CN108924851B - Cognitive wireless sensor network QoS guarantee opportunistic routing method - Google Patents

Cognitive wireless sensor network QoS guarantee opportunistic routing method Download PDF

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CN108924851B
CN108924851B CN201811148566.3A CN201811148566A CN108924851B CN 108924851 B CN108924851 B CN 108924851B CN 201811148566 A CN201811148566 A CN 201811148566A CN 108924851 B CN108924851 B CN 108924851B
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antenna
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CN108924851A (en
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白光伟
王露
沈航
李灵俐
周凌宇
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Nanjing Tech University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • 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]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/06—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on characteristics of available antennas
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H04W40/14—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality based on stability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a cognitive wireless sensor network QoS guarantee opportunistic routing method based on a directional antenna, and belongs to the field of combination of the directional antenna and cognitive wireless sensor network routing. The method comprises the following steps: firstly, analyzing the influence of the adjustment of the directional antenna on the spectrum access and the routing selection; secondly, modeling is carried out on an antenna system and a network model, and a joint optimization problem of antenna sectors, transmission channels and routing is put forward on the basis. Next, an approximation scheme and heuristic algorithm are proposed to select antenna sectors and channels for data transmission, creating a list of candidate forwarding nodes. And finally, carrying out simulation experiments and result analysis. The invention can be well adapted to the cognitive wireless network environment with dynamically changing available frequency spectrum, and provides QoS guarantee with high reliability, low energy consumption and low time delay.

Description

Cognitive wireless sensor network QoS guarantee opportunistic routing method
Technical Field
The invention relates to a cognitive wireless sensor network QoS guarantee opportunistic routing mechanism, and belongs to the field of combination of directional antennas and cognitive wireless sensor network routing.
Background
Existing wireless sensor networks mostly operate in the ISM band without authorization, but these common unlicensed bands become increasingly crowded as new generations of wireless devices become widespread. In order to alleviate the above problems, CRSN (Cognitive Radio Sensor Networks) capable of implementing dynamic spectrum access is developed. In CRSN, sensor nodes may opportunistically occupy these free licensed spectrum (channels) as long as they do not interfere with PU (Primary User) communications. As soon as the PU signal returns, the node needs to immediately evacuate the band and find a new spectrum access opportunity. In such an environment with limited available spectrum, stability of data transmission between nodes is difficult to be guaranteed, and how to design a network layer to meet QoS requirements of upper layer applications is receiving more and more attention.
The design of the routing mechanism of the cognitive wireless sensor network generally needs to consider the selection of a transmission channel and the selection of a forwarding node jointly so as to reduce the adverse effect of the dynamic change of the available channel on the transmission performance. However, most of the current research work in this area is based on: (1) opportunistic spectrum access, namely opportunistic access to an authorized channel when the PU is inactive, and multiplexing spectrum resources on a time domain; (2) the omni-directional transmission, that is, the node uniformly transmits signals to all directions, is easy to cause large interference to the PU, thereby reducing the connectivity of the node in the cognitive network. Based on the above two points, it is considered to apply the directional antenna technology to the routing design, and concentrate the energy to a specific direction for radiation. On one hand, the method is beneficial to reducing the interference to the PU in other directions, and the opportunity of accessing the cognitive sensor node to the authorized channel is further increased from the spatial domain. On the other hand, the method is beneficial to expanding the transmission distance of signals and reducing the end-to-end forwarding hop count, thereby reducing the total delay and the total energy consumption of data transmission.
Opportunistic routing refers to opportunistic packet forwarding by using multiple potential candidate nodes, which can further improve the reliability of a wireless link. However, while directional antennas are utilized to improve the opportunity for licensed spectrum access, they also present a significant challenge to opportunistic routing design: (1) due to the limitation of the beam width, when the directional antenna points to different directions, the node obtains different neighbor node sets; (2) the narrower the beam width of the directional antenna, the longer the transmission distance of the signal, which is more beneficial to reducing the end-to-end forwarding hop count. However, the narrower the beam width is, the smaller the signal coverage area is, which is not favorable for obtaining the forwarding node with high transmission performance; (3) the available channels detected in different directions will also be different due to differences in the primary user geographical location.
The existing method for cognitive routing and directional antenna application is briefly introduced as follows:
liu et al propose a distributed Opportunistic Cognitive Routing (OCR) protocol, and each intermediate forwarding node independently selects its next hop according to local channel use information and geographical location information to quickly adapt to a dynamically changing wireless link. Pan et al propose CRSN multi-channel opportunistic routing that balances the contradiction between energy consumption and delivery rate when creating a candidate node contention set, aiming to achieve as high a delivery rate as possible by consuming less energy. However, the above routing mechanisms all consider omni-directional transmission and cannot be directly applied to CRSN based on directional antenna.
For the problem of applying directional antennas to the traditional wireless network routing, Fang et al propose a directional arbitrary path routing DART protocol, and consider the problem of directional routing in two environments, respectively, fixed beam width and variable beam width. The DSMA proposed by Feng et al combines directional antennas with arbitrary path routing and discusses the problem of joint selection of antenna direction scheduling and transmission rate. However, due to the characteristics of the cognitive radio network itself and the limitation of energy of the sensor nodes, the conventional method of wireless network routing design cannot be fully applied to the CRSN environment.
