CN103561426A - Probability route improving method in delay-tolerance mobile sensor network based on node activeness - Google Patents
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Abstract
The invention relates to a probability route improving method in a delay-tolerance mobile sensor network based on the node activeness. The method adds node activeness factors into prediction of the transmission probability to provide a calculating formula of the self node activeness NA, the NA and a transmission prediction probability value of an original PROPHET algorithm are weighed through a weight alpha, a new transmission prediction probability value is calculated according to node meeting history information and node activeness in the network, whether information is transmitted is decided by comparing transmission prediction probability values of nodes, and buffer memory is managed through a time to live TTL discarding strategy. Simulation results show that a probability estimating method of an NAPR algorithm is more comprehensive than a PROPHET algorithm, fewer copies are generated, the NAPR algorithm reduces the node overhead rate even though the average delay of the information is slightly increased, and the delivery rate of message is improved. In future work, the probability route improving method can be further used for solving the jam problem in the network.
Description
Technical field
The present invention be a kind of in holding slow mobile sensor network (Delay Tolerant Mobile Sensor Networks, DTMSN) the modified model probabilistic routing method based on node liveness, belong to the probability routing algorithm field of delay-tolerant sensor network.
Background technology
DTMSN belongs to the category of delay-tolerant network (Delay Tolerant Networks, DTN), the network transmission data of DTN for being intermittently communicated with.DTN technology was introduced into wireless sensor network in recent years, and according to the difference of node mobility, delay-tolerant sensor network (Delay Tolerant Sensor Networks, DTSN) can be divided into the sensor network of network that sensor node is static and node motion.The sensor network of node motion holds slow mobile sensor network (Delay Tolerant Mobile Sensor Networks, DTMSN).DTMSN has high latency, low data rate, without the feature such as stable end-to-end connection, node resource be limited, for Data Collection widely, it is comprised of two kinds of nodes conventionally: convergent point and portable mobile sensor node.Wherein, sensor node is bundled in movably on object (as people, animal, vehicle etc.), for collecting the information of appointment, and form sparse intermittent communicated wireless network, convergent point position is fixed or movable, be used for the data that receiving sensor sends, and can forward the data to the access point of backbone network.
In DTMSN, random mobility model is often brought research, mainly contains: two kinds, random waypoint mobility model (Random Way-Point model, RWP) and random direction model (Random Direction, RD).Yet, in reality, the Move Mode of a lot of sensor nodes is not completely random, and the pattern based on a kind of repetition is moving often, it is a kind of predictable move mode, such as when some nodes often appear in certain region before, the probability that it appears in this region again so will be very large.By this phenomenon, just can obtain probability route, it is a kind of route of priori formula, or a kind of route based on node contact and handing down history: according to history between node meet information and transmission record, carry out the possible move mode of look-ahead node.In probability route, each node is safeguarded a probability tables separately, any two probability that node meets in this probability tables display network, namely between two nodes, transmit the probability of message, this table is dynamic change, As time goes on, can dynamically show the variation of transmitting message probability size between node.
The PROPHET(Probabilistic Routing Protocol using History of Encounters and Transitivity that the people such as LindgrenA propose) algorithm is a kind of typical probability routing algorithm.Utilize node to meet or deliver transfer history information, estimating that each node success delivery data is to the probability of destination node, i.e. communicating predicted value (arriving probability Delivery Predictability, DP).When two nodes meet, except intercoursing message list, arrival probability tables that also can switching node message transmission.Between node by relatively arriving the forwarding of probability control message.PROPHET algorithm has limited message copy quantity to a certain extent, thereby has obtained being similar to the delay of Epidemic algorithm, and has significantly reduced resource consumption.Yet PROPHET algorithm only decides the forwarding of message by arriving the height of the transmission probability of destination node, have certain blindness, the estimation of probability is short of reasonability, does not consider liveness and the forwarding problem on opportunity of each node itself.About PROPHET algorithm, have some corrective measures, but these improvement algorithms do not consider that the activity of node itself is for the impact of communicating predicted value, the estimated value of the probability obtaining shortcoming reasonability yet.
