CN109874147A - A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy - Google Patents
A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy Download PDFInfo
- Publication number
- CN109874147A CN109874147A CN201910166351.2A CN201910166351A CN109874147A CN 109874147 A CN109874147 A CN 109874147A CN 201910166351 A CN201910166351 A CN 201910166351A CN 109874147 A CN109874147 A CN 109874147A
- Authority
- CN
- China
- Prior art keywords
- node
- link
- hop
- routing
- calculate
- 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.)
- Withdrawn
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
- Mobile Radio Communication Systems (AREA)
Abstract
A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy.First calculate reliable communication region, it is held time according to relative shift between node and link, obtains link-quality assessment, then according to link-quality assessment, the distance of alternate node to destination node and neighbor node degree, metric is obtained, the big node of selectance magnitude is as next-hop node.When there is routing cavity, according to the waiting time of setting, use waiting forward mode, after waiting time, as present node still routes void node, while the utilization right side, left hand criterion, carry out all mid-side node forwardings, by considering that deflection angle, residue energy of node and link are held time, empty two sides are being routed, are selecting preferred value high as next-hop forward node respectively.With GPSR, EMGR and EDGR ratio, this method reduce energy consumption, network delays, and reduce the possibility of link failure, improve the network lifetime and packet-based transport of algorithm.
Description
[technical field]
The invention belongs to mobile ad-hoc network fields, and in particular to a kind of movement based on greedy repeating optimizing strategy from
Organize the routing new method of network.
[background technique]
Mobile ad-hoc network (Mobile Ad hoc Network, MANET) is a kind of special network, is had a large amount of
Mobile sensor node, military battlefield, traffic control, environmental monitoring, the disaster relief and in terms of extensive application,
So that Routing Protocol becomes the hot spot of current research.With the fast development of location technology, mobile node can be accurately by complete
The technologies such as ball positioning system (Global Positioning System, GPS) obtain the location information of itself, therefore based on ground
The Routing Protocol of reason position comes into being.Main foundation of the geographical location agreement using geographical location information as Route Selection, is pressed
Destination node is sent from source node by data packet according to certain forwarding strategy.GPSR Routing Protocol is one based on geographical location
Kind routing algorithm uses greedy forwarding strategies to establish and routes.Existing Routing Protocol is using the distance to destination node as unique
Index, cause to be easily trapped into Local Minimum phenomenon, later using right hand rule carry out periphery forwarding, be easy so that routing cavity
Region becomes larger.
Presently, there are routing algorithm only consider through right hand rule or left hand criterion around routing cavity, on the side in cavity
Boundary carries out the forwarding of data, however, such routing algorithm is likely to result in the increase in routing cavity, or even causes the paralysis of network
Paralysis.
In conclusion there are following several problems for traditional routing algorithm: 1) using the distance to destination node as unique
Index, cause to be easily trapped into Local Minimum phenomenon；2) only consider to bypass routing cavity by right hand rule or left hand criterion,
The boundary in cavity carries out the forwarding of data, and such routing algorithm is likely to result in the increase in routing cavity, or even causes network
Paralysis.
[summary of the invention]
The purpose of the present invention is to solve the above problems of the existing technology, provide a kind of based on greedy repeating optimizing
The routing new method (ELAN) of the mobile ad-hoc network of strategy.This method be based on geographical location greediness stateless route
(GPSR), three kinds of method phases of dual path Geographic routing (EDGR) of Energy-aware multipath Geographic routing (EMGR) and Energy-aware
Than ELAN method reduces energy consumption, network delay, and reduces the possibility of link failure, improves the network of algorithm
Life span and packet-based transport.
When using greedy forwarding strategies, reliable communication region is calculated first, then according to the relative displacement between node
Amount and link hold time, obtain link-quality assessment, then according to link-quality assessment, alternate node to destination node away from
From and neighbor node degree, obtain metric, the big node of selectance magnitude is as next-hop node.It is empty when there is routing
When, according to the waiting time of setting, using forward mode is waited, after the waiting time, if present node still routes cavity section
Point using right hand rule and left hand criterion, while carrying out all mid-side node forwardings, by considering deflection angle and residue energy of node
And link is held time, and in the two sides in routing cavity, selects the high forward node as next-hop of preferred value respectively.Root
It factually tests and shows that method of the invention achieves preferable experiment effect.
