CN105611599A - Routing algorithm for dynamically adjusting forward angle based on residual energy - Google Patents

Abstract

The invention relates to a routing algorithm for dynamically adjusting a forward angle based on residual energy, belonging to the field of communication. When a certain node prepares to select a next-hop node, whether the residual energy of a neighbour node in the forward angle range of a current node is less than a residual energy threshold value of a node or not can be judged; if the residual energy of the neighbour node in the forward angle range of the current node is less than the residual energy threshold value, the forward angle of the node is increased; and, if a node, the residual energy of which is not less than the residual energy threshold value, exists in the neighbour node in the forward angle range of the current node, selection of the next-hop node is carried out by following a probability transition function. The routing algorithm disclosed by the invention is reasonable in design; a key node can be sheltered better; the network energy consumption can be balanced; the energy consumption uniformity degree of the node is improved; the information transmission success rate is also relatively high; the effective network coverage rate can be kept better; the robustness of a wireless sensor network can be improved; and premature paralysis of the network can be effectively prevented.

Description

A kind of routing algorithm of dynamically adjusting forward angle based on dump energy

Technical field

The present invention relates to a kind of routing algorithm of dynamically adjusting forward angle based on dump energy, belong to the communications field.

Background technology

Along with the renewal of MEMS (MEMS) technology, intelligence sensor has obtained development rapidly, makes wireless sensor network(WirelessSensorNetworks, WSNs) becomes a kind of brand-new Information acquisi-tion technology. To monitoring target informationPerception, collection, transmission and processing make wireless sensor network be widely used in environmental monitoring, disaster pollution monitoring, intelligence manThe numerous areas such as residence. Network route mainly realizes the transmission of source node information to destination node, is the basis of network-efficient communication,It is very important that the optimization problem of route also becomes.

But the feature such as the finite energy of wireless sensor network, self-organization of network and topology are unstable, has determined it and has passedThe difference of system network. Nature Food Recruiment In Ants behavior evolution goes out to be nowadays widely used in the ant group system of numerous areas. Ant group systemThe feature such as self-organization, adaptability and robustness, be highly suitable for solving the dynamic routing optimization problem of WSNs, be conducive toThe communication reliability of raising system, is also conducive to realize network energy consumption balance.

Wireless sensor network Sink node is the center that the network information converges, and around key node energy consumption is excessive for Sink node,Very easily cause the too early death of key node will directly impel self sensing range inefficacy, indirectly cause peripheral perception information to passTransport to Sink node, impel network early to enter state of paralysis. Existing route algorithm is often taked the mode of fixed route forward angleEnsure the fast convergence of algorithm and the lower-delay that perception information is uploaded, but this mode will bring next-hop node to selectLimitation and the centrality of route key node, cause part route key node because of traffic load overweight dead too early.

Summary of the invention

For the limited and existing routing algorithm Sink of the network energy node too fast problem of key node energy consumption around, the invention providesA kind of routing algorithm of dynamically adjusting forward angle based on dump energy.

Technical scheme of the present invention is: a kind of routing algorithm of dynamically adjusting forward angle based on dump energy, and when certain node standardWhen alternative next-hop node, judge that in present node forward angle range, whether neighbor node dump energy size is surplus lower than nodeComplementary energy threshold value E_th：

If neighbor node dump energy is all lower than dump energy threshold value E in present node forward angle range_th, node increases forward angleDegree; Wherein, node increases the principle of forward angle: from minimal forward angle A_minTo maximum forward angle A_max, until maximumIn forward angle range, all do not exist and meet dump energy higher than dump energy threshold value E_thNeighbor node time, follow its probabilityTransfer function principle, reselects next-hop node;

If exist dump energy to be not less than dump energy threshold value E in neighbor node in present node forward angle range_thNode,Follow its probability transfer function and carry out the selection of next-hop node;

Wherein, dump energy threshold value E_thChoose 30% of the initial dump energy of respective neighbours node; A_minChoose 60 °; A_maxChoose 240 °, adjust forward angle increment is 60 ° at every turn.

