CN112953649B - Wireless body area network QoS optimization method based on utility function - Google Patents

Wireless body area network QoS optimization method based on utility function Download PDF

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CN112953649B
CN112953649B CN202110129900.6A CN202110129900A CN112953649B CN 112953649 B CN112953649 B CN 112953649B CN 202110129900 A CN202110129900 A CN 202110129900A CN 112953649 B CN112953649 B CN 112953649B
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area network
body area
utility function
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CN112953649A (en
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王晨阳
郭坤祺
陈昱欣
胡效康
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Jiangsu University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B13/00—Transmission systems characterised by the medium used for transmission, not provided for in groups H04B3/00 - H04B11/00
    • H04B13/005—Transmission systems in which the medium consists of the human body
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]

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Abstract

The invention discloses a wireless body area network QoS optimization method based on a utility function. The QoS index in the invention refers to the time delay of the wireless body area network and is expressed by the average waiting time of a data packet. The invention judges whether the queue length and the transmission rate need to be balanced according to the urgency of the collected data. And when the data emergency degree is high, interrupting the optimization algorithm, and preferentially selecting the channel with good channel condition to transmit data. And when the data is not urgent, establishing a utility function optimization model. The process of establishing the optimization model is that firstly, the priority, the node queue length and the transmission rate are determined according to the data type of the sensor node. Then, a utility function model is established, and parameters of the utility function are determined according to the priority. And then establishing a QoS model of the wireless body area network according to the constraint conditions. The invention can effectively improve the service quality of the wireless body area network.

