CN111885532A - Heterogeneous network energy efficiency optimization method based on Internet of things - Google Patents

Abstract

The present application relates to the technical field of the Internet of Things in electric power, and provides an energy efficiency optimization method for heterogeneous networks based on the Internet of Things. By periodically collecting the output signals of each node sensor, the maximum constellation value of the output signal of each node sensor is determined.

and constellation minimum

And use the optimal fast enumeration method to determine the node information of each sensor and the value of the constellation size _bi , so as to obtain the optimal variables of the sensor under the condition of the minimum transmission power of the sensor

And further determine the optimal value of the sampling period _hi

and the optimal value of the packet error probability p _i

Therefore, the energy consumption of the sensor signal transmission and the performance of the monitoring system are optimized through the optimization method proposed in this application.

Description

An energy efficiency optimization method for heterogeneous networks based on the Internet of Things

technical field

The present application relates to the technical field of the Internet of Things in electric power, and in particular, to a method for optimizing the energy efficiency of heterogeneous networks based on the Internet of Things.

Background technique

The power Internet of Things is the application of the Internet of Things in the smart grid, which can effectively integrate the communication infrastructure resources and the power system infrastructure resources to improve the informatization level of the power system, improve the utilization efficiency of the existing infrastructure of the power system, and generate electricity for the power grid. , power transmission, substation, distribution and power consumption links provide important technical support.

The application scenarios of the Internet of Things are very rich, for example, narrowband applications (wireless meter reading, etc.) characterized by low speed and massive devices, and broadband applications (video, etc.) characterized by high speed. In the construction process of the communication network, it is necessary to adopt appropriate communication technology to support both narrowband and broadband applications, as well as the intelligent integration of various devices and terminals, and to have a very high security system to meet the needs of the Internet of Things laboratory. The unique communication needs of information exchange.

Referring to Figure 1, it is the architecture of the Internet of Things and the wide-narrow heterogeneous wireless network. In the power Internet of Things, the wide-narrow heterogeneous wireless network monitoring system is often used. While adapting to the wide range of equipment points and different environments, it also needs to meet the requirements of reliability, real-time security, economy, and bandwidth requirements.

Heterogeneous wireless network monitoring system is a spatially distributed monitoring system in which sensors, actuators and monitors communicate through wireless networks. The communication system design of the wireless network monitoring system requires to ensure the performance and stability of the monitoring system when the battery resources of the sensor nodes are limited. The key parameter that needs to be considered in both the monitoring system and the communication system is the packet error of the sensor nodes in the network. Probability, delay requirement and sampling period, reducing the values of packet error probability, delay requirement and sampling period can improve the performance of the monitoring system; however, when the values of packet error probability, delay requirement and sampling period are reduced, , which will cause the energy consumption of sensor nodes in wireless transmission to increase. That is, there is a trade-off between the energy consumption of the sensor signal delivery and the performance of the monitoring system.

And for the energy consumption of the sensor signal transmission and the performance of the monitoring system. There are two optimization methods currently used. One optimization method is to maximize the performance of the monitoring system according to the packet loss probability of the wireless link and/or the energy constraints of the sensor nodes. However, most of this method assumes that the probability of packet loss on the wireless link is constant, and the energy consumption of each packet transmission is fixed. Therefore, there is a big gap between the theoretical optimization effect and the actual effect. Another optimization method is: M-ary (multi-state) quadrature amplitude modulation as the modulation scheme, and the earliest deadline as the scheduling algorithm, but The optimization framework and its solutions are limited to the goal of minimizing the total power consumption of the communication system, and cannot be widely used in heterogeneous wireless networks of the Internet of Things.

SUMMARY OF THE INVENTION

The present application provides a method for optimizing the energy efficiency of heterogeneous networks based on the Internet of Things, so as to optimize the energy consumption of sensor signal transmission and the performance of the monitoring system.

A method for optimizing energy efficiency of heterogeneous networks based on the Internet of Things provided by this application includes:

和星座最小值

Periodically obtain the sensor output signals of each node of the power grid, and determine the constellation maximum value of the sensor output signals of each node

and constellation minimum

Using the optimal fast enumeration method, determine the node information i and constellation size b _i value of each sensor;

According to the constellation size _bi value of each sensor, get the optimization variable of each sensor

得到采样周期h_i和包错误概率p_i的最优值。According to optimization variables

The optimal values of sampling period _hi and packet error probability _pi are obtained.