The routing new method (ELAN) of mobile ad-hoc network provided by the invention based on greedy repeating optimizing strategy, ginseng
See attached drawing 1, mainly include following committed step:
1st, model foundation:
1.1st, network model；
The initial position obedience Poisson distribution of 1.1.1, node；
1.1.2, node obtain position by included GPS device, exchange bootstrap information packet or destination locations service
It sets and energy information；
1.1.3, link mode use two-way link；
1.2nd, energy model；
1.2.1, calculate node send the energy consumption of data to neighbor node；
The distance between 1.2.2, calculate node；
2nd, greedy forwarding strategies:
2.1st, reliable communication region；
By comparing four reliable communication zone, link-quality, distance and neighbor node degree parameter evaluation mobile node S
Communication stability between the neighbor node of S selects most stable of next-hop node；
2.2nd, link-quality is assessed；
Relative displacement between 2.2.1, calculate node；
Link between 2.2.2, calculate node is held time；
2.2.3, the relative velocity for calculating two nodes；
The quality of 2.2.4, one hop link of calculate node；
2.3rd, distance and the assessment of neighbor node degree；
Range index between 2.3.1, calculate node；
2.3.2, calculate node neighbours' degree next-hop node is measured；
2.4th, greedy forwarder selection strategy；
Calculate the measurement of next-hop greediness forward node；
3rd, it is based on periphery forwarding strategy:
3.1st, angle estimator is forwarded；
3.1.1, it calculates at a distance from alternate node M to present node C and the place destination node D path；
Angle between 3.1.2, calculating present node C and alternate node M；
3.2nd, dump energy proficiency assessment；
The energy level of calculate node；
3.3rd, all mid-side nodes forward selection strategy；
Present node calculates the priority valve of all alternate nodes, and selecting priority is worth maximum node as next-hop section
Point；
4th, algorithm description:
The obtains position and the dump energy of neighbor node by sending bootstrap information packet between 4.1, node；When source node has
The request for sending data, first checks for whether having purpose node in the neighbor node of oneself, if there are purposes in neighbor node
Node then sends the data directly to destination node, conversely, then carrying out the 4.2nd step；
4.2nd, calculate reliable communication region, between node relative shift, link hold time, alternate node to purpose section
The degree and PRI value of the distance, neighbor node put, the node for selecting PRI value big is as next-hop node.When not comparing present node
When bigger PRI value, it is considered herein that there is routing cavity；
4.3rd, when there is routing cavity, a MAX_WaitTime is set, if after which time, routing empty situation
Do not improve, then carries out all mid-side node forwardings simultaneously using right hand rule and left hand criterion；
4.4th, deflection angle and AEL value are calculated, in both direction, select respectively AEL value greatly as next-hop periphery
Forward node, until node more preferably than routing void node occur changes into greedy forwarding strategies or until destination node；
4.5th, when carrying out data transmission, select link existent time long, the high path of energy residual carries out data biography
It is defeated.
The advantages and positive effects of the present invention
The major design of the present invention routing new method of the mobile ad-hoc network based on greedy repeating optimizing strategy
(ELAN), when using greedy forwarding strategies, reliable communication region is calculated first, then according to the relative shift between node
It holds time with link, obtains link-quality assessment, then according to link-quality assessment, the distance of alternate node to destination node
And neighbor node degree, metric is obtained, the big node of selectance magnitude is as next-hop node.When there is routing cavity,
According to the waiting time of setting, using waiting forward mode, after the waiting time, if present node still routes void node,
Utilize right hand rule and left hand criterion simultaneously, carry out all mid-side node forwardings, by consider deflection angle and residue energy of node with
And link is held time, and in the two sides in routing cavity, selects the high forward node as next-hop of preferred value respectively.The party
Method be based on geographical location greediness stateless route (GPSR), Energy-aware multipath Geographic routing (EMGR) and Energy-aware
Three kinds of methods of dual path Geographic routing (EDGR) are compared, and ELAN method reduces energy consumption, network delay, and reduces chain
The possibility of road failure, improves the network lifetime and packet-based transport of algorithm.
[Detailed description of the invention]
Fig. 1 ELAN method for routing flow chart；
Fig. 2 reliable communication area schematic；
Fig. 3 angle schematic diagram；
Corresponding diagram between Fig. 4 heterogeneous networks closeness and energy consumption；
The corresponding diagram of Fig. 5 heterogeneous networks closeness and network lifetime；
The corresponding diagram of Fig. 6 heterogeneous networks closeness and data Packet delivery ratio；
The corresponding diagram of Fig. 7 heterogeneous networks closeness and network delay；
The corresponding relationship of Fig. 8 different communication number and energy consumption；
The corresponding relationship of Fig. 9 different communication number and network lifetime；
The corresponding relationship of Figure 10 different communication number and data Packet delivery ratio；
The corresponding relationship of Figure 11 different communication number and network delay；
The corresponding relationship of Figure 12 difference node motion speed and packet delivery rate；
The corresponding relationship of Figure 13 difference node motion speed and average end-to-end time delay.
[specific embodiment]
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.The given examples are served only to explain the present invention, is not intended to limit the present invention.
In order to more clearly describe ELAN method for routing, the present embodiment carries out emulation experiment using NS-2.35 platform, and right
The agreement of proposition carries out simulation analysis, and the GPSR agreement of the ELAN of proposition and classics, EMGR, EDGR algorithm having proposed are carried out
Comparative analysis.Details are as follows for specific implementation process:
1st, model foundation:
1.1st, the network model of node is established.
1.1.1, the initial position that node is arranged obey Poisson distribution.
Assuming that any two node is in different geographical location, the initial position of all nodes in a network is obeyed
Poisson distribution.
1.1.2, node obtain position by included GPS device, exchange bootstrap information packet or destination locations service
It sets and energy information.
The moving direction of each node is arbitrary, and node can obtain the position of itself by included GPS device
Confidence breath.Node can also obtain the location information and energy information of neighbor node by exchanging bootstrap information packet.Source node S can
To obtain the location information on packet rs destination ground by certain destination locations services.The position of node is with serving as its ID and network
Location.It therefore, there is no need to individual ID and establish agreement.
1.1.3, link mode use two-way link.
