CN108810856B - A kind of M2M terminal service optimization control method - Google Patents

Abstract

The invention discloses an M2M terminal service optimization control method, which comprises the steps of firstly determining the service type according to the characteristics of the service type and determining the weight factor eta of the service type₁And η₂(ii) a Setting a service traffic ratio threshold of each type of service according to an application scene of M2M, and determining whether the service characteristics of the service type are real-time delay sensitive service or non-real-time cost sensitive service according to the service traffic ratio threshold; and (5) introducing a self-adaptive shunting strategy to solve the optimal distribution ratio. The invention realizes the optimization of the combined target by converting the shunting problem into the single target problem of minimizing time delay and cost, thereby improving the performance of the system, reducing the transmission time delay and reducing the cost consumption of a user for using the network.

Description

A kind of M2M terminal service optimization control method

technical field

The invention relates to a multi-service-oriented distributed offloading strategy based on the Internet of Vehicles, in particular to an M2M terminal service optimization control method, which belongs to the technical field of communications.

Background technique

The Internet of Vehicles refers to the establishment of connections between vehicles and people, vehicles and vehicles, and vehicles and the external environment through the intra-vehicle network, the inter-vehicle network and the vehicle-mounted mobile Internet. , road conditions, pedestrians, surrounding environment and other real-time information transmission and interaction, and then realize intelligent transportation, intelligent supervision, intelligent services and other functions. In short, the Internet of Vehicles uses moving vehicles as information nodes, and uses technical means such as network interconnection, global positioning, and wireless communication to achieve the harmonious unity of "people-vehicles-road". Under the framework of IOV (Internet of Vehicle), the vehicle gradually develops into an open and intelligent vehicle terminal system platform that integrates sensor technology, radio frequency identification technology, server computing, cloud computing and other technologies, and realizes the integration with the urban traffic information network. connections, with powerful computing and communication capabilities.

Driven by technological innovation and service demands, the integration and complementation of networks has become a development trend in the field of wireless communications. The Internet of Vehicles is no longer a vehicle-mounted ad hoc network based solely on DSRC communication, but has gradually evolved into a vehicle-mounted heterogeneous wireless network. At the same time, the comprehensive development of the information age has led to a rapid increase in wireless data traffic, and the explosive traffic growth has overwhelmed the Internet of Vehicles based on IEEE 802.11p standard communication. In the future, 5G Internet of Vehicles terminals will carry networking services, communication services and value-added services. Its complex service types and ultra-high traffic requirements have brought huge challenges to traditional wireless resource management.

However, the traditional wireless resource management only provides users with "best effort delivery" services, and its "divide and conquer" management mode and the operation idea of maximizing the utilization of system resources can no longer adapt to the development of the network. The business offloading mechanism starts from the overall situation, through a series of control mechanisms, including: load balancing, access control, etc., to reasonably allocate and effectively integrate various resources, so as to provide users with high-quality services. The core idea of the service offloading mechanism is to divide the service data streams and optimize them one by one through the offloading technology in the parallel transmission of heterogeneous wireless networks according to the different service requirements required by users, the service load and transmission capacity of heterogeneous networks and other information. Data traffic in each wireless network, so as to achieve efficient and reliable transmission of business flow.

However, the existing business offloading strategy only considers a single type of business and the optimization goal is relatively single, and does not consider multi-type business for 5G Internet of Vehicles terminals.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, and to provide a multi-service-oriented optimal offloading strategy and take the delay and cost as joint optimization goals.

In order to solve the above-mentioned technical problems, the present invention provides an M2M terminal service optimization control method, which is characterized by comprising the following steps:

S1: Determine the service type according to the characteristics of the service type and determine the weighting factors η ₁ and η ₂ of the service type according to the service type; preferably, if the terminal service has higher latency requirements, that is, a latency-sensitive service, then η ₁ can be set >η ₂ ; if the terminal service can tolerate a higher time delay and has higher cost requirements, that is, a cost-based service, then η ₂ >η ₁ .

S2: Set the service traffic proportion thresholds of various types of services according to the M2M application scenario, and determine whether the service characteristics of the service types are real-time delay-sensitive services or non-real-time cost-sensitive services according to the service traffic proportion thresholds.

S3: Introduce an adaptive shunting strategy to solve the optimal distribution ratio.

Further, there are three types of services: networking communication, communication between terminals and base stations, and value-added services.

Further, after setting the business flow ratio threshold of each type of business in S2, the business flow of the business type is greater than the threshold, and then the business characteristics of the business type are determined as delay-sensitive services; the business flow is less than the threshold. Characterized as a cost-sensitive business.