和星座最小值

Optionally, according to the transmit power of the sensor not exceeding the maximum allowable power level W ^t,max of the sensor, determine the maximum value of the constellation output signal of the sensor

and constellation minimum

满足下式：in:

Satisfy the following formula:

d _i ≤Δ;

为给定调制的传输功率；

为发射机有源模式下的电路功耗。Among them: where b _i is the number of bits or constellation used for each symbol; for a predetermined modulation scheme, d _i is the transmission delay of the ith node sensor, which can be expressed as a function of b _i ; Δ is the maximum allowable limit of the stable control system Delay;

is the transmission power for a given modulation;

is the circuit power consumption in transmitter active mode.

和星座最小值

还包括：在传感器节点功耗最小化的情况下，确定星座最大值

和星座最小值

Optionally, in order to determine the constellation maximum of the sensor output signal

and constellation minimum

Also includes: Determining the constellation maximum with minimal sensor node power consumption

and constellation minimum

是k_i满足式

的最优解，可以用星座大小b_i的函数

表示；S^feasible为传感器可调度性约束。Among them, Ω is the random maximum allowable transmission interval of the sensor;

is k _i satisfying

The optimal solution of , can use the function of constellation size b _i

represents; S ^feasible is the schedulability constraint of the sensor.

Optionally, the sensor schedulability constraint is the set of transmission delay and sampling period of each node sensor, which can be expressed as:

{(d ₁ (b ₁ )h ₁ ), (d ₂ (b ₂ )h ₃ ),...,...,(d _N (b _N )h _N )}S ^feasible , i∈[1, N].

Optionally, in the step of determining the node information i of each sensor and the value of the constellation size b _i by using the optimal fast enumeration method, the method further includes:

Increase the power consumption of each node sensor and rearrange the constellation size set of each node sensor output signal;

Evaluate the schedulability and objective function of the constellation size vector with the minimum power consumption of each node sensor;

Prune schedulable constellation size vectors and vectors with poor objective function values;

Associate the constellation size vector with the number of vectors it branched into, and regenerate the constellation size vector for evaluation without repeatedly covering all constellation size vectors.

的步骤中，由以下不等式获得传感器的优化变量

Optionally, according to the constellation size _bi value of each sensor, the optimization variable of each sensor is obtained

In the steps of , the optimization variables of the sensor are obtained by the following inequalities

得到采样周期h_i和包错误概率p_i的最优值的步骤中,由以下等式确定采样周期h_i的最优值

和包错误概率p_i的最优值

Optionally, according to the optimization variable

In the step of obtaining the optimal values of sampling period _hi and packet error probability _pi , the optimal value of sampling period _hi is determined by the following equation:

and the optimal value of the packet error probability p _i

Ω is the random maximum allowable transmission interval of the sensor, and δ is the minimum probability to realize the requirement of the random maximum allowable transmission interval of the sensor.

和星座最小值

利用最优快速枚举法，确定每个传感器的节点信息i以及星座大小b_i值；根据每个传感器的星座大小b_i值，得到每个传感器的优化变量

根据优化变量

得到采样周期h_i和包错误概率p_i的最优值。As can be seen from the above technical solutions, the present application provides an energy efficiency optimization method for heterogeneous networks based on the Internet of Things, including: periodically acquiring the output signals of sensors of each node of the power grid, and determining the maximum constellation value of the output signals of the sensors of each node.

and constellation minimum

Using the optimal fast enumeration method, determine the node information _i and constellation size bi value of each sensor; according to the constellation size _bi value of each sensor, get the optimized variable of each sensor

According to optimization variables

The optimal values of sampling period _hi and packet error probability _pi are obtained.

