR nodes is crucial, and each node only sends its TC packet to its corresponding MPR node, so as to reduce the number of control packets between networks. Document [8] mentions that selecting the optimal MPR set is an NP-hard problem. In a traditional mode, a greedy algorithm is usually adopted for selecting the MPR set, and a one-hop node which covers the most two-hop neighbors of a source node is preferentially selected. This results in a large redundancy, which in turn results in an increased overhead for the protocol and an increased latency between networks. Many scholars improve the MPR selection algorithm from different angles. Some learners employ a min-max algorithm to reduce the number of TC packets on each node. The scholars propose an MPR selection algorithm based on a set, which can effectively eliminate invalid redundant nodes by combining loop and set operations. Also, the researchers have proposed MPR selection based on local database of neighboring nodes extended to three hops, MPR selection using existing algorithm in OLSR, and the results show that it is superior to standard OLSR in TC packet number and energy efficiency.
The Ant Colony Optimization (ACO) is an abstraction and improvement of real Ant Colony foraging behavior in the real world, and is a heuristic algorithm of intelligent Optimization. Ants transmit information to other ants through pheromones left by moving in the foraging process, and other ants determine the next path according to the concentration of the pheromones in different paths. The ant colony algorithm has the advantage that the search is global, and the traditional greedy algorithm cannot consider the global situation and tends to fall into local optimum. The ant colony algorithm selects a next-hop path by utilizing random probability, and when pheromone is accumulated and the probability is increased to 1, the algorithm is degraded into a greedy algorithm. The single ant colony algorithm is improved on the basis of the greedy algorithm. But still has the problem that the iteration time is too long to trap into partial optimization
Disclosure of Invention
The invention aims to provide a large-scale ad hoc network multipoint relay selection method based on ant colony optimization, which combines the global search capability of an ant colony algorithm, considers the characteristics of an unmanned aerial vehicle ad hoc network, introduces a local node three-hop neighbor database into a path selection function of the ant colony, considers the speed of the node, improves the original path selection and state updating mechanism of an ACO algorithm, applies the ant colony optimization to solve the MPR set problem, achieves the aim of optimizing the MPR set, and finally integrates the ant colony optimization into a Qualnet network simulation software system, thereby well verifying the performance of the algorithm provided by the invention.
The technical scheme adopted by the invention for solving the technical problem is as follows: a large-scale ad hoc network multipoint relay selection method based on ant colony optimization comprises the following steps:
step 1: initializing a source node one-hop neighbor set S1, a two-hop neighbor set S2 and a three-hop neighbor set S3, a node array visit visited by each ant, the number num _ ants of ants, the number Uncov _ S2 of uncovered two-hop neighbor nodes and the total cycle times total _ iteration of an ant group;
and 2, step: when the current iteration times are less than the total cycle times, the following steps are carried out: initializing an initial node of each ant, recording the initial node into a visit array of each ant, and initializing a current one-hop neighbor set cur _ S1, a current two-hop set cur _ S2 and a current three-hop set cur _ S3 of each ant;
and step 3: for each ant, when the current Unceover _ S2>0, the following steps are carried out: calculating the probability of selecting other nodes by ants according to a path probability selection formula provided by the invention, selecting the next hop selection of the current ants according to a roulette selection method, updating visit arrays, cur _ S1, cur _ S2 and cur _ S3 arrays, covering nodes corresponding to the selected nodes, and updating cur _ Solu and best _ Solu for each ant according to the current visit array condition;
and 4, step 4: according to the pheromone updating method provided by the invention, the current path is updated.
Further, in step 3 of the present invention, the path probability selection formula is:
wherein alpha represents a heuristic factor of pheromone, beta represents a heuristic factor of two-hop weight, gamma represents a heuristic factor of three-hop weight, epsilon represents a heuristic factor of node relative speed, tau (i) represents the concentration of pheromone held on a node i, mu (i) represents the weight of a source node two-hop neighbor covered by the node i, eta (i) represents the weight of a source node three-hop neighbor covered by the node i, and nu (i) represents an influence formula of the moving speed of a target node i on probability selection.
