CN102006237A - Routing decision method for delay tolerant network - Google Patents

Abstract

The invention discloses a routing decision method for a delay tolerant network, which mainly solves the defects that the prior art can not sufficiently use network node motion regulation and link historical information to carry out routing selection. The routing decision method comprises the following specific processes: (1) discretizing time-varying link capacity functions and constructing a three-dimensional matrix for describing network topology transformation; (2) carrying out two-value quantification on link capacity to obtain a homogeneous Markov model of each link; (3) estimating a link connection and disconnection state arrival rate by using the node motion regulation and the link historical information; (4) calculating link connection probabilities at any moments according to the obtained link connection and disconnection state arrival rate; (5) defining the routing decision processes of the delay tolerant network as a stage-limited Markov decision model; and (6) defining a globally optimal equation according to a Markov decision theory and calculating an optimal decision sequence by adopting a backward iteration and recursion algorithm. The invention is capable of satisfying the requirements of the delay tolerant network with larger delay and selecting an optimal routing decision sequence for the delay tolerant network.

Description

The routing decision method that is used for Delay Tolerant Network

Technical field

The invention belongs to communication technical field, relate to the Delay Tolerant Network routing decision, be used in the Delay Tolerant Network lifting overall performance of network.

Background technology

Delay Tolerant Network DTN is a kind of new-type network architecture, and general reference is because node motion etc. are former thereby do not have stable end-to-end transmission path even most of the time to be in a class network of interrupt status.This notion is put forward on famous international conference SIGCOMM2003 by K.Fall the earliest, the many communication networks beyond the internet have been contained in its application, as interspace network, rural network, Warnet, wild animal monitoring and tracking network, mobile Ad Hoc network and wireless sensor network, packet network PSN etc.Research to Delay Tolerant Network will provide the network service of certain quality for these networks under the situation of topological dynamic change, be considered to realize a key technology of " ubiquitous network ", have important Research Significance.

Compare with legacy network, the topological dynamic change of Delay Tolerant Network, there is not stable transmission path between node, even may all there be a complete transmission channel at any time, this makes those traditional Ad hoc network routing mechanisms that depend on the stable transfer path be difficult to play a role in Delay Tolerant Network, therefore, must be according to the new routing mechanism of its characteristics design.Present existing Delay Tolerant Network method for routing comprises based on the algorithm that floods that duplicates thought, infects routing algorithm, distributes and wait for algorithm etc., and these algorithms can guarantee transfer rate well when solving the Delay Tolerant Network routing issue; But because the contradiction between the finite element network resources such as required huge network overhead of these algorithms and nodal cache has seriously limited their use.The researcher has proposed different data drop policies at this situation, as abandoning DOA the earliest and minimum abandons DLE; The notion that has also proposed the probability diffusion is saved network overhead, promptly only to the higher neighbor node diffusion of probability, as probability route calculation method PROPHET; And based on energy constraint and historical information method for routing ERHR, or the like.Nonetheless, traditional method for routing still fails to make full use of the change histories of network topology and the schedule information of following each contact, optimizes the transmission course of message, thereby causes overall performance of network not high.

Summary of the invention

The objective of the invention is to overcome the deficiency of above-mentioned prior art, characteristics at Delay Tolerant Network, a kind of routing decision method that is used for Delay Tolerant Network is provided, the two mobility network bigger to predictable time delay, make full use of node self-operating rule and regulate with the contact probability of peripheral adjacent node, and form a kind of novel routing decision with learning functionality by the Markovian decision model, realize that Delay Tolerant Network node data bag input chance promotes, and is improved the Delay Tolerant Network overall performance.