Still further, preferably, the threshold for the proportion of business traffic is set to 80%, that is, if the business traffic is greater than the 80%, the business characteristics of the business type are determined as delay-sensitive services; if the business traffic is less than 80%, the business type is determined. The characteristics of the business are determined to be cost-sensitive.

Further, step S3 includes the following steps:

S31: Establish M/M/1 queuing theory model;

S32: Determine the optimization objective and establish a utility function;

S33: Establish an optimization model according to the weight factor of the service type and obtain a globally optimal terminal service distribution ratio.

Still further, step S31: establishing the M/M/1 queuing theory model specifically includes the following steps:

S301: Assuming that the arriving data packets obey the Poisson distribution with parameter λ, then λ _i is the data packet arrival rate of the ith wireless link; μ _i is the service rate of the ith wireless link;

其中，λ为终端总的数据包到达率，μ为系统总的服务速率；根据排队论方法，在t时刻系统中存在n个数据包的概率满足平衡方程：If there are a total of m heterogeneous sub-networks in the system, that is, λ is divided into m sub-streams, the following equation holds:

Among them, λ is the total data packet arrival rate of the terminal, μ is the total service rate of the system; according to the method of queuing theory, the probability of the existence of n data packets in the system at time t satisfies the equilibrium equation:

where P _i (0≤i≤n) is the probability that there are i data packets in the system at steady state;

S302: The sum of the system state probabilities is 1, and the expression is as follows:

S303: Calculate the state probability P _n that there are n data packets in the system at steady state, and the expression is as follows:

S304: Assuming that L _si is the average number of data packets in the ith network, according to the method of calculating mathematical expectation, it can be obtained:

S305: Determine the optimization target—the expression of the delay: According to the Little formula, the time that the data packet stays when it is transmitted on the i-th network, that is, the average transmission delay D _i is expressed as:

S306: Determine another single optimization objective—the network cost, that is, the energy consumption C _i of the ith subnet, and the expression is as follows:

where _θi represents the transmission cost required for each packet to be transmitted on the network.

_θi is known when the data size of each packet and the cost per bit of data transmission are known. In actual network transmission, these two are known, so the value of θ _i can be calculated by their simple multiplication.

Still further, step S32 determines the optimization target to establish the utility function with delay and cost as the joint optimization target, and the expression is as follows:

where D(λ) is the total system delay, which is the accumulation of the transmission delay D _i (1≤i≤n) generated by each heterogeneous sub-network; C(λ) is the total network cost consumption, which is the Accumulation of network costs C _i (1≤i≤n); η ₁ and η ₂ are weighting factors, and η ₁ +η ₂ =1.

If the terminal service has high requirements on delay, i.e. delay-sensitive service, then η ₁ >η ₂ can be set, for example: road safety monitoring, if the service generated by this type of application is delayed too long, the consequences will be unimaginable; if the terminal service can tolerate The higher the delay and the higher the cost requirements, that is, the cost-based service, then η ₂ >η ₁ . When η ₁ =1, η ₂ =0, only the system delay is considered; on the contrary, when η ₁ =0, η ₂ =1, only cost consumption is considered. Therefore, the specific values of η ₁ and η ₂ depend on the characteristics of the terminal service.

Still further, S33 includes:

The expression of the optimization model is as follows:

where F(λ) is the utility function, which takes the delay and cost as the final expression of the joint optimization objective; D ₀ is the maximum transmission delay that the system can tolerate. The less time delay and cost consumption, the higher the gain, that is, the optimization model solves the problem of the minimum value of F(λ).

Further, solving the globally optimal terminal service allocation ratio specifically includes:

(1) Introduce the Lagrangian multipliers ω ₀ and ω _i to construct the Lagrangian function, the expression is as follows:

where ω ₀ and ω _i satisfy:

(2) Obtain the globally optimal terminal service distribution ratio:

The beneficial effects achieved by the present invention: the present invention determines the service type according to the characteristics of the service type and sets the weight factor of the service type; the service characteristics of the service type are divided into real-time delay-sensitive services or non-real-time cost-sensitive services business; by transforming the offloading problem into a single objective problem of minimizing delay and cost, joint objective optimization is achieved, thereby improving the performance of the system, reducing the transmission delay and reducing the cost consumption of the network for users.