Summary of the invention
Technical problem: the present invention is directed to the problems referred to above, propose in calculating the process of communicating predicted value, utilize the weighted average of predictive factor of the node liveness factor and original transmitted probability as new communicating predicted value.Based on this, a kind of modified model probability routing algorithm NAPR(Node Activity-based Probabilistic Routing Algorithm based on node liveness has been proposed), the node liveness factor is joined among the prediction of transmission probability.
Technical scheme: of the present inventionly a kind ofly hold the improvement probabilistic routing method based on node liveness in slow mobile sensor network and be, the node liveness factor is joined among the prediction of transmission probability, the computing formula of the liveness NA of node itself is proposed, by weights α, NA and the original communicating predicted probable value of PROPHET algorithm are weighted, according to the node historical information of meeting, and node active degree in network, calculate new communicating predicted probable value, whether the forwarding of the big or small decision message by more communicating predicted probable value between node, utilize life span TTL drop policy to manage buffer memory, its step is as follows:
1) initialization: the transmission probability information table of node initializing oneself, is initialized as initial transmission probability constant P by the transmission probability of all nodes
init, P
initvalue between [0,1],
2) calculate the node liveness of each node: according to the contacted nodes of node and and upper node time of contact, calculate the liveness of each node itself,
3) in conjunction with node liveness and the record that meets, upgrade the communicating predicted probable value of each node,
4) utilize life span TTL drop policy to carry out queue management, when TTL is less than or equal to 0, message is abandoned from queue; When TTL is greater than 0, according to transmission probability, carry out message forwarding.
Described joins the node liveness factor among the prediction of transmission probability, proposes the computing formula of the liveness NA of node itself, and the computing formula of the liveness NA of node itself is defined as follows:
NA
ithe liveness that represents node i, NA
ithe quantity of size other nodes contacted to this node be directly proportional, and and this node and the length of upper node time of contact be inversely proportional to, that is:
Wherein, n
ithe quantity that represents other nodes that node i is contacted, t
ithe time of contact that represents node i and a upper node, λ is constant, and in order to realize normalization, λ is made as 10, i.e. 0≤NA
i≤ 1; For different nodes, NA is larger, represents that this node is more active in network, and the probability that the entrained message of this node is delivered to convergent point is larger.
By weights α, NA and the original communicating predicted probable value of PROPHET algorithm are weighted, specifically describe as follows:
Communicating predicted probable value P (a, b): message is transferred to the transmission probability parameter of Node B from node A, often using P (a, b) as probability route metric standard; At every turn meeting between node all increases the probable value between them, and the probable value between the node meeting is often just high, and this probable value is called transfer probability, and the value of communicating predicted P (a, b) has following two kinds of situations:
Situation 1: when node A and Node B are met:
P(a,b)＝α×[P(a,b)
old+(1-P(a,b)
old)×P
init]+(1-α)×(NA
b-NA
a) （2）
Wherein, α is weights, adjusts weights α and can change the node liveness factor for the impact of transmission probability predicted value, P (a, b)
oldthe last communicating predicted of the node A, the B that obtain from node meets historical record, P
init∈ (0,1] be initial constant, all P (a, b) initialization are all set to P
init;
P(a,b)＝α×P(a,b)
old×γ
k （3）
Wherein, γ ∈ [0,1] represent decay factor, institute's elapsed time after k represents that node A and Node B are last and contacts, the transmission predictability of node is stored with vector form, and can between node, exchange, as time goes by, the collision probability numerical value of two nodes can be progressively minimizing, this process can be called attenuation process again.
Utilize life span TTL drop policy to manage buffer memory, specifically describe as follows:
Life span TTL drop policy based on message is considered the remaining life span of message, each message is when producing, the TTL that has an initial value, message in nodal cache is according to its residue life span sequence, and the short message of life span comes above and obtains preferential transmission route, when this message is forwarded at every turn, ttl value constantly reduces, until TTL is equal to or less than at 0 o'clock, this message can be abandoned automatically, avoids unrestricted and must take Internet resources.
