CN109302747B - MAC layer time slot allocation method for guaranteeing QoS in body area network - Google Patents

MAC layer time slot allocation method for guaranteeing QoS in body area network Download PDF

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CN109302747B
CN109302747B CN201811257244.2A CN201811257244A CN109302747B CN 109302747 B CN109302747 B CN 109302747B CN 201811257244 A CN201811257244 A CN 201811257244A CN 109302747 B CN109302747 B CN 109302747B
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孙罡
王凯
孙健
虞红芳
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University of Electronic Science and Technology of China
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    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0446—Resources in time domain, e.g. slots or frames
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses a method for allocating MAC layer time slots for guaranteeing QoS in a body area network. According to the time slot allocation method, time delay cost and energy cost in the data transmission process of the nodes are considered, the node utility function is innovatively designed by combining node priority and transmission delivery probability, the optimization problem is mathematically modeled according to the definition of the utility function, and a time slot allocation scheme with low complexity and good performance is provided. Therefore, the time slot allocation scheme provided by the invention can improve the comprehensive utility of the network.

Description

MAC layer time slot allocation method for guaranteeing QoS in body area network
Technical Field
The invention relates to the technical field of wireless body area networks, in particular to a method for allocating time slots of an MAC layer for guaranteeing QoS in a body area network.
Background
The wireless body area network is a wireless sensor network which takes a human body as a center and consists of a local processing unit (intelligent terminals such as a mobile phone and a bracelet) and wireless sensors distributed on the surface of the human body or in the human body. Usually, the local processing unit and the common sensor node directly perform data transmission through single-hop communication to form a star network. The sensor nodes collect information of each part of the human body, and then transmit the information to the local processing unit for processing and displaying or further transmit the information to the remote processing center.
Due to the particularities of wireless body area networks themselves, the study of their communication protocols is facing several challenges. Firstly, the blocking and absorption of the electromagnetic wave signals by the human body can cause the deep fading of the channel of the wireless body area network, the deep fading of the body surface channel is as long as 400ms, and the value is far higher than that of the traditional wireless sensor network. Secondly, the network environment and the service requirements of the wireless body area network are also in dynamic change due to the movement of the human body. For example, when a heart patient is moving violently, the data (such as an electrocardiogram) associated with the movement should have a lower transmission delay and a lower packet loss rate. In addition, since the sensor node is usually worn on the human body, and the size and battery capacity of the sensor node are very severely limited, the wireless body area network is an extremely energy-limited system. Finally, due to the weak processing power of the sensor nodes, generally speaking, complex computation tasks should be performed by the local processing unit, while the sensor nodes are only responsible for simple collection and transmission tasks.
The design and optimization of the wireless body area network MAC (medium access control) layer protocol can be mainly divided into two aspects: medium access control and resource optimal scheduling. The MAC protocol mainly controls specific behaviors of a node accessing a channel, such as communication, sleep time, and the like, so that a plurality of nodes can implement collision-free communication in one resource-shared communication link. In addition, the energy efficiency, the time delay, the packet loss rate, and the like of the node for data transmission also have an inseparable relationship with the MAC protocol. The optimal resource scheduling means that resources such as bandwidth, time, energy and the like of the system are reasonably distributed to each communication node in the network, and the QoS (quality of service) and energy requirements of each node are met, so that the system obtains the optimal performance.
The MAC protocols for wireless body area networks are generally divided into three categories: time division multiplexing based, contention based, and hybrid MAC protocols. The MAC protocol based on time division multiplexing divides time into time slots with the same size, and different nodes obtain different time slots. The node wakes up and transmits data when the time slot belonging to the node starts, and enters a sleep state in other time slots. This mechanism can avoid collisions and reduce energy consumption due to the introduction of the standby state, but puts high demands on the slot allocation strategy due to the dynamic variation of channel conditions and the diversity of traffic demands of different nodes. The MAC protocol based on competition does not distribute the resources uniformly, and the nodes adopt a certain mechanism to obtain the shared channel resources in a competition mode. This mechanism is very scalable, however, the simultaneous transmission of highly correlated data introduces a large number of collisions. Hybrid MAC protocols use both time division multiplexing and contention based mechanisms, but are often too complex.
At present, there have been some studies on the MAC layer slot allocation scheme. For example, some schemes model the change in the communication channel between the sensor node and the local processing unit as a two-state markov process. And allocating different time slots for the corresponding nodes according to different states of the channel. Although the above method can dynamically adjust the time slot allocation according to the channel state change, the attributes of the node itself, such as node importance, residual cache, residual energy, etc., are not considered, and the service requirements of a specific node cannot be met.
Researchers design priorities of sensor nodes, consider importance of the sensor nodes, and propose a corresponding time slot allocation scheme according to the importance of the nodes. The method is not comprehensive in consideration of the specific service requirements of the nodes, only the importance of the nodes is considered, and factors such as sampling rate, residual cache, residual energy and the like are not considered. Furthermore, this approach does not model and solve the problem, and therefore lacks the necessary theoretical basis.
Disclosure of Invention
Aiming at the defects in the prior art, the MAC layer time slot allocation method for guaranteeing the QoS in the body area network provided by the invention solves the problem of low utility value of the whole network.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a MAC layer time slot distribution method for guaranteeing QoS in a body area network comprises the following steps:
s1, calculating the node priority through the key index factor, the sampling rate factor, the overtime factor and the residual energy factor of the sensor node;
s2, calculating the delivery probability, the delay cost and the energy cost of the node;
s3, establishing a utility function of the node through the priority, the delivery probability, the delay cost and the energy cost of the node;
s4, modeling time slot allocation in the body area network by using utility functions of a plurality of nodes to obtain an optimization model;
and S5, obtaining a time slot allocation scheme with an optimal model suboptimal by carrying out basic time slot allocation on the nodes and considering constraint conditions.
