A loRa large-capacity networking system communication parameter allocation method, system and device

Abstract

The application discloses a LoRa large-capacity networking system communication parameter allocation method, system and device, and belongs to the technical field of LoRa Internet of Things communication. In order to solve the problem that a large number of LoRa nodes use the same and large spreading factors, resulting in data packet loss, caused by the fact that a LoRa large-capacity networking system does not consider the spreading factor used by all LoRa nodes in the entire LoRa network. The application allocates communication parameters based on a wireless communication model of a LoRa capacity networking system. In the process of allocating communication parameters, the adjustment number N step of a LoRa node is first determined, and then the SF and TP of the LoRa node are adjusted according to the adjustment number N step of the LoRa node, so that the allocation of the spreading factor SF and the signal transmission power TP is realized. The application is used for the communication of a LoRa large-capacity networking system.

Description

Technical Field

[0001] This invention belongs to the field of LoRa Internet of Things communication technology, specifically relating to a method, system, and device for allocating communication parameters in a LoRa high-capacity networking system. Background Technology

[0002] High-capacity, high-density IoT device networks have become a hot topic for future research and development. On the one hand, most of these IoT devices are low-power, low-data-rate, and low-cost sensor nodes, which not only need to operate stably for extended periods but also need to communicate efficiently within the network. The large number of IoT nodes within the network leads to problems such as complex network topology, large data volumes, and high security requirements. Therefore, designing and optimizing the architecture and algorithms of large-scale IoT systems has become an important research direction.

[0003] Existing research on LoRa networking mainly focuses on the improvement and optimization of LoRaWAN-based networking and its application in various scenarios, as well as the analysis of media access problems in single-channel large-scale LoRa networks, proposing new protocols that adapt to LoRa's physical layer parameters and use control packets for coordination to maintain optimal connectivity between terminal nodes and gateways without violating duty cycle constraints. However, if the LoRa-map protocol method is still used in high-capacity LoRa networking, the resulting network communication success rate and node energy consumption are relatively poor. Therefore, research is needed on how to improve the network performance of the networking system under high-capacity networking conditions.

[0004] In the LoRaWAN protocol, on the one hand, when a large number of LoRa nodes join the network, they use the random access method (ALOHA) for uplink communication, which leads to a large number of node collisions. On the LoRa node side, it's impossible to determine whether a collision has occurred during uplink communication. In CLASS A mode, the receive window will be opened twice continuously, resulting in communication failures and significant energy consumption. On the other hand, LoRa nodes obtain LoRa transmission parameters, including the spreading factor (SF) and transmit power (TP), through the ADR algorithm. When sending the same data packets, LoRa nodes using a larger spreading factor will have a longer air transmission time. A longer transmission time leads to a higher probability of collisions, resulting in reduced communication quality and significant energy consumption for LoRa nodes. When using the ADR algorithm to adjust the parameters of nodes within the network, only the node's own situation is considered, without taking into account the spreading factor used by all LoRa nodes in the entire LoRa network. This allocation strategy leads to a large number of LoRa nodes using the same and larger spreading factor. In signal interference, the largest source of interference comes from interference with the same SF, ultimately leading to a large number of data packet collisions and packet loss. However, there is no detailed solution for reducing interference within the same channel. Summary of the Invention

[0005] This invention addresses the issue of data packet loss caused by a large number of LoRa nodes using the same and larger spreading factors, which is a consequence of LoRa high-capacity networking systems not considering the spreading factors used by all LoRa nodes within the entire LoRa network.

[0006] A method for allocating communication parameters in a LoRa high-capacity networking system is proposed. This method allocates communication parameters based on the wireless communication model of the LoRa high-capacity networking system. During the parameter allocation process, the adjustment count N corresponding to each LoRa node is first determined. step Then, based on the adjustment count N corresponding to the LoRa node step Simultaneous adjustment of the spread factor (SF) and transmit power (TP) of the LoRa node achieves the allocation of the spread factor SF and the signal transmit power TP. The specific adjustment process is as follows:

[0007] (1) When Nstep>0,

[0008] If the node does not use the minimum SF, then reduce the SF and decrease Nstep at the same time;

[0009] If the node has already used the minimum SF and the TP used is greater than the minimum transmission power, then reduce TP and Nstep at the same time.

[0010] If the minimum SF and minimum transmission power are used, the SF and TP values ​​of the output node are;

[0011] (2) When Nstep <= 0,

[0012] If TP is greater than the maximum transmission power, then decrease TP and increase Nstep at the same time;

[0013] If TP is less than the maximum transmission power, then increase TP and simultaneously increase Nstep.

[0014] If TP is less than the minimum transmission power, then set it to the minimum transmission power value and increase Nstep at the same time.