Description

Wireless body area network QoS optimization method based on utility function
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a wireless body area network QoS based on a utility function.
Background
At present, the population aging becomes a global development trend, the problems of chronic diseases, medical care, life quality of the old and the like caused by the population aging become important challenges in the social development process, and the wireless body area network is generated under the background. A Wireless Body Area Network (WBAN) is a Wireless Network which takes a human Body as a center and consists of a center node and a plurality of sensor nodes attached to or implanted in the human Body, is used for monitoring various physiological data of the human Body in real time, can effectively relieve medical problems caused by shortage of medical resources and imbalance of medical situations, and promotes the development of electronic medical treatment.
A Wireless Body Area Network (WBAN), which is a branch of a Wireless Sensor Network (WSN), is a short-distance communication network centered on the human body, and a plurality of sensors around, on the surface of, and in the human body can sense and collect some important physiological parameters of the human body (such as body temperature, blood pressure, heart rate, blood oxygen concentration, etc.) or some environmental parameters around the human body (such as temperature, humidity, etc.), and send them to a central server in a wireless transmission manner. The center is usually placed in a relatively stable position such as dark or waist, and after receiving the information, it is transmitted to the center server through WiFi or mobile cellular network. The central node is usually placed in a relatively stable position such as the waist, and after receiving the information, the central node transmits the information to a remote server (such as a hospital, a data center and the like) through a WiFi or mobile cellular network for subsequent processing by professionals. This makes reliable, efficient real-time data delivery a serious challenge for quality of service in WBANs.
The complexity of the human body wireless communication environment, the dynamic nature of the body state, and the finite nature of the node storage resources make the Quality of Service (QoS) of the user face a major challenge in the medical wireless body area network.
Disclosure of Invention
In order to solve the problems, the invention establishes a model of a QoS utility function of the wireless body area network according to the average waiting time of the node for sending the data packet in the non-emergency situation. And when an emergency occurs, the optimization model is interrupted, and a channel with good channel condition is preferentially selected to transmit emergency data. The wireless body area network supports QoS quality service, different data types have different characteristics, the utility function can select different parameters according to the data priority of the node to set different types of data, and the process of model establishment is as follows:
s1, setting node data priority: let a set of nodes in a wireless body area network be I = {1,2, 3. And setting the data priority of each node according to the data type of the node according to the respective monitored different human body parameters of each node in the wireless body area network. The priority of data defined in the IEEE 802.15.6 protocol is 0 to 7 from low to high, and can BE roughly classified into three categories, BE (Best Effort) data (priority 0 to 2), qoS data (priority 3 to 4), and medical data (priority 5 to 7) according to the data characteristics.
S2, determining the length of a data queue of a node and the data arrival bit rate of the node: the average waiting time of the data is related to the data queue length of the current node and the arrival bit rate of the data. Therefore, to determine the average waiting time of data first needs to know the data queue length of the current node and the data arrival bit rate of the current node, and the average arrival ratio of the nodes is used in the inventionSpecific rate eta i And node average queue length
Figure BDA0002924762160000021
And (4) showing. Wherein the node average arrival bit rate η i Is the ratio between the total number of data packets arriving at a node during slot n and the slot length.
Assuming that there is a length T ω The time window of (c). At nT s Time of day, average queue length over time window of node i
Figure BDA0002924762160000022
Can be written as nT s The number of bits in the time node i queue and the last time, i.e., (n-1) T s The sum of the lengths of the remaining queues is transmitted at a time. Can be written as:
Figure BDA0002924762160000023
wherein L is i [n]Is nT s Number of bits in time node i queue, θ = T s /T ω 。
S3, determining average waiting time: the average latency in the utility function is represented by the predicted average latency. At the beginning of time slot n (nT) s Time of day), a given data transmission rate r i [n]Then at the end of time slot n, (n + 1) T s Is obtained by the following equation:
Figure BDA0002924762160000024
given data transmission rate r i [n]Let L be i [n]Is nT s The number of bits of the packet in the queue of time node i. Then (n + 1) T at the end of time slot n s The predicted average latency of a time instant is the data transmission rate r during a time slot n i [n]Function of (c):
Figure BDA0002924762160000025
and S4, constructing a utility function model. The utility function in the invention is used for measuring the satisfaction degree of the node i to the average waiting time of the node data packet. Assuming that the utility of node i is associated with an average latency W, the longer the average latency, the lower the node utility. The relationship between node utility and average latency can be represented by a utility function:
Figure BDA0002924762160000026
where α, β are parameters, the values of α and β may be adjusted according to different data priorities such that the utility function satisfies the utility with respect to average latency at different priorities.
S5, optimizing the target
The optimization objective is to maximize the overall utility with respect to the predicted average latency of each slot in the network, namely:
Figure BDA0002924762160000031
the optimization target is a non-linear function, which the present invention approximates to a linear function to make the calculation process simpler. By means of U i (W i ) The nature of the function yields:
Figure BDA0002924762160000032
suppose there is a set A of nodes i with a data queue length greater than zero n ＝{i：L i [n]>0}. Finally, establishing a QoS model of the wireless body area network as a data transmission rate r i [n]Linear function of (c):
Figure BDA0002924762160000033
the constraint conditions of the model are as follows:
Figure BDA0002924762160000034
the invention has the beneficial effects that:
according to the invention, through establishing the optimization model, a proper threshold value can be obtained to balance the user service quality and the node energy efficiency, the energy efficiency is improved on the premise of ensuring the user service quality, and the life cycle of the node is prolonged.
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FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic diagram of a WBAN node.
Detailed Description
The invention will be further explained with reference to the drawings.
Such as the WBAN system model shown in fig. 2. In a wireless body area network scene, there are N sensing nodes, and these sensor nodes are installed in human different positions, monitor various physiological parameters of human respectively. The set of N sensor nodes in the network is set as: i = {1,2, 3.., N }. The wireless sensor nodes arranged in and on the body surface of the human body form a multi-hop WBAN. Each sensor node (except the Sink central node) is powered by a battery, acquires one or more types of physiological data, transmits the acquired data and the data from the child nodes to the parent node thereof, and finally transmits the data to the Sink in a multi-hop mode; each sensor node has the capability to forward data.
First, the urgency of the data is determined. Whether the data is urgent or not refers to the degree of deviation of the sensed data from a normal value, and the more urgent the data is, the transmission needs to be preferentially carried out. Let the index of the ith node be defined as:
Figure BDA0002924762160000041
in which ξ s Is the measurement quantity of the ith node, and ξ is the medical data of the nodeNormal value of (1), then CI i Indicating the absolute change of the ith node data based on the normal value. When the change situation exceeds the normal human body sign value, the emergency situation is determined, and at the moment, a channel with good channel condition should be selected to transmit the emergency situation preferentially.
When the data is in the range of normal human body sign values, the priority of the data queue and the channel condition are balanced according to the optimization model provided by the invention, and the QoS of the wireless body area network is optimized. As shown in fig. 1, the method of the present invention comprises the following steps:
s1, setting node data priority: the priority of the nodes is divided into eight levels of 0 to 7 according to the data types in the nodes, wherein the nodes can BE roughly divided into three types according to the data characteristics, BE (Best Effort) data with the priority of 0 to 2, qoS data with the priority of 3 to 4 and medical data with the priority of 5 to 7.
S2, determining the length of a data queue of a node and the data arrival bit rate of the node: the model takes the average waiting time of data as an independent variable and the utility of the wireless body area network as a dependent variable. The average waiting time of the data is related to the data queue length of the current node and the arrival bit rate of the data. Therefore, to determine the average waiting time of the data first needs to know the data queue length of the current node and the data arrival bit rate of the current node. Node average arrival bit rate η in the present invention i And node average queue length
Figure BDA0002924762160000042
And (4) showing. Wherein the average arriving bit rate of the node is the ratio of the total number of data packets arriving in the time slot n to the time slot length. Let L i Is nT s The number of bits in the time node i queue. According to the Riter's law, the average latency of node i is W i ＝L i /η i 。
Assuming that there is a length T ω The time window of (c). At nT s Average queue length over time window for time, node i
Figure BDA0002924762160000043
Can be written as nT s The number of bits in the time node i queue and the last time, i.e., (n-1) T s The sum of the lengths of the remaining queues is transmitted at a time. Can be written as:
Figure BDA0002924762160000044
wherein L is i [n]Is nT s Number of bits in time node i queue, θ = T s /T ω 。T s Is the length of a single slot.
S3, determining average waiting time: at the beginning of time slot n (nT) s Time of day), a given data transmission rate r i [n]Let L be i [n]Is nT s The number of bits of the packet in the queue of time node i. Then (n + 1) T at the end of time slot n s The predicted average latency of a time instant is the data transmission rate r during a time slot n i [n]Function of (c):
Figure BDA0002924762160000051
s4, constructing a utility function model: assuming that the utility of node i is associated with an average latency W, the longer the average latency, the lower the node utility. The node utility versus average latency direct relationship can be represented by a utility function:
Figure BDA0002924762160000052
wherein, alpha, beta is a regulating parameter, the size is 0-15, different data types can BE set by adjusting the value of alpha, beta, for BE data (0-2), the coefficient alpha and beta in the utility function can BE relatively smaller; for QoS data (3-4), the utility function has a relatively small value for the coefficient α and a relatively large value for β; for medical data (5-7), the coefficient α and β in the utility function may have relatively large values and small values, respectively. Thereby obtaining the utility relation between the average waiting time W and the QoS of the wireless body area network under different priorities.
S5, optimizing the utility function: an optimization objective is first determined that is to maximize the total utility with respect to the predicted average latency per slot in the network, namely:
Figure BDA0002924762160000053
the problem solving process should be simplified because it is complicated and the calculation amount is too large for the wireless body area network sensor nodes of the wireless body area network with limited energy. When the packet arrival process is determined, then the average latency is actually determined by the data transmission rate r i [n]And (6) determining. The optimization goal can thus be reduced to the data transmission rate r i [n]The linear function of (c):
Figure BDA0002924762160000054
the constraint conditions are as follows:
Figure BDA0002924762160000055
secondly, the solution of the optimization problem has various existing methods, and different methods can be selected according to actual requirements to solve the optimization problem. For example, a particle swarm optimization algorithm can be selected, and the optimal solution of the optimization problem is obtained through optimization iteration of the individual extremum and the population extremum. And will not be described in detail herein.
The above-listed series of detailed descriptions are merely specific illustrations of possible embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent means or modifications that do not depart from the technical spirit of the present invention are intended to be included within the scope of the present invention.