Further: the calculation formula of the node priority in step S1 is:
Figure BDA0001843019980000031
in the above formula, the first and second carbon atoms are,ω1+ω2+ω3＝1，i＝1,2,……,N，ω1、ω2and ω3Are all coefficients, N is the total number of nodes, PriiFor the priority of node i, Priove,iTo take into account the timeout factor Ovepri,iNode priority of, RemEiFor the residual energy of node i, RemEth,iIs the minimum threshold value of the residual energy of the node i, CIpri,iIs a key index factor, f, of node ipri,iIs the sampling rate factor of node i, Ovenum,iThe number of data frames in the next superframe for node i that the node may time out Ovenum,th,iA threshold for the number of data frames in node i's next superframe that may time out, Ovepri,iIs the timeout factor of node i, Ovepri,th,iIs a data frame priority threshold that may be timed out in the next superframe of node i, wherein the sampling rate factor fpriThe calculation formula of (2) is as follows:
Figure BDA0001843019980000041
in the above formula, Nframe＝Nnew+Nold，NframeFor the total number of data frames, N, in the buffer queue during the next superframenewFor the number of new data frames in the next superframe period, NoldFor the number of old data frames in the next superframe period, fpri,newSample rate factor, f, for new data frames in the next superframe periodpri,oldFor the sampling rate factor of the old data frame in the next superframe period, fuIs the upper bound of the sampling rate of the node, flIs the lower bound of the sampling rate of the node, fnewSample rate, f, allocated for a new data frame by a local processing unitoldSample rate allocated for the processing unit for the old data frame, where NnewAnd NoldThe calculation formula of (2) is as follows:
Figure BDA0001843019980000042
Figure BDA0001843019980000043
in the above formula, TsFor sample time, R is the data transmission rate, TslotFor the length of the time slot, Reqframe,oldFor the service requirement of the node in the current superframe, taking a data frame as a unit, Nslots,oldThe number of slots allocated for that node in the current superframe,
Figure BDA0001843019980000044
the average delivery probability of the node data frame in the current superframe is obtained;
residual energy RemE of node iiThe calculation formula of (2) is as follows:
RemEi＝Ecurrent,i-Etran,i
in the above formula, Ecurrent,iTo the current energy level, Etran,iThe energy consumed by node i for the next transmission.
Further: the delivery probability of the node i in the step S2 is the average delivery probability of the node
Figure BDA0001843019980000045
The calculation formula is as follows:
Figure BDA0001843019980000046
in the above formula, slotiAll time slots, N, allocated for node islot,iThe number of time slots allocated to the node i, p (j) is the corresponding delivery probability of the jth time slot, TslotFor the slot length, p (j) is calculated as:
Figure BDA0001843019980000051
in the above formula, PBGProbability of change of channel state from bad to good, PGBIs the probability that the channel state is changed from good to bad, if the data frame can be successfully transmitted to the local processing unit under a certain channel state, the channel state is good, and vice versaIs bad, Pch＝PBG+PGB，PchIs the probability of channel change;
delay cost C of node idelay,iThe calculation formula of (2) is as follows:
Figure BDA0001843019980000052
in the above formula, tsuccTran,j(i) Node i (i) is the data frame set without timeout in node i, j is its angular coordinate, NNodes(i)For the aggregate capacity, tinit,j(i) Is the time t at which the data frame j in the node i enters the queueove,th,j(i) Is the timeout threshold of data frame j in node i, and
tsuccTran,j＝p(j)·tstartTran,j+(1-p(j))·(Tslot+tstartTran,j)
in the above formula, tstartTran,jThe time for starting transmission of the data frame j according to the allocated time slot;
energy cost C of node iE,iThe calculation formula of (2) is as follows:
Figure BDA0001843019980000053
in the above formula, RiIs the transmission rate of the node i and,
Figure BDA0001843019980000054
for the average delivery probability of the data frame of node i,
Figure BDA0001843019980000055
is the minimum value of the average delivery probability of node i, Ptran,iThe transmission data frame of node i consumes power,
Figure BDA0001843019980000056
Etranenergy consumption for transmitting data frames, TtranIs the transmission time.
Further: the node utility function in step S3 is:
Figure BDA0001843019980000057
in the above formula, UiAs a utility function of node i, Pdel,iProbability of delivery as node i, Cdelay,iIs the delay cost of node i, CE,iFor the energy cost of node i, λ1、λ2、λ3And λ4Are all constants greater than 0, and λ4>λ3>λ2>λ1。
Further: the optimization model in step S4 is:
Figure BDA0001843019980000061
in the above formula, order (i) is the transmission sequence of node i, LohFor overhead bits of a unit data frame, Reqbit,iFor the service requirement of node i, taking bit as unit, TframeIs the superframe length.
Further: the specific steps of step S5 are:
s51, allocating basic time slots for the GOOD nodes and the BAD nodes, and calculating the number of transmission continuous time slots;
when the last data frame of the current superframe of the node i is successfully transmitted, and the initial state of the node i is GOOD, the node i is considered to be i ∈ GOOD, otherwise, the node i is i ∈ BAD;
s52, calculating the transmission duration satisfying the time slot total number constraint through the transmission duration time slot number to obtain the time slot section distributed by the node i;
s53, calculating a time slot segment list after the conflict time slot is adjusted through the time slot segments distributed by the node i;
and S54, calculating a final time slot segment list after the time-out node is adjusted through the time slot segment list of the node i, and taking the final time slot segment list as a suboptimal time slot allocation scheme.