Further, in step 4 of the present invention, the pheromone updating formula is:
τ i (t+1)＝(1-ρ)·τ i (t)+Δτ i (t) (2)
where ρ is the volatility of the phased pheromone, τ i (t) pheromone increment on node i before update, τ i (t + 1) is the pheromone increment on the updated node i;
wherein the white iron cur Represents the current number of iterations, ite max Representing a set maximum number of iterations;
wherein
The pheromone, cure, representing ant k releasing on the path to the i-node in the iterative process k The Q value is the initial pheromone constant for the solution of the current iteration.
Has the advantages that:
1. the method and the device can effectively reduce the size of the selected MPR set, further reduce the packet loss rate of the network and improve the throughput of the network. The ant colony algorithm is introduced into the MPR set selection process, a local three-hop neighbor database and speed considerations are added into a node path selection decision, and for the updating of pheromones, a global pheromone updating rule is adopted to update the pheromones on the path so as to prevent the algorithm from converging on a local optimal solution.
2. The invention overcomes the problem that the traditional MPR selection algorithm is easy to generate redundancy and the difficulty that the traditional MPR selection algorithm cannot be suitable for the current large-scale ad hoc network.
3. The method combines the global search capability of the ant colony algorithm, considers the characteristics of the unmanned aerial vehicle ad hoc network, introduces a local node three-hop neighbor database into the path selection function of the ant colony, simultaneously considers the speed of the node, improves the original path selection and state updating mechanism of the ACO algorithm, applies the ant colony optimization to the solving of the MPR set problem, achieves the purpose of optimizing the MPR set, and finally integrates the ant colony optimization in a Qualnet network simulation software system, thereby well verifying the performance of the algorithm provided by the invention.
Detailed Description
The invention will be described in further detail below.
Example one
The invention provides an ant colony optimization-based large-scale ad hoc network multipoint relay selection method, which combines the global search capability of an ant colony algorithm, considers the characteristics of an unmanned aerial vehicle ad hoc network, introduces a local node three-hop neighbor database into a path selection function of an ant colony, simultaneously considers the speed of a node, improves the original path selection and state updating mechanism of an ACO algorithm, applies the ant colony optimization to the solving of the MPR set problem, achieves the purpose of optimizing the MPR set, and finally integrates the ant colony optimization into a Qualnet network simulation software system, thereby well verifying the performance of the algorithm provided by the invention. The method specifically comprises the following steps:
s1: objective function
In the current MPR selection problem, assume that there are m nodes, where a source node is a, a one-hop set S1 of the source node is defined, and | S1| = n, a strict two-hop neighbor set S2 of the source node, and a strict three-hop neighbor set S3 of the source node, where | S1| + | S2| + | S3| +1=m. Suppose there are a total of k ants, res k And representing the number of elements in the MPR solution set obtained by the kth ant in the current iteration. target j E {0,1} (j =1,2.. N) represents whether S1 will be for the current ant or not j And selecting the MPR set. Optimal solution for each ant
For this problem, the global optimal solution bestSolu = min | res k L. The objective of the algorithm is to continuously optimize res while considering the mobility of the network nodes of the unmanned aerial vehicle k The value of (2) is obtained, so that the finally obtained bestSolu obtains an ideal minimum value to meet the requirement of the current complex unmanned aerial vehicle ad hoc network.
S2: node path selection
The global search capability of the ant colony algorithm is that the selection of the next hop path is not absolute, and ants can dynamically update key information such as pheromones, weights and the like in the moving process, and select the most suitable path according to the factors. Order to
Represents ant k selectionThe probability of selecting node i (i =1,2.., n) as the next node to be selected into the MPR set can be obtained:
wherein alpha represents a heuristic factor of pheromone, beta represents a heuristic factor of two-hop weight, gamma represents a heuristic factor of three-hop weight, and epsilon represents a heuristic factor of relative speed of nodes. Wherein, the symbol α belongs to (0,1), the symbol β belongs to (0,1), the symbol γ belongs to (0,1), and the symbol ε belongs to (0,1). The larger the value of alpha, the greater the influence of the current ant on the remaining pheromones of other ants. Beta, gamma, epsilon represent the importance of the corresponding elicitor. When alpha is 1, beta, gamma and epsilon are 0, the ant colony algorithm is degenerated into the traditional greedy algorithm.