Description of drawings

Fig. 1 is the schematic diagram of the service optimization control method of the present invention;

Fig. 2 is the schematic flow chart of the method of the present invention;

FIG. 3 is a comparison diagram of a delay/cost-sensitive service utility function value according to a specific embodiment of the method of the present invention;

4 is a comparison diagram of the utility function value of a delay-sensitive service according to a specific embodiment of the method of the present invention;

FIG. 5 is a comparison diagram of the utility function value of a cost-sensitive business according to a specific embodiment of the method of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.

The following is a specific embodiment of the method of the present invention, as shown in FIG. 2 .

The first step: analyze the characteristics of the 5G Internet of Vehicles terminal service, determine the service type according to the characteristics of the service type, and determine the weight factors η ₁ and η ₂ of the service type with reference to the utility function expression (Equation 7) combined with the service type. Preferably, determining the service type and determining the weight factor of the service type according to the service type specifically include:

It is determined that the service type of the networking communication is composed of session services, and the weighting factors η ₁ =0.9 and η ₂ =0.1 are set;

It is determined that the service type of the communication between the terminal and the base station is composed of the interaction type and the background type service, and the weighting factors η ₁ =0.2, η ₂ =0.8 are set;

It is determined that the service type of the value-added service is a streaming media type service, and the weighting factors η ₁ =0.8 and η ₂ =0.2 are set.

Specifically, the idea of setting the weight factor in this embodiment is as follows: Because the networking communication is mainly responsible for the information exchange between the vehicle-mounted units based on the short-range communication technology, including: voice communication, location sharing, etc., therefore, it belongs to two-way communication, which has a relatively high latency. Therefore, it is assumed that the business service type of networking communication is mainly composed of session services, namely: η ₁ =0.9, η ₂ =0.1; the vehicle terminal obtains information such as geographic location and traffic conditions through the 5G base station set by the operator, so , it is assumed that the business service types of information sharing between the terminal and the operator's base station are mainly composed of interactive and background services, namely: η ₁ =0.2, η ₂ =0.8; value-added services mainly include real-time services such as music and games, which are typical , therefore, its η ₁ =0.8 and η ₂ =0.2.

Step 2: Formulate the 5G Internet of Vehicles application scenario, analyze the main service types of the terminal, and determine the proportion of business flow; it is assumed that the vehicle terminal is driving on a sparsely populated urban road. From the scenario analysis, it can be seen that the main service types of the vehicle terminal are real-time online value-added services based on music, video, etc., and a small amount of non-real-time services such as communicating with the operator's base station to obtain road conditions. Therefore, the simulation parameters are set as shown in Table 1 below. Indicates that the network communication/value-added service and the service flow ratio of the communication between the terminal and the base station described in the table can be customized.

Table 1 shows the parameter settings of the service when the value-added service is prioritized

business type business characteristics Business flow ratio Network communication/value-added services Delay-sensitive services (real-time) 80% Communication between terminal and base station Cost-sensitive business (non-real-time) 20%

It is assumed that the vehicle terminal is driving on roads with complex road conditions such as tunnels and intersections. From the scenario analysis, it can be seen that the in-vehicle terminal needs to communicate with the 5G operator base station from time to time to obtain information such as geographic location, traffic conditions and other information released by the in-vehicle terminal. Therefore, the simulation parameters are set as shown in Table 2 below. The network communication/value-added services described in the table and the service flow ratio of the communication between the terminal and the base station can be customized.

Table 2 is the parameter setting of the service when the communication service between the terminal and the base station is prioritized

business type business characteristics Business flow ratio Network communication/value-added services Delay-sensitive services (real-time) 20% Communication between terminal and base station Cost-sensitive business (non-real-time) 80%

Step 3: Introduce an adaptive shunt strategy to solve the optimal allocation ratio, and obtain the performance comparison charts shown in Figure 3, Figure 4, and Figure 5.

The present invention combines the characteristics of multi-type services of 5G Internet of Vehicles terminals. In the embodiment, the data traffic proportion of various services is determined by formulating application scenarios, and the performance of load balancing, single service offloading scheme and multi-type mixed service offloading scheme is compared and analyzed. That is, the utility function value, which verifies the effectiveness of the proposed scheme. At the same time, it also indirectly proves that the adaptive offloading strategy for a single type of service is equally suitable for the offloading transmission of multiple types of services, and the effect is more significant.