This method only considers two-way link, and assumes that its transimission power can be adjusted to it from 0 by each sensor node
Maximum transmission power.
1.2nd, the energy model of node is established.
1.2.1, calculate node send the energy consumption of data to neighbor node.
Since node p is consisted of three parts to the energy consumption Energy (p, q) that neighbours q sends 1 bit data, and count
Calculation method is as follows:
Wherein,It indicates from node p to the distance of node q.
The distance between 1.2.2, calculate node.
Because node periodically broadcasts bootstrap information packet, node the distance between can obtain according to the following formula
It arrives:
Wherein, (x, y) indicates to send the position of the node p of bootstrap information packet, (xq,yq) indicate that the one of sending node jumps neighbour
Occupy the position of node q.μ is the path loss constant between 2 to 5, is specifically dependent upon transmission environment, and a, b and c are to depend on electronics
The constant of characteristic and wireless device characteristics.Parameter indicates the path loss between node p and q, and b indicates node p
Energy consumed by signal is handled with q,Indicate that node carries out receiving the energy used in the transmission range of sender
Amount.Assuming that each sensor node a, b and c having the same.In the method, energy parameter is provided that μ=2, a=
100pJ/bit/mμ, b=100nJ/bit, and c=60pJ/bit/m2。
2nd, using greedy forwarding strategies.
Traditional greedy forwarding strategies only account for node to the distance of destination node, improved greedy forwarding in this method
Strategy carries out the unstability of processing neighborhood by considering following four parameter: reliable communication zone, link-quality,
Distance and neighbor node degree.
2.1st, reliable communication region is calculated.
As shown in Fig. 2, such as S, A, B, E, C respectively represent mobile node, D represents destination node.When S is attempted data packet
When being sent to D, S will find the mobile node nearest to destination node from adjacent neighbor node.To the nearest of destination node D
Node be B.Based on the distance between B to D calculate it is maximum allowable jump away from.By comparing above-mentioned several parameter evaluation movable joints
Communication stability between point S and the neighbor node of S selects most stable of next-hop node.
In Fig. 2, S sends data packet to D, and the coordinate of S and D are respectively set to (xS,yS) and (xD,yD).Turn when in greediness
When sending grouping in hair, source node S finds the node near D in the neighbor list of their own, and nearest node is
B, coordinate are (xB,yB).Distance d from B to DBDWith the distance d from B to SBSIt is calculated respectively by formula (3) and (4):
Will be centered on D, dmaxFor the radius for dividing reliable area, centered on S, maximum communication distance R is the two of radius
The overlapping region of a circle is defined as reliable communication region, which is named as RCA.Each node in RCA is not
Only close to destination node D, but also in the communication range of node S, it is suitble to be chosen as the next-hop node of S.dmaxCalculating side
Formula is as follows:
dmax=dBD+λ×dBS (5)
In equation (5), λ is to adjust radius dmaxThe coefficient of size, and λ ∈ [0,1].The value for being readily seen λ affects
The size of RCA.When λ is too big, RCA will become larger, then the node near S is easier to be selected as the next-hop in RCA, still
The hop count of the node to D can increase.When λ is too small, RCA will become smaller, then the node near D is easier under being chosen as in Q
One jumps, may be elongated to the distance of the node from S, and link stability possible deviation, and packet loss is caused to increase.By into
Row many experiments have preferable performance in terms of greedy forwarding when λ is set as 0.3.
2.2nd, link-quality assessment is carried out.
Relative displacement between 2.2.1, calculate node.
Due to the movement of node, the variation of network topology structure is resulted in, affects the stability of link.With between node
Relative displacement variable quantity measure node between link stability.At this point, the relative displacement L between nodedisplacementCalculating side
Method is as follows:
Wherein, R is the transmission radius (that is, maximum communication distance) of mobile node, usually a constant.di(t) it indicates
T moment sends the node of bootstrap information packet to the distance of neighbor node i.By considering the relative displacement between node,
LdisplacementSmaller, link stability is better.
Link between 2.2.2, calculate node is held time.
Because the size of node speed and the direction of movement can all cause the variation of link topology, the link being connected to originally is very
It is easy to be broken when sending data packet, therefore, holding time for link is also be worthy of consideration an important factor for.Node
Receive the bootstrap information packet of neighbor node transmission, the T at this point, link between node and node i is held timeiCalculation method such as
Under:
R2=((xi+v×Ti)-x)2+((yi+v×Ti)-y)2 (7)
Wherein, (x, y) indicates to send the position of the node of bootstrap information packet, (xi,yi) indicate sending node a hop neighbor
The position of node i.
2.2.3, the relative velocity for calculating two nodes.
R is the transmission radius of node, and v is the relative velocity of two nodes, and v can be calculated by following equation:
V=vi-vs (8)
viIt is the speed of neighbor node, vsIt is the speed for sending the node of bootstrap information packet.
The quality of 2.2.4, one hop link of calculate node.
Therefore, available using the two indices of holding time of relative displacement and link between above-mentioned node
The quality L of one hop linkquality, calculation method is as follows:
Wherein, ω indicates weight coefficient, LdisplacementIndicate the relative displacement between node, TiIndicate the chain between two nodes
It holds time on road.
2.3rd, distance and the assessment of neighbor node degree are carried out.
Range index between 2.3.1, calculate node.