At present, the technology of applying a directional antenna to a cognitive wireless network is relatively few, and most of the technologies focus on the aspects of spectrum sensing, channel intersection, network connectivity analysis and the like. Zhao et al analyzed the detection probability of the spectrum hole from an angle domain, and Wang et al proposed a theoretical model for analyzing the connectivity of the directional cognitive self-organizing network. For the environment with excessive number of PUs, Song et al propose a channel intersection mechanism based on directional antennas to improve the success rate of cognitive node channel intersection. Dai et al have devised an efficient routing model that uses directional antennas to detect PU signals in different directions to determine whether a cognitive node is at the boundary of the coverage of the PU signals to reduce interference to it.
Through comparative analysis of the related research results, it can be found that the joint design scheme of opportunistic routing and directional antenna adjustment in the current CRSN is relatively few, and the utilization of the directional antenna brings favorable conditions for routing design in the cognitive environment and deserves further discussion and research.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at a cognitive wireless sensor network with limited energy and dynamically changing available frequency spectrum, an effective opportunistic routing mechanism based on a directional antenna is provided so as to realize data transmission with high reliability, low time delay and low energy consumption.
The invention adopts the following technical scheme for solving the technical problems:
a cognitive wireless sensor network QoS guarantee opportunistic routing method based on a directional antenna comprises the following steps:
step (1): establishing a system model comprising an antenna model and a network model by analyzing the influence of directional antenna adjustment on node access authorization spectrum and routing node selection in the cognitive wireless network environment;
step (2): on the basis of the step (1), establishing a joint optimization mathematical model of directional antenna adjustment and cognitive opportunity routing design; decomposing the joint optimization problem into two stages of sector selection, channel selection and candidate forwarding node list creation, and establishing a corresponding optimization model;
and (3): adopting the candidate forwarding node list established in the step (2) to perform routing selection;
and (4): and evaluating and analyzing the routing performance through a Matlab simulation experiment.
Further, the establishing of the antenna model in the step (1) of the present invention specifically includes:
Figure GDA0003166184880000031
wherein, PrIndicating the received power, P, of the nodetIndicating a fixed power at which the signal is transmitted, Gt、GrRespectively representing the antenna gains of a transmitter and a receiver, l represents the distance between two nodes, alpha is a path loss index, and omega is a lognormal random variable with the mean value of zero and the standard deviation of sigma;
for the directional antenna with the beam width theta, the gain G of the transmitting or receiving signal is 2 pi/theta, 0 is more than or equal to theta and less than or equal to 2 pi, and when the directional transmission and the omnidirectional reception are adopted, G ist＝G，GrWhen the signal transmission distance r is 1, the signal transmission distance r is represented by formula (2), and accordingly, the signal coverage area S is represented by formula (3):
Figure GDA0003166184880000032
Figure GDA0003166184880000033
where Δ represents the power attenuation between two nodes, and Δ ═ Pt/Pr，ΔtRepresenting a given power attenuation threshold when alpha, omega, deltatAt a certain time, the smaller the beam width θ, the larger the antenna gain G, and the longer the transmission distance, the smaller the signal coverage area.
Further, the establishing of the network model in the step (1) of the present invention specifically comprises:
the occupation time of the PUs in each data channel is assumed to be an ON/OFF random process following an exponential distribution: is provided withThe ON state indicates that the channel is occupied by the PU, the OFF state indicates that the channel is free, and the average values are respectively zmAnd umIs used to describe the duration of the ON/OFF state of channel M, M is M, M represents a group of data channels, and then the probability of channel M being occupied or idle is expressed as:
Figure GDA0003166184880000034
extending the PU activity model to each sector
Figure GDA0003166184880000035
Representing a node viIn the direction of
Figure GDA0003166184880000036
Probability of detecting that channel m is available on an antenna sector having a beam width θ.
Further, the establishing of the joint optimization mathematical model of directional antenna adjustment and cognitive opportunity routing design in step (2) of the present invention specifically relates to a joint optimization problem of directional antenna parameter adjustment, data channel selection and candidate forwarding node list creation, specifically:
let Ti、RiRepresents a pair node viThe local delay and reliability requirements of single hop forwarding,
Figure GDA0003166184880000037
representing a node viIn the direction of
Figure GDA0003166184880000038
Directional neighbor node set on sector with beam width theta, candidate forwarding node list
Figure GDA0003166184880000039
Representing a node viAn ordered set of candidate forwarding nodes on the corresponding sector and channel M, M e M,
Figure GDA00031661848800000310
n denotes the number of candidate forwarding nodes, then viThe rules for selecting data channels, beam sectors, and candidate forwarding nodes are described as follows:
Figure GDA00031661848800000311
Figure GDA0003166184880000041
Figure GDA0003166184880000042
Figure GDA0003166184880000043
wherein, the node viSelecting a locally best data channel m*Direction of the wave beam
Figure GDA0003166184880000044
Beam width theta*And corresponding candidate forwarding lists
Figure GDA0003166184880000045
Minimizing node energy consumption while satisfying reliability and delay requirements, wherein formula (4) is a target for minimizing energy consumption, and conditions (5) and (6) utilize a candidate node list
Figure GDA0003166184880000046
The opportunistic forwarding needs to meet the reliability and delay requirements.