The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the technical principles of the present invention, several improvements and modifications can be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (8)

1. an M2M terminal service optimization control method, is characterized in that, comprises the following steps: S1: determine the business type according to the business characteristics of the service type and determine the weighting factors n ₁ and n ₂ of the business type according to the business type; S2: Set the business traffic proportion threshold of each type of business according to the M2M application scenario, and determine whether the business characteristics of the business type are real-time delay-sensitive business or non-real-time cost-sensitive business according to the business traffic proportion threshold. The service characteristic of the service type is determined to be a time-delay-sensitive service if it is greater than the threshold value; the service characteristic of the service type is determined as a cost-sensitive service when the traffic flow is less than the threshold value; the sum of n ₁ and n ₂ is 1, and when For extension-sensitive services, set η ₁ >η ₂ ; for cost-sensitive services, then η ₂ >η ₁ ; S3: Introduce an adaptive shunt strategy to solve the optimal allocation ratio, including: S31: Establish M/M/1 queuing theory model; S32: Determine the optimization objective and establish a utility function; S33: Establish an optimization model according to the weight factor of the business type and solve the globally optimal terminal business allocation ratio, where the utility function is expressed as follows:

where D(λ) is the total system delay, which is the accumulation of the transmission delay D _i (1≤i≤n) generated by each heterogeneous sub-network; C(λ) is the total network cost consumption, which is the Accumulation of network costs C _i (1≤i≤n); η ₁ and η ₂ are weighting factors, and η ₁ +η ₂ =1. 2 . The optimization control method according to claim 1 , wherein there are three types of services: networking communication, communication between terminals and base stations, and value-added services. 3 . 3. optimization control method according to claim 2, is characterized in that, after setting the business flow ratio threshold value of each type of business in S2, the business flow rate is greater than described threshold value then the business characteristic of business type is determined as delay-sensitive type. service; if the service flow is less than the threshold, the service characteristic of the service type is determined as a cost-sensitive service. 4. The optimization control method according to claim 3, characterized in that, the threshold for the proportion of business traffic is set to 80%, that is, if the business traffic is greater than the 80%, the business characteristics of the business type are determined as delay-sensitive services ; If the business flow is less than 80%, the business characteristics of the business type are determined as cost-sensitive business. 5. optimization control method according to claim 1, is characterized in that, The weight factors for determining the business type and determining the business type according to the business type include: It is determined that the service type of the networking communication is composed of session services, and the weighting factors η ₁ =0.9 and η ₂ =0.1 are set; It is determined that the service type of the communication between the terminal and the base station is composed of the interaction type and the background type service, and the weighting factors η ₁ =0.2 and η ₂ =0.8 are set; It is determined that the service type of the value-added service is a streaming media type service, and the weighting factors η ₁ =0.8 and η ₂ =0.2 are set. 6. optimization control method according to claim 1 is characterized in that, step S31: establishing M/M/1 queuing theory model specifically comprises the following steps: S301: Assuming that the arriving data packets obey the Poisson distribution with parameter λ, then λ _i is the data packet arrival rate of the ith wireless link; μ _i is the service rate of the ith wireless link; If there are m heterogeneous sub-networks in total in the system, that is, λ is divided into m sub-streams, the following equation holds:

Among them, λ is the total data packet arrival rate of the terminal, and μ is the total service rate of the system; According to the method of queuing theory, the probability that there are n data packets in the system at time t satisfies the equilibrium equation:

where P _i (0≤i≤n) is the probability that there are i data packets in the system at steady state; S302: The sum of the system state probabilities is 1, and the expression is as follows:

S303: Calculate the state probability P _n that there are n data packets in the system at steady state, and the expression is as follows:

S304: Assumption

is the average number of data packets in the i-th network. According to the method of calculating the mathematical expectation, we can get:

S305: Determine the optimization target—the expression of delay: the time the data packet stays in the i-th network when it is transmitted, that is, the average transmission delay D _i is expressed as:

S306: Determine that another single optimization objective is the network cost, and the network cost is the energy consumption C _i of the ith subnet, and the expression is as follows:

where _θi represents the transmission cost required for each packet to be transmitted on the network. 7. optimization control method according to claim 1, is characterized in that, S33 comprises: The expression of the optimization model is as follows:

where F(λ) is the utility function, which takes the delay and cost as the final expression of the joint optimization objective; D ₀ is the maximum transmission delay that the system can tolerate. 8. optimization control method according to claim 1 is characterized in that, solving the terminal service distribution ratio of global optimum specifically comprises: (1) Introduce the Lagrangian multipliers ω ₀ and ω _i to construct the Lagrangian function, the expression is as follows:

where ω ₀ and ω _i satisfy:

(2) Obtain the globally optimal terminal service distribution ratio:

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