τ (i) represents the concentration of pheromones held on node i, and μ (i) represents the weight of the source node two-hop neighbor covered by node i. η (i) represents the weight of the source node three-hop neighbor covered by node i. And v (i) represents an influence formula of the moving speed of the target node i on the probability selection. allowed (S1) k represents the set of nodes that ant k allows to join its MPR set.
The aim of further optimizing MPR can be achieved by adding the local database of the three-hop neighbor to the additional decision function of MPR for eta (i) [10].
For the
When speeddi>When 0, it means that the node is moving to itself, and when speedi<0, indicates that the node is moving away from itself. For the network of the unmanned aerial vehicle system, the stable dynamic balance between the nodes is expected to be achieved as much as possible. When the one-hop node and the source node reach ideally relative quiescence, the probability of being an MPR node is maximized. When the speed of the one-hop node relative to the source node is too high, whether the node is driven to or away from the source node, the node is regarded as unstable, and the probability of the node being the MPR node decreases with the increase of the moving speed.
S3: pheromone update
In the ant colony algorithm, pheromones on paths are accumulated continuously along with the increase of ants passing through, the concentration of pheromones on a large number of walking non-optimal paths is accumulated continuously, the concentration of pheromones on unselected paths is reduced continuously, the true possible optimal paths may exist in the unselected paths, and the algorithm is trapped in a local optimal solution.
To prevent the algorithm from converging on a locally optimal solution, or converging too slowly. The method adopts a global pheromone updating rule to update the pheromone on the path. Compared with the traditional ACO algorithm for updating pheromones on paths passed by all ants reaching a destination node, the method carries out further pheromone accumulation on the optimal path in the current iteration process and carries out further volatilization on the worst path to punish. Therefore, relatively poor paths can be eliminated more quickly, and better paths are highlighted, so that the convergence of the algorithm is accelerated, and the probability of the algorithm falling into local optimum is reduced.
The update formula of pheromone is as follows:
wherein, is the volatility of the stepwise pheromone, tau i (t) pheromone increment on node i before update, τ i (t + 1) is the pheromone increment on the updated node i, and is specifically defined as follows:
where itecur represents the current number of iterations and itemax represents the set maximum number of iterations.
Wherein
The representative ant k releases the pheromone on the path to the inode in an iterative process. currsek is the solution for the current iteration. For currsek, the smaller the value is, the closer the current iteration is to the optimal solution, the larger the deposition amount of pheromone on the path passed by the currsek is, and on the contrary, the larger the value is, the penalty is given to the path passed by the currsek, the deposition amount of pheromone on the path passed by the currsek is obviously reduced, which is beneficial for ants to select smaller MPR sets to obtain the optimal solution.
Because the results of each iteration are possibly different, pheromones on more paths can be accumulated, and the initial value of the volatilization rate is higher in terms of the volatility coefficient of the periodic pheromones, so that ants can search all paths more comprehensively, and the ant is prevented from falling into local optimum. With the increase of iteration times, the volatilization rate is continuously reduced, and ants can be gradually gathered on the optimal path.
When the pheromone is updated, in order to prevent that the pheromone is too small to be selected when a certain optimal node is selected, or the concentration of the pheromone on a certain node is too large, the route searching process is stopped too early, and a more optimal route is missed. Setting an upper limit phe for each node's pheromone max While setting the lower limit phe min So that pheromone of the node is constant [ phe ] min ，phe max ]Within.