It is recognised that transmitting energy consumed by data packet and reaching the distance dependent of destination node in energy model,
Therefore, it if the next-hop node to be selected is node reliable, that quality is high, is fully considered when designing forwarding strategy
To the distance of destination node.The calculation method of range index between node is as follows:
Wherein,Indicate sending node to destination node distance,Indicate a hop neighbor node i to destination node
Distance.
2.3.2, calculate node neighbours' degree next-hop node is measured.
In greedy repeating process, the neighbor node number of next-hop node is also a vital factor, if
Without considering, it is possible to cause the next-hop of the next-hop node of selection not select suitably, lead to overall network performance
Decline.Therefore, thxe present method defines neighbor node degree to measure to next-hop node.WithIndicate i-th of node
Neighbor node degree, and can be calculate by the following formula:
Wherein, niThe neighbor node number of i-th of alternate node is represented, N indicates the number of whole network interior joint.
2.4th, using greedy forwarder selection strategy.
Greediness forward selection next-hop node when, only consider next-hop node to destination node distance, it is possible to lead
It causes the link between the next-hop of selection unstable, influences the performance of network.Herein, selection next-hop node is forwarded in greediness
When, in reliable communication region, while considering three quality of link, the distance of node and neighbor node degree performance indicators.
On the one hand the rate of submitting of data packet can be improved, on the other hand also can be reduced propagation delay time.
This method defines Pri as the measurement for measuring next-hop greediness forward node, in reliable communication region, Pri value
Maximum node can be obtained as next-hop forward node, Pri by following formula:
Wherein,It indicates the neighbor node degree of i-th of node, can be obtained by exchanging bootstrap information packet, LqualityIt represents
The quality of one hop link, distance (s, i) represent sending node at a distance from neighbor node.Meanwhile α, beta, gamma are every
Coefficient weights, alpha+beta+γ=1.As Pri > 0, in reliable communication region, the forward node of next-hop is selected；When Pri≤0
When, it is believed that there is routing cavity, it is contemplated that the randomness of mobile ad-hoc network node motion, network topology are constantly to change
, herein, using waiting forward mode.
3rd, using based on periphery forwarding strategy.
Traditional GPSR algorithm carries out periphery forwarding using right-hand rule, although routing cavity can be laid out, obtains
Path be not usually optimal.This method thinks that only consideration distance factor is inadequate, and herein, this method considers angle
Degree factor, residue energy of node and link are held time.
3.1st, angle estimator is forwarded.
3.1.1, it calculates at a distance from alternate node M to present node C and the place destination node D path.
Different from traditional GPSR, this method takes right-hand rule and lefft-hand rule simultaneously, carries out periphery from both direction
Forwarding.Angle by taking right hand rule as an example, into after the forward mode of periphery, first between calculating present node C and alternate node
θ, it is assumed that the coordinate of node C is (xC,yC), the coordinate of alternate node M is (xM,yM), the coordinate of node D is (xD,yD), such as Fig. 3
It is shown, at this point, using dM→CDNode M is indicated to the distance of CD and can be obtained by following formula:
Angle between 3.1.2, calculating present node C and alternate node M.
So available:
3.2nd, dump energy proficiency assessment is carried out.
Because selecting the node of low energy, it is possible to lead to node energy consumption totally, cause to route empty increase.We
Method also contemplates the dump energy of node, and the high node of selection dump energy as far as possible is forwarded, and uses ElevelIndicate energy level,
Calculation method is as follows:
Wherein, EresidualIndicate the energy of current residual, EinitIndicate the primary power of node.
3.3rd, selection strategy is forwarded using all mid-side nodes.
After using periphery forward mode, present node calculates and the priority valve AEL of more all alternate nodes, selection
As next-hop node, AEL is calculated the maximum node of AEL value according to following formula:
In equation (16), p, q, f are the periphery forwarding of angle factor, dump energy level and link quality factors respectively
Weighting coefficient, and p+q+f=1, LqualityDefinition it is identical as formula (9).P=q=f=1/3 in this method, according to this hair
The method of bright middle proposition selects the biggish next-hop node of deflection angle that can increase the probability for jumping out routing cavity, keeps away as far as possible
Exempt to increase routing cavity, the high node of relative energy and more stable link is selected, when improving the existence of whole network
Between.
When encountering routing cavity, while taking right hand rule and left hand criterion, discovery two from routing void node to
The path of destination node.
4th, algorithm description is carried out.
ELAN agreement is added to based on energy level, link-quality, forwarding angle and neighbours' section on GSPR protocol basis
The mechanism of point degree has biggish improvement, and steps are as follows for specific algorithm.
The description of algorithm ELAN protocol steps
(1) position and the residual energy of the neighbor node of this node are obtained by transmission bootstrap information packet between node and node
Amount.When source node have send data request, first check for whether having purpose node in the neighbor node of oneself, if neighbours save
There are destination nodes in point, then send the data directly to destination node, conversely, then carrying out (2) step；
(2) sending node calculates d according to formula (5)maxValue, obtain reliable communication zone, it is then all reliably to lead to
Letter region be next-hop node alternate node, at this time according between formula calculate node relative shift and link maintain when
Between, the assessment of link-quality is obtained according to formula (9), then the distance of alternate node to destination node and neighbor node degree,
The value of PRI is obtained according to formula (13), the node for selecting PRI value big is as next-hop node.When bigger not than present node
PRI value when, this method thinks to have occurred routing cavity.
