CN103096445A - Method and system of wireless sensor network task scheduling based on actual battery model - Google Patents

Abstract

The invention provides a method and a system of wireless sensor network task scheduling based on an actual battery model. The method comprises that according to operating parameters of a wireless sensor network and the actual battery model, power consumption of different stages in each cycle is obtained, wherein the different stages comprise a data sampling sending stage, a receiving stage and a monitoring and sleeping stage, and the actual battery model is the relationship between energy loss and time and currents; according to the power consumption of the different stages, a total power consumption model of wireless sensor network nodes is obtained, wherein the total power consumption model is related to sleeping time of the wireless sensor network nodes; the optimal sleeping time of the total power consumption model of the wireless sensor network nodes is solved; and tasks of the wireless sensor network are scheduled according to the optimal sleeping time to lower power consumption of the wireless sensor network. According to the method, through solution of the optimal sleeping time of the wireless sensor network, power consumption of the wireless sensor network nodes is lowered, and the service lives of the wireless sensor network nodes are prolonged at the same time.

Description

Wireless sense network method for scheduling task and system based on the actual battery model

Technical field

The present invention relates to technical field of wireless, particularly a kind of wireless sense network method for scheduling task and system based on the actual battery model.

Background technology

Because wireless sense network can be by self battery in the situation that do not carry out artificially interfering in the long period normally operation.Its superiority of field especially abominable at environment, that the mankind can't set foot on is more remarkable.Because the service time of the electric power thus supplied of battery and its useful life and node is closely bound up, how effectively to make the ground energy become key point.

For specific wireless sensor network, the length that sends data is generally fixed, but a very important parameter, and the time span of sleep task is independently to set according to system situation.In the B-MAC agreement, necessary energy consumption is noticeable for the authentic datas of agreement propagate two, and one is the energy consumption that sends introduction, another be receiving node intercept energy consumption.These two energy consumption all are linked together with the length of one's sleep, and the length of one's sleep is long, the corresponding growth of the length of introduction, and sending the introduction energy consumption just increases.Meanwhile, the invalid expected time of intercepting also can increase thereupon.Equally, the length of neither sleeping is the smaller the better, otherwise has been in the state of intercepting always.

In the energy use procedure of existing wireless sense network, idle energy consumption is more, main cause is that node is all to move under desirable battery model, relevant parameter is not optimized in conjunction with the actual battery model, and, do not realize task scheduling on the energy consumption parameter of optimizing, thereby optimally do not reduce the power consumption of system, simultaneously, do not consider the actual battery model, making with actual motion has relatively large deviation.

Summary of the invention

Purpose of the present invention is intended to solve at least one of above-mentioned technological deficiency.

For achieving the above object, the embodiment of one aspect of the present invention proposes a kind of wireless sense network method for scheduling task based on the actual battery model, comprise the following steps: S1: according to the operational factor of wireless sense network and actual battery model to obtain the power consumption of different phase in each cycle, wherein, described different phase comprise data sampling transmission phase, reception stage, intercept and sleep stage and the described actual battery model relation that is energy loss and time and electric current; S2: according to the power consumption of described sampling transmission phase, receive the stage power consumption, intercept the total power consumption model that obtains described wireless sensing net node with the power consumption of sleep stage, wherein, described total power consumption model is relevant to the length of one's sleep of wireless sensing net node; S3: the optimum length of one's sleep of finding the solution described wireless sensing net node total power consumption model; And S4: minimum by the described optimum length of one's sleep task of wireless sense network being dispatched the energy that actual battery is consumed, to reduce the power consumption of wireless sense network.

According to the method for the embodiment of the present invention, by setting up the total power consumption model of wireless sense network, and find the solution the optimum length of one's sleep of wireless sense network, thereby reduced the power consumption of wireless sensing net node, extended simultaneously the useful life of wireless sensing net node.

In an example of the present invention, described actual battery model is the Rakhmatov model, represent by following formula, $\mathrm{\σ}\left(t\right)=\underset{k=0}{\overset{n-1}{\mathrm{\Σ}}}{I}_k{\mathrm{\Δ}}_k+2\underset{k=0}{\overset{n-1}{\mathrm{\Σ}}}\underset{m=1}{\overset{\∞}{\mathrm{\Σ}}}{I}_k\left(\frac{e^{-{\mathrm{\β}}^2{m}^2(t-t_k-\mathrm{\Δk})}-e^{-{\mathrm{\β}}^2{m}^2(t-t_k)}}{{\mathrm{\β}}^2{m}^2}\right),$ Wherein, the loss of σ (t) expression apparent charge, namely battery is in the tolerance of t time self-energy consumption, and n is illustrated in the task quantity in cycle t, I _k, t _k, Δ _kThe current value, time started and the duration that represent respectively k task, β represents the battery variety coefficient.

In an example of the present invention, described total power consumption model represents by following formula, $E_{\mathrm{period}}=E_{\mathrm{SAS}}+N*E_{\mathrm{RD}}+\frac{(1/r-T_1-N*T_2)}{T_3}*E_{\mathrm{LAS}},$ Wherein, E _PeriodThe expression total power consumption, E _SASThe power consumption of expression sampling transmission phase, E _RDThe power consumption in expression reception stage, E _LASThe power consumption with sleep stage is intercepted in expression, and N represents the number of adjacent node in wireless sense network, and r represents sample rate, T ₁The duration of expression sampling transmission phase, T ₂The duration in expression reception stage, T ₃The duration with sleep stage is intercepted in expression.

