CN103096445B - Wireless sense network method for scheduling task based on actual battery model and system - Google Patents

Abstract

The present invention proposes a kind of wireless sense network method for scheduling task based on actual battery model and system.Wherein, method includes: operational factor and actual battery model according to wireless sense network are with the power consumption of different phase in acquisition each cycle, wherein, different phase includes the sampling transmission phase of data, reception stage, intercepts and Sleep stages, and actual battery model is energy loss and time and the relation of electric current；Power consumption according to different phase obtains the total power consumption model of wireless sensing net node, and wherein, total power consumption model is relevant to the length of one's sleep of wireless sensing net node；Solve the optimum length of one's sleep of wireless sensing net node total power consumption model；By the task of wireless sense network is scheduling reducing the optimum length of one's sleep power consumption of wireless sense network.Method according to embodiments of the present invention, by solving the optimum length of one's sleep of wireless sense network, thus reduces the power consumption of wireless sensing net node, extends the service life of wireless sensing net node simultaneously.

Description

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

Technical field

The present invention relates to technical field of wireless, appoint particularly to a kind of wireless sense network based on actual battery model Business dispatching method and system.

Background technology

Due to wireless sense network can by self battery in the case of the most artificially interfering in the long period normal Run.Especially more significantly at its superiority of field that bad environments, the mankind cannot set foot on.Power supply feelings due to battery Condition is closely bound up with the use time of its service life and node, the most effectively makes the ground energy become key point.

For specific wireless sensor network, the length sending data is generally fixing, but one the heaviest The parameter wanted, the time span of sleeping task is independently can to set according to system situation.In B-MAC agreement, Have two in order to agreement infallible datas propagate and necessary energy expenditure is it is noted that one be send introduction energy Amount consume, another be receiving node intercept energy expenditure.The two energy expenditure is all to relate to one with the length of one's sleep Rising, the length of one's sleep is long, and the length of introduction increases accordingly, sends introduction energy consumption and is increased by.Meanwhile, invalid The expected time intercepted also can increase therewith.Equally, it is not that sleep length is the smaller the better, otherwise is constantly in and intercepts State.

During the energy of existing wireless sense network uses, idle energy consumption is more, main cause be node be all in desired electrical Running under pool model, relevant parameter is not bound with actual battery model and is optimized, and, not in the energy consumption optimized Realize task scheduling in parameter, thus the most optimally reduce the power consumption of system, meanwhile, do not account for actual battery model, Make there is relatively large deviation with actual motion.

Summary of the invention

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

For reaching above-mentioned purpose, the embodiment of one aspect of the present invention proposes a kind of wireless sense network based on actual battery model Method for scheduling task, comprises the following steps: S1: operational factor and actual battery model according to wireless sense network are to obtain The power consumption of different phase in each cycle, wherein, described different phase include the sampling transmission phase of data, the reception stage, Intercept and Sleep stages, and described actual battery model is energy loss and time and the relation of electric current；S2: according to described The power consumption of sampling transmission phase, receive the power consumption in stage, intercept power consumption with Sleep stages and obtain described wireless sense network joint The total power consumption model of point, wherein, described total power consumption model is relevant to the length of one's sleep of wireless sensing net node；S3: solve The optimum length of one's sleep of described wireless sensing net node total power consumption model；And S4: by the described optimum length of one's sleep to nothing The energy that the task of line Sensor Network is scheduling making actual battery consume is minimum, to reduce the power consumption of wireless sense network.

Method according to embodiments of the present invention, by setting up the total power consumption model of wireless sense network, and solves wireless sense network The optimum length of one's sleep, thus reduce the power consumption of wireless sensing net node, extend making of wireless sensing net node simultaneously Use the life-span.

In an example of the present invention, described actual battery model is Rakhmatov model, is represented by equation below,Wherein, σ (t) represents apparent charge loss, i.e. battery In the tolerance that t time self-energy consumes, n represents the task quantity in cycle t, I_k, t_k,Δ_kRepresent kth task respectively Current value, time started and persistent period, β represents battery variety coefficient.

In an example of the present invention, described total power consumption model is represented by equation below,Wherein, E_periodRepresent total power consumption, E_SASRepresent that sampling sends The power consumption in stage, E_RDRepresent the power consumption in reception stage, E_LASRepresenting and intercept the power consumption with Sleep stages, N represents wireless The number of adjacent node in Sensor Network, r represents sample rate, T₁Represent the persistent period of sampling transmission phase, T₂Expression connects The persistent period in receipts stage, T₃Represent and intercept the persistent period with Sleep stages.