[0015] Furthermore, the adjustment count N corresponding to the first determined LoRa node is... step =A / 3;

[0016] A = min(SINR) margin RSSI margain );

[0017] SINR margin =SINR max –SINR min –Margin;

[0018] RSSI margain =RSSI max –RSSI min –Margin;

[0019] Among them, SINR margin For the dryness margin, RSSI margain SINR is the received signal strength margin. max SINR min For the LoRa gateway receiver, calculate the maximum and minimum SINR among multiple uplink signals received from a single LoRa node; RSSI max RSSI min This represents the maximum and minimum RSSI values ​​among multiple uplink signals received from a single LoRa node at the LoRa gateway receiver; Margin represents the link margin.

[0020] Furthermore, the wireless communication model includes a path loss model, a link budget model, an interference model, and a gateway reception model, wherein the interference model is as follows:

[0021] In LoRa networking, nodes may experience interference from other nodes when transmitting uplink data. For LoRa networking, the signal-to-interference-plus-noise ratio (SINNR) threshold matrix T = [T...] is used for LoRa signal reception in the common frequency domain. i,j As the interference matrix, Ti,j The element in row i and column j of T represents the signal-to-interference-plus-noise ratio (SNR) margin in dB between the LoRa node's signal SF=i and the interference signal SF=j. The LoRa node signal must be greater than the SNR margin in the interference matrix to be correctly demodulated.

[0022] Furthermore, the path loss model between the LoRa node and the LoRa gateway adopts the Okumura-Hata model.

[0023] Furthermore, the link budget model for the LoRa network wireless link is as follows:

[0024] P rx [dB] = P tx +G tx -L tx -L pl -L m +G rx -L rx (2)

[0025] Among them, P rx P is the power of the signal received at the receiver. tx It is the transmission power, G tx It is the transmitter's antenna gain, L tx It's the transmitter's loss, L pl It is the loss in the transmission path, L m G represents the margin for other losses and fading. rx It is the receiving antenna gain, L rx It refers to the loss at the receiver end.

[0026] Furthermore, the link budget model for the LoRa network wireless link is as follows:

[0027]

[0028] Among them, L pl (d0) represents the path loss at the reference distance d0, where d i This represents the actual distance between the gateway and the LoRa node, where n is the path loss exponent, and X is the distance between the gateway and the LoRa node. σ Let be a Gaussian distributed random variable with zero mean and variance σ.

[0029] Furthermore, the gateway receiving model is as follows:

[0030] In a LoRa networking system, assuming that data from LoRa node a can be successfully received by LoRa gateway b, then the following should be satisfied:

[0031] 1. The RSSI of the signal received when the data sent by LoRa node a reaches the LoRa gateway.a,b (S,T,C) must be greater than the receiver sensitivity of the LoRa gateway;

[0032] 2. The signal-to-interference-plus-noise ratio (SINR) of the uplink signal at the LoRa gateway must be greater than the SINR threshold value for which the LoRa gateway can correctly demodulate the data. threshold ;

[0033] The probability that the uplink data packet of LoRa node a is successfully received by LoRa gateway b is expressed as:

[0034] PDR a,b (S,T,C)=P{RSSI a,b (S,T,C)≥Sensitivity}×P{SINR(S,T,C)≥SINR threshold} (5)

[0035] Where P represents probability; SINR threshold The value corresponds to the element T in matrix T. ij .

[0036] A communication parameter allocation system for a LoRa high-capacity networking system includes a wireless communication model creation unit and a communication parameter allocation unit for the LoRa high-capacity networking system.

[0037] The LoRa capacity networking system wireless communication model creation unit is used to create the wireless communication model of the LoRa capacity networking system and provide a wireless communication model environment for the communication parameter allocation unit.

[0038] Communication parameter allocation unit: Based on the wireless communication model of the LoRa capacity networking system, communication parameters are allocated. During the allocation process, the number of adjustment times N corresponding to the LoRa node is used. step Simultaneous adjustment of the spread factor (SF) and transmit power (TP) of the LoRa node achieves the allocation of the spread factor SF and the signal transmit power TP. The specific adjustment process is as follows:

[0039] (1) When Nstep>0,

[0040] If the node does not use the minimum SF, then reduce the SF and decrease Nstep at the same time;

[0041] If the node has already used the minimum SF and the TP used is greater than the minimum transmission power, then reduce TP and Nstep at the same time.

[0042] If the minimum SF and minimum transmission power are used, the SF and TP values ​​of the output node are;

[0043] (2) When Nstep <= 0,

[0044] If TP is greater than the maximum transmission power, then decrease TP and increase Nstep at the same time;

[0045] If TP is less than the maximum transmission power, then increase TP and simultaneously increase Nstep.

[0046] If TP is less than the minimum transmission power, then set it to the minimum transmission power value and increase Nstep at the same time.

[0047] Furthermore, the adjustment number N corresponding to the LoRa node in the communication parameter allocation unit step =A / 3;

[0048] A = min(SINR) margin RSSI margain );

[0049] SINR margin =SINR max –SINR min –Margin;

[0050] RSSI margain =RSSI max –RSSI min –Margin;

[0051] Among them, SINR margin For the dryness margin, RSSI margain SINR is the received signal strength margin. max SINR min For the LoRa gateway receiver, calculate the maximum and minimum SINR among multiple uplink signals received from a single LoRa node; RSSI max RSSI min This represents the maximum and minimum RSSI values ​​among multiple uplink signals received from a single LoRa node at the LoRa gateway receiver; Margin represents the link margin.