Further, step (2) of the present invention decomposes the joint optimization problem into two stages, namely sector, channel selection, and candidate forwarding node list creation, as follows:
division of the 360 ° angular domain into N antenna sectors with fixed beam width and direction to control the sectorThe selection of zones, each sector spanning an angle of 2 pi/N, θ 2 pi/N, the set of directions of these sectors being indicated as
Figure GDA0003166184880000047
Figure GDA0003166184880000048
For the current node viThe mathematical model for antenna sector and transmission channel selection is as follows:
Figure GDA0003166184880000049
Figure GDA00031661848800000410
Figure GDA00031661848800000411
wherein the content of the first and second substances,
Figure GDA00031661848800000412
representing a node viIn the direction of
Figure GDA00031661848800000413
Probability of detecting availability of channel M on antenna sector with beam width theta, M belongs to M, M represents a group of data channels, M*Indicating the selected locally-best data channel,
Figure GDA00031661848800000414
indicates the direction of the antenna sector corresponding to the best data channel, and gamma indicates viAngle with destination node s, QiRepresenting a threshold for channel availability, initially excluding some combinations of channels and antenna sectors with lower channel availability using an inequality (21), then determining the antenna sector to use for data transmission using equation (22), and selecting the data signal with the highest availability on that sectorA lane;
secondly, a heuristic algorithm is adopted to create a candidate forwarding list, neighbor nodes with high priority are preferentially selected, once the QoS requirement is met, more candidate nodes do not need to be selected, wherein a mathematical model for creating the candidate forwarding node list is as follows:
Figure GDA0003166184880000051
Figure GDA0003166184880000052
Figure GDA0003166184880000053
the heuristic algorithm is created by a recursive search method
Figure GDA0003166184880000054
The method specifically comprises the following steps:
before the start of the search is made,
Figure GDA0003166184880000055
not including any node, each time from a neighboring node
Figure GDA0003166184880000056
Selects a node vjIt is then mixed with
Figure GDA0003166184880000057
Are added together to the temporary node set F and are as follows
Figure GDA0003166184880000058
If the expected reliability and delay of F meet the QoS requirement, finding out the node v which minimizes the expected energy consumption from the neighbor nodes meeting the requirementtAnd adds it into the candidate forwarding list; whether or notThen, will
Figure GDA0003166184880000059
Node v with highest medium priority1Put into the candidate forwarding list and then repeat the traversal process from the remaining neighbor nodes until there are no remaining optional nodes or until a list of nodes meeting the QoS requirements is found.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. by applying the directional antenna in the cognitive wireless network, the signal interference of the cognitive node on the PU is reduced, the opportunity of authorized spectrum access is increased, and the stability of data transmission in the cognitive network is further guaranteed.
2. The adjustment of antenna parameters, the selection of a transmission channel and the creation of a candidate forwarding node list are described as a joint optimization mathematical model, and an approximate solution close to an optimal solution is provided, so that the energy-saving data stream transmission with QoS guarantee is realized, and the computational complexity is effectively reduced.
3. A heuristic algorithm based on recursion is used for creating a candidate forwarding node list, complexity is low, efficiency is high, and routing performance close to an optimal scheme can be obtained.
4. Under the same network environment, the opportunistic routing mechanism based on the directional antenna is superior to a typical QoS routing scheme based on an omnidirectional antenna in the aspects of QoS guarantee, throughput and energy efficiency.
Drawings
Fig. 1 is a diagram of a directional antenna based spectrum access according to the present invention.
Fig. 2 is a diagram of two antenna sectors with different beam widths according to the present invention.
Fig. 3 is a flow chart of the method of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
it will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Currently, CRSN is mostly aimed at time multiplexing, that is, cognitive sensor nodes access a free licensed spectrum when a PU is inactive, and another possible solution for increasing the benefits of CRSN is to utilize spatial multiplexing. The directional antenna is an effective mode for realizing spatial multiplexing in a wireless network, can reduce signal interference and improve the utilization rate of frequency spectrum, and promotes the research on routing in the CRSN based on the directional antenna.
Referring to fig. 3, the process of the present invention is as follows:
the method comprises the following steps of (1) analyzing the influence of the directional antenna adjustment on the cognitive wireless network environment, wherein the influence mainly comprises the influence on node access authorization spectrum and routing node selection.
A. The impact of directional antenna tuning on spectrum access:
as shown in FIG. 1, the dotted circle represents the omni-directional transmission range of a node, the solid sector represents the directional transmission range, channel c1、c2Are occupied by the master user. Since the transmitting node cannot cause signal interference to primary users within its transmission range, when v is1When the omnidirectional antenna is adopted for transmission, only other channels which are not occupied by the primary user are allowed to be used. Conversely, if the node employs directional antenna transmission, the wireless signal will only cover the entire sector. Even if v1Occupation of channel c1Interference to surrounding main users is avoided, and therefore the opportunity that the cognitive node accesses the authorized spectrum is greatly increased.