In an example of the present invention, described total power consumption Optimized model is represented by equation below,Wherein, E_periodRepresent total power consumption, T_sleepTable Showing and represent the length of one's sleep, N the number of adjacent node in wireless sense network, S.T. represents that deferred constraint, r represent sample rate, T_constraintExpression deferred constraint is time constant.

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

For reaching above-mentioned purpose, on the other hand embodiments of the invention propose a kind of wireless sensing based on actual battery model Net task scheduling system, including: the first acquisition module, for the operational factor according to wireless sense network and actual battery mould Type is to obtain the power consumption of different phase in each cycle, wherein, described different phase include data sampling transmission phase, The reception stage, intercept and Sleep stages, and described actual battery model is energy loss and time and the relation of electric current；The Two acquisition modules, for the power consumption according to described sampling transmission phase, the power consumption in reception stage, intercept and Sleep stages Power consumption obtains the total power consumption model of described wireless sensing net node, wherein, described total power consumption model and wireless sensing net node The length of one's sleep be correlated with；Solve module, for solving the optimum length of one's sleep of described wireless sensing net node total power consumption model； And adjusting module, for by being scheduling making actual battery disappear to the task of wireless sense network the described optimum length of one's sleep The energy of consumption is minimum, to reduce the power consumption of wireless sense network.

System according to embodiments of the present invention, by setting up the total power consumption model of wireless sense network, and solves wireless sense network The optimum length of one's sleep, thus reduce the power consumption of wireless sensing net node, extend making of wireless sensing net node simultaneously Use the life-span.

In an example of the present invention, described actual battery model is Rakhmatov model, is represented by equation below,Wherein, σ (t) represents apparent charge loss, i.e. battery In the tolerance that t time self-energy consumes, n represents the task quantity in cycle t, I_k, t_k,Δ_kRepresent kth task respectively Current value, time started and persistent period, β represents battery variety coefficient.

In an example of the present invention, described total power consumption model is represented by equation below,Wherein, E_periodRepresent total power consumption, E_SASRepresent that sampling sends The power consumption in stage, E_RDRepresent the power consumption in reception stage, E_LASRepresenting and intercept the power consumption with Sleep stages, N represents wireless The number of adjacent node in Sensor Network, r represents sample rate, T₁Represent the persistent period of sampling transmission phase, T₂Expression connects The persistent period in receipts stage, T₃Represent and intercept the persistent period with Sleep stages.

In an example of the present invention, described total power consumption Optimized model is represented by equation below,Wherein, E_periodRepresent total power consumption, T_sleepTable Showing and represent the length of one's sleep, N the number of adjacent node in wireless sense network, S.T. represents that deferred constraint, r represent sample rate, T_constraintExpression deferred constraint is time constant.

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

Aspect and advantage that the present invention adds will part be given in the following description, and part will become from the following description Substantially, or by the practice of the present invention recognize.

Accompanying drawing explanation

The present invention above-mentioned and/or that add aspect and advantage will become bright from the following description of the accompanying drawings of embodiments Aobvious and easy to understand, wherein:

Fig. 1 is the stream of the wireless sense network method for scheduling task based on actual battery model according to one embodiment of the invention Cheng Tu；

Fig. 2 is the schematic diagram that the wireless senser according to one embodiment of the invention sent or received task data；

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

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

Fig. 5 is the cartogram changed with neighbor node number the optimal length of one's sleep according to one embodiment of the invention；

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

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

Detailed description of the invention

Embodiments of the invention are described below in detail, and the example of embodiment is shown in the drawings, the most identical or Similar label represents same or similar element or has the element of same or like function.Retouch below with reference to accompanying drawing The embodiment stated is exemplary, is only used for explaining the present invention, and is not construed as limiting the claims.

Fig. 1 is the stream of the wireless sense network method for scheduling task based on actual battery model according to one embodiment of the invention Cheng Tu.As it is shown in figure 1, wireless sense network method for scheduling task based on actual battery model according to embodiments of the present invention, Comprise the following steps:

Step S101, operational factor and actual battery model according to wireless sense network are with different phase in acquisition each cycle Power consumption, wherein, different phase includes the sampling transmission phase of data, reception stage, intercepts and Sleep stages, and real Border battery model is energy loss and time and the relation of electric current.