(3) when there is routing cavity, this method sets a MAX_WaitTime, at this using forward mode is waited
After a time, if present node still routes void node, Zhou Bianjie is carried out simultaneously using right hand rule and left hand criterion
Point forwarding.
(4) deflection angle is calculated using formula (14), when then comprehensively considering the maintenance of residue energy of node and link
Between, calculate the value of AEL, in both direction, select respectively AEL value it is big be used as next-hop periphery forward node, up to there is ratio
More preferably node changes into greedy forwarding strategies or until destination node to routing void node.
(5) when carrying out data transmission, select a link existent time long, the high path of energy residual carries out data biography
It is defeated.
The step of algorithm, is as follows:
The pseudocode of algorithm is as follows:
We carry out emulation experiment in this example, carry out emulation experiment using NS-2.35 platform, and this method is proposed
Agreement carries out simulation analysis, and the GPSR agreement of ELAN and classics that this method proposes, EMGR, EDGR algorithm having proposed are carried out
Comparative analysis.Simulation parameter is as shown in table 1.Each experiment simulation time is 500s, and be averaged 40 simulation results.
1 simulation parameter of table
By changing the density and communication session number of network node, using energy consumption, network lifecycle, data packet is delivered
Rate and delivery four performance indicators of delay, the agreement that assessment this method proposes.
Energy consumption is defined as participating in the gross energy of all the sensors node consumption of data transmitting.The measurement indicates all biographies
Sensor node consumption how many energy are for communicating.
Network lifecycle was defined as since simulation to certain time point, and node exhausts in network 20% or more energy
Amount.Load balance degree between node involved in measurement instruction communication.
The number for the data grouping that grouping transfer ratio is defined as the quantity of the data grouping successfully transmitted and source node generates
Ratio between amount.The index reflects data transmission efficiency.
Transmission delay is defined as being generated to the time delay for being transmitted to destination from grouping.The instruction of this module is saved from source
Point sends the speed of intended recipient after data packet.
As shown in Fig. 4, Fig. 5, Fig. 6 and Fig. 7, in the case where changing network-intensive degree, compare the network of four kinds of routings
Performance.The movement speed of node is identical, is 10m/s, and mobile direction is random.
Can clearly it find out from Fig. 4, with the increase of network-intensive degree, tetra- kinds of algorithms of GPSR, EMGR, EDGR and ELAN
Energy consumption be in downward trend.This method proposes that ELAN algorithm energy consumption is reduced to 2.15J from 2.75J, and EMGR energy disappears
Consumption is reduced to 2.48J from 3.0J, and EDGR energy consumption is reduced to 2.28J from 2.8J, and GPSR energy consumption is reduced to from 3.38J
2.64J.Experimental analysis shows that the ELAN agreement that this method proposes passes through and considers the problems of energy consumption, reduces energy consumption, relatively
In other three kinds of agreements, there is preferable performance.
As seen in Figure 5, with the increase of network-intensive degree, the network of tetra- kinds of algorithms of GPSR, EMGR, EDGR and ELAN is raw
Deposit the time and be in the trend of rising.The network lifetime for the ELAN algorithm that this method proposes increases to 382s, EMGR from 324s
Increase to 359s from 310s, EDGR increases to 373s from 319s, and GPSR rises to 349s from 295s.It can be seen that ELAN algorithm
Network lifetime be substantially higher in other three kinds of algorithms.
As seen in Figure 6, with the increase of network-intensive degree, the packet of tetra- kinds of algorithms of GPSR, EMGR, EDGR and ELAN is passed
Friendship rate is in the trend of rising.The rate of submitting of the data packet for the ELAN algorithm that this method proposes increases to 0.989 from 0.944,
EMGR, which from 0.937 increases to 0.98, EDGR and increases to 0.987, GPSR from 0.94, rises to 0.923 from 0.862.It is demonstrate,proved by experiment
Bright, the packet of ELAN algorithm submits rate higher than other three kinds of algorithms.
Can clearly it find out from Fig. 7, with the increase of network-intensive degree, tetra- kinds of algorithms of GPSR, EMGR, EDGR and ELAN
Network delay decline.The ELAN algorithm network delay that this method proposes is reduced to 82ms from from 102ms, EMGR algorithm from
116ms is reduced to 93ms, and EDGR algorithm drops to 87ms from 105ms, and traditional GPSR algorithm is reduced to 101ms from 126ms.Through
Cross it is demonstrated experimentally that ELAN in performance of the network delay in terms of this better than other three kinds of agreements.
As Fig. 8, Fig. 9, Figure 10 and Figure 11, to verify the performance of four kinds of algorithms, communicate meeting by changing communication session number
Words number is stepped up from 1 to 8, at this point, network node density remains unchanged, the speed of node is all identical, is set as 10m/s.
Fig. 8 shows that the gross energy with different communication session number tetra- kinds of algorithms of ELAN, EMGR, EDGR and GPSR disappears
Consumption.It is easy to see that the energy consumption of EDGR and ELAN algorithm is far below other two kinds of algorithms, this is because EDGR and ELAN is calculated
Method establish two it is different be routed to destination node, in this way, the probability of data packet retransmission reduces, therefore, relatively
For, energy consumption is less.