In an example of the present invention, described total power consumption Optimized model represents by following formula, $\underset{T_{\mathrm{sieep},N}}{\min }{E}_{\mathrm{period}}(T_{\mathrm{sleep}},N)S.T.0<T_{\mathrm{sleep}}<\min (1/r,T_{\mathrm{constraint}}),$ Wherein, E _PeriodThe expression total power consumption, T _SleepRepresent the length of one's sleep, N represents the number of adjacent node in wireless sense network, and S.T. represents deferred constraint, and r represents sample rate, T _ConstraintThe expression deferred constraint is time constant.

In an example of the present invention, the described best length of one's sleep is relevant with the topological situation of network.

For achieving the above object, embodiments of the invention propose a kind of wireless sense network task scheduling system based on the actual battery model on the other hand, comprise: the first acquisition module, be used for according to the operational factor of wireless sense network and actual battery model to obtain the power consumption of different phase in each cycle, wherein, described different phase comprise data sampling transmission phase, reception stage, intercept and sleep stage and the described actual battery model relation that is energy loss and time and electric current; The second acquisition module, be used for according to described sampling transmission phase power consumption, receive the stage power consumption, intercept the total power consumption model that obtains described wireless sensing net node with the power consumption of sleep stage, wherein, described total power consumption model is relevant to the length of one's sleep of wireless sensing net node; Find the solution module, be used for finding the solution the optimum length of one's sleep of described wireless sensing net node total power consumption model; And adjusting module, be used for by the described optimum length of one's sleep, the task of wireless sense network being dispatched the energy minimum that actual battery is consumed, to reduce the power consumption of wireless sense network.

According to the system of the embodiment of the present invention, by setting up the total power consumption model of wireless sense network, and find the solution the optimum length of one's sleep of wireless sense network, thereby reduced the power consumption of wireless sensing net node, extended simultaneously the useful life of wireless sensing net node.

In an example of the present invention, described actual battery model is the Rakhmatov model, represent by following formula, $\mathrm{\&sigma;}\left(t\right)=\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}{I}_k{\mathrm{\&Delta;}}_k+2\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}\underset{m=1}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{I}_k\left(\frac{e^{-{\mathrm{\&beta;}}^2{m}^{2(t-t_k-{\mathrm{\&Delta;}}_k)}-e^{-{\mathrm{\&beta;}}^2{m}^{2(t-t_k)}}}}{{\mathrm{\&beta;}}^2{m}^2}\right),$ Wherein, the loss of σ (t) expression apparent charge, namely battery is in the tolerance of t time self-energy consumption, and n is illustrated in the task quantity in cycle t, I _k, t _k, Δ _kThe current value, time started and the duration that represent respectively k task, β represents the battery variety coefficient.

In an example of the present invention, described total power consumption model represents by following formula, $E_{\mathrm{period}}=E_{\mathrm{SAS}}+N*E_{\mathrm{RD}}+\frac{(1/r-T_1-N*T_2)}{T_3}*E_{\mathrm{LAS}},$ Wherein, E _PeriodThe expression total power consumption, E _SASThe power consumption of expression sampling transmission phase, E _RDThe power consumption in expression reception stage, E _LASThe power consumption with sleep stage is intercepted in expression, and N represents the number of adjacent node in wireless sense network, and r represents sample rate, T ₁The duration of expression sampling transmission phase, T ₂The duration in expression reception stage, T ₃The duration with sleep stage is intercepted in expression.

In an example of the present invention, described total power consumption Optimized model represents by following formula, $\underset{T_{\mathrm{sieep},N}}{\min }{E}_{\mathrm{period}}(T_{\mathrm{sleep}},N)S.T.0<T_{\mathrm{sleep}}<\min (1/r,T_{\mathrm{constraint}}),$ Wherein, E _PeriodThe expression total power consumption, T _SleepRepresent the length of one's sleep, N represents the number of adjacent node in wireless sense network, and S.T. represents deferred constraint, and r represents sample rate, T _ConstraintThe expression deferred constraint is time constant.

In an example of the present invention, the described best length of one's sleep is relevant with the topological situation of network.

The aspect that the present invention adds and advantage part in the following description provide, and part will become obviously from the following description, or recognize by practice of the present invention.

Description of drawings

Above-mentioned and/or the additional aspect of the present invention and advantage will become from the following description of the accompanying drawings of embodiments and obviously and easily understand, wherein:

Fig. 1 is according to an embodiment of the invention based on the flow chart of the wireless sense network method for scheduling task of actual battery model;

Fig. 2 is wireless senser transmission according to an embodiment of the invention or the schematic diagram that receives task data;

Fig. 3 is the graph of a relation in the best according to an embodiment of the invention length of one's sleep and cycle;

Fig. 4 is the energy consumption comparison diagram of actual battery model and ideal battery model according to an embodiment of the invention;

The statistical chart that Fig. 5 changed with the neighbor node number for the best according to an embodiment of the invention length of one's sleep;

Fig. 6 is the graph of a relation of battery life and neighbor node number according to an embodiment of the invention; And

Fig. 7 is according to an embodiment of the invention based on the frame diagram of wireless sense network task scheduling system.