Specifically, the process that data are sent and receive by node is divided into three phases, i.e. the data acquisition transmission phase of node (SAS stage), receive the stage (RD stage) and intercept and Sleep stages (LAS stage).

Fig. 2 is the schematic diagram that the wireless senser according to one embodiment of the invention sent or received task data.Such as Fig. 2 Shown in, there it can be seen that transmission data and reception data are big electric current tasks, and sleeping task is in all tasks Consumed energy is minimum.It is made up of some detailed task in above-mentioned three megastages classification, such as, intercepts and Sleep stages Can be combined with multiple sleeps by multiple intercepting respectively.Tables 1 and 2 is the various operational factors of wireless sense network, Can carry out calculating by the relevant parameter of Tables 1 and 2 and obtain, such as, send the time T of a packet_send, permissible By the transmission data length L of table 1_packetIt is T with the transmission one Bit data required time of table 2_txb, and obtain according to equation below To L_send, T_send=L_packet*T_txb。

Table 1

Table 2

In one embodiment of the invention, the task in each stage is combined by some detailed task, and the these three stage The energy consumed can be by actual battery model formation,Wherein, σ (t) represents apparent charge loss, i.e. battery exists The energy consumed in the t time, n represents the task quantity in cycle t, I_k, t_k,Δ_kRepresent the electricity of kth task respectively Flow valuve, time started and persistent period, the Section 2 of formula contains rate capabilities effect and recovery Effects.We use this Energy expenditure on one sensor node of individual model metrics.β represents battery variety coefficient, and its value is according to different types of dry electricity Pond is different.

In one embodiment of the invention, being deployed in the sensor node in tufted topological network, arbitrary cycle has 1 SAS task and N number of RD task (from N number of neighbor node), the remaining time in cycle then occupies by LAS task.Cycle Middle total power consumption is the sum of three generic task energy expenditures.This three generic task can further be divided into detailed task combination, this A little detailed task can not arbitrarily be dispatched, and namely order is immutable.The once number of the little task that three generic tasks comprise, Time started determines, the energy expenditure of general assignment just can determine therewith.In our model, the parameter that uniquely can change It it is the length of one's sleep.

In one embodiment of the invention, being analyzed as follows of SAS phased mission.According to B-MAC protocol contents, this stage appoints Business execution process is, sensor acquisition data (little task 1), and then sensor node intercepts channel (little task 2), if Channel idle, then send introduction and data (task 3), if busy, then node wait (little task 4) after a period of time again Secondary intercept channel (task 5).Being continued for this process until sending out this data, maximum number of attempt is that M, M can To set according to factors such as concrete network sizes.Arbitrarily persistent period and the current parameters of detailed task can be from table 1 and table Inquire about in 2, be calculated.

The persistent period of detailed task 3 can calculate with equation below.According to B-MAC agreement, length L of introduction_preambleMust Must be more than the length of one's sleep of node.Make L_preambleEqual to the T length of one's sleep_sleepAdditive constant C, in emulation, the default value of C is 20 ratios Special.L_preamble=C+T_sleep/T_txb, therefore the persistent period of detailed task 3 is T_SAS3=(L_preamble+L_packet)*T_txb.If it is first Secondary channel of intercepting for the free time, then occurs without waiting for task, then SAS task only comprises three detailed task, and we are this event Probability of happening is designated as p0.If there occurs and once waiting, then SAS task contains 5 detailed task, is respectively sampling, detects Listening, wait, intercept, send, the probability of this part thing is designated as P1.In like manner, we use A_kRepresentations of events is for listen for k letter Road, wherein k-1 secondary channel is busy, and kth secondary channel is idle.The probability of this event is, P (A_k)=(1-p0)^k-1p0.All Event all can be carried out energy expenditure calculating by above-mentioned formula.Noticing, be up to M-1 wait according to agreement, then we can divide Do not obtain the energy that M event consumes, E can be expressed as_k(k=0,1 ..., M-1).Comprehensive all events, we obtain The catabiotic expected value of SAS task, is shown below,Arbitrarily E_k(k=0,1 ..., M-1) it is all parameter T_sleepFunction, and P_k(k=0,1 ..., M-1) it is all the function of p0.P0 be detection channel be idle probability, here I Suppose that it is all independent for detecting event every time, then p0 should be equal to total sleep time except in the length in whole cycle, i.e. L_receive=L_preamble/2+L_packet, therefore P_kAlso it is the length of one's sleep and the function of neighbours' number.Thus ENERGY E_SASWhen being sleep Between T_sleepFunction with neighbours' number N.