Claims (7)

1. A method for optimizing QoS of a wireless body area network based on a utility function is characterized by comprising the following steps:
s1, setting node data priority;
s2, determining the length of a data queue of a node and the data arrival bit rate of the node;
s3, determining the average waiting time in the utility function;
s4, constructing a utility function model, wherein the utility function is used for measuring the satisfaction degree of the node i on the average waiting time of the node data packet;
the implementation of the S4 comprises the following steps: assuming that the utility of the node i is associated with the average waiting time W, the relationship between the utility of the node and the average waiting time is expressed as a utility function:
Figure FDA0003757077020000011
wherein, alpha and beta are adjustable parameters and the range of the parameters is 0 to 15;
s5, optimizing a utility function, namely determining an optimization target to maximize the total utility of the predicted average waiting time of each time slot in the network;
the optimization objective function of S5 is:
Figure FDA0003757077020000012
the method for solving the optimization objective function is to approximate the optimization objective function to a linear function, and the specific process is as follows:
by means of U i (W i ) The nature of the function yields:
Figure FDA0003757077020000013
suppose there is a set A of nodes i with a data queue length greater than zero n ＝{i:L i [n]>0} and finally establishing a QoS model of the wireless body area network as a data transmission rate r i [n]The linear function of (c):
Figure FDA0003757077020000021
the constraint conditions are as follows:
Figure FDA0003757077020000022
2. the utility function-based wireless body area network QoS optimization method according to claim 1, wherein the specific implementation of S1 is as follows: setting a set of nodes in a wireless body area network as I = {1,2,3,.., N }, wherein each node in the wireless body area network monitors different human body parameters and sets the data priority of the node according to the data type of the node.
3. The utility function-based wireless body area network QoS optimization method of claim 2, wherein the priority is divided into: the priority of the data defined in the IEEE 802.15.6 protocol is 0-7 from low to high respectively, the data can BE divided into three categories according to the data characteristics, the BE data priority is 0-2, the QoS data priority is 3-4, and the medical data priority is 5-7.
4. The utility function-based QoS optimization method for wireless body area network according to claim 1, wherein the data queue length of the node of S2 and the data arrival bit rate of the node adopt the average arrival bit rate η of the node i And node average queue length
Figure FDA0003757077020000023
5. The utility function based QoS optimization method for wireless body area network according to claim 4, wherein the mean arrival bit rate η of the nodes is i Is defined as: the ratio between the total number of bits of a packet arriving by a node during a time slot n and the length of the time slot.
6. The utility function-based QoS optimization method for wireless body area network according to claim 4, wherein the average queue length of the nodes is
Figure FDA0003757077020000024
Is defined as: suppose there is a length T ω Time window of (2) at nT s Time of day, average queue length over time window of node i
Figure FDA0003757077020000025
Can be written as nT s The number of bits in the time node i queue and the last time, i.e., (n-1) T s The sum of the lengths of the time transmission residual queues is expressed as:
Figure FDA0003757077020000026
wherein L is i [n]Is nT s Number of bits in time node i queue, θ = T s /T ω ，T s Is a single slot length.
7. The utility function-based QoS optimization method for wireless body area network according to claim 1, wherein the average waiting time in the utility function in S3 is represented by predicted average waiting time, at the beginning nT of time slot n s At the moment, given the data transmission rate r i [n]Then at the end of time slot n, (n + 1) T s Is obtained by the following equation:
Figure FDA0003757077020000031
at a given data transmission rate r i [n]When, suppose L i [n]Is nT s The number of bits of the data packet in the queue of time node i is (n + 1) T at the end of time slot n s The predicted average latency of a time instant is the data transmission rate r during a time slot n i [n]Function of (c):
Figure FDA0003757077020000032
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