CN110602760A - Multi-utility criterion high-energy-efficiency network selection method oriented to Internet of vehicles - Google Patents

Multi-utility criterion high-energy-efficiency network selection method oriented to Internet of vehicles Download PDF

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CN110602760A
CN110602760A CN201910983593.0A CN201910983593A CN110602760A CN 110602760 A CN110602760 A CN 110602760A CN 201910983593 A CN201910983593 A CN 201910983593A CN 110602760 A CN110602760 A CN 110602760A
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network
utility function
energy efficiency
utility
energy
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蒋定德
孙嘉璐
王雨晴
齐盛
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/17—Selecting a data network PoA [Point of Attachment]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/18—Selecting a network or a communication service
    • 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
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Computer Networks & Wireless Communication (AREA)
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  • Computer Security & Cryptography (AREA)
  • Health & Medical Sciences (AREA)
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Abstract

The invention discloses a multi-utility criterion high-energy-efficiency network selection method for Internet of vehicles, belongs to the field of network selection problems for vehicle infrastructure networks in heterogeneous wireless networks, and particularly relates to a high-energy-efficiency network selection method constructed by using a multi-standard utility theory. The present invention jointly considers a plurality of decision factors to facilitate networking of vehicle infrastructures. The energy efficiency of the network is considered to be an important factor in the selection of the network. Energy efficiency, signal strength, network cost, delay and bandwidth are considered decision factors. Their utility functions are used to characterize the user's preferences and network performance. The multi-standard utility theory is used for constructing an energy-efficient network selection method. Several design strategies are proposed to construct a joint multi-criteria utility function for network selection. A multi-constraint optimization model is also presented to describe network selection for Internet of vehicles applications. A multi-criteria access selection algorithm is proposed to solve the model to provide better network selection performance.

Description

Multi-utility criterion high-energy-efficiency network selection method oriented to Internet of vehicles
Technical Field
The invention belongs to the field of green communication of Internet of vehicles, and particularly relates to a multi-utility criterion high-energy-efficiency network selection method for the Internet of vehicles.
Background
The emerging technology of internet of vehicles has become a hot topic. However, car networking applications are commonly used for heterogeneous wireless networks. In such a context, the user terminal faces the challenge of access network selection. Furthermore, high energy consumption is an important issue in current communication networks.
With the emerging demand and fast-paced development of smart cities, intelligent transportation and vehicle-mounted entertainment, multimedia videos, pictures, photos, real-time transportation and vehicle information, weather information and the like must be quickly uploaded to data processing platforms of the application programs through a wireless vehicle-mounted network. For example, when a vehicle accident occurs, the accident position is rapidly reported to the intelligent transportation platform in real time, so that the life is saved and traffic jam is avoided; through in-vehicle terminals, personal mobile phones and other personal mobile devices (such as ipads and laptops), users can download and upload entertainment data, such as videos and photos, to/from a social network for sharing with friends.
In addition, traffic congestion control and traffic scheduling based on a vehicle infrastructure network are very helpful for improving urban traffic and implementing intelligent traffic. All of these applications need to provide more data. In this case, the terminal device consumes more energy. In particular, when multiple mobile devices are present in a vehicle, interference between them can result in higher receive and transmit power consumption. As described above, energy efficiency has become an important index for the on-vehicle network. Energy efficiency, signal strength, network cost, delay and bandwidth metrics are combined to build an appropriate utility function to capture user preferences and network performance. Further, Dedicated Short-Range Communications (DSRC) is a unidirectional or bidirectional, Short-to-medium Range wireless communication technology for vehicle-to-vehicle communication in an on-board network.
In a vehicle infrastructure network, a user terminal (vehicle) has a plurality of available Networks, such as Wireless Local Area Networks (WLANs), cellar Networks and other Wireless Networks, which may choose them as its access network. It is very difficult for the user terminal to select an appropriate network from a plurality of available networks.