Example two
In order to verify the feasibility of the algorithm, a qualnet network simulation platform is used for simulation. Each node adopts a Random escape model of the self-contained qualnet, the whole scene scale is 1500m × 1500m, an OLSR protocol in the qualnet is adopted, and the algorithm disclosed by the invention is added into the MPR selection process. Compared with the MPR algorithm adopting a greedy strategy under different network topologies and network densities, the traditional Ant Colony Optimization (ACO) algorithm is compared, and the result shows that the MPR algorithm is superior to the MPR algorithm in reducing the MPR set size, reducing the network packet loss rate and improving the network throughput under the condition of large-scale ad hoc network.
The framework of the whole algorithm of the invention is as follows: the number num _ Ants of Ants, the current iteration time ite, the total iteration time total _ iteration, the node array visit visited by each ant, the number Uncealed two-hop neighbor nodes Uncever _ S2, and the current ant selecting the MPR set size cur _ Solu. And (4) the history optimal MPR set size best _ Solu.
Initializing a source node one-hop neighbor set S1, a two-hop neighbor set S2, a three-hop neighbor set S3 and the like.
The start node for each ant is initialized and recorded into the visit array for each ant. The current one-hop neighbor set cur _ S1, the current two-hop set cur _ S2 and the current three-hop set cur _ S3 of each ant are initialized.
And (3) calculating the probability of the ants selecting other nodes according to the previous path probability selection formula (2).
And selecting the next hop of the current ants according to a roulette selection method.
And updating the visit array, cur _ S1, cur _ S2 and cur _ S3 array, and covering the nodes corresponding to the selected nodes.
And updating cur _ Solu and best _ Solu for each ant according to the current visit array condition.
The path pheromone is updated according to the method for updating pheromones.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and there may be a slight structural change in a local part in the implementation process. If various changes or modifications of the present invention are made without departing from the spirit and scope of the present invention and within the claims and the equivalent technical scope of the present invention, it is intended that the present invention also include such changes and modifications.
Claims (3)
1. A large-scale ad hoc network multipoint relay selection method based on ant colony optimization is characterized by comprising the following steps:
step 1: initializing a source node one-hop neighbor set S1, a two-hop neighbor set S2 and a three-hop neighbor set S3, the number num _ ants of ants, a node array visit visited by each ant, the number Uncever _ S2 of the current uncovered two-hop neighbor nodes and the total cycle times total _ iteration of an ant group;
step 2: when the current iteration times are smaller than the total cycle times, initializing an initial node of each ant, recording the initial node into a visit array of each ant, and initializing a current one-hop neighbor set cur _ S1, a current two-hop set cur _ S2 and a current three-hop set cur _ S3 of each ant;
and step 3: for each ant, when the current Unceover _ S2 is greater than 0, calculating the probability of selecting other nodes by the ant according to a path probability selection formula, selecting the next hop selection of the current ant according to a roulette selection method, updating a visit array, a cur _ S1, a cur _ S2 and a cur _ S3 array, covering the nodes corresponding to the selected nodes, and updating cur _ Solu and best _ Solu for each ant according to the condition of the current visit array;
and 4, step 4: and updating pheromone for the current path.
2. The ant colony optimization-based large-scale ad hoc network multipoint relay selection method according to claim 1, wherein in the step 3, the path probability selection formula is:
wherein alpha represents a heuristic factor of pheromone, beta represents a heuristic factor of two-hop weight, gamma represents a heuristic factor of three-hop weight, epsilon represents a heuristic factor of node relative speed, tau (i) represents the concentration of pheromone held on a node i, mu (i) represents the weight of a source node two-hop neighbor covered by the node i, eta (i) represents the weight of a source node three-hop neighbor covered by the node i, and nu (i) represents an influence formula of the moving speed of a target node i on probability selection.