Embodiment

The below describes embodiments of the invention in detail, and the example of embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or the element with identical or similar functions from start to finish.Be exemplary below by the embodiment that is described with reference to the drawings, only be used for explaining the present invention, and can not be interpreted as limitation of the present invention.

Fig. 1 is according to an embodiment of the invention based on the flow chart of the wireless sense network method for scheduling task of actual battery model.As shown in Figure 1, the wireless sense network method for scheduling task based on the actual battery model according to the embodiment of the present invention comprises the following steps:

Step S101, according to the operational factor of wireless sense network and actual battery model to obtain the power consumption of different phase in each cycle, wherein, different phase comprise data sampling transmission phase, reception stage, intercept and sleep stage, and the actual battery model is the relation of energy loss and time and electric current.

Particularly, node is divided into three phases with the process of data input and data output, i.e. the data acquisition transmission phase of node (SAS stage), reception stage (RD stage) and intercept and sleep stage (LAS stage).

Fig. 2 is wireless senser transmission according to an embodiment of the invention or the schematic diagram that receives task data.As shown in Figure 2, therefrom can find out, sending data and receive data is large electric current task, and the sleep task is that in all tasks, consumed energy is minimum.Formed by some details tasks in above-mentioned three megastages classification, for example, intercept with sleep stage and can be combined by a plurality of intercepting with a plurality of sleeps respectively.Table 1 and table 2 are the various operational factors of wireless sense network, can calculate acquisition by the relevant parameter of table 1 and table 2, for example, send the time T of a packet _Send,Can be by the transmission data length L of table 1 _PacketWith the transmission one Bit data required time of table 2 be T _txb, and obtain L according to following formula _Send, T _Send=L _Packet* T _txb

Table 1

Symbol	Describe	Default value
			C sleep	Sleep current (mA)	0.03
L preamble	Introduction length (bytes)	Nothing
			L packet	Data length (bytes)	36
N	Neighbor node number (nodes)	10
			r	Sample rate (per second number of times)	1/300
C listen	Sample rate current (mA)	2.4
			T listen	Sampling time (s)	2.5E-3
T sleep	The length of one's sleep (s)	Nothing

T back

Retreat the time (ms)

27.4

Table 2

In one embodiment of the invention, the task in each stage is combined by some details tasks, and the energy that this three kinds of stages consume can be by the actual battery model formation, $\mathrm{\&sigma;}\left(t\right)=\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}{I}_k{\mathrm{\&Delta;}}_k+2\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}\underset{m=1}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{I}_k\left(\frac{e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k-\mathrm{\&Delta;k})}-e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k)}}{{\mathrm{\&beta;}}^2{m}^2}\right),$ Wherein, the loss of σ (t) expression apparent charge, i.e. the energy that consumes in the time at t of battery, n is illustrated in the task quantity in cycle t, I _k, t _k, Δ _kThe current value, time started and the duration that represent respectively k task, second of formula has comprised rate capabilities effect and recovery Effects.We use the energy consumption on sensor node of this model metrics.β represents the battery variety coefficient, and its value is different according to different types of dry cell.

In one embodiment of the invention, be deployed in the sensor node in bunch shape topological network, there are 1 SAS task and N RD task (from N neighbor node) in arbitrary cycle, and the remaining time in cycle is occupied by the LAS task.In cycle total power consumption be that three generic task energy consume and.This three generic task can further be divided into the combination of details task, and these details tasks can not arbitrarily be dispatched, and namely order is immutable.In case the number of the little task that three generic tasks comprise, the time started is definite, and the energy consumption of general assignment just can be determined thereupon.In our model, unique parameter that can change is the length of one's sleep.

In one embodiment of the invention, the SAS phased mission is analyzed as follows.According to the B-MAC protocol contents, this phased mission implementation is, transducer image data (little task 1), then sensor node is intercepted channel (little task 2), if channel idle, send introduction and data (task 3), if busy, channel (task 5) is intercepted in node wait (little task 4) again after a period of time.Continue this process until send out this data, maximum number of attempt is M always, and M can set according to factors such as concrete network sizes.Duration and the current parameters of details task can be inquired about from table 1 and table 2, calculate arbitrarily.

The duration of details task 3 can calculate by following formula.According to the BMAC agreement, the length L of introduction _PreambleMust be greater than the length of one's sleep of node.Make L _PreambleEqual the T length of one's sleep _SleepAdditive constant C, in emulation, the default value of C is 20 bits.L _Preamble=C+T _Sleep/ T _txb, so the duration of details task 3 be T _SAS3=(L _Preamble+ L _Packet) * T _txbIf intercept first channel for idle, do not have wait task to occur, the SAS task only comprises three details tasks, and we are designated as p0 to this event occurrence rate.If occured once to wait for, the SAS task has comprised 5 details tasks, is respectively sampling, intercepts, and waits for, intercepts, and sends, and the probability of this part thing is designated as P1.We use mistake in like manner! Do not find Reference source.Representations of events is for having intercepted the k secondary channel, and wherein the k-1 secondary channel is busy, and the k secondary channel is idle.The probability of this event is, P (A _k)=(1-p0) ^k-1All events of p0 all can be carried out the energy consumption calculations by above-mentioned formula.Notice according to agreement, M-1 wait arranged at most, we could obtain respectively the energy that M event consumes, and can be expressed as respectively mistake! Do not find Reference source.(k=0,1…,M-1)。Comprehensive all events, we obtain the catabiotic desired value of SAS task, are shown below, An any mistake! Do not find Reference source.(k=0,1 ..., M-1) be all parameter T _SleepFunction, and P _k(k=0,1 ..., M-1) be all the function of p0.P0 detects channel to be idle probability, and here we suppose that each detection event is all independently, and p0 should equal total sleep time except in the length in whole cycle, i.e. L _Receive=L _Preamble/ 2+L _PacketSo T _kIt is also the function of the length of one's sleep and neighbours' number.Thereby ENERGY E _SASThe T length of one's sleep _SleepFunction with neighbours' number N.