Disclosure of Invention
Aiming at the defects of the existing method, the invention provides a multi-utility-criterion high-energy-efficiency network selection method facing to the Internet of vehicles to effectively realize the networking of vehicle infrastructures
The technical scheme of the invention is a multi-utility criterion high-energy-efficiency network selection method facing the Internet of vehicles, which comprises the following steps:
step 1: collecting and providing all parameters required by a network selection algorithm, and selecting a network meeting given constraints and parameter thresholds;
step 2: calculating the energy efficiency of the available network according to the network parameters and the traffic type;
and step 3: calculating energy efficiency weight, cost weight, signal strength weight, delay weight and bandwidth weight of the available network by using a hierarchical analysis method;
and 4, step 4: establishing an efficiency utility function, a cost utility function, a signal strength utility function, a delay utility function and a bandwidth utility function;
and 5: and (4) calculating the utility value of each network according to the utility function in the step (4), and calculating the optimal network according to the weight corresponding to the step (3).
Further, the step 1 of collecting and providing all parameters required by the network selection algorithm, and selecting the network satisfying given constraints and parameter thresholds comprises the following specific processes:
collecting network information: obtaining a list of parameters l for n access networksnp＝{p1,p2,...,pnI.e. the available bandwidth b of the access network iiCost c of access network iiDelay d of access network iiSignal strength s of access network iiWherein p isi＝{bi,ci,di,si1,2, ·, n; initializing the parameters α, β, r1And r2Wherein alpha represents a proportional system in the network energy efficiency utility function, 0 < alpha < 1, and beta represents SProportional system in shape utility function and 0 < beta, r1Representing a parameter in a sigmoid utility function and 2 < r1、r2Representing a proportional system in a piecewise utility function and 2 < r2；
Collecting user information: obtaining a requirement list l of k usersd＝{q1,q2,...,qkI.e. the bandwidth requirement b of the u-th useruCost requirement cuDelaying the demand duSignal strength requirement suWherein q isu＝{bu,cu,du,suAnd u is 1,2, k, let m be 1;
the network filtering module selects a network which meets given constraints and parameter thresholds; constructing an available network set through a network filtering process;
according to the network parameter list lnpDemand List ld；
Establishing an access network list l according to the parameters of n access networksan＝{l1,l2,...,lnWhen the value of i is 0-n, if bi≤buOr ci≤cuOr di≥duOr si≤suFrom the list lanDeleting access network i, and listing access network lanIs updated toan＝{l1,l2,.. }, order lan＝lanObtaining the available access network list l of user man。
Further, the energy efficiency of the available network calculated in step 2 is specifically calculated as follows:
according to the available network list lanGet the list lanThe number of available networks in z ═ lanL, |; when i takes 0-z, the list l is calculatedanEnergy efficiency e of medium access network iiGet the list lanEnergy efficiency list e ═ e of all available networks in the network1,e2,...ez}；
Further, the calculation of the selection weight of the available network in step 3 specifically includes the following steps:
according to bandwidth demand buCost requirement cuDelaying the demand duSignal strength requirement suBuilding a hierarchical structure H ═ H1,h2,...,hn(ii) a Let j equal 1;
step 3.1: constructing a decision matrix R;
step 3.2: calculating the weight of the j layer; then obtaining an energy efficiency weight of the j-th layerCost weightingSignal strength weightingDelay weightBandwidth weighting
Judging whether the jth layer is consistent; if not, returning to execute the step 3.1; otherwise, if j is less than n, returning to execute the step 3.2, and if j is equal to n, executing the step 3.3;
step 3.3: calculating the total weight; then obtaining the energy efficiency of the access network iCost weightingSignal strength weightingDelay weightBandwidth weightingJudging whether the whole layering is consistent; if not, returning to execute the step 3.1;
further, the establishing of the utility function in the step 4 judges whether the optimal utility function is reached, and the specific process is as follows:
according to the utility theory, the utility function satisfies the characteristics of twice micromability, monotonicity and concavity and convexity. Only when the three attributes are met, the optimal point can be found according to the designed utility function;
step 4.1 utility function of network energy efficiency
The network energy efficiency EE is:
wherein T represents throughput, i.e., the maximum mutual information amount delivered, and E represents energy consumption in joules; α is a parameter, where 0<α<1; b represents the average transmission rate of user traffic, unit Kbyte/s; (Σ B) is less than or equal to B, wherein B represents the network bandwidth; r istRepresenting the power consumption of the basic circuit, ensuring the normal operation of the terminal circuit when the terminal is idle, and rdRepresents the energy consumption related to the terminal load only when the terminal transmits the network data;
and characterizing utility metrics of the network energy efficiency by adopting a sigmoid function, wherein the sigmoid utility function is as follows:
the utility function for obtaining the network energy efficiency is:
wherein e and eavgRepresents the energy efficiency and the average energy efficiency of the network, and gamma1≧ 2 denotes the sensitivity of user traffic to energy efficiency, which determines the steepness of the curve of the utility function, steep γ1Result inHigh sensitivity;
step 4.2: the utility function of signal strength is as follows:
wherein s isminAnd smaxRespectively representing the lower and upper limits of the signal strength, gamma2Sensitivity is expressed by more than or equal to 2;
step 4.3: utility function of cost:
u(c)＝1-u′(c) (9)
wherein
c is the current cost of the network, cmaxIs the maximum cost acceptable to the user;
step 4.4: utility function of network delay:
u(d)＝1-u′(d)
wherein d ismaxDenotes the maximum delay, dmIs half of the maximum delay, gamma3Sensitivity,. gamma.,. gtoreq.23The larger the value, the higher the sensitivity;
step 4.5: utility function of network bandwidth:
wherein b isminAnd bmaxRepresenting the minimum and maximum network bandwidth, respectively, b representing the bandwidth demanded by the user, the utility function of the network delay is similar to that of the signal strength.
The symbols involved in the present invention are defined as follows:
lanrepresenting a list of available networks, buIndicating tapeWide demand, cuRepresents the cost demand, duIndicating the delay requirement, suRepresenting the signal strength requirement, biRepresenting the available bandwidth of the access network i, ciRepresenting the cost of the access network i, diIndicating the delay, s, of the access network iiIndicating the signal strength of the access network i, eiRepresentation list lanThe energy efficiency of the medium access network i,the energy efficiency weight for level j is represented,the weight of the cost is represented by,a weight representing the strength of the signal is represented,the weight of the delay is represented by,the weight of the bandwidth is represented by,representing the energy efficiency of the access network i,representing the cost weight of the access network i,representing the signal strength weight of the access network i,representing the delay weight of the access network i,denotes the bandwidth weight of the access network i, and T denotes the throughput (i.e. maximum mutual confidence of delivery)Information quantity), E represents the energy consumption in joules, α represents a parameter where 0<α<1，rdRepresenting the energy consumption to transmit K bytes, rtRepresenting the energy consumption of the circuit of the subscriber terminal per second, b representing the average transmission rate of the subscriber traffic in Kbyte/s, rtRepresenting the energy consumption of the basic circuit, ensuring the normal operation of the terminal circuit when the terminal is idle, rdRepresenting the energy consumption of the terminal only in relation to the terminal load when it transmits network data, e and eavgRepresenting the energy efficiency and the average energy efficiency, gamma, of the network1(wherein γ is1≧ 2) represents the sensitivity of user traffic to energy efficiency, sminAnd smaxDenotes the lower and upper limits of the signal intensity, γ2(wherein γ is2≧ 2) represents sensitivity, c represents current cost of the networkmaxRepresents the maximum cost acceptable to the user, dmaxDenotes the maximum delay, dmIs half of the maximum delay, gamma3(wherein γ is3≧ 2) represents sensitivity, bminRepresents the minimum network bandwidth, bmaxRepresenting the maximum network bandwidth and b representing the bandwidth demanded by the user.