Further: the specific steps of step S51 are:
s511, dividing the nodes into GOOD nodes and BAD nodes respectively according to the sensor factor FsensorSorting from big to small, setting GOOD node as 1G,2G,……,nGGet GOOD node transmission start time
Figure BDA0001843019980000062
S512, the time slot of the BAD node is processed according to the channel factor FchannelSorting from big to small, set BAD node to {1B,2B,……,nBGet BAD node transmission start time
Figure BDA0001843019980000071
S513, calculating the number N of the transmission continuous time slotsslots,base,iThe calculation formula is as follows:
Figure BDA0001843019980000072
further: the calculation formula of the transmission duration in step S52 is:
Figure BDA0001843019980000073
in the above formula, Nslots,iFor the duration of the transmission, Reqframe,iFor the service requirement of the node i, taking the data frame as a unit, k is a node, and k is i +1, i +2, … …, N, k satisfies the following conditions:
Figure BDA0001843019980000074
further: the specific steps of step S53 are:
s531, calculating all time slot segments where collision occurs between GOOD nodes and BAD nodes and generating a list L ist (t)col,k,Ncol,k)，k＝1,2,……,N；
(tcol,k,Ncol,k) Generating a conflict for node kThe starting time and the number of the sustained time slots of a certain time slot period;
s532, calculating (t) respectivelycol,k,Ncol,k) Utility values corresponding to GOOD node and BAD node of time slot segment
Figure BDA0001843019980000075
And
Figure BDA0001843019980000076
s533, if
Figure BDA0001843019980000077
Moving the time slot divided by the goose node, and updating the time slot segment and the conflict time slot segment allocated by the following node, otherwise, entering step S434, where the move formula of the goose node is:
Figure BDA0001843019980000078
in the above formula, the first and second carbon atoms are,
Figure BDA0001843019980000081
starting time of a certain time slot segment for generating conflict for an i node in a GOOD node;
s534, moving the time slot divided by the BAD node, and updating the time slot segment and the conflict period distributed by the following node, wherein the moving formula of the BAD node is as follows:
Figure BDA0001843019980000082
in the above formula, the first and second carbon atoms are,
Figure BDA0001843019980000083
the starting time of a certain time slot segment for generating conflict for an i node in the BAD node;
s535, obtaining the adjusted time slot segment list L ist (t)col,k,Ncol,k)，k＝1,2,……,N。
Further: the specific steps of step S54 are:
s541, calculating time slot segment list L ist (t) after conflict time slot adjustmentcol,k,Ncol,k) The overtime nodes are sorted from small to large according to the overtime threshold to form an ordered overtime node set {1,2, … …, m }, wherein m is the number of the overtime nodes;
s542, when
Figure BDA0001843019980000084
If yes, deleting the node i from the overtime node set, otherwise, entering the step S443;
Figure BDA0001843019980000085
for node i in the time slot segment just without timeout
Figure BDA0001843019980000086
Utility value of UkFor being currently in a time slot segment
Figure BDA0001843019980000087
The utility value of node k;
s543, adjusting the node i to the time slot segment
Figure BDA0001843019980000088
S544, if the node k is a GOOD node, sequentially moving the time slots of the node k and nodes behind the node k through a moving formula of the GOOD node, and deleting the node i from the overtime set;
s545, if the node k is a BAD node, sequentially moving the time slots of the node k and nodes behind the node k through a moving formula of the BAD node, and deleting the node i from the overtime set;
s546, obtaining Final time slot list Final L ist (t) of node istart,i,Nslots,i)。
The invention has the beneficial effects that:
(1) the invention carefully considers the overtime condition and the residual energy level of the node data frame, and combines the importance degree and the emergency degree of the data frame to finely design the priority of the node, thereby meeting different service requirements of different sensor nodes.
(2) Because the channel modeling is a Markov process, different scenes only need to calculate different channel state transition matrixes in advance, and the requirements of different scenes can be met.
(3) According to the time slot allocation method, time delay cost and energy cost in the data transmission process of the nodes are considered, the node utility function is innovatively designed by combining node priority and transmission delivery probability, the optimization problem is mathematically modeled according to the definition of the utility function, and a time slot allocation scheme with low complexity and good performance is provided. Therefore, the time slot allocation scheme provided by the invention can improve the comprehensive utility of the network.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a flowchart of step S5 according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a method for allocating a time slot of a MAC layer for guaranteeing QoS in a body area network includes the following steps:
s1, calculating the node priority through the key index factor, the sampling rate factor, the overtime factor and the residual energy factor of the sensor node, wherein the calculation formula of the node priority is as follows:
Figure BDA0001843019980000091
in the above formula, ω1+ω2+ω3＝1，i＝1,2,……,N，ω1、ω2And ω3Are all coefficients, N is the total number of nodes, PriiFor the priority of node i, Priove,iTo take into account the timeout factor Ovepri,iNode priority of, RemEiFor the residual energy of node i, RemEth,iIs the minimum threshold value of the residual energy of the node i, CIpri,iIs a key index factor, f, of node ipri,iIs the sampling rate factor of node i, Ovenum,iThe number of data frames in the next superframe for node i that the node may time out Ovenum,th,iA threshold for the number of data frames in node i's next superframe that may time out, Ovepri,iIs the timeout factor of node i, Ovepri,th,iAnd the node i is a data frame priority threshold which may be overtime in the next superframe, wherein the sampling rate factor reflects the urgency of the data sensed by the node, and the higher the urgency is, the higher the node priority is. We use the deviation of the sampling rate from the standard sampling rate to represent the urgency of the data frame generated by the node, and the more distant from the standard value represents the more urgent the data frame is. After each superframe is finished, the local processing unit simply analyzes the data of each node received in the current superframe period, appoints a proper sampling rate of a data frame to be transmitted in the next superframe period for each node, and broadcasts through a beacon frame. Sample rate factor fpriThe calculation formula of (2) is as follows:
Figure BDA0001843019980000101
in the above formula, Nframe＝Nnew+Nold，NframeFor the total number of data frames, N, in the buffer queue during the next superframenewFor the number of new data frames in the next superframe period, NoldFor the number of old data frames in the next superframe period, fpri,newSample rate factor, f, for new data frames in the next superframe periodpri,oldFor the sampling rate factor of the old data frame in the next superframe period, fuIs the upper bound of the sampling rate of the node, flIs the lower bound of the sampling rate of the node, fnewSample rate, f, allocated for a new data frame by a local processing unitoldSample rate allocated for the processing unit for the old data frame, where NnewAnd NoldThe calculation formula of (2) is as follows:
Figure BDA0001843019980000102
Figure BDA0001843019980000103
in the above formula, TsFor sample time, R is the data transmission rate, TslotFor the length of the time slot, Reqframe,oldFor the service requirement of the node in the current superframe, taking a data frame as a unit, Nslots,oldThe number of slots allocated for that node in the current superframe,
Figure BDA0001843019980000111
the average delivery probability of the node data frame in the current superframe, and the number of the new data frames are the product of the sampling rate and the sampling time distributed in the next superframe; the number of old data frames is the difference between the traffic demand in the current superframe and the number of successfully transmitted data frames.
In the priority evaluation strategy, in addition to considering the two basic indexes, the invention also reasonably introduces a timeout factor and a residual energy factor to enable the evaluation strategy to be more accurate. The timeout factor adjusts the priority in consideration of the timeout condition of the node data, and the residual energy factor adjusts in consideration of the residual energy of the node.
Due to the existence of the node packet loss rate, data frames may not be transmitted during the time slot arranged in the last super frame and are stored in the buffer, but due to the fact that different nodes transmit data in different time slot segments, the data are not transmitted until the next super frame, and in the process, the data of the nodes may be discarded due to time-out. The strategy proposed by the invention is to avoid the node overtime situation as much as possible. Therefore, for those nodes that contain data that may time out, it is reasonable to mentionTheir priority is high so that they transmit data as early as possible, while for those nodes that do not contain data that may time out, the critical exponential factor CI is still only followedpriAnd a sampling rate factor fpriIts priority is calculated.