At every turn meeting between node all increases the probable value between them, and the probable value between the node meeting is often just high, and transfer probability namely specifically describes as follows:
If node A is often connected with Node B, and Node B is often connected with node C, this just means can think that node C and node A can be with high success rate forwarding messages, and probability transitivity can be expressed as:
P (a, c)=α * [P (a, c)
old+ (1-P (a, c)
old) * P (a, b) * P (b, c) * β]+(1-α) * [(NA
b-NA
a)+(NA
c-NA
b)] obtain after abbreviation:
P(a,c)＝α×[P(a,c)
old+(1-P(a,c)
old)×P(a,b)×P(b,c)×β]+(1-α)×(NA
c-NA
a) （4）
Wherein, β ∈ [0,1] being constant, is to transmit factor of influence, P (a, b) be the communicating predicted probable value between node A and Node B, similarly, P (b, c) is the communicating predicted probable value between Node B and node C, communicating predicted probable value between P (a, c) node A and node C.
Beneficial effect: the present invention is studied the probability route PROPHET algorithm meeting based on node in DTMSN or deliver transfer history information, and has proposed a kind of modified model probabilistic routing method based on node liveness, i.e. NAPR algorithm.The node liveness factor is joined among the prediction of transmission probability to the estimation of the transmission probability of perfect node.Simulation result shows, the Probabilistic estimation of NAPR algorithm is more comprehensive compared with PROPHET algorithm, and the number of copies of generation is less, although cause the average delay of message to increase to some extent, NAPR algorithm has reduced node overhead rate, has improved the rate of submitting of message.In future work, will further study and how tackle the congestion problems in network.
Accompanying drawing explanation
Fig. 1 NAPR algorithm flow chart,
During the different weights of Fig. 2, transmissibility changes with number of nodes,
During the different weights of Fig. 3, the average retardation of message changes with number of nodes,
During the different weights of Fig. 4, transmissibility changes with simulation time,
During the different weights of Fig. 5, the average retardation of message changes with simulation time,
The message count that Fig. 6 successfully submits changes with spatial cache,
The average retardation of Fig. 7 message changes with spatial cache,
Fig. 8 transmissibility changes with number of nodes,
The average number of copies of Fig. 9 changes with number of nodes,
Figure 10 message average retardation changes with number of nodes,
Figure 11 transmissibility changes with simulation time,
Figure 12 message average retardation changes with simulation time,
Figure 13 overhead rate changes with simulation time.
Embodiment
Basic thought: the present invention is directed to routing algorithm PROPHET in the DTMSN blindness problem that transmission probability estimates that E-Packets, defined the liveness NA(node of node active own) computing formula, introduce weights α NA and original PROPHET algorithm predicts transmission probability are weighted, the weighted average obtaining is as communicating predicted DP(Delivery Predictability).According to node, meet or deliver transfer history information, and node active degree in network, each node success delivery data calculated to the new communicating predicted probable value of destination node.When node meets, will judge whether to carry out data retransmission according to new communicating predicted value.When node does not meet, according to the attenuation process decay in PROPHET algorithm.Last utilize TTL drop policy the same as PROPHET algorithm manages buffer memory, according to transmission probability, carries out message forwarding.
Variable-definition:
Definition 1: liveness (NA): NA
ithe liveness that represents node i.NA
ithe quantity of size other nodes contacted to this node be directly proportional, and and this node and the length of upper node time of contact be inversely proportional to, that is:
Wherein, n
ithe quantity that represents other nodes that node i is contacted, t
ithe time of contact that represents node i and a upper node.λ is that constant (in order to realize normalization, is made as 10, i.e. 0≤NA
i≤ 1).For different nodes, NA is larger, represents that this node is more active in network, and the probability that the entrained message of this node is delivered to convergent point is larger.