In one embodiment of the invention, as follows to RD task analysis: according to B-MAC agreement, following event is sent out in order Raw: to intercept channel (detailed task 1), if activity being detected, intercept until introduction terminates, if finding sending out of data the most always Sending destination is this node, then receive data (detailed task 2), otherwise return sleep state.If being not detected by activity, Directly returning sleep state, be so actually a sleeping task, this kind of situation is left in LAS task discuss.Because Node intercepts the randomness of channel time, and we can simply suppose to receive the introduction length addend that desired length is half of introduction According to the length of bag, L_receive=L_preamble/2+L_packet.This it is assumed to be reasonably, from macroscopically, and average reception introduction Length is the half of actual introduction length really.According to this it is assumed that we can obtain the consumption of RD task from above formula Energy.It should be noted that the persistent period of second little task should pass through L_receiveCalculate.Certainly, we can also Take fully enough L_receiveSample, calculate after energy expenditure respectively and average, but this method is except increasing computation complexity Outside, in raising computational accuracy, there is not any contribution.We use symbol E_RDRepresent the energy that RD task consumes, with The analysis of sample, it is the function of the length of one's sleep and N, i.e.

In one embodiment of the invention, LAS phased mission is analyzed as follows.Here task has two kinds of situations.The first Situation, intercepting channel is idle condition, and node immediately enters sleep state.The second situation, channel status is busy, then etc. Treat until introduction terminates, find that destination is not this node, then proceed to sleep state.In the first situation, only two thin Joint task, respectively intercepts, sleeps, it is easy to calculating by formula 1, consumed energy is denoted as E_LAS1.In second case, The desired a length of L of reception introduction_preamble/2.There are three detailed task in this case, respectively intercept, receive introduction number According to, sleep, again by above-mentioned formula calculate energy, be designated as E_LAS2.Analysis like above, the two energy is all slept Dormancy time and the function of neighbours' number N.The probability of happening of the second type tasks depends on the busy extent of channel.We This probability of happening is designated as c.In general, channel busy probability depends on the information of neighbor node and sends situation, but saves from self From the point of view of some observation, channel occupancy probabilities can be estimated as the time of the total reception information of node own except in total cycle time.So The reasonability done is, we itself discuss is the sensor network of low load-factor, and information just must be in through wait repeatedly The probability sent is very little.Node itself to send a secondary data, once receives successfully, and N-1 time possible mistake is intercepted, Thus we obtain following formula and are, c=(T₁+(N-1)*T₂)/(1/r), wherein, T₁And T₂Represent that node sends data task respectively Average holding time and reception data average holding time.Thus, the energy of LAS task consumption can be expressed as, E_LAS=(1-c) * E_LAS1+c*E_LAS2.By upper, a cycle only has 1 SAS task and N number of RD task, the cycle Remaining time is all occupied by LAS task.

Step S102, according to the power consumption of sampling transmission phase, receives the power consumption in stage, intercepts the power consumption with Sleep stages and obtain Obtaining the total power consumption model of wireless sensing net node, wherein, 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 in a cycle is calculated as follows:Wherein, E_periodRepresent total power consumption, E_SASRepresent The power consumption of sampling transmission phase, E_RDRepresent the power consumption in reception stage, E_LASRepresent and intercept the power consumption with Sleep stages, N Representing the number of adjacent node in wireless sense network, r represents sample rate, T₁Represent the persistent period of sampling transmission phase, T₂ Represent the persistent period in reception stage, T₃Represent and intercept the persistent period with Sleep stages.

Step S103, solves the 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, it is understood that there may be deferred constraint, namely sleep Overlong time causes the reduction of the rate of information throughput.It is tired that this point does not exist substantially for our model Difficult.Plus deferred constraint, we final Optimized model are:Wherein, E_periodRepresent total power consumption, T_sleepTable Showing and represent the length of one's sleep, N the number of adjacent node in wireless sense network, S.T. represents that deferred constraint, r represent sample rate, T_constraintExpression deferred constraint is time constant.