The invention firstly provides a method for judging whether a certain data frame of a node is possible to overtime in an upcoming superframe. In the scheme, the sampling time of the node in the next superframe is a time point near the transmission starting time, and the sampling is not performed after the synchronization, and then the node sleeps until the time slot arrives, so that the waiting time delay and the queuing time delay of the data frame are greatly reduced, and therefore, the newly sampled data can be considered not to be overtime. For old data frames, a definition is given here that a data frame may time out in an upcoming superframe if its time-out threshold is within the time slot range of the superframe. Namely:
Figure BDA0001843019980000112
in which IndoveA time-out flag indicating a data frame, a value of 1 indicating that an upcoming superframe of the data frame may time out, and a value of 0 indicating no. t is tove,thRepresents the overtime threshold, t, of the corresponding node of the data framecurrentAnd tinitRespectively representing the current time and the time at which the data frame entered the queue, TframeIndicating the length of the superframe.
This is because the local processing unit cannot acquire the complete priority information of all nodes and make specific time slot arrangement when making preliminary estimation on the timeout condition of each data frame, and thus cannot make accurate estimation on the specific timeout condition of the data frame. Therefore, only rough predictions can be made based on the above principles.
Based on the above definition, the number Ove of data frames that may be overtime can be easily obtainednumAnd its priority Ovepri. Because the invention adopts retransmission mechanism and first-come first-serve principle, and the data overtime threshold t of each nodeove,thWill not generally change muchTherefore, it is known that the data frame waiting time in the buffer queue closer to the head is longer, and the timeout is more likely to occur. Therefore, it is only necessary to perform the timeout validation from the data frame at the head of the queue to the tail of the queue, i.e. to check its timeout flag Indove. If a certain data frame is detected, it is found to satisfy tove,th-(tcurrent-tinit)>TframeThe data frame following the data frame in the queue will not time out either and can be easily calculated OvenumI.e. the number of all data frames in the queue that precede the data frame. The priority Ove corresponding to these data framespriOr directly through the stored record of the local processing unit.
The present invention discusses a policy for adjusting priority in consideration of a timeout. The present invention introduces a timeout factor Ovepri,iAnd adjusting the node priority according to the node data timeout condition. Generally speaking, if the node's data frame is during the next superframe period TframeThe number of data frames that may time out is high and in order to avoid more data frames being dropped by the node due to a time out, these data frames need to be sent as early as possible in the upcoming superframe and therefore the priority of the node should be increased. In addition, among all the data frames which are overtime, the data frame with the higher priority represents the higher importance degree, and the negative influence of the data frame after the overtime happens is larger, so that the node corresponding to the data frame should obtain the higher transmission priority. Ove used in the present inventionnum,thAnd Ovepri,thTo indicate the number threshold and priority threshold of data frames that may time out in the next superframe (both of which are initialized to one 0, 1 during system deployment phase]Constant value therebetween) and the basic priority Pri is set according to a combination of the twobase,iThe adjustment is made to take into account the timeout factor Ovepri,iNode priority of (Pri)ove,iThe method comprises the following steps:
Figure BDA0001843019980000131
wherein ω is1+ω2+ω31,2, … …, N, notably Ovepri,iThe value set of (a) is {0, 0.5,1 }. The impact of timeouts on node priority is reasonably divided into four levels by discussing the number and priority of timeout data frames. Number of data frames Ove when node i may time outnum,iIts timeout priority Ovepri,iWith their respective thresholds Ovenum,th,i、Ovepri,th,iSatisfy Ovenum,i>Ovepri,iAnd Ovepri,i>Ovepri,th,iIf the node i is determined to have a high possibility that a part of data frames in the node i are overtime in the next superframe period and the negative influence caused by the overtime is large, therefore, the node should arrange to transmit data as early as possible, and the total priority of the node i is set to be the highest value 1; similarly, when Ovenum,i>Ovenum,th,iAnd Ovepri,i>Ovepri,th,,iThat is, only one of the number or the priority of the data frames that timeout occurs in the node is greater than the threshold, it is considered that the data frames in the node i are less likely to timeout in the next superframe period or the negative effect of the timeout is not great, and therefore, we will use the timeout factor Ove to determine that the data frames in the node i will timeout in the next superframe periodpri,i1 is set to improve the priority; when Ovenum,i>Ovenum,th,iAnd Ovepri,i>Ovepri,th,,iNeither of these conditions is met, i.e. the node timeout data frame number and priority are not greater than the threshold (but greater than 0), then it is considered that the data frame in node i will timeout less likely in the next superframe period and the negative impact of timeout is small, therefore, the timeout factor Ove is used to determine the timeout factorpri,iSet 0.5. When Ovenum,iWhen all data frames of the node i cannot be timed out, the influence of the time-out on the priority is not considered, and only a key index factor CI is consideredpriAnd a sampling rate factor fpriTo calculate its priority.
In defining the energy factor of the node priority, most of the studies have considered the energy efficiency when the node transmits the data frame, for example, the energy consumption of transmitting a unit data frame is used as an evaluation index. However, in a real environmentThe energy efficiency of the node is related to the transmission power of the data frame and the link quality, for example, when the link quality is poor, more energy is required for successfully transmitting the same amount of data frame. That is, energy efficiency is not merely an individual property of a sensor node, and thus the present invention discusses the node as it is being computed. It is assumed here that the residual energy of node i has a minimum threshold value RemEth,i(RemEth,iInitialized to a fixed value in the system deployment stage), when the residual energy of the node i meets RemEi≤RemEth,iIn the process, the node i is considered to be incapable of normal communication, namely no matter how good the channel quality is and no matter how many data frames to be transmitted are, the node i cannot complete transmission; otherwise, when the node i residual energy satisfies RemEi>RemEth,iWhen the indexes such as the traffic volume, the channel condition and the like meet certain conditions, the node can complete the transmission. Residual energy RemE of node iiThe calculation formula of (2) is as follows:
RemEi＝Ecurrent,i-Etran,i
in the above formula, Ecurrent,iTo the current energy level, Etran,iThe energy consumed by node i for the next transmission.