For achieving the above object, technical scheme of the present invention is: at first, to the time become the link capacity function and carry out discretization and handle, construct a three-dimensional matrice and will describe network topology change; Then, discrete link capacity function is carried out two-value quantize, make every link obtain homogeneous Markov model; At last, with the routing decision procedure definition of Delay Tolerant Network is the Markovian decision model of limited discrete time decision-making of stage, and utilize global optimum's equation definition and back in the Markovian decision theory to contents such as iterative algorithms, and calculate the optimizing decision sequence, the specific implementation process is as follows:

(1) link with discretization is communicated with characteristic description network topology situation:

(1a) in the observation time section, long with given sampling interval Δ peace sliding steps, with the time change link capacity function c between arbitrary network node i and j _Ij(t) discretization obtains discrete series c _Ij(t ₀+ a Δ).

(1b) each node gained discrete series of will sampling spreads all over the whole network, makes each node integrate out a three-dimensional matrice that characterizes with discrete form:

A＝{c _ij(t ₀+aΔ)} _|V|×|V|×K，

Wherein, V, E represent respectively consider node and limit in the network, | V|, | E| represents the number on node and limit respectively; I, j=0,1, L, | V|-1; T is the network operation time, t ₀Be time statistics starting point, a is the time series of sampling, a=0, and 1, L K, K are the sample sequence maximum.

(2) according to the three-dimensional matrice A that has obtained, adopt two-value quantization means link capacity, every link obtains following homogeneous Markov model and is:

$c_{\mathrm{ij}}(t_0+\mathrm{a\Δ})=\left\{\begin{array}{c}0\\ 1\end{array}\right.,$

It is a markoff process that shifts between " 0 " and one state that this model has embodied each link capacity; In the formula, " 0 " expression link is communicated with, and " 1 " expression disconnects.

(3) according to the Markov model that obtained, the link historical information that obtains with each node characteristics of motion and statistics in the network is utilized the random process method for parameter estimation as a reference, estimates the connected state of each link and the arrival rate μ and the λ of off-state.

(4), obtain following differential equation group and be according to connection and off-state arrival rate μ, λ and the Kolmogorov equation of estimating gained:

$\left\{\begin{array}{c}{P^{\′}}_0\left(t\right)=-\mathrm{\λ}{P}_0\left(t\right)+\mathrm{\μ}{P}_1\left(t\right)\\ {P^{\′}}_1\left(t\right)=\mathrm{\λ}{P}_0\left(t\right)-\mathrm{\μ}{P}_1\left(t\right)\end{array}\right.,$

Finding the solution above differential equation group gets:

$P_0\left(t\right)=\frac{\mathrm{\μ}}{\mathrm{\λ}+\mathrm{\μ}}+\frac{\mathrm{\λ}}{\mathrm{\λ}+\mathrm{\μ}}{e}^{-(\mathrm{\λ}+\mathrm{\μ})t},$

$P_1\left(t\right)=\frac{\mathrm{\λ}}{\mathrm{\λ}+\mathrm{\μ}}+\frac{\mathrm{\μ}}{\mathrm{\λ}+\mathrm{\μ}}{e}^{-(\mathrm{\λ}+\mathrm{\μ})t}.$

In the following formula, P ₀(t), P ₁(t) be illustrated respectively in the t probability of stability of 0 state and 1 state constantly, P ' ₀(t), P ' ₁(t) be illustrated respectively in t constantly 0 state and 1 state produce probability.

(5), be the Markovian decision model of limited discrete time decision-making of stage with the routing decision procedure definition of Delay Tolerant Network according to each link Markov state model of obtaining: T, S, A (i), p (j ' | i, e _Ij), r (i, e _Ij), wherein:

T represents decision-making constantly, T={0, and 1, L, | V|-1}, | V|-1 represents the maximum permissible value of hop count, promptly allows to skip all other nodes except that source node at most.

The state that S expresses possibility, S=V, promptly a node is represented a state.

The action collection that A (i) expresses possibility, A (i)=e _Ij, e _Ij∈ E, i, j represents network node, e _IjExpression connects the limit of network node i and node j.