The invention has the beneficial effects that:
in contrast to previous approaches, the present invention jointly considers multiple decision factors to facilitate networking of the vehicle infrastructure. The energy efficiency of the network is considered to be an important factor in the selection of the network. Energy efficiency, signal strength, network cost, delay and bandwidth are considered decision factors. Their utility functions are used to characterize the user's preferences and network performance. The multi-standard utility theory is used for constructing an energy-efficient network selection method. Several design strategies are proposed to construct a joint multi-criteria utility function for network selection. A multi-constraint optimization model is also presented to describe network selection for Internet of vehicles applications. A multi-criteria access selection algorithm is proposed to solve the model to provide better network selection performance.
Drawings
FIG. 1 is a program flow chart of a multi-utility criterion energy-efficient network selection method for Internet of vehicles according to the present invention;
FIG. 2 is a utility function of energy efficiency;
FIG. 3 is a utility function of signal strength
FIG. 4 is a utility function of cost
Fig. 5 is an access selection for multiple available networks in a vehicle infrastructure network, where the gray circles represent WLAN APs, the blue circles represent 3G towers, the green circles represent WiMax towers, and the red rectangles represent vehicles moving from left to right.
FIG. 6 is a flow chart of the MCAS algorithm
FIG. 7 shows the traffic distribution of users on different access networks under MCAS algorithm
FIG. 8 shows power consumption distribution of different access networks
Fig. 9 is the energy efficiency of the three algorithms.
Fig. 10 shows the energy consumption of the three algorithms.
Fig. 11 is the average satisfaction of the three algorithms.
Fig. 12 shows the number of network selection failures for the three algorithms.
Fig. 13 shows the average allocated bandwidth for the three algorithms.
Fig. 14 is the average handover delay for the three algorithms.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
a multi-utility criterion high-energy-efficiency network selection method for Internet of vehicles comprises the following specific steps:
and establishing an optimal model of network selection in the equation according to the utility functions of the five decision factors, wherein the optimal model is a multi-constraint optimal model. Solving the optimal model is very difficult. Thus, a heuristic Multi-Criteria Access Selection (MCAS) algorithm is proposed.
The MCAS algorithm flow chart is shown in figure 6.
Step one, collecting and providing all parameters required by the network selection algorithm, and selecting the network meeting the given constraint and parameter threshold, the specific process is as follows:
collecting network information; obtaining parameters for n access networksNumber list lnpP1, p2, pn, i.e. the available bandwidth bi of the access network i, the cost c of the access network iiDelay d of access network iiSignal strength s of access network iiWherein pi ═ bi,ci,di,si1,2, n; initializing the parameters α, β, r1And r2。
Collecting user information; obtaining a requirement list l of k usersd＝{q1,q2,...,qkI.e. wide demand b for the u-th useruCost requirement cuDelaying the demand duSignal strength requirement suWherein q isu＝{bu,cu,du,suAnd u ═ 1, 2. Let m equal to 1.
The network filtering module selects a network that satisfies given constraints and parameter thresholds. Through the network filtering process, a set of available networks is constructed. Meanwhile, the calculation complexity and the decision time can be effectively reduced.