3. The method according to claim 1, wherein in step 4, the pheromone updating formula is as follows:
τ i (t+1)＝(1-ρ)·τ i (t)+Δτ i (t) (2)
where ρ is the volatility of the phased pheromone, τ i (t) pheromone increment on node i before update, τ i (t + 1) is the pheromone increment on the node i after updating;
wherein the white iron cur Represents the current number of iterations, ite max Representing a set maximum number of iterations;
wherein
The pheromone, cure, representing ant k releasing on the path to the i-node in the iterative process k The Q value is the initial pheromone constant for the solution of the current iteration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210934581.0A CN115696494A (en) | 2022-08-04 | 2022-08-04 | Large-scale ad hoc network multipoint relay selection method based on ant colony optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210934581.0A CN115696494A (en) | 2022-08-04 | 2022-08-04 | Large-scale ad hoc network multipoint relay selection method based on ant colony optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115696494A true CN115696494A (en) | 2023-02-03 |
Family
ID=85061354
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210934581.0A Pending CN115696494A (en) | 2022-08-04 | 2022-08-04 | Large-scale ad hoc network multipoint relay selection method based on ant colony optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115696494A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117042014A (en) * | 2023-10-10 | 2023-11-10 | 北京航空航天大学 | Unmanned aerial vehicle ad hoc network multipath transmission method considering speed and safety |
-
2022
- 2022-08-04 CN CN202210934581.0A patent/CN115696494A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117042014A (en) * | 2023-10-10 | 2023-11-10 | 北京航空航天大学 | Unmanned aerial vehicle ad hoc network multipath transmission method considering speed and safety |
| CN117042014B (en) * | 2023-10-10 | 2023-12-22 | 北京航空航天大学 | Unmanned aerial vehicle ad hoc network multipath transmission method considering speed and safety |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101945432B (en) | A kind of multi tate chance method for routing for wireless mesh network | |
| CN102970722B (en) | Multicasting route algorithm of low-time-delay delay tolerant and disruption tolerant sensor network | |
| CN114025330B (en) | Air-ground cooperative self-organizing network data transmission method | |
| CN103634842B (en) | Method for routing between a kind of distributed satellite network group | |
| CN104168620A (en) | Route establishing method in wireless multi-hop backhaul network | |
| CN106131916B (en) | Wireless network route establishing method based on ant colony algorithm | |
| CN113141592B (en) | Long-life-cycle underwater acoustic sensor network self-adaptive multi-path routing method | |
| CN101594281A (en) | Collecting network data of wireless sensor method, system and relevant device | |
| CN112996019B (en) | Terahertz frequency band distributed constellation access control method based on multi-objective optimization | |
| CN110191413B (en) | Method and system for broadcasting in mobile ad hoc network based on greedy ant colony algorithm | |
| He et al. | A fuzzy logic reinforcement learning-based routing algorithm for flying ad hoc networks | |
| CN110225569A (en) | A method of based on the WSNs clustering and multi-hop Routing Protocol for improving particle swarm algorithm | |
| CN115696494A (en) | Large-scale ad hoc network multipoint relay selection method based on ant colony optimization | |
| Lyu et al. | Qngpsr: A q-network enhanced geographic ad-hoc routing protocol based on gpsr | |
| Albayrak et al. | Bee-MANET: a new swarm-based routing protocol for wireless ad hoc networks | |
| CN114339661A (en) | Aircraft self-organizing network multipath routing mechanism based on whale optimization | |
| Li et al. | Ant-based on-demand clustering routing protocol for mobile ad-hoc networks | |
| CN108684065A (en) | Relay selection method based on ant group optimization in a kind of car networking | |
| CN108632785B (en) | Ant colony self-adaptive Internet of vehicles routing method based on link quality | |
| CN111614559B (en) | Method, system and medium for realizing global optimization of AODV routing | |
| Siddiqui et al. | A survey on data aggregation mechanisms in wireless sensor networks | |
| CN110808911B (en) | Networking communication routing method based on ant colony pheromone | |
| CN108551661A (en) | A kind of efficiency optimal method based on the connection prediction of Ant Routing algorithm | |
| Chen et al. | An efficient neural network-based next-hop selection strategy for multi-hop VANETs | |
| CN108282791A (en) | A method of the Ad Hoc transmission datas based on directive antenna |
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 |