When Fig. 9 shows the network survivability with different communication session number tetra- kinds of algorithms of ELAN, EMGR, EDGR and GPSR
Between.Compared with other algorithms, ELAN algorithm considers the energy of the consumption of node and both candidate nodes in the forwarding strategy of periphery, and
And have found two different paths.Therefore, with the increase of communication session quantity, the ELAN network life that this method proposes is excellent
In other agreements.
Figure 10 describes the relationship between different communication session number and data Packet delivery ratio.With the increasing of communication session quantity
Add, the transfer ratio decline of data grouping.This is primarily due to that data collision has occurred on the empty boundary of routing.Because of this method
Data grouping can be assigned to the two sides in routing cavity, therefore the opposite drop of probability of data collision by ELAN the and EDGR algorithm of proposition
It is low, that is to say, that packet-based transport will not significantly reduce.
Figure 11 shows the transfer delay with different communication number of sessions.Emulation experiment shows with communication session number
The increase of amount, the ELAN algorithm that this method proposes had there are two the main reason for minimum time delay, performance gap.Firstly, this
The agreement that method proposes establishes two paths, the data collision occurred in data transmission can be reduced, to reduce number of retransmissions
And reduce transmission delay.Secondly, alternative section of the node as next-hop node that the agreement that this method proposes selects energy more
Point reduces the possibility of link failure.
In order to verify the practicability for the ELAN algorithm that this method proposes, ELAN algorithm is applied in car networking, mobile
Vehicle regard mobile node as, using 30 vehicles, the average speed of vehicle carries out in 30km/h-50km/h in intersection real
It tests.Compare the performance of ELAN, PBR [17] and tri- kinds of algorithms of GPSR.
Figure 12 shows in intersection, influence of the different node motion speed to packet delivery rate, with vehicle
The increase of speed, packet delivery rate decline, is primarily due to, and speed becomes larger, and results in link and is easier to disconnect, this method
The ELAN agreement of proposition maintains preferable performance, is worth appreciation.
Figure 13 shows influence of the different node motion speed to average end-to-end time delay.The experimental results showed that with vehicle
The increase of speed, average end-to-end time delay become larger, this is because speed increases, network topology are caused to change, existing
Path may no longer meet the needs of communication, result in the need for restarting route finding process, this propagation delay time undoubtedly increased, but phase
Than other two kinds of algorithms, ELAN has preferable performance.
Therefore, by considering the parameters such as relative displacement, neighbor node degree between node, this method on the basis of GPSR,
Propose mobile ad hoc network routing new method (ELAN) based on greedy forwarding improvement strategy.When utilize greedy forwarding strategies
When, first calculating reliable communication region, then according between node relative shift and link hold time, obtain link matter
Amount assessment is measured then according to link-quality assessment, the distance of alternate node to destination node and neighbor node degree
Value, the big node of selectance magnitude is as next-hop node.When there is routing cavity, according to the waiting time of setting, use
Forward mode is waited, after the waiting time, if present node still routes void node, while quasi- using right hand rule and left hand
Then, all mid-side node forwardings are carried out, it is empty in routing by considering holding time for deflection angle and residue energy of node and link
The two sides in hole select the high forward node as next-hop of preferred value respectively.Show that method of the invention obtains according to experiment
Preferable experiment effect.For this method with based on geographical location greediness stateless route (GPSR), Energy-aware multipath is geographical
Routing (EMGR) is compared with three kinds of methods of dual path Geographic routing (EDGR) of Energy-aware, and ELAN method reduces energy and disappears
Consumption, network delay, and reduce the possibility of link failure, improve the network lifetime and packet-based transport of algorithm.
Claims (1)
1. a kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy, it is characterised in that this method includes
Following steps:
1st, model foundation:
1.1st, network model；
The initial position obedience Poisson distribution of 1.1.1, node；
1.1.2, node by included GPS device, exchange bootstrap information packet or destination locations service obtain position and
Energy information；
1.1.3, link mode use two-way link；
1.2nd, energy model；
1.2.1, calculate node send the energy consumption of data to neighbor node；
The distance between 1.2.2, calculate node；
2nd, greedy forwarding strategies:
2.1st, reliable communication region；
By comparing reliable communication zone, link-quality, distance and neighbor node degree four parameter evaluation mobile nodes S and S
Neighbor node between communication stability, select most stable of next-hop node；
2.2nd, link-quality is assessed；
Relative displacement between 2.2.1, calculate node；
Link between 2.2.2, calculate node is held time；