According to the network parameter list lnpDemand List ldEstablishing an access network list l according to the parameters of n access networksan＝{l1,l2,...,lnWhen the value of i is 0-n, if bi≤buOr ci≤cuOr di≥duOr si≤suFrom the list lanDeleting access network i, and listing access network lanIs updated toan＝{l1,l2,.. }, order lan＝lanObtaining the available access network list l of user man。
Step two, calculating the energy efficiency of the available network, specifically comprising the following steps:
according to the available network list lanGet the list lanThe number of available networks in z ═ lanL, |; when i takes 0-z, the list l is calculatedanEnergy efficiency e of medium access network iiGet the list lanEnergy efficiency list e ═ e of all available networks in the network1,e2,...ez}。
Step three, calculating the selection weight of the available network, specifically comprising the following steps:
according to bandwidth demand buCost requirement cuDelaying the demand duSignal strength requirement suBuilding a decision hierarchy H ═ H1,h2,...,hn(ii) a Let j equal 1;
the first step is as follows: constructing a decision matrix R;
the second step is that: calculating the weight of the level j; an energy efficiency weight for level j is then obtainedCost weightingSignal strength weightingDelay weightBandwidth weighting
Determining whether the grades j are consistent; if not, returning to the first step; if j is less than n, returning to the second step;
calculating the total weight; then obtaining the energy efficiency of the access network iCost weightingSignal strength weightingDelay weightBandwidth weightingDetermining whether the levels of the whole hierarchical structure are consistent; if not, the first step is executed again;
designing a utility function to judge whether the utility function is optimal or not, wherein the specific process is as follows:
according to the utility theory, the utility function satisfies the characteristics of twice micromability, monotonicity and concavity and convexity. Only when the three attributes are satisfied can the optimum be found from the designed utility function.
A. Utility function of energy efficiency
Defining the energy efficiency of the network as
Where T denotes throughput (i.e. maximum mutual information delivered), E denotes energy consumption in joules; α is a parameter, where 0<α<1；rdRepresents the energy consumption to transmit K bytes; r istRepresents the power consumption of the circuit of the user terminal per second; b represents the average transmission rate of user traffic, unit Kbyte/s; (Σb) ≦ B, where B represents the network bandwidth. In equation (1), rtRepresenting the power consumption of the basic circuit, ensuring the normal operation of the terminal circuit when the terminal is idle, and rdRepresenting the energy consumption associated with the terminal load only when the terminal is transmitting network data.
The sigmoid function is a good threshold function, continuous, smooth, and strictly monotonic. Thus, a sigmoid function is used to characterize the utility metric of the network energy efficiency.
Sigmoidal utility function, i.e.
Is twice differentiable, monotonic and concave-convex. Therefore, the utility function of the network energy efficiency is defined as
Wherein e and eavgRepresents the energy efficiency and the average energy efficiency of the network, and gamma1(wherein γ is1≧ 2) represents the sensitivity of user traffic to energy efficiency, which determines the steepness of the curve of the utility function. Steep gamma1Resulting in higher sensitivity.
The utility function of energy efficiency is shown in fig. 3. It can be seen from fig. 3 that the utility function is monotonic and concave-convex. A higher network energy efficiency e results in a larger utility function u (e) and a more preferred network. This indicates that the defined utility function u (e) can be effectively used for decisions in network selection.
Defining a utility function of network energy efficiency as
Wherein e and eavgRepresents the energy efficiency and the average energy efficiency of the network, and gamma1(wherein γ is1≧ 2) represents the sensitivity of user traffic to energy efficiency, which determines the steepness of the curve of the utility function. Steep gamma1Resulting in higher sensitivity.
The utility function of energy efficiency is shown in fig. 2. It can be seen from fig. 2 that the utility function is monotonic and concave-convex. The physical meaning of equation (2) is that a higher network energy efficiency e results in a larger utility function u (e) and a more preferred network. This indicates that the defined utility function u (e) can be effectively used for decisions in network selection.
B. Utility function of signal strength
Generally, when the received signal is below a certain threshold, it can be considered that the network cannot guarantee normal operation. In this case, the utility value of the signal strength is 0.
The received signal determines the strength range. If x is assumedαIs a lower limit and xβFor the upper limit, the upper and lower limits of the utility function need to be considered. Due to the fact thatFor this, the following additional conditions are introduced for the utility function of the signal strength:
u(xmid)＝0.5,x＝xmid (5)
wherein
Utility functions for:
wherein gamma is2Not less than 2 and
is twice differentiable, monotonous and concave-convex. In addition, it satisfies the constraint in equations (3) - (5).