[0052] A LoRa high-capacity networking system communication parameter allocation device, the device includes a processor and a memory, the memory stores at least one instruction, the at least one instruction is loaded and executed by the processor to implement the LoRa high-capacity networking system communication parameter allocation method.

[0053] Beneficial effects:

[0054] In the LoRaWAN protocol, this invention uses the joint evaluation of the SINR and RSSI of LoRa nodes as the modification number for data rate adjustment. Through the network server, it simultaneously changes the SF and TP of the nodes to balance the communication rate and power of the nodes. This ensures a balanced use of various SF ratios even with a large number of nodes in the network, with a focus on using smaller SFs to reduce the probability of collisions at the receiver. This guarantees the optimal allocation result with the fewest modifications, thereby improving the energy efficiency of LoRa nodes and network performance. Furthermore, this invention effectively improves network performance and reduces LoRa node power consumption through LoRa node spreading factor configuration and transmission power optimization within a single channel.

[0055] This invention modifies the control commands of the MAC protocol within the LoRa network to achieve the ability to adjust the communication rate of LoRa nodes and to efficiently manage nodes in a large-capacity LoRa network. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the network structure of a LoRa high-capacity networking system.

[0057] Figure 2 It is the transmission protocol model of LoRa nodes in a LoRa high-capacity networking system.

[0058] Figure 3 This refers to the impact of using different SF (Secondary Information Store) data packets on airborne transmission time.

[0059] Figure 4 This describes the distribution of LoRa nodes using the spreading factor (SF) within the original ADR algorithm network range.

[0060] Figure 5 This describes the distribution of the spreading factor (SF) among LoRa nodes within the network range, based on the improved high-capacity LoRa communication parameter allocation algorithm.

[0061] Figure 6 It is the ratio of the spreading factor allocated by the original algorithm and the improved high-capacity LoRa communication parameter allocation algorithm.

[0062] Figure 7 This is a comparison of Packet Delivery Rate (PDR) for different algorithms.

[0063] Figure 8 This is a comparison of the energy consumption results of different algorithms.

[0064] Figure 9This invention presents an improved high-capacity LoRa communication parameter allocation algorithm. The network conditions are shown in different gateway deployments with different numbers of LoRa nodes. (a) is the network uplink data success rate (PDR), and (b) is the average energy consumption of nodes in the network.

[0065] Figure 10 This is a schematic diagram of the probability model for communication collisions. Detailed Implementation

[0066] Specific implementation method one: This implementation method is explained with reference to the figure.

[0067] This invention relates to a method for allocating communication parameters in a LoRa high-capacity networking system, comprising the following steps:

[0068] S1. Establish a wireless communication model for a LoRa capacity networking system:

[0069] First, we analyze the impact of the LoRa gateway core device on the signal reception of LoRa nodes. The device hardware itself determines the communication bandwidth; communication between LoRa nodes and the gateway can only use a fixed 125kHz bandwidth. Considering the impact on data transmission reliability and LoRa node energy consumption in LoRa networking, we adjust network performance using three parameters: spreading factor SF, signal transmit power TP, and wireless channel CH.

[0070] The LoRa gateway's wireless channels (CH) are each configured with different offsets, preventing interference from other channels. Therefore, uplink transmission interference for LoRa nodes only considers interference from other LoRa nodes within the same channel. Assuming that within the coverage area of ​​a single gateway, N LoRa nodes need to connect to the gateway on the same uplink channel, and at least two LoRa nodes' uplink data packets arrive at the gateway's receiver simultaneously, overlapping reception times will occur at the receiver. Figure 10 As shown, a communication collision will occur, causing the LoRa node to fail to upload data.

[0071] Interference is explained by establishing a collision model. In the same wireless channel, when data packets sent by two or more LoRa nodes using the same spreading factor overlap in transmission time, regardless of the length of the overlap, a signal collision will occur. This results in the two data packets failing to be successfully parsed at the gateway, leading to data upload failure. Communication collisions cause data transmission failures and reduced network efficiency. Furthermore, in high-capacity LoRa networking systems, due to the large coverage area of ​​the LoRa gateway and the large number of nodes, the uplink transmission failures of many nodes lead to significant energy consumption, thus affecting network quality. The shortened lifespan of some nodes within the network also greatly increases the maintenance cost of the network system. Therefore, the fairness of energy consumption among nodes and the reliability of transmission within the LoRa network must be considered.