和星座最小值

并利用最优快速枚举法，确定每个传感器的节点信息以及星座大小b_i值，从而在传感器传输功率最小的情况下，获得传感器的优化变量

并进一步确定采样周期h_i的最优值

和包错误概率p_i的最优值

从而通过本申请提出的优化方法，优化传感器信号传递的能量消耗与监控系统的性能By collecting the output signal of each node sensor, the constellation maximum value of the output signal of each node sensor is determined

and constellation minimum

And use the optimal fast enumeration method to determine the node information of each sensor and the value of the constellation size _bi , so as to obtain the optimal variables of the sensor under the condition of the minimum transmission power of the sensor

And further determine the optimal value of the sampling period _hi

and the optimal value of the packet error probability p _i

Therefore, through the optimization method proposed in this application, the energy consumption of the sensor signal transmission and the performance of the monitoring system are optimized.

Description of drawings

In order to illustrate the technical solutions of the present application more clearly, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative work, the Additional drawings can be obtained from these drawings.

Figure 1 shows the IoT architecture and wide-narrow heterogeneous wireless network;

Figure 2 is a structural diagram of a wireless network monitoring system;

Fig. 3 is the sequence diagram of the wireless communication of sensor and monitor;

4 is a flowchart of a method for optimizing energy efficiency of a heterogeneous network based on the Internet of Things provided by an embodiment of the present application;

FIG. 5 is a flowchart of determining node information and constellation size of a sensor by using an optimal fast enumeration method according to an embodiment of the present application.

Detailed ways

Embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following examples are not intended to represent all implementations consistent with this application. are merely exemplary of systems and methods consistent with some aspects of the present application as recited in the claims.

Referring to FIG. 2, it is a structural diagram of a wireless network monitoring system.

Referring to FIG. 3, it is a timing diagram of the wireless communication of the sensor and the monitor.

In order to better illustrate the method for optimizing the energy efficiency of heterogeneous networks based on the Internet of Things provided by the embodiments of the present application, the hardware structure and functions on which the technical solutions of the present application are based are now described. Each power plant equipment is controlled through a wireless communication network. The power plant equipment is a physical part of the wireless network monitoring system, connected to the sensor nodes on the power plant equipment, and periodically samples the sensor output, and then transmits the data through the wireless channel to the monitoring system. Monitors for power plant equipment. A wireless network monitoring system consists of multiple monitors, each of which monitors a certain physical domain of the system. One of the monitors is assigned as the coordinator. The coordinating monitor is responsible for time synchronization in the network, resource allocation of network elements; that is, running resource allocation algorithms and informing nodes about decisions in a centralized framework, and monitoring network topology and channel conditions.

Referring to FIG. 4 , a flowchart of a method for optimizing energy efficiency of a heterogeneous network based on the Internet of Things provided by the embodiment of the present application.

An IoT-based heterogeneous network energy efficiency optimization method provided by an embodiment of the present application includes:

和星座最小值

S101 periodically obtains the sensor output signals of each node of the power grid, and determines the constellation maximum value of the sensor output signals of each node

and constellation minimum

The periodic information transfer between sensors attached to a certain power plant equipment and the monitors that control the plant is shown in Figure 3. The sensor sampling period, transmission delay and packet error probability of node i are denoted by h _i , d _i and p _i , respectively. Among them, if d _i ≧ h _i , it means that the data packet is outdated and replaced by new sampled data. Outdated or lost packets are retransmitted due to larger transmission delays and packet errors, respectively, so the sampling period is set to a value greater than the transmission delay.

和星座最小值

Obtain the constellation maximum value of the sensor output signal of each node through the periodically collected sensor output signal

and constellation minimum

Due to the limited weight and size of sensor nodes, the sensor transmit power cannot exceed the maximum allowable power level W ^t,max , and the maximum transmit power constraint is:

满足下式：in:

Satisfy the following formula:

d _i ≤Δ.

为给定调制的传输功率；

为发射机有源模式下的电路功耗。Among them: where b _i is the number of bits or constellation used for each symbol; for a predetermined modulation scheme, d _i is the transmission delay of the ith node sensor, which can be expressed as a function of b _i ; Δ is the maximum allowable limit of the stable control system Delay;

is the transmission power for a given modulation;

is the circuit power consumption in transmitter active mode.

The Time Division Multiple Access (TDMA) of the monitoring system is a medium access control (MAC, Media Access Control Address) protocol. The explicit scheduling of node transmission in TDMA can meet the strict delay and reliability requirements of the monitoring system. , while not sending or receiving any data packets, put the radio of the sensor node into sleep mode, thereby minimizing the energy consumption of the sensor node.