P _t(j ' | i, e _Ij) the expression transition probability,

This formula represents that the i node adopts action e constantly at t _IjArrive the probability of node j ', p (e _Ij) expression link e _IjThe connection probability, p (e _Ij)=P ₀(t), i, j, j ' ∈ S, e _Ij∈ E, t ∈ [0, | V|-1].

r _t(i, e _Ij) expression recompense value,

This formula represents that the i node adopts action e constantly at t _IjResulting, wherein, c _Ij(t) be link e _IjAt t link capacity constantly, i, j ∈ S, e _Ij∈ E, t ∈ T, N are the finish time, N ∈ [0, | V|-1].

(6) according to Markovian decision model and the Markovian decision theory set up, definition global optimum equation is as follows:

In this formula, u _t(h _t) be t optimal value constantly; r _t(i, e _Ij) be t i state employing constantly action e _IjThe recompense value; p _t(j|i, e _Ij) be t i state employing constantly action e _IjTransfer to the probability of j; h _tBe the preceding constantly track vector of t, state that is experienced before comprising and the action of being taked.

(7) according in the Markovian decision theory, the optimal policy solution procedure of finite horizon model, under the situation of known initial state and state of termination, adopt the back to the described global optimization equation of iterative recursive algorithm computation, obtain one group of optimizing decision sequence that connects initial condition and state of termination.

The present invention compared with prior art has following advantage:

1) the present invention handles by continuous channel capacity function being carried out discretization, with the network topology change of complexity, is converted into discrete static topology, is convenient to calculate sequence of decisions.

2) the present invention adopts Markov model to describe the connection situation of network, and is more objective, clearly described link break-make situation.

3) the present invention utilizes the node characteristics of motion and link historical information to estimate the arrival rate of link connected state and off-state, makes the routing decision process more reliable.

4) the present invention utilizes the Markovian decision model of limited discrete time decision-making of stage to solve Delay Tolerant Network routing decision problem, makes algorithm integral body have the function of intensified learning.

Description of drawings

Fig. 1 is a schematic flow sheet of the present invention;

Fig. 2 is the network topology change schematic diagram that the present invention constructs;

Fig. 3 is the data packet transmission success rate performance comparison diagram with the inventive method and traditional method for routing;

Fig. 4 is the packet average delay performance comparison diagram with the inventive method and traditional method for routing;

Fig. 5 is the average forwarding packet number of copies performance comparison diagram with the inventive method and traditional method for routing.

Embodiment

Followingly technical scheme of the present invention is described in further detail with reference to accompanying drawing.

With reference to Fig. 1, it is as follows that route of the present invention is set up process:

Step 1 becomes the link capacity function during discretization.

With reference to Fig. 2, long with given sampling interval Δ peace sliding steps in the observation time section, with link e between arbitrary network node i and j _IjThe time become link capacity function c _Ij(t) discretization obtains discrete series c _Ij(t ₀+ a Δ).The continuous variation of network topology is dispersed is each static topological diagram, wherein, i, j=0,1, L, | V|-1; e _Ij∈ E; V, E represent respectively consider node and limit in the network, | V|, | E| represents the number on node and limit respectively; T is the network operation time, t ₀Be time statistics starting point, a is the time series of sampling, a=0, and 1, L K, K are the sample sequence maximum.

Step 2, the three-dimensional matrice of structure description network topology change.

Each node gained discrete series of will sampling spreads all over the whole network, makes each node integrate out a three-dimensional matrice that characterizes with discrete form.

A＝{c _ij(t ₀+aΔ) _|V|×|V|×K。

Following formula has write down in the observation time section, the full detail of network topology change.

Step 3 according to the three-dimensional matrice A that has obtained, adopts two-value quantization means link capacity, and every link obtains following homogeneous Markov model and is:

$c_{\mathrm{ij}}(t_0+\mathrm{a\Δ})=\left\{\begin{array}{c}0\\ 1\end{array}\right.,$

It is a markoff process that shifts between " 0 " and one state that this model has embodied each link capacity, and in the formula, " 0 " expression link is communicated with, and " 1 " expression disconnects, and the connection characteristic of whole network is defined as | V| * | V| markoff process.