And S2, calculating the delivery probability, the delay cost and the energy cost of the node.
The delivery probability defined in the present invention refers to the probability that a certain data frame transmitted by a sensor node in a certain time slot successfully reaches the local processing unit or the proportion of data frames successfully reaching the local processing unit in all data frames transmitted by nodes in a section of time slot. When discussing node delivery probabilities, it has been studied to assume that the delivery probability of a node in a super-frame range does not change with the change of channel state or only consider the average value. The delivery probability of GOOD nodes is monotonously decreased along with time, the delivery rate of BAD nodes is increased along with time and converged to a constant value
Figure BDA0001843019980000141
Meanwhile, the delivery probability corresponding to the data transmission of any node in any time slot can be calculated. Node pointi is the average delivery probability of the node
Figure BDA0001843019980000142
The calculation formula is as follows:
Figure BDA0001843019980000151
in the above formula, slotiAll time slots, N, allocated for node islot,iThe number of time slots allocated to the node i, p (j) is the corresponding delivery probability of the jth time slot, TslotFor the time slot length, it can be known by considering the definitions of the node utility and the delivery probability given by the present invention, in the data transmission process, not all data frames sent by the node can generate utility, and even if the data frames which are lost due to interference, loss and the like in the transmission process are important or urgent, the values of the data frames cannot be exerted, and the energy consumption of the node and the time delay cost of other nodes are increased. Thus, only data frames that successfully arrive at the local processing unit contribute to the utility of the node. And the higher the delivery probability of the node is, the larger the value of the utility function is, namely the correlation between the utility function and the node is positive. In addition, since the node delivery probability has a close relationship with the channel state and the allocation of the time slot, it is necessary to arrange the transmission time slot of each node appropriately so that the node generates the maximum utility.
The calculation formula of p (j) is:
Figure BDA0001843019980000152
in the above formula, PBGProbability of change of channel state from bad to good, PGBIs the probability that the channel status changes from good to bad, if the data frame can be successfully transmitted to the local processing unit under a certain channel status, the channel status is good, otherwise, the channel status is bad, Pch＝PBG+PGB，PchIs the probability of channel change;
waiting time delay of data frame under the premise of not overtimeThe ratio of the time delay threshold is larger, the delay cost is larger, and the delay cost C of the node idelay,iThe calculation formula of (2) is as follows:
Figure BDA0001843019980000153
in the above formula, tsuccTran,j(i) Node i (i) is the data frame set without timeout in node i, j is its angular coordinate, NNodes(i)For the aggregate capacity, tinit,j(i) Is the time t at which the data frame j in the node i enters the queueove,th,j(i) Is the timeout threshold, C, of the data frame j in node idelayIs defined as the mean value of the delay cost of the data frame without timeout in the superframe, and has a value range of (0, 1), and tsuccTran,j(i) The larger the value, the larger the delay cost. It is noted that the node latency cost is defined herein only considering the data frames that have not timed out, because the specific timeout condition of the node data frame has been analyzed in the timeout factor part of the priority and reflected to the priority of the node, and therefore the timeout data frame is not considered herein.
As can be known from the analysis of the overtime factor part, the data frame generated by the node sampling has a delay threshold, and when t of the data frame is tstartTran-tinit>tove,thThe data frame is discarded. However, even if the data frame satisfies tstartTran-tinit≤tove,thSince the timeliness of the data frame is lower as the waiting time is longer, the accuracy of reflecting the condition of the sampling site is also lower. t is tstartTran-tinit>tove,thObviously, the two are inversely related. In addition, t issuccTran,j(i) Refers to the time when the data frame j of the node i is successfully transmitted to the local processing unit, but not the time t when the data frame starts to be transmitted according to the allocated time slotstartTran,j(i) This is because the delivery probability of the data frame in the time slot also affects the delay cost. It is well understood that if the delivery probability is lowThen, the data frame has a high probability of packet loss, and therefore has to be sent in the next time slot, so that the delay cost is increased, and the utility of the node is reduced. For the same node i, we give its tstartTran,j(i) And tsuccTran,j(i) The relationship of (1):
tsuccTran,j＝p(j)·tstartTran,j+(1-p(j))·(Tslot+tstartTran,j)
in the above formula, tstartTran,jThe time for starting transmission of the data frame j according to the allocated time slot;
in the body area network, the energy of the local processing unit can be assumed to be unlimited, and only the energy consumption of the common sensor node needs to be considered. The node energy consumption mainly comprises synchronous energy consumption EsynEnergy consumption E for waking up from sleepwakeEnergy consumption for sampling EsampleEnergy consumption for treatment EproEnergy consumption for transmitting data frame EtranAnd energy consumption E for receiving confirmation messageACKAnd the like. Since the solution of the invention provides for each node to be allocated a continuous time slot and to perform synchronization only once, EsynAnd EwakeIs constant for each node; and Esample、EACKAnd EproRelative to EtranSo to speak, can be ignored. Therefore we only consider E in this contexttranAt a power of
Figure BDA0001843019980000171
TtranIs the transmission time. We define the energy cost of node i as the energy consumption of node i to transmit a unit bit. Energy cost C of node iE,iThe calculation formula of (2) is as follows:
Figure BDA0001843019980000172
in the above formula, RiIs the transmission rate of the node i and,
Figure BDA0001843019980000173
for the average delivery probability of the data frame of node i,
Figure BDA0001843019980000174
is the minimum value of the average delivery probability of node i, Ptran,iThe transmission data frame of node i consumes power,
Figure BDA0001843019980000175
Etranenergy consumption for transmitting data frames, TtranIs the transmission time.
The higher the node energy cost, the more energy it consumes to transmit the same amount of data, and obviously the less efficient it is at this time. Therefore, it is easy to find that the node utility is negatively correlated with its energy cost relationship. It is worth noting that we only consider the energy efficiency of a node as its energy cost rather than the proportion of the energy consumed by the node in the transmission to the remaining energy. This is because the amount of power consumed does not reflect the actual value of the transmission, for example, a node transmits 10 units of data and another node transmits 10 units of data consumes 20 units of power. Although the latter consumes more energy, it is clearly more effective. In addition, we do not consider whether the energy is sufficient during the transmission process, because the problem is already discussed in the remaining energy factor part of the priority and reflected in the priority value, and therefore, it is not described herein again.