It should be noted that there are multi-mode operation of sensor nodes, namely sleep mode (not scheduled to send or receive data packets), active mode (scheduled to send or receive data packets) and transient mode (switch from active mode to sleep mode) ), since the power consumption in the active mode is much greater than the power consumption in the sleep mode and the transient mode, only the power consumption of the sensor to transmit data packets is considered in the optimization process of the embodiments of the present application.

和星座最小值

Determining the constellation maximum with minimal power consumption at the sensor node

and constellation minimum

是k_i满足式

的最优解，可以用星座大小b_i的函数

表示；S^feasible为传感器可调度性约束。Among them, Ω is the random maximum allowable transmission interval of the sensor;

is k _i satisfying

The optimal solution of , can use the function of constellation size b _i

represents; S ^feasible is the schedulability constraint of the sensor.

For a predetermined scheduling algorithm, the schedulability constraint expresses the feasibility of allocating corresponding time slots according to a given network node constellation size and sampling period without allowing sensor nodes to transmit concurrently. In other words, it indicates whether a schedule can be constructed given the transmission duration and period of each node in the network under a predetermined scheduling algorithm. The schedulability constraint is expressed as:

{(d ₁ (b ₁ )h ₁ ), (d ₂ (b ₂ )h ₂ ),...,...,(d _N (b _N )h _N )}∈S ^feasible .

In the formula, ^feasible represents the set of transmission delay and sampling period of each node sensor, namely {(d ₁ (b ₁ )h ₁ ), (d ₂ (b ₂ )h ₂ ),...,...,( d _N (b _N )h _N )}, according to which a feasible schedule can be constructed.

S102 uses the optimal fast enumeration method to determine the node information i and the constellation size b _i value of each sensor.

Referring to FIG. 5 , it is a flowchart of determining the node information and the constellation size of the sensor by using the optimal fast enumeration method according to the embodiment of the present application.

In OFE (Optimal Fast Enumeration) algorithm, each vector is generated only once, and all possible constellation size vectors are generated without pruning. When there are schedulable constellation size vectors, OFE The algorithm can find the optimal constellation size vector in finite time. Include the following steps:

S201 increases the power consumption of each node sensor and rearranges the constellation size set of each node sensor output signal.

S202 evaluates the schedulability and objective function of the constellation size vector under the condition of the minimum power consumption of each node sensor.

S203 prunes the schedulable constellation size vector and the vector with poor objective function value.

S204 associates the constellation size vector with the number of vectors into which it is branched, and regenerates the constellation size vector for evaluation without repeatedly covering all constellation size vectors.

个可能的星座大小值b_ij，i∈[1，N]，j∈[1，A]，设b_ij为节点i到第j个最小功耗时对应的星座大小。设deg(b)表示b＝(b₁₁，b₂₁，...，...，b_N1)的度，定义为向量b分支成的向量的个数。每个星座大小向量的度数分配,保证了算法只生成一个特定的向量b一次，并且在不进行修剪的情况下生成所有可能的向量。算法从每个节点的最小功耗的星座大小开始，得到根向量b＝(b₁₁，b₂₁，...，...，b_N1)的度数为N。向量集B定义为在当前算法迭代中求值的星座大小向量集合。对于B中的每个向量b，算法首先确定它是否可以用较小的目标函数值改进到目前为止的最优解，如果是，则算法检查向量b的可调度性。如果向量b也是可调度的，则用向量b和向量b对应的目标函数值，分别更新最优星座大小向量b^*和最优解f^*，f^*表示与可行星座大小向量相对应的目标函数的最小值，并初始化为∞。如果向量b不可调度，则算法将向量b分成deg(b)向量。对于[1，deg(b)]区间内的每一个j值，通过将向量b中的N-j+1位值的星座大小设置为下一个星座大小，设置度数为j生成向量b⁺。每个新生成的向量b⁺都包含在集合B⁺中，集合B⁺将在结束时与集合B相等，以便在算法的下一次迭代中进行计算。当集合B中的所有向量都是可调度的或目标值大于或等于迄今为止的最优解时，算法终止。The specific description is as follows: For each sensor node i ∈ [1, N], the input