Step 4, according to the Markov model that has obtained, the link historical information that obtains with each node characteristics of motion and statistics in the network is utilized the random process method for parameter estimation as a reference, estimate the connected state of each link and the arrival rate μ and the λ of off-state, this estimation comprises two kinds of situations:

The one, for the internodal link of characteristics of motion the unknown, be the link historical information of utilizing statistics to obtain, adopt maximum-likelihood method or Bayes's method to estimate the arrival rate μ of this link connected state and the arrival rate λ of off-state;

The 2nd, for the known internodal link of the characteristics of motion, be to utilize deep space communication channel loss Model Calculation, become link capacity function c in the time of must this link _Ij(t); Again to the time become link capacity function c _Ij(t) disperse and two-value quantizes to obtain the arrival rate μ of this link connected state and the arrival rate λ of off-state.

Step 5 according to connection and off-state arrival rate μ, λ and the Kolmogorov equation of estimating gained, obtains following differential equation group and is:

$\left\{\begin{array}{c}{P^{\′}}_0\left(t\right)=-\mathrm{\λ}{P}_0\left(t\right)+\mathrm{\μ}{P}_1\left(t\right)\\ {P^{\′}}_1\left(t\right)=\mathrm{\λ}{P}_0\left(t\right)-\mathrm{\μ}{P}_1\left(t\right)\end{array}\right.,$

Finding the solution above differential equation group gets:

$P_0\left(t\right)=\frac{\mathrm{\μ}}{\mathrm{\λ}+\mathrm{\μ}}+\frac{\mathrm{\λ}}{\mathrm{\λ}+\mathrm{\μ}}{e}^{-(\mathrm{\λ}+\mathrm{\μ})t},$

$P_1\left(t\right)=\frac{\mathrm{\λ}}{\mathrm{\λ}+\mathrm{\μ}}+\frac{\mathrm{\μ}}{\mathrm{\λ}+\mathrm{\μ}}{e}^{-(\mathrm{\λ}+\mathrm{\μ})t};$

In the following formula, P ₀(t), P ₁(t) be illustrated respectively in the t probability of stability of 0 state and 1 state constantly, P ' ₀(t), P ' ₁(t) be illustrated respectively in t constantly 0 state and 1 state produce probability; By the P that obtains ₀(t), P ₁(t) link that can calculate at network operation any time t is communicated with and the disconnection probability, is about to concrete discrete time point t ₀+ a Δ is brought P into ₀(t), P ₁(t) link that calculates any time t in the equation is communicated with and the disconnection probability.

Step 6, definition Delay Tolerant Network routing decision model.

According to each the link Markov state model that obtains, be the Markovian decision model of limited discrete time decision-making of stage with the routing decision procedure definition of Delay Tolerant Network: T, S, A (i), p (j ' | i, e _Ij), r (i, e _Ij), wherein:

T represents decision-making constantly, T={0, and 1, L, | V|-1}, | V|-1 represents the maximum permissible value of hop count, promptly allows to skip all other nodes except that source node at most.

The state that S expresses possibility, S=V, promptly a node is represented a state.

The action collection that A (i) expresses possibility, A (i)=e _Ij, e _Ij∈ E, i, j represents network node, e _IjExpression connects the limit of network node i and node j.

P _t(j ' | i, e _Ij) the expression transition probability,

This formula represents that the i node adopts action e constantly at t _IjArrive the probability of node j ', p (e _Ij) expression link e _IjThe connection probability, p (e _Ij)=P ₀(t), i, j, j ' ∈ S, e _Ij∈ E, t ∈ [0, | V|-1].

r _t(i, e _Ij) expression recompense value,

This formula represents that the i node adopts action e constantly at t _IjResulting, wherein, c _Ij(t) be link e _IjAt t link capacity constantly, i, j ∈ S, e _Ij∈ E, t ∈ T, N are the finish time, N ∈ [0, | V|-1];

By the variation of recompense value in the Markovian decision model, constantly revise the action of being taked, make this routing decision process have the ability of intensified learning.