Definition 2: communicating predicted probable value P (a, b): message is transferred to the transmission probability parameter of Node B from node A, often using P (a, b) as probability route metric standard.At every turn meeting between node all increases the probable value between them, and the probable value between the node meeting is often just high.The value of communicating predicted P (a, b) has following two kinds of situations:
Situation 1: when node A and Node B are met:
P(a,b)＝α×[P(a,b)
old+(1-P(a,b)
old)×P
init]+(1-α)×(NA
b-NA
a) （2）
Wherein, α is weights.Adjust weights α and can change the node liveness factor for the impact of transmission probability predicted value.P (a, b)
oldthe last communicating predicted of the node A, the B that obtain from node meets historical record, P
init∈ (0,1] be initial transmission probability constant, all P (a, b) initialization are all set to P
init.
P(a,b)＝α×P(a,b)
old×γ
k （3）
Wherein, γ ∈ [0,1] represents decay factor, institute's elapsed time after k represents that node A and Node B are last and contacts.The transmission predictability of node is stored with vector form, and can between node, exchange.As time goes by, the minimizing that the collision probability numerical value of two nodes can be progressively, this process can be called attenuation process again.
Definition 3: probability transmission: if node A is often connected with Node B, and Node B is often connected with node C, and this just means can think that node C and node A can be with high success rate forwarding messages, and probability transitivity can be expressed as:
P (a, c)=α * [P (a, c)
old+ (1-P (a, c)
old) * P (a, b) * P (b, c) * β]+(1-α) * [(NA
b-NA
a)+(NA
c-NA
b)] obtain after abbreviation:
P(a,c)＝α×[P(a,c)
old+(1-P(a,c)
old)×P(a,b)×P(b,c)×β]+(1-α)×(NA
c-NA
a) （4）
Wherein, β ∈ [0,1] is constant, is to transmit factor of influence.
According to algorithm idea, algorithm steps can be obtained as follows.
1) initialization.The transmission probability information table of node initializing oneself, is initialized as P by the transmission probability of all nodes
init, P
initvalue between [0,1].
2) calculate the node liveness of each node.According to the contacted nodes of node and and upper node time of contact, according to formula (1), calculate the liveness of each node itself.
3) in conjunction with node liveness and the record that meets, according to formula (2), (3), upgrade the communicating predicted probable value of each node.
4) utilize TTL to carry out queue management.When TTL is less than or equal to 0, message is abandoned from queue; When TTL is greater than 0, according to transmission probability, carry out message forwarding.
Operation principle:
The described operation principle of this algorithm refers to the node liveness factor is joined among the prediction of transmission probability, specifically refer to according to the node historical information of meeting, and node active degree in network, calculate new communicating predicted probable value, whether the forwarding of the big or small decision message by more communicating predicted probable value between node, utilizes TTL drop policy to manage buffer memory.
At ONE(Opportunistic Networking Environment) NAPR algorithm is carried out to simulation analysis on emulation platform, and carry out contrast experiment with Epidemic algorithm and PROPHET algorithm.
Simulated environment configuration is as shown in table 1: simulating scenes adopts Helsinki city city map as moving area, size is 4500m*3400m, number of nodes is respectively 3*10, 3*20, 3*30, 3*40, 3*50, 3*60, 3*70, 3*80, 3*90, 3*100, be divided into walking, driving and by bike three kinds of nodes, 100 every kind, the range for wireless communication of node (transmission radius) is 10m, bandwidth is 250kbps, cache size is respectively 2M, 4M, 30M, mobility model adopts Helsinki city map model of ONE acquiescence, the translational speed of node is respectively 1.34m/s, 8.94m/s, 4.0m/s, represent respectively walking, drive and three kinds of different average speeds of activity by bike.Data package size is 50KB, and the rise time is spaced apart 50s.The simulation run time is 2,4 ..., 30ks.
Table 3ONE simulated environment
In NAPR algorithm simulating process, adopt following 4 performance index:
1) transmissibility: the message count that success is transmitted and the ratio of message sum.
Delivery probability=delivered messages/created messages
2) overhead rate: be forwarded but the ratio of the message number that does not have successfully to transmit and the message number of successfully transmitting.
Overhead ratio=(relayed messages-delivered messages)/delivered messages
3) average number of copies: the copy sum that message produces and the ratio of message sum.The number of copies that message i produces is designated as c
i, message sum is designated as n.