In one embodiment of the invention, as follows to the RD task analysis: according to the B-MAC agreement, following event occurs in order: intercept channel (details task 1), if activity detected, intercept until introduction finishes always, if find that the transmission destination of data is this node, receive data (details task 2), otherwise return to sleep state.If sleep state is directly returned in the activity that do not detect, be actually like this a sleep task, this kind situation is left in the LAS task and discusses.Because node is intercepted the randomness of channel time, we can suppose simply that the desired length that receives introduction adds the length of packet, L for half introduction length _Receive=L _Preamble/ 2+L _PacketThis supposition is that reasonably on macroscopic view, average reception introduction length is half of actual introduction length really.According to this supposition, we can obtain from above formula the consumed energy of RD task.The duration that it should be noted that second little task should be passed through L _ReceiveCalculate.Certainly, we also can take fully enough L _ReceiveSample, average after calculating energy consumption respectively, but this method is except increasing computation complexity, any contribution is not arranged improving on computational accuracy.We use symbol error! Do not find Reference source.The energy that expression RD task consumes, same analysis, it is the function of the length of one's sleep and N, namely

In one embodiment of the invention, the LAS phased mission is analyzed as follows.The task here has two kinds of situations.The first situation, intercepting channel is idle condition, node enters sleep state immediately.The second situation, channel status are busy, wait for until introduction finishes, and find that the destination is not this node, change sleep state over to.Under the first situation, only have two details tasks, be respectively and intercept, sleep, be easy to calculate with formula 1, the consumed energy note is E _LAS1In second case, the reception introduction length of expectation is L _Preamble/ 2.Three details tasks are arranged in this case, are respectively and intercept, receive preamble data, sleep, by above-mentioned formula calculating energy, be designated as mistake equally! Do not find Reference source.。Analysis above similar, these two energy are all the functions of the length of one's sleep and neighbours' number N.The probability of happening of the second type task depends on the busy extent of channel.We are designated as c to this probability of happening.In general, the information that the channel busy probability depends on neighbor node sends situation, but observes from self node, and the time that the channel occupancy probability can be estimated as the total reception information of node own is except in total cycle time.The reasonability of doing like this is, what we discussed itself is the sensor network of low load-factor, and information just must be very little in the probability that sends through wait repeatedly.Node itself will send a secondary data, once receives successfully, and N-1 time possible mistake is intercepted, thereby we obtain following formula and are, c=(T ₁+ (N-1) * T ₂)/(1/r), wherein, T ₁And T ₂Represent that respectively node sends data task average holding time and receive data average holding time.Thereby the energy of LAS task consumption can be expressed as, E _LAS=(1-c) * E _LAS1+ c*E _LAS2By upper, one-period only has 1 SAS task and N RD task, and all occupied by the LAS task remaining time in cycle.

Step S102, according to the power consumption of sampling transmission phase, receive the stage power consumption, intercept the total power consumption model that obtains wireless sensing net node with the power consumption of sleep stage, wherein, the total power consumption model is relevant to the length of one's sleep of wireless sensing net node.

In one embodiment of the invention, the total power consumption of one-period is calculated as follows: $E_{\mathrm{period}}=E_{\mathrm{SAS}}+N*E_{\mathrm{RD}}+\frac{(1/r-T_1-N*T_2)}{T_3}*E_{\mathrm{LAS}},$ Wherein, E _PeriodThe expression total power consumption, E _SASThe power consumption of expression expression expression sampling transmission phase, E _RDThe power consumption in expression reception stage, E _LASThe power consumption with sleep stage is intercepted in expression, and N represents the number of adjacent node in wireless sense network, and r represents sample rate, T ₁The duration of expression sampling transmission phase, T ₂The duration in expression reception stage, T ₃The duration with sleep stage is intercepted in expression.

Step S103 finds the solution optimum length of one's sleep of wireless sensing net node total power consumption model.

In one embodiment of the invention, in some application problem, may exist deferred constraint, namely the long reduction that causes the rate of information throughput length of one's sleep.There is not difficulty in essence in this point for our model.Add deferred constraint, we are at final Optimized model: $\underset{T_{\mathrm{sieep},N}}{\min }{E}_{\mathrm{period}}(T_{\mathrm{sleep}},N)S.T.0<T_{\mathrm{sleep}}<\min (1/r,T_{\mathrm{constraint}}),$ Wherein, E _PeriodThe expression total power consumption, T _SleepRepresent the length of one's sleep, N represents the number of adjacent node in wireless sense network, and S.T. represents deferred constraint, and r represents sample rate, T _ConstraintThe expression deferred constraint is time constant.