Although the use of directional antennas creates more spectrum access opportunities for cognitive nodes, appropriate antenna directions and beam widths need to be set in the routing stage. Due to the difference of the geographical positions of the primary users, if the directional antenna direction of the cognitive node is not properly selected, interference may be caused to the primary users communicating with the cognitive node, and thus the chance of accessing a partial channel is lost.
B. Influence of directional antenna adjustment on routing:
as shown in fig. 2, the main lobe of the directional antenna beam approximates a sector of radius r, theta,
Figure GDA0003166184880000061
Respectively, the beam width and the beam direction. Two sectors with different beam widths are shown, theta' > theta. The larger the beamwidth, the larger the signal coverage area, and the more potential forwarding nodes are available, as compared to the narrow beamwidth. However, the larger the beam width, the shorter the transmission distance, and the smaller the forwarding rate of the data packet. On the other hand, the narrower the beam width means that the longer the signal transmission distance is, the better the end-to-end forwarding hop count and transmission delay are reduced. But routing performance may also suffer due to the narrow selection range of forwarding nodes.
On the other hand, due to the limitation of the beam width, when the antenna beam points to different directions, the node will obtain different sets of neighbor nodes. If the antenna direction is not properly selected, network transmission performance may also be reduced.
And (2) establishing a system model which comprises an antenna model and a network model.
a. An antenna model:
considering a multi-channel CRSN, the sensor node is provided with a smart antenna which can provide two working modes of orientation and omnidirectional. Two separate antennas can be envisaged: a steerable single beam antenna and an omni-directional antenna, which antenna system can be realized by integrating a directional transmitter and an omni-directional/directional receiver. The omni-directional mode is mainly used for receiving, and in the directional mode, the node can only select a beam in a certain direction to concentrate energy for signal transmission or reception.
Antenna gain is typically used to simulate the transmitting or receiving capabilities of an antenna. For the directional antenna with the beam width theta, the gain G of the transmitting or receiving signal is 2 pi/theta, and 0-theta-2 pi. If with a constant power PtTo carry outSignal transmission, the received power of the node may represent:
Figure GDA0003166184880000071
wherein l represents the distance between two nodes, Gt、GrRepresenting the antenna gain of the transmitter and receiver. Alpha is a path loss exponent, typically 2 ≦ alpha ≦ 6. Omega is a logarithmic normal random variable with the mean value of zero and the standard deviation of sigma and is used for simulating the influence of the shadow fading effect. In practice, the quality of signal transmission is usually measured by the power attenuation Δ between two nodes, where Δ Pt/Pr. The invention limits the power attenuation to a given threshold value deltatAbove, it can be deduced from equation (1) that when directional transmission, omni-directional reception is used (G)t＝G，Gr1), the transmission distance r of the signal can be expressed as shown in equation (2). Accordingly, the signal coverage area S can be expressed as shown in equation (3):
Figure GDA0003166184880000072
Figure GDA0003166184880000073
it can be seen that when α, ω, ΔtAt a certain time, the smaller the beam width θ, the larger the antenna gain G, and the longer the transmission distance, the smaller the signal coverage area.
b. And (3) network model:
assuming that a Common Control Channel (CCC) and a group of data channels M exist in the network, the CCC is used for the interaction of control messages between nodes, all the data channels are authorized to a master user, and the cognitive sensor node can opportunistically access idle channels in the master user only on the premise of not interfering the communication of the master user. When a source node initiates data communication to a destination node, channel access opportunity is obtained through spectrum sensing, then a proper forwarding node is selected in an idle channel according to a routing mechanism, and finally data transmission is completed. The invention relates to opportunistic routing of QoS guarantee, therefore, a group of neighbor nodes are required to be selected as candidate forwarding nodes in the relay selection process, certain priority is distributed to the nodes, and then a next hop node is selected opportunistically to forward data according to the actual transmission condition. Each node may obtain its location information via GPS or other positioning device.
Due to the randomness of PU activity, the availability of channels may differ for cognitive nodes during the spectrum sensing phase and the data transmission phase. To characterize this dynamic nature of the available channels, the present invention assumes the occupancy time of the PUs in each data channel as an exponentially distributed ON/OFF stochastic process: the ON state indicates that the channel is occupied by the PU; the OFF state indicates that the channel is idle. With mean values of z respectivelymAnd umDescribes the duration of the ON, OFF state of channel M, and M ∈ M, the probability that channel M is occupied or idle can be expressed as:
Figure GDA0003166184880000081
and
Figure GDA0003166184880000082
a larger channel means that the channel m is more stable and suitable for data transmission. Considering that the channel availability of the node on each antenna sector is dynamic, the invention extends the PU activity model to each sector
Figure GDA0003166184880000083
Indicates that node vi is in the direction of
Figure GDA0003166184880000084
Probability of detecting that channel m is available on an antenna sector having a beam width θ.