[0072] In network deployment, the energy consumption of LoRa nodes mainly includes data transmission and reception, standby and sleep processes, with the majority of energy consumed during data transmission and reception. LoRa nodes employ a Class A communication mechanism. In this mode, LoRa nodes achieve bidirectional communication with the gateway and server. After joining the network, the node first transmits uplink data, and then the gateway and server respond with downlink data. In the uplink data transmission, if the data packet is not received by the gateway, the gateway will not send a reply. The LoRa node will periodically open two receive windows, consuming a significant amount of energy during communication failures. However, improving the downlink success rate can reduce the node's energy consumption to some extent. Therefore, in the LoRa parameter allocation process, improving the network communication success rate and reducing the energy consumption of LoRa nodes within the network are the core issues to be addressed in the algorithm design. The resources allocated in a high-capacity LoRa network mainly include the allocation of the spreading factor (SF) and signal transmit power (TP) of the LoRa nodes. Next, a communication model for the high-capacity network system will be established.

[0073] (1) Path loss model

[0074] LoRa networking systems use radio communication. Electromagnetic waves experience propagation losses in free space, and the path loss model between nodes and gateways is considered a logarithmic distance path model. This path loss model is highly dependent on the environment. Taking into account factors such as the operating frequency band of LoRa nodes, the coverage area of ​​LoRa gateways, the height of installed antennas, and the operating area, the Okumura-Hata model is used as the theoretical model for LoRa transmission for calibration. The expression for the propagation loss in this model is:

[0075] L pl (dB)=69.55+26.16lgf-13.82lgh b -α(h m )+(44.9-6.55lghb )×lgd (1)

[0076] Where f is the operating frequency of the wireless signal, h b To raise the base station's transmitting antenna, h m α(h) represents the effective height of the mobile station's receiving antenna, d represents the horizontal distance between the base station's transmitting antenna and the mobile station's receiving antenna, and α(h) represents the effective height of the receiving antenna. m ) is the antenna correction factor.

[0077] (2) Link Budget Model

[0078] In high-capacity LoRa networks, the actual coverage of the LoRa gateway directly determines the density and effectiveness of LoRa node deployment. The coverage range of the LoRa gateway is affected by hardware and transmission losses. A path loss model is established to measure the link quality of the wireless system in a LoRa network. The link budget refers to the measure of all gains and transmission losses from the LoRa node transmitter to the gateway receiver. The link budget of a LoRa network wireless link is:

[0079] P rx [dB] = P tx +G tx -L tx -L pl -L m +G rx -L rx (2)

[0080] Among them, P rx P is the power of the signal received at the receiver. tx It is the transmission power, G tx It is the transmitter's antenna gain, L tx It's the transmitter's loss, L pl It is the loss in the transmission path, L m G represents the margin for other losses and fading. rx It is the receiving antenna gain, L rx This represents the receiver's losses. The link budget can be simplified as follows:

[0081]

[0082] Among them, L pl (d0) represents the path loss at the reference distance d0, where d i This represents the actual distance between the gateway and the LoRa node, where n is the path loss exponent, and X is the distance between the gateway and the LoRa node. σ Let be a Gaussian distributed random variable with zero mean and variance σ.

[0083] (3) Interference Model

[0084] In LoRa networking, nodes transmitting uplink data are susceptible to interference from other nodes. Assuming no other technical interference besides the LoRa signal, and considering the imperfect orthogonality of different signal transmission lines (SFs), the impact of other LoRa nodes on the network is discussed. The effects of different SFs on data packet transmission time are illustrated below. Figure 3 As shown.

[0085] When LoRa nodes communicate, signals transmitted by LoRa nodes using the same or different signal frequency (SF) can overlap in time and frequency at the receiver. Whether the receiver demodulator can correctly demodulate the data when data packets are received simultaneously depends on the signal-to-interference-plus-noise ratio (SINR) threshold of the receiver-side signal. The SF used by any LoRa node... k The signal can only be correctly decoded when the SINR of the signal on the receiver side is higher than the threshold of interference plus noise. In order to accurately evaluate and analyze the signal interference in the LoRa network, Equation (4) gives the signal-to-interference-plus-noise ratio threshold matrix for LoRa signal reception in the common frequency domain.

[0086]

[0087] In the aforementioned interference matrix, interference from the same SF has the greatest impact compared to interference caused by signals using different SFs. A signal-to-interference-plus-noise ratio (SNR) threshold matrix was used to determine whether the LoRa signal could be received by the receiver. The element T in this matrix... i,j This represents the signal-to-interference-plus-noise ratio (SIR) margin, in dB, between the LoRa node's signal SF=i and the interfering signal SF=j, used to determine whether the receiver can correctly decode the signal. If there are multiple interfering signals, the signal must have an SIR margin greater than that of the largest interfering signal in the interference to be correctly demodulated.

[0088] (4) Gateway Reception Model

[0089] In a LoRa networking system, assuming that data from LoRa node a can be successfully received by LoRa gateway b, then the following should be satisfied:

[0090] 1. The RSSI of the signal received when the data sent by LoRa node a reaches the LoRa gateway. a,b (S,T,C) must be greater than the receiver sensitivity of the LoRa gateway;

[0091] 2. The signal-to-interference-plus-noise ratio (SINR) of the uplink signal at the LoRa gateway must be greater than the SINR threshold value for which the LoRa gateway can correctly demodulate the data. threshold .