Here, the utility function of obtaining the signal strength is as follows:
wherein s isminAnd smaxRespectively representing the lower limit and the upper limit of the signal intensity; gamma ray2(wherein γ is2≧ 2) represents sensitivity and
the physical meaning of equation (8) is that a higher signal strength s results in a larger utility function u(s) and a more preferred network. This indicates that the defined utility function u(s) can be efficiently used for network selection. FIG. 3 shows the utility function of signal strength.
C. Utility function of cost
Network cost is a more intuitive indicator for users. The costs of different networks can be directly compared to each other. Herein, a utility function of cost is represented using a linear function. It can therefore be denoted as u (c) 1-u' (c) (9)
Wherein
c is the current cost of the network, cmaxIs the maximum cost acceptable to the user. The physical meaning of equation (9) is that a lower cost c results in a larger utility function u (c) and a more preferred network. This indicates that the defined utility function u (c) can be efficiently used for network selection. Figure 5 plots the utility function of cost.
D. Utility function of network delay
Generally, the network delay should have a maximum value. In designing the utility function, it is expected that a larger network delay will have a lower corresponding utility value. The delay criteria is a decreasing metric. Therefore, the utility function of the network delay is defined as u (d) 1-u' (d)
Wherein d ismaxDenotes the maximum delay, dmIs half of the maximum delay, gamma3(wherein γ is3≧ 2) represents sensitivity. Gamma ray3The larger the value, the higher the sensitivity.
The utility function of the network delay is similar to that of the signal strength, which is twice differentiable, monotonic and concave-convex. Due to space limitations, no detailed proof procedures are given. The physical meaning of equation (10) is that a higher cost d results in a larger utility function u (d) and a more preferred network. This indicates that the defined utility function u (d) can be efficiently used for network selection.
E. Utility function of network bandwidth
This will result in lost user requests when the network bandwidth is below the minimum requirements for user traffic. This is very difficult for the user. When the network bandwidth is higher than the maximum requirement of the user traffic, the satisfaction of the user is not further improved. Thus, the following utility function is defined for the network bandwidth:
wherein b isminAnd bmaxRepresenting the minimum and maximum network bandwidth, respectively, and b representing the bandwidth demanded by the user. The utility function of the network delay is similar to the utility function of the signal strength. Due to space limitations, no detailed proof procedures are given. The physical meaning of equation (11) is that a higher cost b results in a larger utility function u (b) and a more preferred network. This shows that the definition function u (b) can be efficiently used for network selection.
Thus, according to the above discussion and design, two differentiable, monotonic and concave-convex respective utility functions can be effectively designed for the metrics related to energy efficiency, signal strength, network cost, delay and bandwidth. Depending on the utility function of these designs, an appropriate access network may be selected.
Examples
A series of tests were conducted using the NS3 simulation platform to verify the MCAS algorithm for vehicle infrastructure network applications. The simulation scenario is that the user is in the coverage area of three types of radio access networks, namely 3G cellular network, WLAN network and WiMAX network.
The method comprises the following steps: parameters are collected. The basic parameters of the three networks are listed in table 1, where the symbols "si" and "bw" represent "signal strength" and "bandwidth". As shown in table 1:
TABLE 1 basic parameters of three networks
In the simulated scenario, four different types of applications were analyzed, including voice, video, web downloads, and online gaming. All required bandwidths and durations are fixed without loss of generality.
Detailed parameters for all applications are listed in table 2, where the symbols "du", "bwd" and "sod" denote "duration", "bandwidth requirement" and "sensitivity to delay", respectively. As listed in Table 2
TABLE 2 requirement parameters
Step two: energy efficiency of the available network is calculated. As shown in Table 1, for 3G, WiMAX and WLAN, their energy efficiency values (J/KB) are 0.01540,0.02000 and 0.00412, respectively.
And step three, calculating the selection weight of the available network by RAS, SAW and MCAS algorithms respectively.