Step (3) establishing a joint optimization mathematical model of directional antenna adjustment and opportunistic routing design
The invention discusses a cognitive wireless sensor network QoS guarantee opportunistic routing DAOR based on a directional antenna, and the algorithm meets the QoS constraint condition and simultaneously realizes the low-energy-consumption transmission of data flow by utilizing available channel information, local network state information and the like sensed by nodes. The joint optimization problem of directional antenna adjustment and opportunistic routing is described first, then the link criteria and the energy model are given, and finally the reliability and delay requirements are analyzed.
Description of joint optimization problem:
unlike traditional wireless network opportunistic routing, a cognitive node must determine its transmit antenna sector (determined by antenna direction and beam width), data channels, and candidate forwarding nodes before sending data. The sensor network application has no loss of generality on the requirements of QoS, and the invention considers two requirements of time delay and reliability. Due to the dynamic nature of the wireless medium, it is not possible to obtain real-time, accurate end-to-end link status. Therefore, the present invention divides the end-to-end QoS requirement into a single-hop QoS requirement, and if the requirement of each hop is satisfied, the end-to-end QoS is basically guaranteed.
Let Ti、RiRepresents a pair node viThe local delay and reliability requirements of single hop forwarding,
Figure GDA0003166184880000085
representing a node viIn the direction of
Figure GDA0003166184880000086
Directional neighbor node set on sector with beam width theta, candidate forwarding node list
Figure GDA0003166184880000087
Representing a node viAn ordered set of candidate forwarding nodes on the corresponding sector and channel M, M e M,
Figure GDA0003166184880000088
the rule for selecting data channels, beam sectors, and candidate forwarding nodes is described as follows:
Given：Ti,Ri,
Figure GDA0003166184880000089
M
Find：m*，
Figure GDA00031661848800000810
θ*，
Figure GDA00031661848800000811
Min：
Figure GDA00031661848800000812
Subject to：
Figure GDA00031661848800000813
Figure GDA0003166184880000091
node viSelecting a locally best data channel m*Direction of the wave beam
Figure GDA0003166184880000092
Beam width theta*And corresponding candidate forwarding lists
Figure GDA0003166184880000093
The node energy consumption can be minimized while the requirements of reliability and time delay are met. Equation (4) is the minimize energy consumption target, and conditions (5) and (6) are the utilization of the candidate node list
Figure GDA0003166184880000094
The opportunistic forwarding needs to meet the reliability and delay requirements.
Neighbor discovery:
as shown in FIG. 2, with (x)i,yi)、(xj,yj) Representing a node v on a planeiAnd node vjGeographic coordinates of (1), thenThe Euclidean distance d (v) between themi,vj) And included angle β can be expressed as:
Figure GDA0003166184880000095
Figure GDA0003166184880000096
if the distance between two nodes is less than or equal to the directional transmission distance, d (i, j) is less than or equal to r, and the included angle does not exceed the beam boundary,
Figure GDA0003166184880000097
description node vjFalls on viWithin the signal coverage of
Figure GDA0003166184880000098
In a conventional wireless network, two nodes may establish a communication link between them if they fall within each other's transmission area. However, in the wireless cognitive network, whether a communication link can be formed between two nodes depends not only on the signal transmission range but also on the spectrum availability. Node v is a node that is a nodeiAnd vjA communication link can be established between: (1) v. ofiAnd vjAll falling within each other's transmission area, i.e. vi、vjNodes which are mutually adjacent; (2) v. ofiAnd vjThere is a common available channel, which means that there are no active primary users in the node transmission area.
Energy loss:
according to the energy model in the prior literature, if the node viTransmitting the Lbit data packet to another node beyond the distance l, wherein the transmission energy consumption is as follows:
Et,i＝Eelec·L+εamp·lα·L (9)
wherein E iselecIndicating transceiver circuitry transmission or reception 1bEnergy consumed by it data, εampRepresents the operating energy consumption of the power amplifier and alpha is the path loss exponent. Considering the packet loss problem in the data transmission process, the node vjMay only receive
Figure GDA0003166184880000099
Data packet of bits, wherein
Figure GDA00031661848800000910
Representing a node vi、vjThe delivery rate of the link between the channels on the channel m, the receiving energy consumption can be expressed as:
Figure GDA00031661848800000911
it is assumed that the nodes only listen to the transmission activity sent to them and that the energy consumption is mainly used for the transmission and reception of data. In order to examine the influence of the activities of the main users on the node communication, a new index is defined to evaluate the cognitive expected energy consumption of opportunistic forwarding of the node, as shown in a formula (11). Where n represents the number of candidate forwarding nodes,
Figure GDA0003166184880000101
Figure GDA0003166184880000102
reliability guarantee:
and evaluating the reliability of the link by using the packet delivery rate, namely the percentage of the packets successfully transmitted to the target node to the total sent packets. If the links per hop on the path are required to provide equal reliability, node viThe local delivery rate that needs to be satisfied to select the candidate node list may be estimated as:
Figure GDA0003166184880000103
wherein R isi,sAnd HiRespectively represent from viReliability requirements and expected number of transmission hops to the destination node s, i.e.:
Figure GDA0003166184880000104
wherein the content of the first and second substances,
Figure GDA0003166184880000105
representing the current node v from the source nodeiAverage single hop advance distance.