Described probability transfer function:Wherein,For jointBetween some i and the next-hop node j of node i the t moment k ant probability transfer function, τ_ij(t) be node i and node iNext-hop node j between the pheromones function in t moment, η_ij(t) be the t moment between the next-hop node j of node i and node iDistance heuristic function,For the energy heuristic function in t moment between the next-hop node j of node i and node i, A_iWhenThe set of all neighbor nodes of front nodal point i, α is the heuristic factor of pheromones, and β is the heuristic factor of distance, and γ is that energy inspiresThe formula factor.

Described pheromones function upgrades in the following way:

τ_ij(t+1)＝(1-ρ)·τ_ij(t)+Δτ_ij(t)

${\mathrm{\Δ\τ}}_{ij}\left(t\right)={\mathrm{\Σ}}_{k=1}^m{\mathrm{\Δ\τ}}_{ij}^k\left(t\right)$

In formula, ρ is pheromones volatilization element, Δ τ_ij(t) be routing information element increment between node i, j in this iteration, τ_ij(t) at the beginning ofInitial value is 0,Be k the pheromones that ant stays, m is ant group ant total number.

Described k the pheromones that ant staysIn renewal process:

If when k ant is Front ant, the pheromones that k ant stayed is carried out local updating:

${\mathrm{\Δ\τ}}_{ij}^k\left(t\right)=\frac{Q{L}_{}}{d_{ij}}+{\mathrm{\δE}}_j\left(t\right)$

In formula, Q_LFor local message element intensity and initial value are 0, d_ijRepresent between node i and the next-hop node j of node iDistance, δ be energy upgrade coefficient; E_j(t) represent that node j is in the dump energy in t moment (at the beginning of the dump energy in t moment representsBeginning dump energy deducts the energy of t moment node j consumption before);

If when k ant is Back ant, the pheromones that k ant stayed is carried out the overall situation and is upgraded:

${\mathrm{\Δ\τ}}_{ij}^k\left(t\right)=\frac{Q}{L_k}\·{\left(\frac{E_{\min }\·n_k}{{\mathrm{\Σ}}_{l=1}^{n_k}{E}_l}\right)}^v$

In formula, Q is that global information element intensity and initial value are 0, L_kBe the jumping figure of k ant, E_minBe k antThe dump energy of dump energy minimum node on the path of representative, n_kBe the interstitial content on the path of k ant representative,E_lBe the residue energy of node of arbitrary node l on the path of k ant representative, v is energy factors coefficient, characterizes energy and existsProportion in pheromones renewal.

The invention has the beneficial effects as follows: reasonable in design, residue energy of node and distance are introduced to ant group probability transfer function simultaneously,And route is proposed to the thought that forward angle is dynamically adjusted, can protect preferably key node, equalising network energy consumption. And dynamicallyThe introducing of forward angle, had both ensured hour ductility that network initial stage perception information is uploaded, again can be in the network later stage when sacrificial sectionProlong to exchange for the continuity of network useful life. And emulation experiment shows that REAFAD node energy consumption uniformity coefficient is improved,The success rate of communication is also better, and network Efficient Coverage Rate is better maintained, and has strengthened the strong of wireless sensor networkStrong property, has effectively avoided network to paralyse too early.

Brief description of the drawings

Fig. 1 is the indicator diagram of deviation angle of the present invention and forward angle;

Fig. 2 is the network energy consumption variance comparison diagram of the embodiment of the present invention 5 simulation results;

Fig. 3 is the communication success rate comparison diagram of the embodiment of the present invention 5 simulation results;

Fig. 4 is the network coverage comparison diagram of the embodiment of the present invention 5 simulation results.