[0092] The probability that the uplink data packet of LoRa node a is successfully received by LoRa gateway b is expressed as:

[0093] PDR a,b (S,T,C)=P{RSSI a,b (S,T,C)≥Sensitivity}×P{SINR(S,T,C)≥SINR threshold} (5)

[0094] Where P represents probability; the signal-to-interference-plus-noise ratio (SINR) of the uplink data transmitted by LoRa node a at the gateway receiver is mainly affected by the LoRa node's transmit power TP, and by the interference from other nodes transmitting signals simultaneously in the same channel using spreading factor j when it uses spreading factor i. threshold The value corresponds to the element T in matrix T. ij .

[0095] Thus, the analysis of wireless communication interference in the LoRa high-capacity networking system was completed, a theoretical communication model was established, and the propagation impact factors of LoRa nodes using different communication parameters during the communication process were given, providing model and data support for further utilization of the improved high-capacity LoRa parameter allocation algorithm.

[0096] S2. An improved LoRa high-capacity networking system communication parameter allocation algorithm is used for communication parameter allocation:

[0097] Given the characteristics of LoRaWAN networks, the proper configuration of LoRa node parameters has a significant impact on node energy consumption, network performance, and reliability. Therefore, parameter allocation is a crucial method for LoRa network optimization. To reduce LoRa node energy consumption and improve network performance, various ADR algorithms have been proposed and applied in practical networks. The official LoRaWAN network uses a reference algorithm—the ADR algorithm—provided by Semtech, which allows each LoRa node within the network to adjust its uplink communication rate and transmit power.

[0098] Addressing the shortcomings of traditional ADR algorithms in high-capacity LoRa node networking scenarios, an improved allocation strategy is proposed. This strategy uses a joint evaluation of LoRa node SNR and RSSI as the basis for determining the number of modifications needed for data rate adjustment. Through a network server, the SF and TP of each node are simultaneously modified to balance the communication rate and power of the nodes. This ensures a balanced use of various SF ratios even with a large number of nodes, prioritizing the use of smaller SFs to reduce the probability of collisions at the receiver. This approach guarantees the optimal allocation result with minimal modifications, thereby improving the energy efficiency of LoRa nodes and network performance. Furthermore, by modifying the control commands of the MAC protocol within the LoRa network, the ability to adjust the communication rate of LoRa nodes and efficiently manage nodes in high-capacity LoRa networks is achieved.

[0099] In this invention, the maximum and minimum SINR and RSSI values ​​among the 20 uplink signals received from a LoRa node at the LoRa gateway receiver are used as the signal-to-dryness ratio margin (SINR) after adding a certain link margin. margin ) and Received Signal Strength Margin (RSSI) margain And use this as the criterion for determining the number of times the LoRa node's communication parameters have been adjusted (N). step ) conditions:

[0100] SINR margin =SINR max –SINR min –Margin;

[0101] RSSI margain =RSSI max –RSSI min –Margin;

[0102] A = min(SINR) margin RSSI margain );

[0103] N step =A / 3.

[0104] Based on the adjustment number N corresponding to the LoRa node step This allows for the simultaneous adjustment of the node's SF and TP.

[0105] (1) When Nstep>0,

[0106] If the node does not use the minimum SF, then reduce the SF and decrease Nstep at the same time;

[0107] If the node has already used the minimum SF and the TP used is greater than the minimum transmission power, then reduce TP and Nstep at the same time.

[0108] If the minimum SF and minimum transmission power are used, then the SF and TP values ​​of the output node are determined.

[0109] (2) When Nstep <= 0,

[0110] If TP is greater than the maximum transmission power, then decrease TP and increase Nstep at the same time;

[0111] If TP is less than the maximum transmission power, then increase TP and simultaneously increase Nstep.

[0112] If TP is less than the minimum transmission power, then set it to the minimum transmission power value and increase Nstep at the same time.

[0113] Uplink data transmission tests are conducted based on the allocation of communication parameters among nodes within the network. LoRa nodes transmit uplink data at a random start time. After one uplink cycle, the gateway calculates the number of received data packets. The nodes calculate their cycle energy consumption based on the transmission parameters used and whether downlink data packets were received. The gateway calculates the uplink communication success rate of the high-capacity LoRa network by counting the number of received data packets, and comprehensively evaluates the network performance based on the average energy consumption of nodes within the network. Specific Implementation Method Two:

[0115] This embodiment is a communication parameter allocation system for a LoRa high-capacity networking system, which includes a wireless communication model creation unit and a communication parameter allocation unit for the LoRa high-capacity networking system.

[0116] The LoRa capacity networking system wireless communication model creation unit is used to create the wireless communication model of the LoRa capacity networking system and provide a wireless communication model environment for the communication parameter allocation unit. The wireless communication model includes a path loss model, a link budget model, an interference model, and a gateway reception model.