Can find out from above-mentioned Optimized model, what of node energy consumption are relevant with number and the sample rate of adjacent node in the length of one's sleep, wireless sense network, and the best length of one's sleep of sensor node is only relevant with the topological situation of network.Because what of neighbor node have directly reacted the possible busy situation of channel, thereby affect the length of the length of one's sleep.As for the actual battery model, just when calculating the channel busy situation on the affecting of energy consumption and the ideal battery model different tolerance is arranged.

Step S104, minimum by the optimum length of one's sleep task of wireless sense network being dispatched the energy that actual battery is consumed, to reduce the power consumption of wireless sense network.

Fig. 3 is the graph of a relation in the best according to an embodiment of the invention length of one's sleep and cycle.As shown in Figure 3, in figure, to count N be 10 to neighbor node, and as we can see from the figure, the cycle is longer, and all increase the best length of one's sleep under two kinds of models, and the actual battery model than ideal battery model corresponding increase the best length of one's sleep faster.Under the task and network topology situation of as much, the cycle lengthens the reduction that is equivalent to network congestion, and node can have the longer time to enter sleep state.And our Optimized model obtains best length of one's sleep, increasing degree was larger than the increasing degree of ideal battery, and recovery Effects of this explanation dry cell is very obvious.We can also observe in Fig. 3, and along with the minimizing of Cycle Length, be tending towards identical two best lengths of one's sleep.This is because throughput is large, and the too busy energy that recovers at sleep stage that causes of channel is compared and can be ignored with desirable battery model.But in the wireless sensor network of low load-factor, this situation seldom, the actual battery model is very important.

Fig. 4 is the energy consumption comparison diagram of actual battery model and ideal battery model according to an embodiment of the invention.As shown in Figure 4, in figure, the neighbor node number is fixed value, and we calculate the energy that consumes separately under two kinds of models.Result shows that the unit bit energy that calculates with the actual battery model is less than the respective value under desirable battery model.This can find out that energy recovers really to have remarkable result after being provided with the best parameter length of one's sleep.When enough large (the less cycle) of the quantitative change of handling up, difference between the two can be left in the basket.But in the wireless sensor network of low load-factor, it is very important that this energy is saved.

The statistical chart that Fig. 5 changed with the neighbor node number for the best according to an embodiment of the invention length of one's sleep.As shown in Figure 5, hold period length is at 300s, and changing the neighbor node number can find out, no matter which kind of model, along with the neighbor node number increases, reduce the best length of one's sleep.In the length of one's sleep under the actual battery model always greater than the value under the ideal battery model.When the neighbor node number is enough large, reach more than 100, difference can reduce to and ignore.

Fig. 6 is the graph of a relation of battery life and neighbor node number according to an embodiment of the invention.As shown in Figure 6, under Optimized model, the energy saving that N equals 10 o'clock the bests can reach 14%.If the cycle, the life-span that extends was reduced to 2% less than 300s.

According to the method for the embodiment of the present invention, by setting up the total power consumption model of wireless sense network, and find the solution the optimum length of one's sleep of wireless sense network, thereby reduced the power consumption of wireless sensing net node, extended simultaneously the useful life of wireless sensing net node.

Fig. 7 is according to an embodiment of the invention based on the frame diagram of the wireless sense network task scheduling system of actual battery model.As shown in Figure 7, the wireless sense network task scheduling system based on the actual battery model according to the embodiment of the present invention comprises the first acquisition module 100, the second acquisition module 200, optimizes module 300 and adjusting module 400.

The first acquisition module 100 is used for according to the operational factor of wireless sense network and actual battery model to obtain the power consumption of different phase in each cycle, wherein, different phase comprise data sampling transmission phase, reception stage, intercept and sleep stage, and the actual battery model is the relation of energy loss and time and electric current.

Particularly, node is divided into three phases with the process of data input and data output, i.e. the data acquisition transmission phase of node (SAS stage), reception stage (RD stage) and intercept and sleep stage (LAS stage).

Fig. 2 is wireless senser transmission according to an embodiment of the invention or the schematic diagram that receives task data.As shown in Figure 2, therefrom can find out, sending data and receive data is large electric current task, and the sleep task is that in all tasks, consumed energy is minimum.Formed by some details tasks in above-mentioned three megastages classification, for example, intercept with sleep stage and can be combined by a plurality of intercepting with a plurality of sleeps respectively.Table 1 and table 2 are the various operational factors of wireless sense network, can calculate acquisition by the relevant parameter of table 1 and table 2, for example, send the time T of a packet _Send, can be by the transmission data length L of table 1 _PacketWith the transmission one Bit data required time of table 2 be T _txb, and obtain L according to following formula _Send, T _Send=L _Packet* T _txb

Table 1

Symbol	Describe	Default value
			C sleep	Sleep current (mA)	0.03
L preamble	Introduction length (bytes)	Nothing
			L packet	Data length (bytes)	36
N	Neighbor node number (nodes)	10
			r	Sample rate (per second number of times)	1/300
C listen	Sample rate current (mA)	2.4
			T listen	Sampling time (s)	2.5E-3
T sleep	The length of one's sleep (s)	Nothing
			T back	Retreat the time (ms)	27.4