The MCAS algorithm calculation results are shown in fig. 7, and when the traffic request is low, the WLAN assumes most of the traffic request. As the number of traffic requests increases, the percentage of traffic allocated to the WLAN gradually decreases, while the percentage of traffic for the WiMAX access network gradually increases. In this case, the percentage of traffic allocated to the 3G access network will initially increase but will subsequently decrease. The WLAN is always responsible for providing more traffic requests to 3G and WiMAX access networks. Compared to WLAN and 3G access networks, WiMAX forwards minimal traffic requests. When the number of traffic requests increases, the WLAN does not have sufficient network resources to provide to the user terminal. In this case, some traffic requests are allocated to 3G and WiMAX networks.
Fig. 8 depicts the energy consumption profile as the number of traffic requests increases gradually. As traffic requests increase, the power consumption of the WLAN may slowly decrease. However, in this case, the energy consumption of WiMAX gradually increases, and the energy consumption of the 3G access network first increases and then slowly decreases. When the number of traffic requests is greater than 18, the energy consumption of the WLAN is lower than the energy consumption of the other two access networks. When there are 16 to 22 traffic requests, the energy consumption of the 3G access network is greater than that of WLAN and WiMAX. When there are less than 16 traffic requests, the WLAN displays the highest energy consumption. From table 1, it can be seen that when the number of traffic requests is small, the MCAS first selects the WLAN as the best available access network. Since the WLAN is burdened with most traffic requests in this case, it consumes the most energy. Furthermore, MCAS uses a number of metrics as the best utility value for selecting the best access network. Thus, when faced with many traffic requests, some will be allocated to the other two access networks according to a multi-criteria selection policy. The energy consumption percentage of the WLAN will slowly decrease compared to the other two access networks.
Step four: the network selection performance of the three algorithms is discussed in terms of energy consumption, average user satisfaction and failure times, as shown in fig. 10-12. The network handover performance of the three algorithms is discussed in terms of average allocated bandwidth and average handover latency, as shown in fig. 13 and 14, respectively. Thus, the MCAS algorithm has better network selection performance.

Claims (4)

1. A multi-utility criterion high-energy-efficiency network selection method for Internet of vehicles comprises the following steps:
step 1: collecting and providing all parameters required by a network selection algorithm, and selecting a network meeting given constraints and parameter thresholds;
step 2: calculating the energy efficiency of the available network according to the network parameters and the traffic type;
and step 3: calculating energy efficiency weight, cost weight, signal strength weight, delay weight and bandwidth weight of the available network by using a hierarchical analysis method;
and 4, step 4: establishing an efficiency utility function, a cost utility function, a signal strength utility function, a delay utility function and a bandwidth utility function;
and 5: and (4) calculating the utility value of each network according to the utility function in the step (4), and calculating the optimal network according to the weight corresponding to the step (3).
All parameters required by the network selection algorithm are collected and provided in the step 1, and a network meeting given constraints and parameter thresholds is selected, and the specific process is as follows:
collecting network information: obtaining a list of parameters l for n access networksnp＝{p1,p2,...,pnI.e. the available bandwidth b of the access network iiCost c of access network iiDelay d of access network iiSignal strength s of access network iiWherein p isi＝{bi,ci,di,si1,2, ·, n; initializing the parameters α, β, r1And r2Wherein alpha represents a proportional system in the network energy efficiency utility function and 0 < alpha < 1, beta represents a proportional system in the sigmoid utility function and 0 < beta, r1Representing a parameter in a sigmoid utility function and 2 < r1、r2Representing a proportional system in a piecewise utility function and 2 < r2；
Collecting user information: obtaining a requirement list l of k usersd＝{q1,q2,...,qkI.e. the bandwidth requirement b of the u-th useruCost requirement cuDelaying the demand duSignal strength requirement suWherein q isu＝{bu,cu,du,suAnd u is 1,2, k, let m be 1;