From above-mentioned Optimized model it can be seen that node energy consumption number with the length of one's sleep, wireless sense network in adjacent node Number relevant with sample rate, and the optimal length of one's sleep of sensor node is only the most relevant with the topological situation of network.Because The busy situation that how many direct reaction of neighbor node channel is possible, thus affect the length of the length of one's sleep.As for reality Battery model, simply calculate channel busy situation on energy expenditure when affecting and ideal battery model has different degree Amount.

Step S104, is scheduling the energy making actual battery consume by the optimum length of one's sleep to the task of wireless sense network Minimum, to reduce the power consumption of wireless sense network.

Fig. 3 is the graph of a relation of the optimal length of one's sleep according to one embodiment of the invention and cycle.As it is shown on figure 3, in figure Neighbor node number N is 10, from the figure, it can be seen that the cycle is the longest, the optimal length of one's sleep under two kinds of models all increases, And actual battery model increases faster optimal length of one's sleep more corresponding than ideal battery model.In task as much and In the case of network topology, the cycle lengthens the reduction being equivalent to network congestion, and node can have longer time entrance to sleep Dormancy state.And our Optimized model obtains optimal length of one's sleep, increasing degree was bigger than the increasing degree of ideal battery, The recovery Effects of this explanation aneroid battery is obviously.We are it could be observed that along with Cycle Length in figure 3 Reduce, tend to identical two optimal lengths of one's sleep.This is because handling capacity is big, channel is the busiest to be caused at Sleep stages The energy recovered is negligible compared with preferable battery model.But in the wireless sensor network of low load-factor, This situation is little, and actual battery model is very important.

Fig. 4 is the energy consumption comparison diagram of the actual battery model according to one embodiment of the invention and ideal battery model.Such as figure Shown in 4, in figure, neighbor node number is fixed value, and we calculate the energy each consumed under two kinds of models.Result shows The energy per bit calculated with actual battery model is less than the respective value under preferable battery model.This can be seen that and is setting After having put parameter optimal length of one's sleep, energy recovers really there is remarkable result.Sufficiently large (less week when quantitative change of handling up Phase), difference between the two can be left in the basket.But in the wireless sensor network of low load-factor, this energy saves Province is very important.

Fig. 5 is the cartogram changed with neighbor node number the optimal length of one's sleep according to one embodiment of the invention.As Shown in Fig. 5, hold period length at 300s, changes neighbor node number it can be seen that no matter which kind of model, along with neighbour Occupying node number to increase, the optimal length of one's sleep reduces.The length of one's sleep under actual battery model is always greater than ideal battery Value under model.When neighbor node number is sufficiently large, reach more than 100, then difference can be reduced to ignore.

Fig. 6 is the graph of a relation of the battery life according to one embodiment of the invention and neighbor node number.As shown in Figure 6, Under Optimized model, energy optimal when N is equal to 10 is saved can reach 14%.If the cycle is less than 300s, then extend Service life reduction to 2%.

Method according to embodiments of the present invention, by setting up the total power consumption model of wireless sense network, and solves wireless sense network The optimum length of one's sleep, thus reduce the power consumption of wireless sensing net node, extend making of wireless sensing net node simultaneously Use the life-span.

Fig. 7 is the frame of the wireless sense network task scheduling system based on actual battery model according to one embodiment of the invention Frame figure.As it is shown in fig. 7, wireless sense network task scheduling system based on actual battery model according to embodiments of the present invention Including first acquisition module the 100, second acquisition module 200, optimize module 300 and adjusting module 400.

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

Specifically, the process that data are sent and receive by node is divided into three phases, i.e. the data acquisition transmission phase of node (SAS stage), receive the stage (RD stage) and intercept and Sleep stages (LAS stage).

Fig. 2 is the schematic diagram that the wireless senser according to one embodiment of the invention sent or received task data.Such as Fig. 2 Shown in, there it can be seen that transmission data and reception data are big electric current tasks, and sleeping task is in all tasks Consumed energy is minimum.It is made up of some detailed task in above-mentioned three megastages classification, such as, intercepts and Sleep stages Can be combined with multiple sleeps by multiple intercepting respectively.Tables 1 and 2 is the various operational factors of wireless sense network, Can carry out calculating by the relevant parameter of Tables 1 and 2 and obtain, such as, send the time T of a packet_send, permissible By the transmission data length L of table 1_packetIt is T with the transmission one Bit data required time of table 2_txb, and obtain according to equation below To L_send, T_send=L_packet*T_txb。