Table 2

In one embodiment of the invention, the task in each stage is combined by some details tasks, and the energy that this three kinds of stages consume can be by the actual battery model formation, $\mathrm{\&sigma;}\left(t\right)=\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}{I}_k{\mathrm{\&Delta;}}_k+2\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}\underset{m=1}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{I}_k\left(\frac{e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k-\mathrm{\&Delta;k})}-e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k)}}{{\mathrm{\&beta;}}^2{m}^2}\right),$ Wherein, the loss of σ (t) expression apparent charge, i.e. the energy that consumes in the time at t of battery, n is illustrated in the task quantity in cycle t, Ik, t _k, Δ _kThe current value, time started and the duration that represent respectively k task, second of formula has comprised rate capabilities effect and recovery Effects.We use the energy consumption on sensor node of this model metrics.β represents the battery variety coefficient, and its value is different according to different types of dry cell

In one embodiment of the invention, be deployed in the sensor node in bunch shape topological network, there are 1 SAS task and N RD task (from N neighbor node) in arbitrary cycle, and the remaining time in cycle is occupied by the LAS task.In cycle total power consumption be that three generic task energy consume and.This three generic task can further be divided into the combination of details task, and these details tasks can not arbitrarily be dispatched, and namely order is immutable.In case the number of the little task that three generic tasks comprise, the time started is definite, and the energy consumption of general assignment just can be determined thereupon.In our model, unique parameter that can change is the length of one's sleep.

In one embodiment of the invention, the SAS phased mission is analyzed as follows.According to the B-MAC protocol contents, this phased mission implementation is, transducer image data (little task 1), then sensor node is intercepted channel (little task 2), if channel idle, send introduction and data (task 3), if busy, channel (task 5) is intercepted in node wait (little task 4) again after a period of time.Continue this process until send out this data, maximum number of attempt is M always, and M can set according to factors such as concrete network sizes.Duration and the current parameters of details task can be inquired about from table 1 and table 2, calculate arbitrarily.

The duration of details task 3 can calculate by following formula.According to the B-MAC agreement, the length L of introduction _PreambleMust be greater than the length of one's sleep of node.Make L _PreambleEqual the T length of one's sleep _SlepAdditive constant C, in emulation, the default value of C is 20 bits.L _Preamble=C+T _Sleep/ T _txb, so the duration of details task 3 be T _SAS3=(L _Preamble+ L _PackeT) * T _txbIf intercept first channel for idle, do not have wait task to occur, the SAS task only comprises three details tasks, and we are designated as p0 to this event occurrence rate.If occured once to wait for, the SAS task has comprised 5 details tasks, is respectively sampling, intercepts, and waits for, intercepts, and sends, and the probability of this part thing is designated as P1.We use mistake in like manner! Do not find Reference source.Representations of events is for having intercepted the k secondary channel, and wherein the k-1 secondary channel is busy, and the k secondary channel is idle.The probability of this event is, P (A _k)=(1-p0) ^k-1All events of p0 all can be carried out the energy consumption calculations by above-mentioned formula.Notice according to agreement, M-1 wait arranged at most, we could obtain respectively the energy that M event consumes, and can be expressed as respectively mistake! Do not find Reference source.(k=0,1…,M-1)。Comprehensive all events, we obtain the catabiotic desired value of SAS task, are shown below,

An any mistake! Do not find Reference source.(k=0,1 ..., M-1) be all parameter T _SleepFunction, and P _k(k=0,1 ..., M-1) be all the function of p0.P0 detects channel to be idle probability, and here we suppose that each detection event is all independently, and p0 should equal total sleep time except in the length in whole cycle, i.e. L _Receive=L _Preamble/ 2+L _PacketSo P _kIt is also the function of the length of one's sleep and neighbours' number.Thereby ENERGY E _SASThe T length of one's sleep _SleepFunction with neighbours' number N.

In one embodiment of the invention, as follows to the RD task analysis: according to the BMAC agreement, following event occurs in order: intercept channel (details task 1), if activity detected, intercept until introduction finishes always, if find that the transmission destination of data is this node, receive data (details task 2), otherwise return to sleep state.If sleep state is directly returned in the activity that do not detect, be actually like this a sleep task, this kind situation is left in the LAS task and discusses.Because node is intercepted the randomness of channel time, we can suppose simply that the desired length that receives introduction adds the length of packet, L for half introduction length _Receive=L _Preamble/ 2+L _PacketThis supposition is that reasonably on macroscopic view, average reception introduction length is half of actual introduction length really.According to this supposition, we can obtain from above formula the consumed energy of RD task.The duration that it should be noted that second little task should be passed through L _ReceiveCalculate.Certainly, we also can take fully enough L _ReceiveSample, average after calculating energy consumption respectively, but this method is except increasing computation complexity, any contribution is not arranged improving on computational accuracy.We use symbol error! Do not find Reference source.The energy that expression RD task consumes, same analysis, it is the function of the length of one's sleep and N, namely