the network filtering module selects a network which meets given constraints and parameter thresholds; constructing an available network set through a network filtering process;
according to the network parameter list lnpDemand List ld；
Establishing an access network list l according to the parameters of n access networksan＝{l1,l2,...,lnWhen the value of i is 0-n, if bi≤buOr ci≤cuOr di≥duOr si≤suFrom the list lanDeleting access network i, and listing access network lanIs updated toan＝{l1,l2,.. }, order lan＝lanObtaining the available access network list l of user man。
2. The internet-of-vehicles-oriented multi-utility criterion energy-efficient network selection method according to claim 1, wherein the energy efficiency of the available network is calculated in the step 2 by the following specific process:
according to the available network list lanGet the list lanThe number of available networks in z ═ lanL, |; when i takes 0-z, the list l is calculatedanEnergy efficiency e of medium access network iiGet the list lanEnergy efficiency list e ═ e of all available networks in the network1,e2,...ez}。
3. The internet-of-vehicles-oriented multi-utility criterion energy-efficient network selection method according to claim 1, characterized in that the selection weight of the available network is calculated in step 3 by the following specific process:
according to bandwidth demand buCost requirement cuDelaying the demand duSignal strength requirement suBuilding a hierarchical structure H ═ H1,h2,...,hn(ii) a Let j equal 1;
step 3.1: constructing a decision matrix R;
step 3.2: calculating the weight of the j layer; then obtaining an energy efficiency weight of the j-th layerCost weightingSignal strength weightingDelay weightBandwidth weighting
Judging whether the jth layer is consistent; if not, returning to execute the step 3.1; otherwise, if j is less than n, returning to execute the step 3.2, and if j is equal to n, executing the step 3.3;
step 3.3: calculating the total weight; then obtaining the energy efficiency of the access network iCost weightingSignal strength weightingDelay weightBandwidth weightingJudging whether the whole layering is consistent; if not, return to execute step 3.1.
4. The internet-of-vehicles-oriented multi-utility criterion energy-efficient network selection method according to claim 1, wherein the utility function is established in the step 4, and whether the optimal utility function is achieved is judged, and the specific process is as follows:
according to the utility theory, the utility function satisfies the characteristics of twice micromability, monotonicity and concavity and convexity. Only when the three attributes are met, the optimal point can be found according to the designed utility function;
step 4.1 utility function of network energy efficiency
The network energy efficiency EE is:
where T represents throughput, i.e., maximum mutual information of deliveryAmount, E represents energy consumption in joules; α is a parameter, where 0<α<1; b represents the average transmission rate of user traffic, unit Kbyte/s; (Σ B) is less than or equal to B, wherein B represents the network bandwidth; r istRepresenting the power consumption of the basic circuit, ensuring the normal operation of the terminal circuit when the terminal is idle, and rdRepresents the energy consumption related to the terminal load only when the terminal transmits the network data;
and characterizing utility metrics of the network energy efficiency by adopting a sigmoid function, wherein the sigmoid utility function is as follows:
the utility function for obtaining the network energy efficiency is:
wherein e and eavgRepresents the energy efficiency and the average energy efficiency of the network, and gamma1≧ 2 denotes the sensitivity of user traffic to energy efficiency, which determines the steepness of the curve of the utility function, steep γ1Resulting in higher sensitivity;
step 4.2: the utility function of signal strength is as follows:
wherein s isminAnd smaxRespectively representing the lower and upper limits of the signal strength, gamma2Sensitivity is expressed by more than or equal to 2;
step 4.3: utility function of cost:
u(c)＝1-u′(c) (9)
wherein
c is the current of the networkCost, cmaxIs the maximum cost acceptable to the user;
step 4.4: utility function of network delay:
u(d)＝1-u′(d)
wherein d ismaxDenotes the maximum delay, dmIs half of the maximum delay, gamma3Sensitivity,. gamma.,. gtoreq.23The larger the value, the higher the sensitivity;
step 4.5: utility function of network bandwidth:
wherein b isminAnd bmaxRepresenting the minimum and maximum network bandwidth, respectively, b representing the bandwidth demanded by the user, the utility function of the network delay is similar to that of the signal strength.
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