A possible constellation size value b _ij , i∈[1, N], j∈[1, A], let b _ij be the constellation size corresponding to the node i to the jth minimum power consumption. Let deg( _b ) represent the degree of b=(b ₁₁ , b ₂₁ , . . . , . The degree assignment of each constellation size vector ensures that the algorithm only generates a particular vector b once, and generates all possible vectors without pruning. The algorithm starts with the constellation size of the minimum power consumption of each node, and obtains the root vector b=(b ₁₁ , b ₂₁ , . . . , . . , b _N1 ) of degree N. Vector set B is defined as the set of constellation size vectors evaluated in the current algorithm iteration. For each vector b in B, the algorithm first determines whether it can improve the optimal solution so far with a smaller objective function value, and if so, the algorithm checks the schedulability of vector b. If the vector b is also schedulable, update the optimal constellation size vector b ^* and the optimal solution f ^* with the objective function values corresponding to the vector b and the vector b, respectively, where f ^* represents the objective function corresponding to the feasible constellation size vector The minimum value of , and is initialized to ∞. If vector b is not schedulable, the algorithm divides vector b into deg(b) vectors. For each value of j in the interval [1, deg(b)], a vector b ⁺ is generated by setting the constellation size of the N-j+1-bit value in vector b to the next constellation size, setting the degree to j. Each newly generated vector b ⁺ is contained in set B ^{+ ,} which will end up being equal to set B for computation in the next iteration of the algorithm. The algorithm terminates when all vectors in set B are schedulable or the target value is greater than or equal to the optimal solution so far.

S103, according to the constellation size _bi value of each sensor, obtain the optimization variable of each sensor

The optimized variables for the sensor are obtained from the following inequalities

为满足上式不等式的k_i最优值。That is, the optimization variable

is the optimal value of k _i that satisfies the inequality above.

得到采样周期h_i和包错误概率p_i的最优值。S104 According to the optimization variable

The optimal values of sampling period _hi and packet error probability _pi are obtained.

和包错误概率p_i的最优值

The optimal value of the sampling period _hi is determined by the following equation

and the optimal value of the packet error probability p _i

The performance and stability conditions of a wireless network monitoring system are expressed in the form of a random maximum allowable transmission interval (MATI), defined as keeping the time interval between subsequent state vector reports from the sensor node to the monitor with a predetermined probability below MATI value; and Maximum Allowed Delay (MAD), defined as the maximum packet delay allowed for transmission from the sensor node to the monitor.

where the random MATI constraint is expressed as:

P _r [u _i (h _i , d _i , p _i )≤Ω]≥δ.

where ui is the time interval between subsequent state vector reports of node _i as a function of h _i , d _i and p _i , Ω is the random maximum allowable transmission interval (MATI) value of the sensor, and δ is the minimum required to achieve MATI probability, and the Ω and δ values are determined by the actual control application.

故，可以将式

改写为

In each interval of length Ω, the number of receiver opportunities reported by the state vector is equal to

Therefore, the formula can be

rewrite as

和星座最小值

利用最优快速枚举法，确定每个传感器的节点信息i以及星座大小b_i值；根据每个传感器的星座大小b_i值，得到每个传感器的优化变量

根据优化变量

得到采样周期h_i和包错误概率p_i的最优值。It can be seen from the above technical solutions that the embodiments of the present application provide a method for optimizing energy efficiency of heterogeneous networks based on the Internet of Things, including: periodically acquiring the output signals of sensors of each node of the power grid, and determining the maximum constellation value of the output signals of the sensors of each node.

and constellation minimum

Using the optimal fast enumeration method, determine the node information _i and constellation size bi value of each sensor; according to the constellation size _bi value of each sensor, get the optimized variable of each sensor

According to optimization variables

The optimal values of sampling period _hi and packet error probability _pi are obtained.