In one embodiment of the invention, the LAS phased mission is analyzed as follows.The task here has two kinds of situations.The first situation, intercepting channel is idle condition, node enters sleep state immediately.The second situation, channel status are busy, wait for until introduction finishes, and find that the destination is not this node, change sleep state over to.Under the first situation, only have two details tasks, be respectively and intercept, sleep, be easy to calculate with formula 1, the consumed energy note is E _LAS1In second case, the reception introduction length of expectation is L _Preamble/ 2.Three details tasks are arranged in this case, are respectively and intercept, receive preamble data, sleep, by above-mentioned formula calculating energy, be designated as mistake equally! Do not find Reference source.。Analysis above similar, these two energy are all the functions of the length of one's sleep and neighbours' number N.The probability of happening of the second type task depends on the busy extent of channel.We are designated as c to this probability of happening.In general, the information that the channel busy probability depends on neighbor node sends situation, but observes from self node, and the time that the channel occupancy probability can be estimated as the total reception information of node own is except in total cycle time.The reasonability of doing like this is, what we discussed itself is the sensor network of low load-factor, and information just must be very little in the probability that sends through wait repeatedly.Node itself will send a secondary data, once receives successfully, and N-1 time possible mistake is intercepted, thereby we obtain following formula and are, c=(T ₁+ (N-1) * T ₂)/(1/r) wherein, T ₁And T ₂Represent that respectively node sends data task average holding time and receive data average holding time.Thereby the energy of LAS task consumption can be expressed as, E _LAS=(1-c) * E _LAS1+ c*E _LAS2By upper, one-period only has 1 SAS task and N RD task, and all occupied by the LAS task remaining time in cycle.

The second acquisition module 200 be used for according to the power consumption of sampling transmission phase, receive the stage power consumption, intercept the total power consumption model that obtains wireless sensing net node with the power consumption of sleep stage, wherein, the total power consumption model is relevant to the length of one's sleep of wireless sensing net node.

In one embodiment of the invention, the total power consumption of one-period is calculated as follows: $E_{\mathrm{period}}=E_{\mathrm{SAS}}+N*E_{\mathrm{RD}}+\frac{(1/r-T_1-N*T_2)}{T_3}*E_{\mathrm{LAS}},$ Wherein, E _PeriodThe expression total power consumption, E _SASThe power consumption of expression expression expression sampling transmission phase, E _RDThe power consumption in expression reception stage, E _LASThe power consumption with sleep stage is intercepted in expression, and N represents the number of adjacent node in wireless sense network, and r represents sample rate, T ₁The duration of expression sampling transmission phase, T ₂The duration in expression reception stage, T ₃The duration with sleep stage is intercepted in expression.

Find the solution module 300 for the optimum length of one's sleep of finding the solution wireless sensing net node total power consumption model.

In one embodiment of the invention, in some application problem, may exist deferred constraint, namely the long reduction that causes the rate of information throughput length of one's sleep.There is not difficulty in essence in this point for our model.Add deferred constraint, we are at final Optimized model: $\underset{T_{\mathrm{sieep},N}}{\min }{E}_{\mathrm{period}}(T_{\mathrm{sleep}},N)S.T.0<T_{\mathrm{sleep}}<\min (1/r,T_{\mathrm{constraint}}),$ Wherein, E _PeriodThe expression total power consumption, T _SleepRepresent the length of one's sleep, N represents the number of adjacent node in wireless sense network, and S.T. represents deferred constraint, and r represents sample rate, T _ConstraintThe expression deferred constraint is time constant.

Can find out from above-mentioned Optimized model, what of node energy consumption are relevant with number and the sample rate of adjacent node in the length of one's sleep, wireless sense network, and the best length of one's sleep of sensor node is only relevant with the topological situation of network.Because what of neighbor node have directly reacted the possible busy situation of channel, thereby affect the length of the length of one's sleep.As for the actual battery model, just when calculating the channel busy situation on the affecting of energy consumption and the ideal battery model different tolerance is arranged.

Adjusting module 400 is used for by the optimum length of one's sleep task of wireless sense network being dispatched the energy that actual battery is consumed minimum, to reduce the power consumption of wireless sense network.

In one embodiment of the invention, the topological situation of the optimum length of one's sleep and network, namely the number of adjacent node is relevant, and by the number of adjustment adjacent node, thereby the power-dissipation-reduced power consumption of wireless sense network is adjusted in realization.

According to the system of the embodiment of the present invention, by setting up the total power consumption model of wireless sense network, and find the solution the optimum length of one's sleep of wireless sense network, thereby reduced the power consumption of wireless sensing net node, extended simultaneously the useful life of wireless sensing net node.

Although the above has illustrated and has described embodiments of the invention, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, those of ordinary skill in the art is not in the situation that break away from principle of the present invention and aim can change above-described embodiment within the scope of the invention, modification, replacement and modification.

Claims (10)

1. the wireless sense network method for scheduling task based on the actual battery model, is characterized in that, comprises the following steps:

S1: according to the operational factor of wireless sense network and actual battery model to obtain the power consumption of different phase in each cycle, wherein, described different phase comprise data sampling transmission phase, reception stage, intercept and sleep stage and the described actual battery model relation that is energy loss and time and electric current;

S2: according to the power consumption of described sampling transmission phase, receive the stage power consumption, intercept the total power consumption model that obtains described wireless sensing net node with the power consumption of sleep stage, wherein, described total power consumption model is relevant to the length of one's sleep of wireless sensing net node;

S3: the optimum length of one's sleep of finding the solution described wireless sensing net node total power consumption model; And

S4: minimum by the described optimum length of one's sleep task of wireless sense network being dispatched the energy that actual battery is consumed, to reduce the power consumption of wireless sense network.

2. the wireless sense network method for scheduling task based on the actual battery model according to claim 1, is characterized in that, described actual battery model is the Rakhmatov model, represent by following formula,

$\mathrm{\&sigma;}\left(t\right)=\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}{I}_k{\mathrm{\&Delta;}}_k+2\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}\underset{m=1}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{I}_k\left(\frac{e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k-\mathrm{\&Delta;k})}-e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k)}}{{\mathrm{\&beta;}}^2{m}^2}\right),$

Wherein, the loss of σ (t) expression apparent charge, namely battery is in the tolerance of t time self-energy consumption, and n is illustrated in the task quantity in cycle t, I _k, t _k, Δ _kThe current value, time started and the duration that represent respectively k task, β represents the battery variety coefficient.