Detailed description of the invention

Embodiment 1: as Figure 1-4, a kind of routing algorithm of dynamically adjusting forward angle based on dump energy, a kind of based on surplusComplementary energy is dynamically adjusted the routing algorithm of forward angle, in the time that certain node is prepared to select next-hop node, judges before present nodeTo neighbor node dump energy size in angular range whether lower than residue energy of node threshold value E_th：

If neighbor node dump energy is all lower than dump energy threshold value E in present node forward angle range_th, node increases forward angleDegree; Wherein, node increases the principle of forward angle: from minimal forward angle A_minTo maximum forward angle A_max, until maximumIn forward angle range, all do not exist and meet dump energy higher than dump energy threshold value E_thNeighbor node time, follow its probabilityTransfer function principle, reselects next-hop node;

If exist dump energy to be not less than dump energy threshold value E in neighbor node in present node forward angle range_thNode,Follow its probability transfer function and carry out the selection of next-hop node;

Wherein, dump energy threshold value E_thChoose 30% of the initial dump energy of respective neighbours node; A_minChoose 60 °; A_maxChoose 240 °, adjust forward angle increment is 60 ° at every turn.

Described probability transfer function:Wherein,For jointBetween some i and the next-hop node j of node i the t moment k ant probability transfer function, τ_ij(t) be node i and node iNext-hop node j between the pheromones function in t moment, η_ij(t) be the t moment between the next-hop node j of node i and node iDistance heuristic function,For the energy heuristic function in t moment between the next-hop node j of node i and node i, A_iWhenThe set of all neighbor nodes of front nodal point i, α is the heuristic factor of pheromones, and β is the heuristic factor of distance, and γ is that energy inspiresThe formula factor.

Described pheromones function upgrades in the following way:

τ_ij(t+1)＝(1-ρ)·τ_ij(t)+Δτ_ij(t)

${\mathrm{\Δ\τ}}_{ij}\left(t\right)={\mathrm{\Σ}}_{k=1}^m{\mathrm{\Δ\τ}}_{ij}^k\left(t\right)$

In formula, ρ is pheromones volatilization element, Δ τ_ij(t) be routing information element increment between node i, j in this iteration, τ_ij(t) at the beginning ofInitial value is 0,Be k the pheromones that ant stays, m is ant group ant total number.

Described k the pheromones that ant staysIn renewal process:

If when k ant is Front ant, the pheromones that k ant stayed is carried out local updating:

${\mathrm{\Δ\τ}}_{ij}^k\left(t\right)=\frac{Q_L}{d_{ij}}+{\mathrm{\δE}}_j\left(t\right)$

In formula, Q_LFor local message element intensity and initial value are 0, d_ijRepresent between node i and the next-hop node j of node iDistance, δ be energy upgrade coefficient; E_j(t) represent the dump energy of node j in the t moment;

If when k ant is Back ant, the pheromones that k ant stayed is carried out the overall situation and is upgraded:

${\mathrm{\Δ\τ}}_{ij}^k\left(t\right)=\frac{Q}{L_k}\·{\left(\frac{E_{\min }\·n_k}{{\mathrm{\Σ}}_{l=1}^{n_k}{E}_l}\right)}^v$

In formula, Q is that global information element intensity and initial value are 0, L_kBe the jumping figure of k ant, E_minBe k antThe dump energy of dump energy minimum node on the path of representative, n_kBe the interstitial content on the path of k ant representative,E_lBe the residue energy of node of arbitrary node l on the path of k ant representative, v is energy factors coefficient, characterizes energy and existsProportion in pheromones renewal.

Embodiment 2: as Figure 1-4, a kind of routing algorithm of dynamically adjusting forward angle based on dump energy, a kind of based on surplusComplementary energy is dynamically adjusted the routing algorithm of forward angle, in the time that certain node is prepared to select next-hop node, judges before present nodeTo neighbor node dump energy size in angular range whether lower than residue energy of node threshold value E_th：

If neighbor node dump energy is all lower than dump energy threshold value E in present node forward angle range_th, node increases forward angleDegree; Wherein, node increases the principle of forward angle: from minimal forward angle A_minTo maximum forward angle A_max, until maximumIn forward angle range, all do not exist and meet dump energy higher than dump energy threshold value E_thNeighbor node time, follow its probabilityTransfer function principle, reselects next-hop node;

If exist dump energy to be not less than dump energy threshold value E in neighbor node in present node forward angle range_thNode,Follow its probability transfer function and carry out the selection of next-hop node;

Wherein, dump energy threshold value E_thChoose 30% of the initial dump energy of respective neighbours node; A_minChoose 60 °; A_maxChoose 240 °, adjust forward angle increment is 60 ° at every turn.