Step 7, definition global optimum equation.

According to Markovian decision model of having set up and Markovian decision theory, definition global optimum equation is as follows:

$u_t\left(h_t\right)=\left\{\begin{array}{cc}\underset{e_{\mathrm{ij}}\∈A\left(i\right)}{\min }[r_t(i,e_{\mathrm{ij}})+\underset{j\∈S}{\mathrm{\Σ}}{p}_t\left(j^{\′}\right|i,e_{\mathrm{ij}})u_{t+1}\left(h_{t+1}\right)],& t=(\mathrm{0,1},\mathrm{LN}-1)\\ 0,& t=N\end{array}\right.,$

In the formula, u _t(h _t) be t optimal value constantly; r _t(i, e _Ij) be t i state employing constantly action e _IjThe recompense value; p _t(j|i, e _Ij) be t i state employing constantly action e _IjTransfer to the probability of j; h _tBe the preceding constantly track vector of t, state that is experienced before comprising and the action of being taked.

Step 8 is calculated the optimizing decision sequence.

In the Markovian decision theory, the optimal policy solution procedure of finite horizon model, under the situation of known initial state and state of termination, adopt the back to the described global optimization equation of iterative recursive algorithm computation, obtain one group of optimizing decision sequence that connects initial condition and state of termination.Concrete steps are as follows:

(1) known initial state is s and state of termination d, s, and d ∈ S makes t=0, h _t=(s), work as h _t=(s, L s), promptly get back to the sequence of decisions of initial condition, u _t(h _t)=∞; Work as h _t=(s, L d), promptly stop sequence of decisions constantly, u _t(h _t)=0;

(2) upgrade decision-making t=t+1 constantly,, then enter (3) if t ∈ is T; Otherwise return (1);

(3) with arbitrary network node and action, i ∈ S, u _t(h _t) ≠ ∞, e _Ij∈ A (i _t) bring global optimum's equation into, calculate the t optimal value u that makes a strategic decision constantly _t(h _t), if, u _t(h _t)=0 then enters (4); Otherwise, upgrade track vector h _t=(h _t, i, e _Ij), return (2);

(4) storage track vector h _t, obtain optimizing decision sequence: h _t=(s, iL j, d).

Effect of the present invention can further specify by following simulation result:

1. simulated conditions

Suppose that the solar system nine major planets and the moon are the Delay Tolerant Network node, it is circular that its orbit is approximately, and be in the same plane; Each major planet has two artificial aircraft that are positioned at its Lagrangian point of safes, with the via node of this aircraft as major planet.Channel speed is 2Mbps, and bandwidth is 1Mhz, and simulation time was 52 weeks; The packet size is 1024bits; It is 5000s that packet sends spaced apart; The number of retransmissions that allows is 2 times; 86410s is at interval upgraded in the position.

2. emulation content

This emulation is divided into following four parts:

1) adopts the inventive method, in the Delay Tolerant Network environment of hypothesis, select the emulation of route, Data transmission bag.In emulation, statistical data packet transmission success rate, packet average delay reach the average packet number of copies of transmitting respectively.

2) adopt the infection method for routing, in the Delay Tolerant Network environment of hypothesis, select the emulation of route, Data transmission bag.In emulation, the transmission success rate of statistical data packet, packet average delay reach the average packet number of copies of transmitting respectively.

3) adopt probabilistic routing method, in the Delay Tolerant Network environment of hypothesis, select the emulation of route, Data transmission bag.In emulation, statistical data packet transmission success rate, packet average delay reach the average packet number of copies of transmitting respectively.