[0117] (1) Path loss model

[0118] LoRa networking systems use radio communication. Electromagnetic waves experience propagation losses in free space, and the path loss model between nodes and gateways is considered a logarithmic distance path model. This path loss model is highly dependent on the environment. Taking into account factors such as the operating frequency band of LoRa nodes, the coverage area of ​​LoRa gateways, the height of installed antennas, and the operating area, the Okumura-Hata model is used as the theoretical model for LoRa transmission for calibration. The expression for the propagation loss in this model is:

[0119] L pl (dB)=69.55+26.16lgf-13.82lgh b -α(h m )+(44.9-6.55lgh b )×lgd

[0120] Where f is the operating frequency of the wireless signal, h b To raise the base station's transmitting antenna, h m α(h) represents the effective height of the mobile station's receiving antenna, d represents the horizontal distance between the base station's transmitting antenna and the mobile station's receiving antenna, and α(h) represents the effective height of the receiving antenna. m ) is the antenna correction factor.

[0121] (2) Link Budget Model

[0122] In high-capacity LoRa networks, the actual coverage of the LoRa gateway directly determines the density and effectiveness of LoRa node deployment. The coverage range of the LoRa gateway is affected by hardware and transmission losses. A path loss model is established to measure the link quality of the wireless system in a LoRa network. The link budget refers to the measure of all gains and transmission losses from the LoRa node transmitter to the gateway receiver. The link budget of a LoRa network wireless link is:

[0123] P rx [dB] = P tx +G tx -L tx -L pl -L m +G rx -L rx

[0124] Among them, P rx P is the power of the signal received at the receiver. tx It is the transmission power, G tx It is the transmitter's antenna gain, L tx It's the transmitter's loss, L pl It is the loss in the transmission path, L m G represents the margin for other losses and fading. rx It is the receiving antenna gain, L rx This represents the receiver's losses. The link budget can be simplified as follows:

[0125]

[0126] Among them, L pl (d0) represents the path loss at the reference distance d0, where d iThis represents the actual distance between the gateway and the LoRa node, where n is the path loss exponent, and X is the distance between the gateway and the LoRa node. σ Let be a Gaussian distributed random variable with zero mean and variance σ.

[0127] (3) Interference Model

[0128] In LoRa networking, nodes transmitting uplink data are susceptible to interference from other nodes. Assuming no other technical interference besides the LoRa signal, and considering the imperfect orthogonality of different signal transmission lines (SFs), the impact of other LoRa nodes on the network is discussed. The effects of different SFs on data packet transmission time are illustrated below. Figure 3 As shown.

[0129] When LoRa nodes communicate, signals transmitted by LoRa nodes using the same or different signal frequency (SF) can overlap in time and frequency at the receiver. Whether the receiver demodulator can correctly demodulate the data when data packets are received simultaneously depends on the signal-to-interference-plus-noise ratio (SINR) threshold of the receiver-side signal. The SF used by any LoRa node... k The signal can only be correctly decoded when the SINR of the signal on the receiver side is higher than the threshold of interference plus noise. In order to accurately evaluate and analyze the signal interference in the LoRa network, Equation (4) gives the signal-to-interference-plus-noise ratio threshold matrix for LoRa signal reception in the common frequency domain.

[0130]

[0131] In the aforementioned interference matrix, interference from the same SF has the greatest impact compared to interference caused by signals using different SFs. A signal-to-interference-plus-noise ratio (SNR) threshold matrix was used to determine whether the LoRa signal could be received by the receiver. The element T in this matrix... i,j This represents the signal-to-interference-plus-noise ratio (SIR) margin, in dB, between the LoRa node's signal SF=i and the interfering signal SF=j, used to determine whether the receiver can correctly decode the signal. If there are multiple interfering signals, the signal must have an SIR margin greater than that of the largest interfering signal in the interference to be correctly demodulated.

[0132] (4) Gateway Reception Model

[0133] In a LoRa networking system, assuming that data from LoRa node a can be successfully received by LoRa gateway b, then the following should be satisfied:

[0134] 1. The RSSI of the signal received when the data sent by LoRa node a reaches the LoRa gateway. a,b (S,T,C) must be greater than the receiver sensitivity of the LoRa gateway;

[0135] 2. The signal-to-interference-plus-noise ratio (SINR) of the uplink signal at the LoRa gateway must be greater than the SINR threshold value for which the LoRa gateway can correctly demodulate the data. threshold .

[0136] The probability that the uplink data packet of LoRa node a is successfully received by LoRa gateway b is expressed as:

[0137] PDR a,b (S,T,C)=P{RSSI a,b (S,T,C)≥Sensitivity}×P{SINR(S,T,C)≥SINR threshold}

[0138] Where P represents probability; the signal-to-interference-plus-noise ratio (SINR) of the uplink data transmitted by LoRa node a at the gateway receiver is mainly affected by the LoRa node's transmit power TP, and by the interference from other nodes transmitting signals simultaneously in the same channel using spreading factor j when it uses spreading factor i. threshold The value corresponds to the element T in matrix T. ij .