4) average retardation: the ratio of submitting the time sum of successful all packets from source to destination and total packet number.Message i time of reception is designated as t
ir, message i transmitting time is designated as t
is, message sum is designated as n.
In simulation process, carry out four groups of experiments, test respectively in the situation that different weights α, cache size, number of nodes and simulation run time the situation of change of the routing performance of three kinds of algorithms.In emulation experiment, first through experiment, determine the value of α, then utilize NAPR algorithm in this paper and Epidemic algorithm, PROPHET algorithm to carry out contrast experiment, obtain final experimental result.
(1), in first group of experiment, by adjusting weights α, change the node liveness factor at communicating predicted middle proportion, the impact that the change of observation weights α produces the performance index of NAPR algorithm.When α value is 0.1,0.3,0.5,0.7,0.9 o'clock, the result of variations of the performance index of NAPR algorithm as shown in Figure 2-5.
(a) the message generation time interval is set to 50s, nodal cache space is for being set to 2M, simulation time is set to 30ks, number of nodes increases to 300 gradually from 30, represent that nodes dense degree increases gradually, test NAPR algorithm in α=0.1, the performance change situation of α=0.3, α=0.5, α=0.7, α=0.9 o'clock.Result as shown in Figure 2,3.
As seen from Figure 2, along with number of nodes increases gradually, the message that can successfully arrive convergent point reduces gradually, and the transmissibility of NAPR algorithm reduces gradually.When number of nodes is fixedly time, along with the increase of α value, the impact of historical record in NAPR algorithm of meeting increases gradually, and the message that can successfully arrive convergent point reduces gradually, thereby the transmissibility of algorithm reduces gradually.
As seen from Figure 3, along with number of nodes increases gradually, the node density in network increases gradually, and the average retardation of NAPR algorithm also increases gradually.When number of nodes is fixedly time, along with α value increases, the impact of historical record in NAPR algorithm of meeting increases gradually, and message arrives the speed of convergent point to be accelerated, and the average retardation of NAPR algorithm reduces gradually.
(b) the message generation time interval is set to 50s, nodal cache space is for being set to 2M, simulation time is set to 30ks, number of nodes is set to 150, simulation time is increased to 30ks gradually from 2ks, test NAPR algorithm in α=0.1, the performance change situation of α=0.3, α=0.5, α=0.7, α=0.9 o'clock.Result as shown in Figure 4,5.
As can be seen from Figure 4, along with the increase gradually of simulation time, the message number that can arrive convergent point increases gradually, so the rate of submitting of NAPR algorithm is increasing gradually.When simulation time is fixed as some determined values, along with α value increases, the impact of historical record in NAPR algorithm of meeting increases gradually, and the message that can successfully arrive convergent point reduces gradually, thereby the transmissibility of algorithm reduces gradually.
As can be seen from Figure 5, along with the increase gradually of simulation time, message institute's time spent that can arrive convergent point increases gradually, so the message average retardation of NAPR algorithm is increasing gradually.When simulation time is fixed as some determined values, along with α value increases, the impact of historical record in NAPR algorithm of meeting increases gradually, and message arrives the speed of convergent point to be accelerated, and the average retardation of NAPR algorithm reduces gradually.
Test by first group, we can find: along with α value increases, the impact of historical record in NAPR algorithm of meeting increases gradually, and the message that can successfully arrive convergent point reduces gradually, thereby the transmissibility of algorithm reduces gradually; But message arrives the speed of convergent point to be accelerated, the average retardation of NAPR algorithm reduces gradually.Weigh the advantages and disadvantages, consider two factors of transmissibility and average retardation, weights α value is 0.5.
(2) in second group of experiment, the message generation time interval is set to 50s, and number of nodes is set to 3*50=150, and simulation time is set to 30ks, and α value is 0.5.Observe when nodal cache space is increased to 30M from 2M the situation of change of the message count of successfully submitting and message average retardation.Simulation result as shown in Figure 6,7.