2.2.3, the relative velocity for calculating two nodes；
The quality of 2.2.4, one hop link of calculate node；
2.3rd, distance and the assessment of neighbor node degree；
Range index between 2.3.1, calculate node；
2.3.2, calculate node neighbours' degree next-hop node is measured；
2.4th, greedy forwarder selection strategy；
Calculate the measurement of next-hop greediness forward node；
3rd, it is based on periphery forwarding strategy:
3.1st, angle estimator is forwarded；
3.1.1, it calculates at a distance from alternate node M to present node C and the place destination node D path；
Angle between 3.1.2, calculating present node C and alternate node M；
3.2nd, dump energy proficiency assessment；
The energy level of calculate node；
3.3rd, all mid-side nodes forward selection strategy；
Present node calculates the priority valve of all alternate nodes, and selecting priority is worth maximum node as next-hop node；
4th, algorithm description:
The obtains position and the dump energy of neighbor node by sending bootstrap information packet between 4.1, node；When source node has transmission
The request of data first checks for whether having purpose node in the neighbor node of oneself, if there are destination node in neighbor node,
Destination node is then sent the data directly to, conversely, then carrying out the 4.2nd step；
4.2nd, calculate that reliable communication region, relative shift between node, link is held time, alternate node arrives destination node
Distance, the degree of neighbor node and PRI value, the node for selecting PRI value big is as next-hop node.When bigger not than present node
PRI value when, it is considered herein that occur routing cavity；
4.3rd, when there is routing cavity, a MAX_WaitTime is set, if after which time, routing empty situation does not have
Improve, then carries out all mid-side node forwardings simultaneously using right hand rule and left hand criterion；
4.4th, deflection angle and AEL value are calculated, in both direction, selects AEL value is big to forward as next-hop periphery respectively
Node, until node more preferably than routing void node occur changes into greedy forwarding strategies or until destination node；
4.5th, when carrying out data transmission, select link existent time long, the high path of energy residual carries out data transmission.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910166351.2A CN109874147A (en) | 2019-03-06 | 2019-03-06 | A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910166351.2A CN109874147A (en) | 2019-03-06 | 2019-03-06 | A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109874147A true CN109874147A (en) | 2019-06-11 |
Family
ID=66919824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910166351.2A Withdrawn CN109874147A (en) | 2019-03-06 | 2019-03-06 | A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109874147A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110267321A (en) * | 2019-06-14 | 2019-09-20 | 西安电子科技大学 | Greedy multicast route protocol design method in three-dimensional scenic |
| CN110601976A (en) * | 2019-08-12 | 2019-12-20 | 浙江工业大学 | Self-adaptive deflection routing control method for electromagnetic nano network |
| CN111093172A (en) * | 2019-11-27 | 2020-05-01 | 上海工程技术大学 | GPSR (general purpose request) Internet of vehicles routing data forwarding method |
| CN111542096A (en) * | 2020-04-29 | 2020-08-14 | 沈阳理工大学 | Routing method based on request domain expansion and hole processing |
| CN112911544A (en) * | 2021-01-19 | 2021-06-04 | 汉纳森（厦门）数据股份有限公司 | Self-adaptive routing method for expressway Internet of vehicles |
| CN113891421A (en) * | 2021-09-24 | 2022-01-04 | 西安理工大学 | Method suitable for solving routing void occurring in greedy forwarding in three-dimensional space |
| CN114827000A (en) * | 2022-03-25 | 2022-07-29 | 华南理工大学 | GPSR routing protocol forwarding method based on link survival time position prediction |
| CN114978984A (en) * | 2022-07-01 | 2022-08-30 | 天津大学 | High-efficiency data placement method for bandwidth sensing in edge computing |
| CN115087069A (en) * | 2022-06-28 | 2022-09-20 | 重庆大学 | Self-adaptive geographical position routing method based on link duration |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030043827A1 (en) * | 2001-01-19 | 2003-03-06 | Steven Teig | LP method and apparatus for identifying route propagations |
| CN102131269A (en) * | 2011-04-29 | 2011-07-20 | 南京邮电大学 | Geographical-position-based routing method in wireless mesh network |
| CN102893666A (en) * | 2010-05-21 | 2013-01-23 | 皇家飞利浦电子股份有限公司 | Method and device for forwarding data packets |
| CN103702381A (en) * | 2012-09-28 | 2014-04-02 | 山东大学（威海） | Routing void processing method for wireless sensor network |
| CN106211257A (en) * | 2016-07-08 | 2016-12-07 | 广州大学 | A kind of energy acquisition routing algorithm for wireless sensor based on geographical position |
| US9832705B1 (en) * | 2016-09-02 | 2017-11-28 | The University Of North Carolina At Chapel Hill | Methods, systems, and computer readable media for topology management and geographic routing in mobile ad-hoc networks |
| CN107645417A (en) * | 2017-10-09 | 2018-01-30 | 天津理工大学 | Towards the adaptive routing method of expressway car networking scene |
-
2019
- 2019-03-06 CN CN201910166351.2A patent/CN109874147A/en not_active Withdrawn
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030043827A1 (en) * | 2001-01-19 | 2003-03-06 | Steven Teig | LP method and apparatus for identifying route propagations |
| CN102893666A (en) * | 2010-05-21 | 2013-01-23 | 皇家飞利浦电子股份有限公司 | Method and device for forwarding data packets |