Defining expected delivery rates
Figure GDA0003166184880000106
Is composed of
Figure GDA0003166184880000107
In which at least one candidate node successfully receives the node viThe probability of the transmitted data packet is that the packet loss events on different links are independent of each other
Figure GDA0003166184880000108
Can be expressed as:
Figure GDA0003166184880000109
and (3) time delay guarantee:
the invention provides a routing algorithm based on spectrum sensing, and in the data transmission process of each hop, a node firstly detects an idle channel and then transmits data information by using the idle channel. After receiving the data packet, the candidate node replies an acknowledgement message ACK according to the preset evasion time. The higher the priority, the shorter the avoidance time and the earlier the return. Once receiving the ACK reply, the transmitting node broadcasts a complete reception message CTR on the CCC to notify other candidate nodes that the sender has received the ACK message, thereby reducing unnecessary data packet repeat forwarding. Suppose node vjIs a priorityThe candidate node ranked at the j-th bit,
Figure GDA00031661848800001010
Figure GDA00031661848800001011
then utilize node vjThe single-hop delay of the forwarded data can be mainly expressed as:
ti,j＝tS+tDATA+tW (15)
wherein, tS、tDATAIndicating the perceived channel and data transmission delay, tWRepresenting the mutually coordinated time delay, t, between the candidate nodesW＝j·(2μ+tACK+tCTR) I.e., the time the node waits to reply with an ACK after receiving the data packet. Mu denotes the minimum short frame slot interval, tACK、tCTRIndicating the time to reply to the ACK, CLR messages, respectively.
The data transmission delay constraint can also be regarded as a space-time constraint of data packet migration in nature, so the invention utilizes a mechanism based on geographic position to estimate the requirement of single-hop delay through the limitation of the forward speed:
Figure GDA0003166184880000111
Figure GDA0003166184880000112
wherein, Ti,sRepresents from viThe delay requirement to the destination node s,
Figure GDA0003166184880000113
respectively utilize
Figure GDA0003166184880000114
The expected distance of advance and the expected delay for opportunistic forwarding are performed as shown in equations (18) and (19). a isij＝d(vi,s)-d(vjS), tableShow node viTo each candidate node vjThe distance of advance of (a) is,
Figure GDA0003166184880000115
Figure GDA0003166184880000116
Figure GDA0003166184880000117
many factors need to be considered in the process of carrying out route design and adjusting the parameters of the directional antenna, and it is impossible to obtain an optimal solution by only depending on a single index. Next, an approximation scheme will be introduced to simplify the above joint optimization problem regarding routing and antenna adjustment.
And (4): and (4) aiming at the joint optimization problem described in the step (3), an approximate solution close to the optimal solution is provided.
Theoretically, the variation space of the antenna direction and the beam width is continuous, so that an infinite number of antenna sectors can be selected, but in real life, the directional antenna cannot be designed to be complex, and an exhaustive search under each sector is impractical to find an optimal solution. Therefore, the present invention divides an angular domain of 360 ° into N antenna sectors with fixed beam width and direction to control the selection range of the sectors. Each sector spanning an angle of 2 pi/N, theta 2 pi/N, the set of directions of these sectors being denoted as
Figure GDA0003166184880000118
For each node, the beam width of its antenna sector is fixed, so only the direction of the sector needs to be determined.
In order to obtain the best data channel, transmission sector and corresponding candidate forwarding list, the most intuitive method is to perform exhaustive search: combining all data channels with sector direction for eachIn one combination, all subsets of neighbor nodes are traversed, and a forwarding node list which meets QoS requirements and energy loss and is minimum is screened out. If M is equal to C,
Figure GDA0003166184880000119
then all together have
Figure GDA00031661848800001110
And (4) selecting. When N, C or n is very large, this is impractical for CRSNs that are limited in energy and processing power. In order to reduce the complexity of the calculation, it is necessary to design an approximate solution that is close to the optimal solution.
Therefore, the invention decomposes the optimization problem into two stages: firstly, determining a forwarding sector and a transmission channel; secondly, under the selected antenna sector and data channel, a forwarding node list which meets the QoS requirement and energy loss and is minimum is constructed.
Selection of antenna sectors and data channels:
in the opportunistic forwarding route based on the geographic location, the forward distance of a node is often used as an index for next hop selection to approach the shortest path route. If the antenna sector pointing to the destination node can be selected, the forwarding node with a larger forwarding distance is necessarily obtained, so that the forwarding speed of the data packet is increased, and the transmission delay is reduced. However, merely selecting a transmission sector based on the antenna direction does not guarantee a high availability of channels on that sector. The worse the channel availability, the more likely the communication link between cognitive nodes is broken by the influence of the primary user, which will result in an increased number of retransmissions. On the other hand, if the present invention simply follows the principles of maximizing channel availability to select antenna sectors and communication channels, it is likely that the selected transmission path will be longer and more delayed.