3. the wireless sense network method for scheduling task based on the actual battery model according to claim 1, is characterized in that, described total power consumption model represents by following formula,

$E_{\mathrm{period}}=E_{\mathrm{SAS}}+N*E_{\mathrm{RD}}+\frac{(1/r-T_1-N*T_2)}{T_3}*E_{\mathrm{LAS}},$

Wherein, E _PeriodThe expression total power consumption, E _SASThe power consumption of expression sampling transmission phase, E _RDThe power consumption in expression reception stage, E _LASThe power consumption with sleep stage is intercepted in expression, and N represents the number of adjacent node in wireless sense network, and r represents sample rate, T ₁The duration of expression sampling transmission phase, T ₂The duration in expression reception stage, T ₃The duration with sleep stage is intercepted in expression.

4. the wireless sense network method for scheduling task based on the actual battery model according to claim 1, is characterized in that, described total power consumption Optimized model represents by following formula,

$\underset{T_{\mathrm{sieep},N}}{\min }{E}_{\mathrm{period}}(T_{\mathrm{sleep}},N)S.T.0<T_{\mathrm{sleep}}<\min (1/r,T_{\mathrm{constraint}}),$

Wherein, E _PeriodThe expression total power consumption, T _SleepRepresent the length of one's sleep, N represents the number of adjacent node in wireless sense network, and S.T. represents deferred constraint, and r represents sample rate, T _ConstraintThe expression deferred constraint is time constant.

5. the low-power consumption method for scheduling task of the wireless senser based on the actual battery model according to claim 1, is characterized in that, the described best length of one's sleep is relevant with the topological situation of network.

6. the wireless sense network task scheduling system based on the actual battery model, is characterized in that, comprising:

The first acquisition module, be used for according to the operational factor of wireless sense network and actual battery model to obtain the power consumption of different phase in each cycle, wherein, described different phase comprise data sampling transmission phase, reception stage, intercept and sleep stage and the described actual battery model relation that is energy loss and time and electric current;

The second acquisition module, be used for according to described sampling transmission phase power consumption, receive the stage power consumption, intercept the total power consumption model that obtains described wireless sensing net node with the power consumption of sleep stage, wherein, described total power consumption model is relevant to the length of one's sleep of wireless sensing net node;

Find the solution module, be used for finding the solution the optimum length of one's sleep of described wireless sensing net node total power consumption model; And

Adjusting module is used for by the described optimum length of one's sleep, the task of wireless sense network being dispatched the energy minimum that actual battery is consumed, to reduce the power consumption of wireless sense network.

7. according to claim 6ly it is characterized in that based on the wireless sense network task scheduling system, described actual battery model is the Rakhmatov model, represent by following formula,

$\mathrm{\&sigma;}\left(t\right)=\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}{I}_k{\mathrm{\&Delta;}}_k+2\underset{k=0}{\overset{n-1}{\mathrm{\&Sigma;}}}\underset{m=1}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{I}_k\left(\frac{e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k-\mathrm{\&Delta;k})}-e^{-{\mathrm{\&beta;}}^2{m}^2(t-t_k)}}{{\mathrm{\&beta;}}^2{m}^2}\right),$

Wherein, the loss of σ (t) expression apparent charge, namely battery is in the tolerance of t time self-energy consumption, and n is illustrated in the task quantity in cycle t, I _k, t _k, Δ _kThe current value, time started and the duration that represent respectively k task, β represents the battery variety coefficient.

8. the low-power consumption task scheduling system of wireless senser according to claim 6, is characterized in that,

Described total power consumption model represents by following formula,

$E_{\mathrm{period}}=E_{\mathrm{SAS}}+N*E_{\mathrm{RD}}+\frac{(1/r-T_1-N*T_2)}{T_3}*E_{\mathrm{LAS}},$

Wherein, E _PeriodThe expression total power consumption, E _SASThe power consumption of expression sampling transmission phase, E _RDThe power consumption in expression reception stage, E _LASThe power consumption with sleep stage is intercepted in expression, and N represents the number of adjacent node in wireless sense network, and r represents sample rate, T ₁The duration of expression sampling transmission phase, T ₂The duration in expression reception stage, T ₃The duration with sleep stage is intercepted in expression.

9. the low-power consumption task scheduling system of wireless senser according to claim 6, is characterized in that, described total power consumption Optimized model represents by following formula,

$\underset{T_{\mathrm{sieep},N}}{\min }{E}_{\mathrm{period}}(T_{\mathrm{sleep}},N)S.T.0<T_{\mathrm{sleep}}<\min (1/r,T_{\mathrm{constraint}}),$

Wherein, E _PeriodThe expression total power consumption, T _SleepRepresent the length of one's sleep, N represents the number of adjacent node in wireless sense network, and S.T. represents deferred constraint, and r represents sample rate, T _ConstraintThe expression deferred constraint is time constant.

10. the low-power consumption task scheduling system of wireless senser according to claim 6, is characterized in that, the described best length of one's sleep is relevant with the topological situation of network.

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