S3, establishing a utility function of the node through the priority, the delivery probability, the delay cost and the energy cost of the node, wherein the utility function of the node is as follows:
Figure BDA0001843019980000176
in the above formula, UiAs a utility function of node i, Pdel,iProbability of delivery as node i, Cdelay,iIs the delay cost of node i, CE,iFor the energy cost of node i, λ1、λ2、λ3And λ4Are all constants greater than 0, and λ4>λ3>λ2>λ1。
S4, modeling the time slot allocation in the body area network by using the utility functions of the plurality of nodes to obtain an optimization model, wherein the optimization model is as follows:
Figure BDA0001843019980000181
in the above formula, order (i) is the transmission sequence of node i, LohFor overhead bits of a unit data frame, Reqbit,iFor the service requirement of node i, taking bit as unit, TframeIs the superframe length.
S5, obtaining a suboptimal time slot allocation scheme of the optimization model by performing basic time slot allocation on the nodes and considering the constraint conditions, as shown in fig. 2, the specific steps are:
s51, allocating basic time slots for GOOD nodes and BAD nodes, calculating the number of continuous transmission time slots, when the last data frame of the current superframe of the node i is successfully transmitted, if the initial state of the node i is GOOD, considering i ∈ GOOD, otherwise, i ∈ BAD, and the concrete steps are:
s511, dividing the nodes into GOOD nodes and BAD nodes respectively according to the sensor factor FsensorSorting from big to small, setting GOOD node as 1G,2G,……,nGGet GOOD node transmission start time
Figure BDA0001843019980000182
S512, the time slot of the BAD node is processed according to the channel factor FchannelSorting from big to small, set BAD node to {1B,2B,……,nBGet BAD node transmission start time
Figure BDA0001843019980000183
S513, calculating the number N of the transmission continuous time slotsslots,base,iThe calculation formula is as follows:
Figure BDA0001843019980000184
s52, calculating the transmission duration satisfying the time slot total number constraint through the transmission duration time slot number to obtain the time slot section allocated by the node i, wherein the calculation formula of the transmission duration time is as follows:
Figure BDA0001843019980000191
in the above formula, Nslots,iFor the duration of the transmission, Reqframe,iFor the service requirement of the node i, taking the data frame as a unit, k is a node, and k is i +1, i +2, … …, N, k satisfies the following conditions:
Figure BDA0001843019980000192
s53, calculating the adjusted time slot segment list of the conflict time slot through the time slot segments distributed by the node i, and the concrete steps are as follows:
s531, calculating all time slot segments where collision occurs between GOOD nodes and BAD nodes and generating a list L ist (t)col,k,Ncol,k)，k＝1,2,……,N；
(tcol,k,Ncol,k) The starting time and the number of the continuous time slots of a certain time slot period for generating conflict for the node k;
s532, calculating (t) respectivelycol,k,Ncol,k) Utility values corresponding to GOOD node and BAD node of time slot segment
Figure BDA0001843019980000193
And
Figure BDA0001843019980000194
s533, if
Figure BDA0001843019980000195
Moving the time slot divided by the goose node, and updating the time slot segment and the conflict time slot segment allocated by the following node, otherwise, entering step S434, where the move formula of the goose node is:
Figure BDA0001843019980000196
in the above formula, the first and second carbon atoms are,
Figure BDA0001843019980000197
starting time of a certain time slot segment for generating conflict for an i node in a GOOD node;
s534, moving the time slot divided by the BAD node, and updating the time slot segment and the conflict period distributed by the following node, wherein the moving formula of the BAD node is as follows:
Figure BDA0001843019980000198
in the above formula, the first and second carbon atoms are,
Figure BDA0001843019980000199
the starting time of a certain time slot segment for generating conflict for an i node in the BAD node;
s535, obtaining the adjusted time slot segment list L ist (t)col,k,Ncol,k)，k＝1,2,……,N。
S54, calculating the final time slot segment list after the adjustment of the overtime node through the time slot segment list of the node i, and taking the final time slot segment list as a suboptimal time slot allocation scheme, which comprises the following specific steps:
s541, calculating time slot segment list L ist (t) after conflict time slot adjustmentcol,k,Ncol,k) The overtime nodes are sorted from small to large according to the overtime threshold to form an ordered overtime node set {1,2, … …, m }, wherein m is the number of the overtime nodes;
s542, when
Figure BDA0001843019980000201
If yes, deleting the node i from the overtime node set, otherwise, entering the step S443;
Figure BDA0001843019980000202
for node i in the time slot segment just without timeout
Figure BDA0001843019980000203
Utility value of UkFor being currently in a time slot segment
Figure BDA0001843019980000204
The utility value of node k;
s543, adjusting the node i to the time slot segment
Figure BDA0001843019980000205
S544, if the node k is a GOOD node, sequentially moving the time slots of the node k and nodes behind the node k through a moving formula of the GOOD node, and deleting the node i from the overtime set;
s545, if the node k is a BAD node, sequentially moving the time slots of the node k and nodes behind the node k through a moving formula of the BAD node, and deleting the node i from the overtime set;
s546, obtaining Final time slot list Final L ist (t) of node istart,i,Nslots,i)。
The invention uses the rules of the wireless body area network official IEEE 802.15.6 protocol to divide the time axis into periodic time intervals called superframes. Each superframe includes three parts, a beacon frame phase, an active phase and an inactive phase. The beacon frame phase local processing unit broadcasts a beacon frame to the sensor nodes. The active phase is composed of a plurality of continuous time slots, and each time slot is used for carrying out complete data frame transmission once, including data frame sending, receiving confirmation messages and the like. The inactive phase does not make any transmissions. The invention divides the communication process between the local processing unit and the sensor node into the following steps:
1. the local processing unit broadcasts an initial beacon frame. The initial beacon frame contains synchronization information, superframe length, slot length, initial sampling rate, initial transmission schedule, etc.
2. The sensor node receives the beacon frame and synchronizes with the local processing unit.
3. The sensor node wakes up when the transmission time slot obtained by the sensor node starts, and data is collected and transmitted by the received sampling rate. And the data transmission adopts a first-come first-serve principle, and transmits the data in the buffer and the data generated by new sampling together. The data frame includes fields of CI, remaining buffer, remaining energy, etc. of the service data to be transmitted. The size of the residual buffer area is used for reasonably arranging the sampling time; in addition, the node is not scheduled to transmit data and upload a message that the node is low in energy if the remaining energy is low.