Table 1

Symbol	Describe	Default value
			Csleep	Sleep current (mA)	0.03
Lpreamble	Introduction length (bytes)	Nothing
			Lpacket	Data length (bytes)	36
N	Neighbor node number (nodes)	10
			r	Sample rate (number of times per second)	1/300
Clisten	Sample rate current (mA)	2.4
			Tlisten	Sampling time (s)	2.5E-3
Tsleep	The length of one's sleep (s)	Nothing
			Tback	Backoff time (ms)	27.4

Table 2

In one embodiment of the invention, the task in each stage is combined by some detailed task, and the these three stage The energy consumed can be by actual battery model formation,Wherein, σ (t) represents apparent charge loss, i.e. battery exists The energy consumed in the t time, n represents the task quantity in cycle t, Ik, t_k,Δ_kRepresent the electricity of kth task respectively Flow valuve, time started and persistent period, the Section 2 of formula contains rate capabilities effect and recovery Effects.We use this Energy expenditure on one sensor node of individual model metrics.β represents battery variety coefficient, and its value is according to different types of dry electricity Pond is different

In one embodiment of the invention, being deployed in the sensor node in tufted topological network, arbitrary cycle has 1 SAS task and N number of RD task (from N number of neighbor node), the remaining time in cycle then occupies by LAS task.Cycle Middle total power consumption is the sum of three generic task energy expenditures.This three generic task can further be divided into detailed task combination, this A little detailed task can not arbitrarily be dispatched, and namely order is immutable.The once number of the little task that three generic tasks comprise, Time started determines, the energy expenditure of general assignment just can determine therewith.In our model, the parameter that uniquely can change It it is the length of one's sleep.

In one embodiment of the invention, being analyzed as follows of SAS phased mission.According to B-MAC protocol contents, this stage appoints Business execution process is, sensor acquisition data (little task 1), and then sensor node intercepts channel (little task 2), if Channel idle, then send introduction and data (task 3), if busy, then node wait (little task 4) after a period of time again Secondary intercept channel (task 5).Being continued for this process until sending out this data, maximum number of attempt is that M, M can To set according to factors such as concrete network sizes.Arbitrarily persistent period and the current parameters of detailed task can be from table 1 and table Inquire about in 2, be calculated.

The persistent period of detailed task 3 can calculate with equation below.According to B-MAC agreement, length L of introduction_preambleMust Must be more than the length of one's sleep of node.Make L_preambleEqual to the T length of one's sleep_sleepAdditive constant C, in emulation, the default value of C is 20 ratios Special.L_preamble=C+T_sleep/T_txb, therefore the persistent period of detailed task 3 is T_SAS3=(L_preamble+L_packet)*T_txb.If it is first Secondary channel of intercepting for the free time, then occurs without waiting for task, then SAS task only comprises three detailed task, and we are this event Probability of happening is designated as p0.If there occurs and once waiting, then SAS task contains 5 detailed task, is respectively sampling, detects Listening, wait, intercept, send, the probability of this part thing is designated as P1.In like manner, we use A_kRepresentations of events is for listen for k letter Road, wherein k-1 secondary channel is busy, and kth secondary channel is idle.The probability of this event is, P (A_k)=(1-p0)^k-1p0.All Event all can be carried out energy expenditure calculating by above-mentioned formula.Noticing, be up to M-1 wait according to agreement, then we can divide Do not obtain the energy that M event consumes, E can be expressed as_k(k=0,1 ..., M-1).Comprehensive all events, we obtain The catabiotic expected value of SAS task, is shown below,Arbitrarily E_k(k=0,1 ..., M-1) it is all parameter T_sleepFunction, and P_k(k=0,1 ..., M-1) it is all the function of p0.P0 be detection channel be idle probability, here I Suppose that it is all independent for detecting event every time, then p0 should be equal to total sleep time except in the length in whole cycle, i.e. L_receive=L_preamble/2+L_packet, therefore P_kAlso it is the length of one's sleep and the function of neighbours' number.Thus ENERGY E_SASWhen being sleep Between T_sleepFunction with neighbours' number N.