4) with three groups of transmission success rate data of statistics gained in above three emulation, make Fig. 3.With three groups of packet average delay data of statistics gained in above three emulation, make Fig. 4.Statistics in above three emulation is on average transmitted the packet copy data by three groups of gained, make Fig. 5.

3. simulation result

Fig. 3 shows that the present invention compares with probabilistic routing method with adopting the infection method for routing, can make Delay Tolerant Network obtain higher data packet transmission success rate.

Fig. 4 shows that the present invention compares with probabilistic routing method with adopting the infection method for routing, can make Delay Tolerant Network obtain lower packet average delay.

Fig. 5 shows that the present invention compares with probabilistic routing method with adopting the infection method for routing, can make Delay Tolerant Network obtain lower average forwarding packet number of copies.

In sum, adopt routing decision method of the present invention, the overall performance of Delay Tolerant Network is got a promotion.

The term note

The DTN Delay Tolerant Network

The PSN packet network

DOA abandons the earliest

The DLE minimum abandons

PROPHET probability route calculation method

ERHR energy constraint and historical information method for routing.

Claims (3)

1. routing decision method that is used for Delay Tolerant Network comprises following process:

(1) link with discretization is communicated with characteristic description network topology situation:

(1a) in the observation time section, long with given sampling interval Δ peace sliding steps, with the time change link capacity function c between arbitrary network node i and j _Ij(t) discretization obtains discrete series c _Ij(t ₀+ a Δ);

(1b) each node gained discrete series of will sampling spreads all over the whole network, makes each node integrate out a three-dimensional matrice that characterizes with discrete form:

A＝{c _ij(t ₀+aΔ) _|V|×|V|×K，

Wherein, V, E represent respectively consider node and limit in the network, | V|, | E| represents the number on node and limit respectively; I, j=0,1, L, | V|-1; T is the network operation time, t ₀Be time statistics starting point, a is the time series of sampling, a=0, and 1, L K, K are the sample sequence maximum;

(2) according to the three-dimensional matrice A that has obtained, adopt two-value quantization means link capacity, every link obtains following homogeneous Markov model and is:

$c_{\mathrm{ij}}(t_0+\mathrm{a\Δ})=\left\{\begin{array}{c}0\\ 1\end{array}\right.,$

It is a markoff process that shifts between " 0 " and one state that this model has embodied each link capacity; In the formula, " 0 " expression link is communicated with, and " 1 " expression disconnects;

(3) according to the Markov model that obtained, the link historical information that obtains with each node characteristics of motion and statistics in the network is utilized the random process method for parameter estimation as a reference, estimates the connected state of each link and the arrival rate μ and the λ of off-state;

(4), obtain following differential equation group and be according to connection and off-state arrival rate μ, λ and the Kolmogorov equation of estimating gained:

$\left\{\begin{array}{c}{P^{\′}}_0\left(t\right)=-\mathrm{\λ}{P}_0\left(t\right)+\mathrm{\μ}{P}_1\left(t\right)\\ {P^{\′}}_1\left(t\right)=\mathrm{\λ}{P}_0\left(t\right)-\mathrm{\μ}{P}_1\left(t\right)\end{array}\right.,$

Finding the solution above differential equation group gets:

$P_0\left(t\right)=\frac{\mathrm{\μ}}{\mathrm{\λ}+\mathrm{\μ}}+\frac{\mathrm{\λ}}{\mathrm{\λ}+\mathrm{\μ}}{e}^{-(\mathrm{\λ}+\mathrm{\μ})t},$

$P_1\left(t\right)=\frac{\mathrm{\λ}}{\mathrm{\λ}+\mathrm{\μ}}+\frac{\mathrm{\μ}}{\mathrm{\λ}+\mathrm{\μ}}{e}^{-(\mathrm{\λ}+\mathrm{\μ})t};$

In the following formula, P ₀(t), P ₁(t) be illustrated respectively in the t probability of stability of 0 state and 1 state constantly, P ' ₀(t), P ' ₁(t) be illustrated respectively in t constantly 0 state and 1 state produce probability;