Fig. 6 is when nodal cache space is reduced to 2M from 30M, the situation of change of the message count that the success of NAPR, Epidemic and tri-kinds of algorithms of PROPHET is submitted.The information that Epidemic algorithm is successfully submitted is minimum, and this is to take spatial cache because inundation mechanism can produce bulk redundancy copy.NAPR algorithm and PROPHET algorithm have all reduced copy amount, and the Information Number of therefore successfully submitting is more than Epidemic algorithm.Along with spatial cache constantly reduces, can produce congestion situation, node need to discharge spatial cache by packet loss, and therefore the transmissibility of three kinds of algorithms all declines gradually.Epidemic and PROPHET algorithm adopt random loss strategy, and therefore the situation that the message of most possibly successfully being transmitted is abandoned may occur.And NAPR algorithm adopts the packet loss strategy based on TTL, abandon the message that transfer probability is minimum, therefore in the situation that buffer memory is very little, can keep higher transmissibility.
Fig. 7 is nodal cache space while being increased to 30M from 2M, the situation of change of the message average delay of NAPR, Epidemic and tri-kinds of algorithms of PROPHET.Along with spatial cache increases gradually, message has longer wait memory time to be forwarded in buffer memory, and therefore the time delay of three kinds of algorithms increases gradually.The time delay of Epidemic algorithm is minimum, because message copy quantity is maximum, message is successfully delivered to the fastest of convergent point.PROPHET algorithm copy amount reduces to some extent, so time delay also increases to some extent.NAPR algorithm in this paper joins the node liveness factor among the prediction of transmission probability, the estimation of the transmission probability of perfect node, thereby the message copy minimum number forwarding, so message average delay is greater than PROPHET algorithm.
(3) in the 3rd group of experiment, the message generation time interval is set to 50s, nodal cache space is for being set to 2M, simulation time is set to 30ks, α value is 0.5, number of nodes increases to 300 gradually from 30, represents that nodes dense degree increases gradually, tests the performance change situation of three kinds of routing algorithms.Simulation result is as shown in Fig. 8-10.
Figure 8 shows that the transmissibility of three kinds of algorithms is with the situation of change of number of nodes.When number of nodes increases to 300 gradually from 30, the node density in network constantly increases, in the situation that the spatial cache of node is only 2M, along with the generation of congestion situation, transmissibility will constantly reduce.In Epidemic algorithm, exist bulk redundancy copy to take spatial cache, so transmissibility is minimum.Seemingly, transmissibility is not high yet for the situation of PROPHET algorithm and the Epidemic class of algorithms.The message copy minimum number of NAPR algorithm repeating, can avoid producing too much redundant copy, and not adopt random loss strategy, so transmissibility is apparently higher than other two kinds of algorithms.
Fig. 9 is for increasing to gradually 300 when number of nodes from 30, when the node density in network constantly increases, and the situation of change of the average number of copies of three kinds of algorithms.Epidemic algorithm is a kind of evolution of flooding algorithm, and the average number of copies of generation is maximum; PROPHET algorithm is similar to Epidemic, is also many copy replication transmission, and the average number of copies of generation is only second to Epidemic algorithm; And NAPR algorithm consummation the transmission probability of PROPHET algorithm estimate, reduced the quantity of copy, provide cost savings expense.
As shown in Fig. 9,10, when number of nodes increases to 300 gradually from 30, the node density in network constantly increases, and the time delay of three kinds of algorithms slightly increases.The time delay of Epidemic is the shortest maximum because of message copy number, and transmission speed is the fastest, and PROPHET algorithm and Epidemic are similar, number of copies is many compared with NAPR algorithm, time delay is less, and the number of copies that NAPR algorithm produces is minimum, so the average delay of NAPR algorithm is the longest.
(4) in the 4th group of experiment, the message generation time interval is set to 50s, and number of nodes is set to 3*50=150, nodal cache space is for being set to 2M, α value is 0.5, and simulation time is increased to 30ks from 2ks, tests the performance change situation of three kinds of routing algorithms.Simulation result is as shown in Figure 11-13.