In summary, the selection of a suitable antenna sector and transmission channel depends not only on the sector direction, but also on the channel availability on that sector. Therefore, the present invention designs a practical selection scheme for combining sectors and channels, aiming to reduce the transmission cost as much as possible and simultaneously reducing the transmission costGuarantees of high channel availability are provided. For the current node viThe selection rules of the antenna sectors and the data channels are as follows:
Figure GDA0003166184880000121
Figure GDA0003166184880000122
Figure GDA0003166184880000123
wherein γ represents viAngle with destination node s, QiRepresenting a threshold for channel availability. Using inequality (21), it is possible to initially exclude some combinations of channels and antenna sectors with lower channel availability, and then on this basis, using equation (22), to determine the antenna sector to use for data transmission, and to select the data channel with the highest availability on this sector.
Next, the present invention will use the concept of probability guarantee to set the threshold Q of channel availabilityiNamely let
Figure GDA0003166184880000124
The probability of (d) is not lower than λ, expressed as:
Figure GDA0003166184880000125
order to
Figure GDA0003166184880000126
Equation (23) can be transformed into:
Figure GDA0003166184880000127
given the mean μ and variance σ of the random variable X2According to the unilateral chebyshev inequality, it satisfies the condition:
Figure GDA0003166184880000128
by applying the Chebyshev inequality to equation (24), it can be derived:
Figure GDA0003166184880000131
Figure GDA0003166184880000132
wherein the content of the first and second substances,
Figure GDA0003166184880000133
and
Figure GDA0003166184880000134
respectively representing variables
Figure GDA0003166184880000135
Mean and variance of. According to the transfer properties of inequalities (24) and (26), if a condition is satisfied
Figure GDA0003166184880000136
The availability as defined in equation (23) will be guaranteed with probability. The corresponding thresholds may be set as:
Figure GDA0003166184880000137
heuristic algorithm to create a list of candidate forwarding nodes:
with a given antenna sector and data channel, it is then necessary to select and assign priorities to candidate forwarding nodes. In general, if rootThe priority is assigned according to the single-hop forward distance of the candidate node, and the forward distance gain is expected to be maximized. The expected transmission delay can be reduced if the priority is assigned according to the packet delivery rate. Considering the dynamic wireless link environment, the farther the signal transmission distance, the lower the packet delivery rate. Therefore, the invention adopts a compromise strategy and utilizes
Figure GDA0003166184880000138
To assign priorities to the candidate forwarding nodes. The selection rules for candidate forwarding nodes are as follows:
Given:m*,
Figure GDA0003166184880000139
Ti,Ri,
Figure GDA00031661848800001310
Find:
Figure GDA00031661848800001311
Minimize:
Figure GDA00031661848800001312
Subject to：
Figure GDA00031661848800001313
Figure GDA00031661848800001314
the intuitive method is exhaustive search to obtain the optimal forwarding node list, but this results in high computational complexity, and is not suitable for sensor nodes with limited energy and processing capability. In addition, the candidate forwarding list should not contain too many nodes, since the cost of energy consumption and coordination delay also increases as the number of candidate forwarding nodes increases. Therefore, the invention designs an effective heuristic algorithm to establish the candidate forwarding list, preferentially selects the neighbor nodes with high priority, and does not need to select more candidate nodes once the QoS requirement is met.
Figure GDA0003166184880000141
The algorithm is performed as shown in algorithm 1, where h represents the maximum number of candidate forwarding nodes. The algorithm is created by a recursive search method
Figure GDA0003166184880000142
Before the start of the search is made,
Figure GDA0003166184880000143
does not contain any nodes. Each time from a neighboring node
Figure GDA0003166184880000144
Selects a node vjIt is then mixed with
Figure GDA0003166184880000145
Are added together to the temporary node set F and are as follows
Figure GDA0003166184880000146
In descending order of size. If the expected reliability and delay of F meet the QoS requirement, finding out the node v which minimizes the expected energy consumption from the neighbor nodes meeting the requirementtAnd adds it to the candidate forwarding list. Otherwise, it will
Figure GDA0003166184880000147
Node v with highest medium priority1Put into the candidate forwarding list and then repeat the traversal process from the remaining neighbor nodes until there are no remaining optional nodes or until a list of nodes meeting the QoS requirements is found. When in use
Figure GDA0003166184880000148
The time complexity of algorithm 1 is O (n)2). If an enumeration algorithm is used for the search, the time complexity is O (n!). It can be seen that once n becomes very large, the heuristic algorithm will be more efficient than an exhaustive search.
It will be understood by those within the art that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the methods specified in the block or blocks of the block diagrams and/or flowchart block or blocks.