| CN102131269A (en) * | 2011-04-29 | 2011-07-20 | 南京邮电大学 | Geographical-position-based routing method in wireless mesh network |
| CN103702381A (en) * | 2012-09-28 | 2014-04-02 | 山东大学（威海） | Routing void processing method for wireless sensor network |
| CN106211257A (en) * | 2016-07-08 | 2016-12-07 | 广州大学 | A kind of energy acquisition routing algorithm for wireless sensor based on geographical position |
| US9832705B1 (en) * | 2016-09-02 | 2017-11-28 | The University Of North Carolina At Chapel Hill | Methods, systems, and computer readable media for topology management and geographic routing in mobile ad-hoc networks |
| CN107645417A (en) * | 2017-10-09 | 2018-01-30 | 天津理工大学 | Towards the adaptive routing method of expressway car networking scene |
Non-Patent Citations (1)
| Title |
|---|
| 喻嘉等: "无线传感器网络中分段贪婪地理路由算法", 《控制与决策》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110267321A (en) * | 2019-06-14 | 2019-09-20 | 西安电子科技大学 | Greedy multicast route protocol design method in three-dimensional scenic |
| CN110267321B (en) * | 2019-06-14 | 2022-03-04 | 西安电子科技大学 | Greedy multicast routing protocol design method in three-dimensional scene |
| CN110601976A (en) * | 2019-08-12 | 2019-12-20 | 浙江工业大学 | Self-adaptive deflection routing control method for electromagnetic nano network |
| CN110601976B (en) * | 2019-08-12 | 2021-07-20 | 浙江工业大学 | Self-adaptive deflection routing control method for electromagnetic nano network |
| CN111093172B (en) * | 2019-11-27 | 2023-07-21 | 上海工程技术大学 | GPSR (gigabit passive distributed system) internet of vehicles routing data forwarding method |
| CN111093172A (en) * | 2019-11-27 | 2020-05-01 | 上海工程技术大学 | GPSR (general purpose request) Internet of vehicles routing data forwarding method |
| CN111542096A (en) * | 2020-04-29 | 2020-08-14 | 沈阳理工大学 | Routing method based on request domain expansion and hole processing |
| CN111542096B (en) * | 2020-04-29 | 2022-02-11 | 沈阳理工大学 | Routing method based on request domain expansion and hole processing |
| CN112911544A (en) * | 2021-01-19 | 2021-06-04 | 汉纳森（厦门）数据股份有限公司 | Self-adaptive routing method for expressway Internet of vehicles |
| CN113891421A (en) * | 2021-09-24 | 2022-01-04 | 西安理工大学 | Method suitable for solving routing void occurring in greedy forwarding in three-dimensional space |
| CN113891421B (en) * | 2021-09-24 | 2023-10-24 | 西安理工大学 | Method for solving routing void occurrence of greedy forwarding in three-dimensional space |
| CN114827000A (en) * | 2022-03-25 | 2022-07-29 | 华南理工大学 | GPSR routing protocol forwarding method based on link survival time position prediction |
| CN115087069A (en) * | 2022-06-28 | 2022-09-20 | 重庆大学 | Self-adaptive geographical position routing method based on link duration |
| CN115087069B (en) * | 2022-06-28 | 2024-06-07 | 重庆大学 | Self-adaptive geographic position routing method based on link duration |
| CN114978984A (en) * | 2022-07-01 | 2022-08-30 | 天津大学 | High-efficiency data placement method for bandwidth sensing in edge computing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109874147A (en) | A kind of method for routing of the mobile ad-hoc network based on greedy repeating optimizing strategy | |
| Zhang et al. | A new method of mobile ad hoc network routing based on greed forwarding improvement strategy | |
| Ullah et al. | Advances in position based routing towards its enabled fog-oriented vanet–a survey | |
| Zhang et al. | A multi-path routing protocol based on link lifetime and energy consumption prediction for mobile edge computing | |
| Nzouonta et al. | VANET routing on city roads using real-time vehicular traffic information | |
| Wolny | Modified DMAC clustering algorithm for VANETs | |
| CN101415248B (en) | Method for establishing layer-striding dynamic source route protocol based on load balance | |
| Harold Robinson et al. | PSOBLAP: particle swarm optimization-based bandwidth and link availability prediction algorithm for multipath routing in mobile ad hoc networks | |
| CN103476086B (en) | Dynamic multi-path AODV method for routing based on the prediction of node congestion degree | |
| Shrestha et al. | Vertex-based multihop vehicle-to-infrastructure routing for vehicular ad hoc networks | |
| Tan et al. | Power and mobility aware routing in wireless ad hoc networks | |
| Attia et al. | Advanced greedy hybrid bio-inspired routing protocol to improve IoV | |
| CN101932062A (en) | Multipath routing method in Ad Hoc network environment | |
| Silva et al. | An adaptive GPSR routing protocol for VANETs | |
| Satyajeet et al. | Heterogeneous approaches for cluster based routing protocol in vehicular ad hoc network (vanet) | |
| Noorani et al. | Routing in VANETs based on intersection using SDN and fog computing | |
| Shrivastava et al. | A survey on congestion adaptive routing protocols for mobile ad-hoc networks | |
| Kumar et al. | A survey on topology and position based routing protocols in vehicular ad hoc network (VANET) | |
| Zhao et al. | A vehicle density and load aware routing protocol for VANETs in city scenarios | |
| CN108541040A (en) | A kind of cross-layer routing protocol suitable under City scenarios | |
| Shafi et al. | A trust based energy and mobility aware routing protocol to improve infotainment services in VANETs | |
| Garrosi et al. | Geo-routing in urban Vehicular Ad-hoc Networks: A literature review | |
| Naresh et al. | Link prediction algorithm for efficient routing in VANETs | |
| Zhang et al. | V2V routing in VANET based on fuzzy logic and reinforcement learning | |
| Amjad et al. | Road aware QoS routing in VANETs |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20190611 |
|
| WW01 | Invention patent application withdrawn after publication |