Described probability transfer function:Wherein,For jointBetween some i and the next-hop node j of node i the t moment k ant probability transfer function, τ_ij(t) be node i and node iNext-hop node j between the pheromones function in t moment, η_ij(t) be the t moment between the next-hop node j of node i and node iDistance heuristic function,For the energy heuristic function in t moment between the next-hop node j of node i and node i, A_iWhenThe set of all neighbor nodes of front nodal point i, α is the heuristic factor of pheromones, and β is the heuristic factor of distance, and γ is that energy inspiresThe formula factor.

Described pheromones function upgrades in the following way:

τ_ij(t+1)＝(1-ρ)·τ_ij(t)+Δτ_ij(t)

${\mathrm{\Δ\τ}}_{ij}\left(t\right)={\mathrm{\Σ}}_{k=1}^m{\mathrm{\Δ\τ}}_{ij}^k\left(t\right)$

In formula, ρ is pheromones volatilization element, Δ τ_ij(t) be routing information element increment between node i, j in this iteration, τ_ij(t) at the beginning ofInitial value is 0,Be k the pheromones that ant stays, m is ant group ant total number.

Embodiment 3: as Figure 1-4, a kind of routing algorithm of dynamically adjusting forward angle based on dump energy, a kind of based on surplusComplementary energy is dynamically adjusted the routing algorithm of forward angle, in the time that certain node is prepared to select next-hop node, judges before present nodeTo neighbor node dump energy size in angular range whether lower than residue energy of node threshold value E_th：

If neighbor node dump energy is all lower than dump energy threshold value E in present node forward angle range_th, node increases forward angleDegree; Wherein, node increases the principle of forward angle: from minimal forward angle A_minTo maximum forward angle A_max, until maximumIn forward angle range, all do not exist and meet dump energy higher than dump energy threshold value E_thNeighbor node time, follow its probabilityTransfer function principle, reselects next-hop node;

If exist dump energy to be not less than dump energy threshold value E in neighbor node in present node forward angle range_thNode,Follow its probability transfer function and carry out the selection of next-hop node;

Wherein, dump energy threshold value E_thChoose 30% of the initial dump energy of respective neighbours node; A_minChoose 60 °; A_maxChoose 240 °, adjust forward angle increment is 60 ° at every turn.

Described probability transfer function:Wherein,For jointBetween some i and the next-hop node j of node i the t moment k ant probability transfer function, τ_ij(t) be node i and node iNext-hop node j between the pheromones function in t moment, η_ij(t) be the t moment between the next-hop node j of node i and node iDistance heuristic function,For the energy heuristic function in t moment between the next-hop node j of node i and node i, A_iWhenThe set of all neighbor nodes of front nodal point i, α is the heuristic factor of pheromones, and β is the heuristic factor of distance, and γ is that energy inspiresThe formula factor.

Embodiment 4: as Figure 1-4, a kind of routing algorithm of dynamically adjusting forward angle based on dump energy, a kind of based on surplusComplementary energy is dynamically adjusted the routing algorithm of forward angle, in the time that certain node is prepared to select next-hop node, judges before present nodeTo neighbor node dump energy size in angular range whether lower than residue energy of node threshold value E_th：

If neighbor node dump energy is all lower than dump energy threshold value E in present node forward angle range_th, node increases forward angleDegree; Wherein, node increases the principle of forward angle: from minimal forward angle A_minTo maximum forward angle A_max, until maximumIn forward angle range, all do not exist and meet dump energy higher than dump energy threshold value E_thNeighbor node time, follow its probabilityTransfer function principle, reselects next-hop node;

If exist dump energy to be not less than dump energy threshold value E in neighbor node in present node forward angle range_thNode,Follow its probability transfer function and carry out the selection of next-hop node;

Wherein, dump energy threshold value E_thChoose 30% of the initial dump energy of respective neighbours node; A_minChoose 60 °; A_maxChoose 240 °, adjust forward angle increment is 60 ° at every turn.