Those of skill in the art will appreciate that the various operations, methods, steps in the processes, acts, or solutions discussed in the present application can be interchanged, modified, combined, or eliminated. Further, various operations, methods, steps in the flows, which have been discussed in the present application, may be interchanged, modified, rearranged, decomposed, combined, or eliminated. Further, steps, measures, schemes in the various operations, methods, procedures disclosed in the prior art and the present invention can also be alternated, changed, rearranged, decomposed, combined, or deleted.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (2)

1. A cognitive wireless sensor network QoS guarantee opportunistic routing method based on a directional antenna is characterized by comprising the following steps:
step (1): establishing a system model comprising an antenna model and a network model by analyzing the influence of directional antenna adjustment on node access authorization spectrum and routing node selection in the cognitive wireless network environment;
step (2): on the basis of the step (1), establishing a joint optimization mathematical model of directional antenna adjustment and cognitive opportunity routing design, further decomposing the joint optimization problem into two stages of sector and channel selection, creating a candidate forwarding node list and establishing a corresponding optimization model;
and (3): adopting the candidate forwarding node list established in the step (2) to perform routing selection;
the step (1) of establishing the antenna model specifically comprises the following steps:
Figure FDA0003184611910000011
wherein, PrIndicating the received power, P, of the nodetIndicating a fixed power at which the signal is transmitted, Gt、GrRespectively representing the antenna gains of a transmitter and a receiver, l represents the distance between two nodes, alpha is a path loss index, and omega is a lognormal random variable with the mean value of zero and the standard deviation of sigma;
for the directional antenna with the beam width theta, the gain G of the transmitting or receiving signal is 2 pi/theta, 0 is more than or equal to theta and less than or equal to 2 pi, and when the directional transmission and the omnidirectional reception are adopted, G ist＝G，GrWhen the signal transmission distance r is 1, the signal transmission distance r is represented by formula (2), and accordingly, the signal coverage area S is represented by formula (3):
Figure FDA0003184611910000012
Figure FDA0003184611910000013
where Δ represents the power attenuation between two nodes, and Δ ═ Pt/Pr，ΔtIndicating a given power attenuation thresholdValues when α, ω, ΔtAt a certain time, the smaller the beam width theta is, the larger the antenna gain G is, the farther the transmission distance is, and the smaller the signal coverage area is;
the step (1) of establishing the network model specifically comprises the following steps:
the occupation time of the primary users in each data channel is assumed as an ON/OFF random process which follows an exponential distribution: setting ON state to indicate that the channel is occupied by a master user, and OFF state to indicate that the channel is idle, wherein the average values are respectively zmAnd umIs used to describe the duration of the ON/OFF state of channel M, M is M, M represents a group of data channels, and then the probability of channel M being occupied or idle is expressed as:
Figure FDA0003184611910000014
extending master user activity model to each sector
Figure FDA0003184611910000015
Representing a node viIn the direction of
Figure FDA0003184611910000016
Day with wave beam width theta
Figure FDA0003184611910000021
Wherein the content of the first and second substances,
Figure FDA0003184611910000022
representing a node viIn the direction of
Figure FDA0003184611910000023
Probability of detecting availability of channel M on antenna sector with beam width theta, M belongs to M, M represents a group of data channels, M*Indicating the selected locally-best data channel,
Figure FDA0003184611910000024
indicates the direction of the antenna sector corresponding to the best data channel, and gamma indicates viAngle with destination node s, QiA threshold representing channel availability, preliminarily excluding some combinations of channels and antenna sectors with lower channel availability using inequality (21), then determining antenna sectors for data transmission using equation (22), and selecting the data channel with highest availability on the sector;
secondly, a heuristic algorithm is adopted to create a candidate forwarding list, neighbor nodes with high priority are preferentially selected, once the QoS requirement is met, more candidate nodes do not need to be selected, wherein a mathematical model for creating the candidate forwarding node list is as follows:
Figure FDA0003184611910000025
Figure FDA0003184611910000026
Figure FDA0003184611910000027
Subject to：
Figure FDA0003184611910000028
Figure FDA0003184611910000029
the heuristic algorithm is created by a recursive search method
Figure FDA00031846119100000210
The method specifically comprises the following steps:
before the start of the search is made,
Figure FDA00031846119100000211
not including any node, each time from a neighboring node
Figure FDA00031846119100000212
Selects a node vjIt is then mixed with
Figure FDA00031846119100000213
Are added together to the temporary node set F and are as follows
Figure FDA00031846119100000214
In descending order of size, aij＝d(vi,s)-d(vj,s)，aijRepresenting a node viTo each candidate node vjThe distance of advance of (a) is,
Figure FDA00031846119100000215
representing a node vi、vjThe delivery rate of the link between on the channel m, if the expected reliability and delay of F meet the QoS requirement, finding out the node v which can minimize the expected energy consumption from the neighbor nodes meeting the requirementtAnd adds it into the candidate forwarding list; otherwise, it will
Figure FDA00031846119100000216
Node v with highest medium priority1Put into the candidate forwarding list and then repeat the traversal process from the remaining neighbor nodes until there are no remaining optional nodes or until a list of nodes meeting the QoS requirements is found.
2. The cognitive wireless sensor network QoS guarantee opportunity routing method based on the directional antenna as claimed in claim 1, further comprising the step (4): and evaluating and analyzing the routing performance through a Matlab simulation experiment.
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