[0139] Communication parameter allocation unit: Based on the wireless communication model of the LoRa capacity networking system, communication parameters are allocated. During the allocation process, the number of adjustment times N corresponding to the LoRa node is used. step Simultaneous adjustment of the spread factor (SF) and transmit power (TP) of the LoRa node achieves the allocation of the spread factor SF and the signal transmit power TP. The specific adjustment process is as follows:

[0140] (1) When Nstep>0,

[0141] If the node does not use the minimum SF, then reduce the SF and decrease Nstep at the same time;

[0142] If the node has already used the minimum SF and the TP used is greater than the minimum transmission power, then reduce TP and Nstep at the same time.

[0143] If the minimum SF and minimum transmission power are used, the SF and TP values ​​of the output node are;

[0144] (2) When Nstep <= 0,

[0145] If TP is greater than the maximum transmission power, then decrease TP and increase Nstep at the same time;

[0146] If TP is less than the maximum transmission power, then increase TP and simultaneously increase Nstep.

[0147] If TP is less than the minimum transmission power, then set it to the minimum transmission power value and increase Nstep at the same time.

[0148] The adjustment count N corresponding to the LoRa node in the communication parameter allocation unit step =A / 3;

[0149] A = min(SINR) margin RSSI margain );

[0150] SINR margin =SINR max –SINR min –Margin;

[0151] RSSI margain =RSSI max –RSSI min –Margin;

[0152] Among them, SINR margin For the dryness margin, RSSI margain SINR is the received signal strength margin. max SINR min For the LoRa gateway receiver, calculate the maximum and minimum SINR among multiple uplink signals received from a single LoRa node; RSSI max RSSI min This represents the maximum and minimum RSSI values ​​among multiple uplink signals received from a single LoRa node at the LoRa gateway receiver; Margin represents the link margin. Specific implementation method three:

[0154] This embodiment is a communication parameter allocation device for a LoRa high-capacity networking system. The device includes a processor and a memory. It should be understood that it includes any device including a processor and a memory described in this invention. The device may also include other units or modules that perform display, interaction, processing, control, and other functions through signals or instructions.

[0155] The memory stores at least one instruction, which is loaded and executed by the processor to implement the LoRa high-capacity networking system communication parameter allocation method.

[0156] It should be understood that the instructions include computer program products, software, or computerized methods corresponding to any method described in this invention; the instructions can be used to program computer systems or other electronic devices. The memory may include a readable medium on which instructions are stored, and may include, but is not limited to, magnetic storage media, optical storage media; magneto-optical storage media include read-only memory (ROM), random access memory (RAM), erasable programmable memory (e.g., EPROM and EEPROM), and flash memory layers, or other types of media suitable for storing electronic instructions.

[0157] Example

[0158] The effectiveness of this invention can be verified and illustrated by the following experiments. The network structure of the LoRa high-capacity networking system is as follows: Figure 1 As shown, the transmission protocol model of LoRa nodes in a LoRa high-capacity networking system is as follows: Figure 2 As shown in Table 1, the parameters related to the LoRa communication model are summarized.

[0159] Table 1

[0160]

[0161] First, it is necessary to verify the network communication experiment of deploying 2000 nodes within a 10-kilometer range of a single gateway, ignoring changes in electromagnetic scattering intensity:

[0162] Figure 4 The image shows the distribution of spreading factors used by LoRa nodes within the original ADR algorithm region. Figure 5 The paper presents the distribution of spreading factors used by LoRa nodes within the region of the improved high-capacity LoRa communication parameter allocation algorithm, and compares them. Figure 5 As a result, the network communication success rate using the original ADR algorithm was 34.7%, while the communication success rate of the LoRa high-capacity network using the improved algorithm was 42.5%, representing a 7.8% improvement in network communication performance.

[0163] Figure 6 This paper demonstrates the proportion of spreading factors used by LoRa nodes within the region using the original ADR algorithm and the improved high-capacity LoRa communication parameter allocation algorithm. Compared to the original algorithm, the improved algorithm reduces the proportion of LoRa nodes using larger spreading factors, resulting in shorter data packet transmission time in the uplink channel and thus reducing the probability of collisions. Meanwhile, the energy consumption of LoRa nodes is also a crucial system indicator. Reducing node energy consumption is an important way to improve the lifespan of a high-capacity LoRa network system. This can be achieved by improving the communication success rate and reducing the number of retransmissions to lower energy consumption per cycle, and by using periodic sleep cycles to further reduce LoRa node energy consumption.

[0164] Secondly, the following experiments further verify the feasibility of the improved high-capacity LoRa communication parameter allocation algorithm. Compared with the NET-ADR algorithm, the original ADR algorithm and the improved ADR algorithm of this invention are compared. The verification is carried out in the same simulation environment. The network coverage is compared to verify the network deployment of 100, 500, 1000, 1500, 2000, 2500 and 3000 LoRa nodes, and the impact of various allocation algorithms on network performance.