As can be seen from Figure 11, along with the increase of simulation time, message is through copying forwarding, and the quantity that is successfully delivered to convergent point increases gradually, and the transmissibility of three kinds of algorithms all increases to some extent.The amplification of NAPR algorithm is larger, and Epidemic algorithm takes second place, and PROPHET algorithm amplification is minimum.NAPR algorithm has been considered the factor of the liveness of node own, and transmission probability is estimated more comprehensive, and the message number that is successfully delivered to convergent point is more, so the transmissibility of NAPR algorithm is the highest.
As shown in figure 12, when simulation time is increased to 30ks from 2ks, in tri-kinds of algorithms of Epidemic, PROPHET and NAPR, Epidemic message copy number is maximum, is delivered to the fastest of convergent point, and required time delay is the shortest, and PROPHET algorithm time delay is taken second place; Along with the increase of simulation time, the Probabilistic estimation of NAPR algorithm is more comprehensive compared with PROPHET algorithm, and the number of copies that NAPR algorithm produces is less than PROPHET algorithm, so be delivered to the required average delay of convergent point, is greater than PROPHET algorithm.
As seen from Figure 13, along with the increase of simulation time, message is through copying forwarding, and required number of copies increases gradually, and the overhead rate of three kinds of algorithms constantly increases.Epidemic message produces many no-good copies, the increase that brings transmission cost.PROPHET algorithm and Epidemic are similar, and number of copies is many compared with NAPR algorithm, so the required expense of PROPHET algorithm is only second to Epidemic algorithm.And the number of copies that NAPR algorithm produces is minimum, so the required expense of NAPR algorithm is minimum.
Claims (5)
1. one kind is held the improvement probabilistic routing method based on node liveness in slow mobile sensor network, it is characterized in that the node liveness factor to join among the prediction of transmission probability, the computing formula of the liveness NA of node itself is proposed, by weights α, NA and the original communicating predicted probable value of PROPHET algorithm are weighted, according to the node historical information of meeting, and node active degree in network, calculate new communicating predicted probable value, whether the forwarding of the big or small decision message by more communicating predicted probable value between node, utilize life span TTL drop policy to manage buffer memory, its step is as follows:
1) initialization: the transmission probability information table of node initializing oneself, is initialized as initial transmission probability constant P by the transmission probability of all nodes
init, P
initvalue between [0,1],
2) calculate the node liveness of each node: according to the contacted nodes of node and and upper node time of contact, calculate the liveness of each node itself,
3) in conjunction with node liveness and the record that meets, upgrade the communicating predicted probable value of each node,
4) utilize life span TTL drop policy to carry out queue management, when TTL is less than or equal to 0, message is abandoned from queue; When TTL is greater than 0, according to transmission probability, carry out message forwarding.
2. according to the improvement probabilistic routing method based on node liveness in the slow mobile sensor network of appearance claimed in claim 1, it is characterized in that the described node liveness factor being joined among the prediction of transmission probability, the computing formula that proposes the liveness NA of node itself, the computing formula of the liveness NA of node itself is defined as follows:
NA
ithe liveness that represents node i, NA
ithe quantity of size other nodes contacted to this node be directly proportional, and and this node and the length of upper node time of contact be inversely proportional to, that is:
Wherein, n
ithe quantity that represents other nodes that node i is contacted, t
ithe time of contact that represents node i and a upper node, λ is constant, and in order to realize normalization, λ is made as 10, i.e. 0≤NA
i≤ 1; For different nodes, NA is larger, represents that this node is more active in network, and the probability that the entrained message of this node is delivered to convergent point is larger.