In one embodiment of the invention, as follows to RD task analysis: according to B-MAC agreement, following event is sent out in order Raw: to intercept channel (detailed task 1), if activity being detected, intercept until introduction terminates, if finding sending out of data the most always Sending destination is this node, then receive data (detailed task 2), otherwise return sleep state.If being not detected by activity, Directly returning sleep state, be so actually a sleeping task, this kind of situation is left in LAS task discuss.Because Node intercepts the randomness of channel time, and we can simply suppose to receive the introduction length addend that desired length is half of introduction According to the length of bag, L_receive=L_preamble/2+L_packet.This it is assumed to be reasonably, from macroscopically, and average reception introduction Length is the half of actual introduction length really.According to this it is assumed that we can obtain the consumption of RD task from above formula Energy.It should be noted that the persistent period of second little task should pass through L_receiveCalculate.Certainly, we can also Take fully enough L_receiveSample, calculate after energy expenditure respectively and average, but this method is except increasing computation complexity Outside, in raising computational accuracy, there is not any contribution.We use symbol E_RDRepresent the energy that RD task consumes, with The analysis of sample, it is the function of the length of one's sleep and N, i.e.

In one embodiment of the invention, LAS phased mission is analyzed as follows.Here task has two kinds of situations.The first Situation, intercepting channel is idle condition, and node immediately enters sleep state.The second situation, channel status is busy, then etc. Treat until introduction terminates, find that destination is not this node, then proceed to sleep state.In the first situation, only two thin Joint task, respectively intercepts, sleeps, it is easy to calculating by formula 1, consumed energy is denoted as E_LAS1.In second case, The desired a length of L of reception introduction_preamble/2.There are three detailed task in this case, respectively intercept, receive introduction number According to, sleep, again by above-mentioned formula calculate energy, be designated as E_LAS2.Analysis like above, the two energy is all slept Dormancy time and the function of neighbours' number N.The probability of happening of the second type tasks depends on the busy extent of channel.We This probability of happening is designated as c.In general, channel busy probability depends on the information of neighbor node and sends situation, but saves from self From the point of view of some observation, channel occupancy probabilities can be estimated as the time of the total reception information of node own except in total cycle time.So The reasonability done is, we itself discuss is the sensor network of low load-factor, and information just must be in through wait repeatedly The probability sent is very little.Node itself to send a secondary data, once receives successfully, and N-1 time possible mistake is intercepted, Thus we obtain following formula and are, c=(T₁+(N-1)*T₂)/(1/r), wherein, T₁And T₂Represent that node sends data task respectively Average holding time and reception data average holding time.Thus, the energy of LAS task consumption can be expressed as, E_LAS=(1-c) * E_LAS1+c*E_LAS2.By upper, a cycle only has 1 SAS task and N number of RD task, the cycle Remaining time is all occupied by LAS task.

Second acquisition module 200 is for the power consumption according to sampling transmission phase, the reception power consumption in stage, rank of intercepting and sleep The power consumption of section obtains the total power consumption model of wireless sensing net node, and wherein, total power consumption model is slept with wireless sensing net node Dormancy time correlation.

In one embodiment of the invention, the total power consumption in a cycle is calculated as follows:Wherein, E_periodRepresent total power consumption, E_SASRepresent The power consumption of sampling transmission phase, E_RDRepresent the power consumption in reception stage, E_LASRepresent and intercept the power consumption with Sleep stages, N Representing the number of adjacent node in wireless sense network, r represents sample rate, T₁Represent the persistent period of sampling transmission phase, T₂ Represent the persistent period in reception stage, T₃Represent and intercept the persistent period with Sleep stages.

Solve module 300 for solving the 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, it is understood that there may be deferred constraint, namely sleep Overlong time causes the reduction of the rate of information throughput.It is tired that this point does not exist substantially for our model Difficult.Plus deferred constraint, we final Optimized model are:Wherein, E_periodRepresent total power consumption, T_sleepTable Showing and represent the length of one's sleep, N the number of adjacent node in wireless sense network, S.T. represents that deferred constraint, r represent sample rate, T_constraintExpression deferred constraint is time constant.

From above-mentioned Optimized model it can be seen that node energy consumption number with the length of one's sleep, wireless sense network in adjacent node Number relevant with sample rate, and the optimal length of one's sleep of sensor node is only the most relevant with the topological situation of network.Because The busy situation that how many direct reaction of neighbor node channel is possible, thus affect the length of the length of one's sleep.As for reality Battery model, simply calculate channel busy situation on energy expenditure when affecting and ideal battery model has different degree Amount.

Adjusting module 400 is for by being scheduling making actual battery consumption to the task of wireless sense network the optimum length of one's sleep Energy minimum, to reduce the power consumption of wireless sense network.