4. During the transmission of data by other nodes, the current node stands by to save energy.
5. The local processing unit calculates the following indexes according to the received data:
1) a new sampling rate is calculated for each node. The sampling rate is determined by the deviation degree of the index value of the monitored part of the current sensor relative to the normal value, and reflects the emergency degree of the current front part data.
2) And calculating the new service requirement of the sensor node according to the sampling rate, the sampling time and the data volume of the node buffer area (obtained by the service requirement of the current superframe sensor node and the delivery probability).
3) And calculating a new transmission plan (comprising transmission time and transmission duration) according to the new service requirement, the node key index, the sampling rate, the size of the residual cache region, the residual energy and the like.
6. The local processing unit broadcasts a beacon frame during the next superframe. The beacon frame contains the new sampling rate and transmission schedule.
7. And (5) repeating the steps 2-6.
The time slot allocation scheme provided by the invention can be deployed in a wireless body area network MAC layer adopting single-hop communication in the scenes of military affairs, medical treatment, sports, entertainment and the like. The local body area network environment is composed of sensor equipment worn on the body surface or implanted in the body and a local processing unit (intelligent equipment such as a mobile phone and a tablet). The sensor device generally comprises three structural units, namely a radio frequency module, a sensor module and a storage module. The sensor module converts the energy form into an analog electric signal, and the analog electric signal is filtered by a filter and digitized by a data converter. The storage module is used for storing messages, and the radio frequency module is mainly used for receiving and transmitting signals. In addition, the sensor device and the local processing unit have corresponding software platforms for function control and information display.
The MAC layer time slot allocation scheme provided by the invention is written into a corresponding platform in a software form, and then behaviors of collecting, transmitting data, receiving a confirmation frame and the like of a sensor are controlled.
The sensor device collects human body related data and maintains a local message buffer queue, and then sends the data to a local processing unit and receives acknowledgement frame messages according to the communication process proposed by the invention. After receiving the information of the sensor equipment, the local processing unit comprehensively considers the self priority of the nodes and the change condition of the channel according to the time slot allocation scheme provided by the invention, uniformly schedules and allocates the time slots divided by the next superframe of all the sensor nodes, and then encapsulates the transmission plan and the sampling rate of the next superframe in a beacon frame and broadcasts the beacon frame to the sensor equipment. In the process, the data frame, the beacon frame and the confirmation frame all adopt the format specified by the IEEE 802.15.6 official protocol.
In addition, for more important data or abnormal data, the local processing unit uploads the data to a remote server for further processing, and the process can use various network communication modes such as WSN, WPAN, W L AN, Internet, cellular network and the like.

Claims (5)

1. A MAC layer time slot distribution method for guaranteeing QoS in a body area network is characterized by comprising the following steps:
s1, calculating the node priority through the key index factor, the sampling rate factor, the overtime factor and the residual energy factor of the sensor node;
the calculation formula of the node priority in step S1 is:
Figure FDA0002442131330000011
in the above formula, ω1+ω2+ω3＝1，i＝1,2,……,N，ω1、ω2And ω3Are all coefficients, N is the total number of nodes, PriiFor the priority of node i, Priove,iTo take into account the timeout factor Ovepri,iNode priority of, RemEiFor the residual energy of node i, RemEth,iIs the minimum threshold value of the residual energy of the node i, CIpri,iIs a key index factor, f, of node ipri,iIs the sampling rate factor of node i, Ovenum,iThe number of data frames in the next superframe for node i that the node may time out Ovenum,th,iA threshold for the number of data frames in node i's next superframe that may time out, Ovepri,iIs the timeout factor of node i, Ovepri,th,iIs a data frame priority threshold that may be timed out in the next superframe of node i, wherein the sampling rate factor fpriThe calculation formula of (2) is as follows:
Figure FDA0002442131330000012
in the above formula, Nframe＝Nnew+Nold，NframeFor the total number of data frames, N, in the buffer queue during the next superframenewFor the number of new data frames in the next superframe period, NoldFor the number of old data frames in the next superframe period, fpri,newSample rate factor, f, for new data frames in the next superframe periodpri,oldFor the sampling rate factor of the old data frame in the next superframe period, fuIs the upper bound of the sampling rate of the node, flIs the lower bound of the sampling rate of the node, fnewSample rate, f, allocated for a new data frame by a local processing unitoldSample rate allocated for the processing unit for the old data frame, where NnewAnd NoldThe calculation formula of (2) is as follows:
Figure FDA0002442131330000021
Figure FDA0002442131330000022
in the above formula, TsFor sample time, R is the data transmission rate, TslotFor the length of the time slot, Reqframe,oldFor the service requirement of the node in the current superframe, taking a data frame as a unit, Nslots,oldThe number of slots allocated for that node in the current superframe,
Figure FDA0002442131330000023
the average delivery probability of the node data frame in the current superframe is obtained;
residual energy RemE of node iiThe calculation formula of (2) is as follows:
RemEi＝Ecurrent,i-Etran,i
in the above formula, Ecurrent,iTo the current energy level, Etran,iThe energy consumed by node i in the next transmission;
s2, calculating the delivery probability, the delay cost and the energy cost of the node;
the delivery probability of the node i in the step S2 is the average delivery probability of the node
Figure FDA0002442131330000024
The calculation formula is as follows:
Figure FDA0002442131330000025
in the above formula, slotiAll time slots, N, allocated for node islot,iThe number of time slots allocated to the node i, p (j) is the corresponding delivery probability of the jth time slot, TslotFor the slot length, p (j) is calculated as:
Figure FDA0002442131330000026
in the above formula, PBGProbability of change of channel state from bad to good, PGBIs the probability that the channel status changes from good to bad, if the data frame can be successfully transmitted to the local processing unit under a certain channel status, the channel status is good, otherwise, the channel status is bad, Pch＝PBG+PGB，PchIs the probability of channel change;
delay cost C of node idelay,iThe calculation formula of (2) is as follows:
Figure FDA0002442131330000031