和星座最小值

并利用最优快速枚举法，确定每个传感器的节点信息以及星座大小b_i值，从而在传感器传输功率最小的情况下，获得传感器的优化变量

并进一步确定采样周期h_i的最优值

和包错误概率p_i的最优值

从而通过本申请提出的优化方法，优化传感器信号传递的能量消耗与监控系统的性能。By collecting the output signal of each node sensor, the constellation maximum value of the output signal of each node sensor is determined

and constellation minimum

And use the optimal fast enumeration method to determine the node information of each sensor and the value of the constellation size _bi , so as to obtain the optimal variables of the sensor under the condition of the minimum transmission power of the sensor

And further determine the optimal value of the sampling period _hi

and the optimal value of the packet error probability p _i

Therefore, the energy consumption of the sensor signal transmission and the performance of the monitoring system are optimized through the optimization method proposed in this application.

Similar parts between the embodiments provided in the present application may be referred to each other. The specific embodiments provided above are just a few examples under the general concept of the present application, and do not constitute a limitation on the protection scope of the present application. For those skilled in the art, any other implementations expanded according to the solution of the present application without creative work fall within the protection scope of the present application.

Claims (7)

1. A heterogeneous network energy efficiency optimization method based on the Internet of things is characterized by comprising the following steps:

periodically acquiring output signals of each node sensor of the power grid, and determining the maximum value of the constellation of the output signals of each node sensor

Sum constellation minimum

Determining node information i and constellation size b of each sensor by using an optimal rapid enumeration method_iA value;

according to the constellation size b of each sensor_iValue, obtaining optimized variables for each sensor

According to the optimization variable

Obtaining a sampling period h_iSum packet error probability p_iThe optimum value of (c).

2. The energy efficiency optimization method for the heterogeneous network based on the Internet of things according to claim 1, wherein the maximum transmitting power of the sensor is not exceededAllowable power level W^t，maxDetermining the maximum value of the constellation of the sensor output signal

Sum constellation minimum

Wherein:

satisfies the following formula:

d_i≤Δ；

wherein: wherein b is_iThe number of bits used or the constellation size for each symbol; for a predetermined modulation scheme, d_iThe transmission delay for the ith node sensor, which can be denoted as b_iA function of (a); Δ is the maximum allowable delay of the stability control system;

a transmission power for a given modulation;

power consumption of the circuit in the active mode of the transmitter.

3. The energy efficiency optimization method for the heterogeneous network based on the Internet of things of claim 2, wherein the maximum value of the constellation of the output signal of the sensor is determined

Sum constellation minimum

Further comprising: determining a constellation maximum with minimized power consumption of a sensor node

Sum constellation minimum

Wherein Ω is a random maximum allowable transmission interval of the sensor;

is k_iSatisfy the formula

Can be given a constellation size b_iFunction of (2)

Represents; s^feasibleIs a sensor schedulability constraint.

4. The energy efficiency optimization method for the heterogeneous network based on the internet of things according to claim 3, wherein the sensor schedulability constraint is a set of transmission delay and sampling period of each node sensor, and can be expressed as:

{(d₁(b₁)h₁)，(d₂(b₂)h₃)，...，...，(d_N(b_N)h_N)}S^feasible，i∈[l，N]。

5. the energy efficiency optimization method for the heterogeneous network based on the Internet of things of claim 1, wherein node information i and constellation size b of each sensor are determined by using an optimal rapid enumeration method_iThe step of value further comprises:

increasing the power consumption of each node sensor, and rearranging the constellation size set of the output signals of each node sensor;

under the condition of minimum power consumption of each node sensor, estimating schedulability and a target function of a constellation size vector;

pruning vectors with the schedulable constellation size and vectors with the poorer objective function values;

the constellation size vector is correlated with the number of vectors into which it branches, and the constellation size vector used for evaluation is regenerated without repeatedly covering all constellation size vectors.

6. The energy efficiency optimization method for the heterogeneous network based on the Internet of things according to claim 1, wherein the constellation size b according to each sensor is_iValue, obtaining optimized variables for each sensor

In the step (2), the optimized variables of the sensor are obtained by the following inequality

7. The foundation of claim 1The energy efficiency optimization method of the networked heterogeneous network is characterized in that the optimization variables are optimized according to

Obtaining a sampling period h_iSum packet error probability p_iIn the step of the optimal value of (b), the sampling period h is determined by the following equation_iOptimum value of (2)

Sum packet error probability p_iOptimum value of (2)

And omega is the random maximum allowable transmission interval of the sensor, and is the minimum probability required by realizing the random maximum allowable transmission interval of the sensor.

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