In one embodiment of the invention, the optimum length of one's sleep and the topological situation of network, i.e. the number of adjacent node has Close, by adjusting the number of adjacent node, thus realize adjusting the lower power consumption power consumption of wireless sense network.

System according to embodiments of the present invention, by setting up the total power consumption model of wireless sense network, and solves wireless sense network The optimum length of one's sleep, thus reduce the power consumption of wireless sensing net node, extend making of wireless sensing net node simultaneously Use the life-span.

Although above it has been shown and described that embodiments of the invention, it is to be understood that above-described embodiment is exemplary , it is impossible to being interpreted as limitation of the present invention, those of ordinary skill in the art is without departing from the principle of the present invention and objective In the case of above-described embodiment can be changed within the scope of the invention, revise, replace and modification.

Claims (4)

1. a wireless sense network method for scheduling task based on actual battery model, it is characterised in that include following step Rapid:

S1: operational factor and actual battery model according to wireless sense network are with the merit of different phase in acquisition each cycle Consumption, wherein, described different phase includes the sampling transmission phase of data, reception stage, intercepts and Sleep stages, and Described actual battery model is energy loss and time and the relation of electric current；

S2: the power consumption that according to the power consumption of described sampling transmission phase, receives the stage, intercept the power consumption with Sleep stages and obtain Obtaining the total power consumption model of described wireless sensing net node, wherein, described total power consumption model is slept with wireless sensing net node Dormancy time correlation；

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

S4: by the described optimum length of one's sleep, the task of wireless sense network is scheduling the energy making actual battery consume Minimum, to reduce the power consumption of wireless sense network,

Described total power consumption model is represented by equation below,

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

Wherein, E_periodRepresent total power consumption, E_SASRepresent the power consumption of sampling transmission phase, E_RDRepresent the merit in reception stage Consumption, E_LASRepresenting and intercept the power consumption with Sleep stages, N represents the number of adjacent node in wireless sense network, and r represents and adopts Sample rate, T₁Represent the persistent period of sampling transmission phase, T₂Represent the persistent period in reception stage, T₃Represent intercept and The persistent period of Sleep stages,

The optimization of described total power consumption model is represented by equation below,

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

Wherein, E_periodRepresent total power consumption, T_sleepRepresent the length of one's sleep, N represent adjacent node in wireless sense network Number, S.T. represents that deferred constraint, r represent sample rate, T_constraintExpression deferred constraint is time constant.

Wireless sense network method for scheduling task based on actual battery model the most according to claim 1, its feature Being, the described optimum length of one's sleep is relevant with the topological situation of network.

3. a wireless sense network task scheduling system based on actual battery model, it is characterised in that including:

First acquisition module, is used for the operational factor according to wireless sense network and actual battery model to obtain each cycle The power consumption of interior different phase, wherein, described different phase includes the sampling transmission phase of data, reception stage, intercepts And Sleep stages, and described actual battery model is energy loss and time and the relation of electric current；

Second acquisition module, for the power consumption according to described sampling transmission phase, the power consumption in reception stage, intercepts and sleeps The power consumption in dormancy stage obtains the total power consumption model of described wireless sensing net node, and wherein, described total power consumption model is with wireless The length of one's sleep of sensing net node is correlated with；

Solve module, for solving the optimum length of one's sleep of described wireless sensing net node total power consumption model；And

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

Described total power consumption model is represented by equation below,

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

Wherein, E_periodRepresent total power consumption, E_SASRepresent the power consumption of sampling transmission phase, E_RDRepresent the merit in reception stage Consumption, E_LASRepresenting and intercept the power consumption with Sleep stages, N represents the number of adjacent node in wireless sense network, and r represents and adopts Sample rate, T₁Represent the persistent period of sampling transmission phase, T₂Represent the persistent period in reception stage, T₃Represent intercept and The persistent period of Sleep stages,

The optimization of described total power consumption model is represented by equation below,

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

Wherein, E_periodRepresent total power consumption, T_sleepRepresent the length of one's sleep, N represent adjacent node in wireless sense network Number, S.T. represents that deferred constraint, r represent sample rate, T_constraintExpression deferred constraint is time constant.

Wireless sense network task scheduling system based on actual battery model the most according to claim 3, its feature Being, the described optimum length of one's sleep is relevant with the topological situation of network.