in the above formula, tsuccTran,j(i) Node i (i) is the data frame set without timeout in node i, j is its angular coordinate, NNodes(i)For the aggregate capacity, tinit,j(i) Is the time t at which the data frame j in the node i enters the queueove,th,j(i) Is the timeout threshold of data frame j in node i, and
tsuccTran,j＝p(j)·tstartTran,j+(1-p(j))·(Tslot+tstartTran,j)
in the above formula, tstartTran,jThe time for starting transmission of the data frame j according to the allocated time slot;
energy cost C of node iE,iThe calculation formula of (2) is as follows:
Figure FDA0002442131330000032
in the above formula, RiIs the transmission rate of the node i and,
Figure FDA0002442131330000033
for the average delivery probability of the data frame of node i,
Figure FDA0002442131330000034
is the minimum value of the average delivery probability of node i, Ptran,iThe transmission data frame of node i consumes power,
Figure FDA0002442131330000035
Etranenergy consumption for transmitting data frames, TtranIs the transmission time;
s3, establishing a utility function of the node through the priority, the delivery probability, the delay cost and the energy cost of the node;
the node utility function in step S3 is:
Figure FDA0002442131330000036
in the above formula, UiAs a utility function of node i, Pdel,iProbability of delivery as node i, Cdelay,iIs the delay cost of node i, CE,iFor the energy cost of node i, λ1、λ2、λ3And λ4Are all constants greater than 0, and λ4>λ3>λ2>λ1；
S4, modeling time slot allocation in the body area network by using utility functions of a plurality of nodes to obtain an optimization model;
the optimization model in step S4 is:
Figure FDA0002442131330000041
Figure FDA0002442131330000042
Figure FDA0002442131330000043
Figure FDA0002442131330000044
in the above formula, order (i) is the transmission sequence of the node i,Lohfor overhead bits of a unit data frame, Reqbit,iFor the service requirement of node i, taking bit as unit, TframeIs the superframe length;
s5, obtaining a suboptimal time slot allocation scheme of an optimization model by carrying out basic time slot allocation on the nodes and considering constraint conditions;
the specific steps of step S5 are:
s51, allocating basic time slots for the GOOD nodes and the BAD nodes, and calculating the number of transmission continuous time slots;
when the last data frame of the current superframe of the node i is successfully transmitted, and the initial state of the node i is GOOD, the node i is considered to be i ∈ GOOD, otherwise, the node i is i ∈ BAD;
s52, calculating the transmission duration satisfying the time slot total number constraint through the transmission duration time slot number to obtain the time slot section distributed by the node i;
s53, calculating a time slot segment list after the conflict time slot is adjusted through the time slot segments distributed by the node i;
and S54, calculating a final time slot segment list after the time-out node is adjusted through the time slot segment list of the node i, and taking the final time slot segment list as a suboptimal time slot allocation scheme.
2. The method for allocating time slots of a MAC layer for QoS guarantee in a body area network according to claim 1, wherein the specific step of the step S51 is:
s511, dividing the nodes into GOOD nodes and BAD nodes respectively according to the sensor factor FsensorSorting from big to small, setting GOOD node as 1G,2G,……,nGGet GOOD node transmission start time
Figure FDA0002442131330000051
S512, the time slot of the BAD node is processed according to the channel factor FchannelSorting from big to small, set BAD node to {1B,2B,……,nBGet BAD node transmission start time
Figure FDA0002442131330000052
S513, calculating the number N of the transmission continuous time slotsslots,base,iThe calculation formula is as follows:
Figure FDA0002442131330000053
3. the method of allocating time slots of a MAC layer for QoS guarantee in a body area network according to claim 2, wherein the transmission duration in step S52 is calculated as:
Figure FDA0002442131330000054
in the above formula, Nslots,iFor the duration of the transmission, Reqframe,iFor the service requirement of the node i, taking the data frame as a unit, k is a node, and k is i +1, i +2, … …, N, k satisfies the following conditions:
Figure FDA0002442131330000055
4. the method for allocating time slots of a MAC layer for QoS guarantee in a body area network according to claim 2, wherein the step S53 specifically comprises the steps of:
s531, calculating all time slot segments where collision occurs between GOOD nodes and BAD nodes and generating a list L ist (t)col,k,Ncol,k)，k＝1,2,……,N；
(tcol,k,Ncol,k) The starting time and the number of the continuous time slots of a certain time slot period for generating conflict for the node k;
s532, calculating (t) respectivelycol,k,Ncol,k) Utility values corresponding to GOOD node and BAD node of time slot segment
Figure FDA0002442131330000061
And
Figure FDA0002442131330000062
s533, if
Figure FDA0002442131330000063
Moving the time slot divided by the goose node, and updating the time slot segment and the conflict time slot segment allocated by the following node, otherwise, entering step S434, where the move formula of the goose node is:
Figure FDA0002442131330000064
in the above formula, the first and second carbon atoms are,
Figure FDA0002442131330000065
starting time of a certain time slot segment for generating conflict for an i node in a GOOD node;
s534, moving the time slot divided by the BAD node, and updating the time slot segment and the conflict period distributed by the following node, wherein the moving formula of the BAD node is as follows:
Figure FDA0002442131330000066
in the above formula, the first and second carbon atoms are,
Figure FDA0002442131330000067
the starting time of a certain time slot segment for generating conflict for an i node in the BAD node;
s535, obtaining the adjusted time slot segment list L ist (t)col,k,Ncol,k)，k＝1,2,……,N。
5. The method for allocating time slots of a MAC layer for QoS guarantee in a body area network according to claim 4, wherein the step S54 comprises the following steps:
s541, calculating time slot segment list L ist (t) after conflict time slot adjustmentcol,k,Ncol,k) In which all timeout conditions existThe nodes sort the overtime nodes from small to large according to the overtime threshold to form an ordered overtime node set {1,2, … …, m }, wherein m is the number of the overtime nodes;
s542, when
Figure FDA0002442131330000068
If yes, deleting the node i from the overtime node set, otherwise, entering the step S443;
Figure FDA0002442131330000069
for node i in the time slot segment just without timeout
Figure FDA00024421313300000610
Utility value of UkFor being currently in a time slot segment
Figure FDA00024421313300000611
The utility value of node k;
s543, adjusting the node i to the time slot segment
Figure FDA00024421313300000612
S544, if the node k is a GOOD node, sequentially moving the time slots of the node k and nodes behind the node k through a moving formula of the GOOD node, and deleting the node i from the overtime set;
s545, if the node k is a BAD node, sequentially moving the time slots of the node k and nodes behind the node k through a moving formula of the BAD node, and deleting the node i from the overtime set;
s546, obtaining Final time slot list Final L ist (t) of node istart,i,Nslots,i)。
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