(5), be the Markovian decision model of limited discrete time decision-making of stage with the routing decision procedure definition of Delay Tolerant Network according to each link Markov state model of obtaining: T, S, A (i), p (j ' | i, e _Ij), r (i, e _Ij), wherein:

T represents decision-making constantly, T={0, and 1, L, | V|-1}, | V|-1 represents the maximum permissible value of hop count, promptly allows to skip all other nodes except that source node at most;

The state that S expresses possibility, S=V, promptly a node is represented a state;

The action collection that A (i) expresses possibility, A (i)=e _Ij, e _Ij∈ E, i, j represents network node, e _IjExpression connects the limit of network node i and node j;

P _t(j ' | i, e _Ij) the expression transition probability,

This formula represents that the i node adopts action e constantly at t _IjArrive the probability of node j ', p (e _Ij) expression link e _IjThe connection probability, p (e _Ij)=P ₀(t), i, j, j ' ∈ S, e _Ij∈ E, t ∈ [0, | V|-1];

r _t(i, e _Ij) expression recompense value, This formula represents that the i node adopts action e constantly at t _IjResulting, wherein, c _Ij(t) be link e _IjAt t link capacity constantly, i, j ∈ S, e _Ij∈ E, t ∈ T, N are the finish time, N ∈ [0, | V|-1];

(6) according to Markovian decision model and the Markovian decision theory set up, definition global optimum equation is as follows:

In this formula, u _t(h _t) be t optimal value constantly; r _t(i, e _Ij) be t i state employing constantly action e _IjThe recompense value; p _t(j|i, e _Ij) be t i state employing constantly action e _IjTransfer to the probability of j; h _tBe the preceding constantly track vector of t, state that is experienced before comprising and the action of being taked;

(7) according in the Markovian decision theory, the optimal policy solution procedure of finite horizon model, under the situation of known initial state and state of termination, adopt the back to the described global optimization equation of iterative recursive algorithm computation, obtain one group of optimizing decision sequence that connects initial condition and state of termination.

2. according to claims 1 described routing decision method that is used for Delay Tolerant Network, wherein the described random process method for parameter estimation that utilizes of process (3) estimates the connected state of each link and the arrival rate μ and the λ of off-state, comprises two kinds of situations:

The one, for the internodal link of characteristics of motion the unknown, be the link historical information of utilizing statistics to obtain, adopt maximum-likelihood method or Bayes's method to estimate the arrival rate μ of this link connected state and the arrival rate λ of off-state;

The 2nd, for the known internodal link of the characteristics of motion, be to utilize deep space communication channel loss Model Calculation, become link capacity function c in the time of must this link _Ij(t); Again to the time become link capacity function c _Ij(t) disperse and two-value quantizes to obtain the arrival rate μ of this link connected state and the arrival rate λ of off-state.

3. according to claims 1 described routing decision method that is used for Delay Tolerant Network, wherein calculate as follows to the described global optimization equation of iterative recursive algorithm computation the described employing of process (7) back:

(7a) known initial state is s and state of termination d, s, and d ∈ S makes t=0, h _t=(s), work as h _t=(s, L s), promptly get back to the sequence of decisions of initial condition, u _t(h _t)=∞; Work as h _t=(s, L d), promptly stop sequence of decisions constantly, u _t(h _t)=0;

(7b) upgrade decision-making t=t+1 constantly,, then enter (7c) if t ∈ is T; Otherwise return (7a);

(7c) with arbitrary network node and action, i ∈ S, u _t(h _t) ≠ ∞, e _Ij∈ A (i _t) bring global optimum's equation into, calculate the t optimal value u that makes a strategic decision constantly _t(h _t), if, u _t(h _t)=0 then enters (7d); Otherwise, upgrade track vector h _t=(h _t, i, e _Ij), return (7b);

(7d) storage track vector h _t, obtain optimizing decision sequence: h _t=(s, iL j, d).

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