3. according to the improvement probabilistic routing method based on node liveness in the slow mobile sensor network of appearance claimed in claim 1, it is characterized in that by weights α, NA and the original communicating predicted probable value of PROPHET algorithm being weighted, specifically describe as follows:
Communicating predicted probable value P (a, b): message is transferred to the transmission probability parameter of Node B from node A, often using P (a, b) as probability route metric standard; At every turn meeting between node all increases the probable value between them, and the probable value between the node meeting is often just high, and this probable value is called transfer probability, and the value of communicating predicted P (a, b) has following two kinds of situations:
Situation 1: when node A and Node B are met:
P(a,b)＝α×[P(a,b)
old+(1-P(a,b)
old)×P
init]+(1-α)×(NA
b-NA
a) （2）
Wherein, α is weights, adjusts weights α and can change the node liveness factor for the impact of transmission probability predicted value, P (a, b)
oldthe last communicating predicted of the node A, the B that obtain from node meets historical record, P
init∈ (0,1] be initial constant, all P (a, b) initialization are all set to P
init;
Situation 2, when node A and Node B are not met:
P(a,b)＝α×P(a,b)
old×γ
k （3）
Wherein, γ ∈ [0,1] represent decay factor, institute's elapsed time after k represents that node A and Node B are last and contacts, the transmission predictability of node is stored with vector form, and can between node, exchange, as time goes by, the collision probability numerical value of two nodes can be progressively minimizing, this process can be called attenuation process again.
4. according to the improvement probabilistic routing method based on node liveness in the slow mobile sensor network of appearance described in claims 1, it is characterized in that utilizing life span TTL drop policy to manage buffer memory, specifically describe as follows:
Life span TTL drop policy based on message is considered the remaining life span of message, each message is when producing, the TTL that has an initial value, message in nodal cache is according to its residue life span sequence, and the short message of life span comes above and obtains preferential transmission route, when this message is forwarded at every turn, ttl value constantly reduces, until TTL is equal to or less than at 0 o'clock, this message can be abandoned automatically, avoids unrestricted and must take Internet resources.
5. according to the improvement probabilistic routing method based on node liveness in the slow mobile sensor network of appearance described in claims 3, it is characterized in that at every turn meeting between node all increases the probable value between them, probable value between the node meeting is often just high, transfer probability namely, specifically describes as follows:
If node A is often connected with Node B, and Node B is often connected with node C, this just means can think that node C and node A can be with high success rate forwarding messages, and probability transitivity can be expressed as:
P (a, c)=α * [P (a, c)
old+ (1-P (a, c)
old) * P (a, b) * P (b, c) * β]+(1-α) * [(NA
b-NA
a)+(NA
c-NA
b)] obtain after abbreviation:
P(a,c)＝α×[P(a,c)
old+(1-P(a,c)
old)×P(a,b)×P(b,c)×β]+(1-α)×(NA
c-NA
a) （4）
Wherein, β ∈ [0,1] being constant, is to transmit factor of influence, P (a, b) be the communicating predicted probable value between node A and Node B, similarly, P (b, c) is the communicating predicted probable value between Node B and node C, communicating predicted probable value between P (a, c) node A and node C.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110261692A1 (en) * | 2010-04-21 | 2011-10-27 | Josep Maria Pujol Serra | Method for balancing loads in mobile wireless ad-hoc networks |
| CN102740395A (en) * | 2012-07-12 | 2012-10-17 | 南京邮电大学 | Self-organizing routing method facing mobile sensor network |
| CN102857989A (en) * | 2012-07-13 | 2013-01-02 | 南京邮电大学 | Self-adaptive routing method oriented to mobile sensor network |
| CN103269506A (en) * | 2013-04-24 | 2013-08-28 | 陕西师范大学 | Mobile wireless sensor network routing method of interference sensing |
-
2013
- 2013-11-04 CN CN201310533854.1A patent/CN103561426B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110261692A1 (en) * | 2010-04-21 | 2011-10-27 | Josep Maria Pujol Serra | Method for balancing loads in mobile wireless ad-hoc networks |
| CN102740395A (en) * | 2012-07-12 | 2012-10-17 | 南京邮电大学 | Self-organizing routing method facing mobile sensor network |
| CN102857989A (en) * | 2012-07-13 | 2013-01-02 | 南京邮电大学 | Self-adaptive routing method oriented to mobile sensor network |
| CN103269506A (en) * | 2013-04-24 | 2013-08-28 | 陕西师范大学 | Mobile wireless sensor network routing method of interference sensing |
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