Embodiment 5: as Figure 1-4, a kind of routing algorithm of dynamically adjusting forward angle based on dump energy,

Can learn that by Fig. 1 the deviation angle of node refers to that the line of source node N and Sink node and this Node B and source node N connectThe angle of line. The deviation angle of node is less just more levels off to the line of source node and Sink node, and data transmission delay is also just got overLittle. For making source node information can comparatively fast be transferred to aggregation node, conventionally allow node in forward angle ∠ CND, select down hop jointPoint, guarantees the rapidity of communication.

Simulating scenes parameter based on as shown in table 1 below is carried out emulation to algorithm:

Table 1 simulating scenes parameter

Parameter	Numerical value	Parameter	Numerical value
				Regional extent	100×100	β	0.96
Interstitial content	100	γ	0.5
				Communication radius	25	ρ	0.96
Perception radius	10	δ	1.5
				Sink coordinate	(50,50)	ν	2
α	1

1, network energy consumption variance contrast: maintain the balanced energy consumption of the each node of network, the robustness of energy maintaining network, directly effectively prolongsThe life-span of long radio sensing network, but repeatedly transfer of data will bring node energy consumption unbalanced. Under network energy consumption can be passed throughFormula is obtained:Wherein, n is network node number, E_iFor node i institute in a data transferEnergy consumption,For the energy consumption average of network one data transfer, as seen from Figure 2 REAFAD (ResidualEnergyAdjustForwardAngleDynamically, dump energy is dynamically adjusted forward angle) algorithm the convergence speed is very fast, network energy consumption varianceTo be starkly lower than FMEPNF (FunctionofMulti-objectEvaluationandPositivenegativeFeedb ack, many ordersMark evaluation function and positive-negative feedback mechanism) algorithm, show that network energy consumption is more even.

The network energy consumption Variance feature that experiment simulation draws is as follows:

Iteration 20 take second place before the rising of variance come from the route construction initial stage, node energy is abundant, optimal path is relatively stable to be causedDue to energy consumption inequality, the minimizing of variance thereafter, is the result that dynamic routing selection and forward angle are adjusted, and part key node obtainsTo protecting, exit critical path, energy obtains due to protection.

2, information transmits success rate contrast: the information of obtaining in sensing range is the object that radio sensing network is set up. Good routeAlgorithm contributes to perception information can be transferred to fast and effectively Sink node, completes information gathering. The too early death of key node not onlyAffect uploading of own perception information, also affect uploading of path ends perception information.

REAFAD algorithm (being the inventive method) is compared with FMEPNF algorithm as seen in Figure 3, the success rate of communicationObviously better.

It is as follows that the information that experiment simulation draws transmits success rate contrast characteristic:

FMEPN algorithm is in 40 left and right of iteration, and due to Sink, around node energy consumption is huge, can't bear the heavy load and death, causesPeripheral information can not successfully arrive at Sink node, and success rate reduces rapidly, and along with the increase REAFAD algorithm of iterationsCan remain success rate slow decreasing, effectively avoid network to paralyse too early.

3, network Efficient Coverage Rate contrast: the effective coverage range of network is exactly effective district that Sink node can be received perception informationTerritory. The big or small direct relation of effective coverage range the perception degree of network to relevant range.

Can draw along with the uploading of perception information by Fig. 4, the effective coverage range of FMEPN algorithm after iteration 40 times,Because of key node depleted of energy, end still has the sensing node perception information of energy cannot converge to Sink node, causes effectively coveringLid scope declines rapidly; And REAFAD algorithm is because having taked the protection to key node energy, better maintaining remains valid coversLid scope reduces gradually.

By reference to the accompanying drawings the specific embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned enforcement sideFormula in the ken possessing, can also be made those of ordinary skill in the art under the prerequisite that does not depart from aim of the present inventionVarious variations.

Claims (4)

1. a routing algorithm of dynamically adjusting forward angle based on dump energy, is characterized in that:

In the time that certain node is prepared to select next-hop node, judge that in present node forward angle range, neighbor node dump energy is largeLittle whether lower than residue energy of node threshold value E_th：

If neighbor node dump energy is all lower than dump energy threshold value E in present node forward angle range_th, node increases forward angleDegree; Wherein, node increases the principle of forward angle: from minimal forward angle A_minTo maximum forward angle A_max, until maximumIn forward angle range, all do not exist and meet dump energy higher than dump energy threshold value E_thNeighbor node time, follow its probabilityTransfer function principle, reselects next-hop node;

If exist dump energy to be not less than dump energy threshold value E in neighbor node in present node forward angle range_thNode,Follow its probability transfer function and carry out the selection of next-hop node;

Wherein, dump energy threshold value E_thChoose 30% of the initial dump energy of respective neighbours node; A_minChoose 60 °; A_maxChoose 240 °, adjust forward angle increment is 60 ° at every turn.

2. the routing algorithm of dynamically adjusting forward angle based on dump energy according to claim 1, is characterized in that: instituteState probability transfer function:Wherein,For node i and jointPoint i next-hop node j between the t moment k ant probability transfer function, τ_ij(t) be the down hop of node i and node iThe pheromones function in t moment between node j, η_ij(t) for the distance in t moment between node i and the next-hop node j of node i opensThe number of sending a letter,For the energy heuristic function in t moment between the next-hop node j of node i and node i, A_iPresent node iThe set of all neighbor nodes, α is the heuristic factor of pheromones, and β is the heuristic factor of distance, and γ is the heuristic factor of energy.

3. the routing algorithm of dynamically adjusting forward angle based on dump energy according to claim 2, is characterized in that: instituteStating pheromones function upgrades in the following way:

τ_ij(t+1)＝(1-ρ)·τ_ij(t)+Δτ_ij(t)

${\mathrm{\Δ\τ}}_{ij}\left(t\right)={\mathrm{\Σ}}_{k=1}^m{\mathrm{\Δ\τ}}_{ij}^k\left(t\right)$

In formula, ρ is pheromones volatilization element, Δ τ_ij(t) be routing information element increment between node i, j in this iteration, τ_ij(t) at the beginning ofInitial value is 0,Be k the pheromones that ant stays, m is ant group ant total number.

4. the routing algorithm of dynamically adjusting forward angle based on dump energy according to claim 3, is characterized in that: instituteState the pheromones that k ant staysIn renewal process:

If when k ant is Front ant, the pheromones that k ant stayed is carried out local updating:

${\mathrm{\Δ\τ}}_{ij}^k\left(t\right)=\frac{Q_L}{d_{ij}}+{\mathrm{\δE}}_j\left(t\right)$

In formula, Q_LFor local message element intensity and initial value are 0, d_ijRepresent between node i and the next-hop node j of node iDistance, δ be energy upgrade coefficient; E_j(t) represent the dump energy of node j in the t moment;

If when k ant is Back ant, the pheromones that k ant stayed is carried out the overall situation and is upgraded:

${\mathrm{\Δ\τ}}_{ij}^k\left(t\right)=\frac{Q}{L_k}\·{\left(\frac{E_{\min }\·n_k}{{\mathrm{\Σ}}_{l=1}^{n_k}{E}_l}\right)}^v$

In formula, Q is that global information element intensity and initial value are 0, L_kBe the jumping figure of k ant, E_minBe k antThe dump energy of dump energy minimum node on the path of representative, n_kBe the interstitial content on the path of k ant representative,El is the residue energy of node of arbitrary node l on the path of k ant representative, and v is energy factors coefficient, characterizes energy and existsProportion in pheromones renewal.