[0165] Figure 7 The simulation compares the network communication success rates of the three algorithms under different LoRa node conditions. The comparisons are performed under identical conditions, and it can be seen that the improved ADR algorithm, when applied to the network, increases the packet delivery rate, showing significantly better results regardless of the number of LoRa nodes. This verifies the correctness of the improved scheme.

[0166] Figure 8 The table shows the average energy consumption of LoRa nodes in the network under different LoRa node conditions for the three algorithms. The NET-ADR algorithm consumes significantly more energy than the original ADR algorithm. The improved ADR algorithm consumes more energy when the number of LoRa nodes is small, but the average energy consumption decreases significantly as the number of LoRa nodes in the network increases. This indicates that the improved ADR algorithm is more suitable for the application requirements of large-capacity LoRa node networking.

[0167] Finally, the following experiments demonstrate the effectiveness of LoRa network performance improvements in a multi-gateway deployment scenario.

[0168] Figure 9 The paper presents the impact of different numbers of LoRa gateways on network performance when receiving uplink data from LoRa nodes within the network, where (a) represents the network uplink data success rate (PDR) and (b) represents the average energy consumption of nodes within the network. Comparative analysis of the network communication success rate and energy consumption of LoRa nodes within the network obtained with different numbers of gateways verifies the effectiveness of the proposed solution.

[0169] This invention improves the performance of high-capacity LoRa networks. LoRa nodes within the network tend to use smaller SFs (Short Fibre Channels), resulting in shorter data transmission times and effectively reducing the probability of communication collisions within the same channel. According to... Figure 4 , Figure 5 , Figure 6In high-capacity LoRa networks, the allocation method of this invention increases the proportion of LoRa nodes using smaller SFs (Small Messages). Compared to the original ADR method, this invention improves the communication success rate in high-capacity LoRa networks from 34.7% to 42.5%, and enhances network communication performance by 7.8%. After six uplink cycles, the average energy consumption of LoRa nodes using the original algorithm is 900.24 mJ, while the average energy consumption of LoRa nodes using this invention is 862.35 mJ, representing a 4.2% reduction in average energy consumption and thus improving network lifetime to some extent.

[0170] The above examples of the present invention are merely illustrative of the computational model and process of the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is impossible to exhaustively list all possible implementations here. Any obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for allocating communication parameters in a LoRa high-capacity networking system, characterized in that, Based on the wireless communication model of the LoRa capacity networking system, communication parameters are allocated. During the allocation process, the adjustment count N corresponding to each LoRa node is first determined. step The number of adjustments N corresponding to the LoRa node step = A / 3; A = min(SINR) margin RSSI margain ); SINR margin = SINR max – SINR min – Margin; RSSI margain = RSSI max – RSSI min – Margin; Among them, SINR margin For the dryness margin, RSSI margain SINR is the received signal strength margin. max SINR min For the LoRa gateway receiver, calculate the maximum and minimum SINR among multiple uplink signals received from a single LoRa node; RSSI max RSSI min This represents the maximum and minimum RSSI values ​​among multiple uplink signals received from a single LoRa node at the LoRa gateway receiver; Margin represents the link margin. Then, based on the adjustment count N corresponding to the LoRa node... step Simultaneous adjustment of the spread factor (SF) and transmit power (TP) of the LoRa node achieves the allocation of the spread factor SF and the signal transmit power TP. The specific adjustment process is as follows: (1) When Nstep > 0, If the node does not use the minimum SF, then reduce the SF and decrease Nstep at the same time; If the node has already used the minimum SF and the TP used is greater than the minimum transmission power, then reduce TP and Nstep at the same time. If the minimum SF and minimum transmission power are used, the SF and TP values ​​of the output node are; (2) When Nstep <= 0, If TP is greater than the maximum transmission power, then decrease TP and increase Nstep at the same time; If TP is less than the maximum transmission power, then increase TP and simultaneously increase Nstep. If TP is less than the minimum transmission power, then set it to the minimum transmission power value and increase Nstep at the same time.

2. The method for allocating communication parameters in a LoRa high-capacity networking system according to claim 1, characterized in that, The wireless communication model includes a path loss model, a link budget model, an interference model, and a gateway reception model, wherein the interference model is as follows: In LoRa networking, nodes may experience interference from other nodes when transmitting uplink data. Therefore, a signal-to-interference-plus-noise ratio (SINNR) threshold matrix for LoRa signal reception in the common frequency domain is used in LoRa networking. As an interference matrix for The element in the i-th row and j-th column represents the signal-to-interference-plus-noise ratio (SNR) margin in dB between the LoRa node's signal SF=i and the interference signal SF=j. The LoRa node signal must be greater than the SNR margin in the interference matrix to be correctly demodulated.

3. The method for allocating communication parameters in a LoRa high-capacity networking system according to claim 2, characterized in that, The path loss model between LoRa nodes and LoRa gateways uses the Okumura-Hata model.

4. The method for allocating communication parameters in a LoRa high-capacity networking system according to claim 3, characterized in that, The link budget model for LoRa network wireless links is as follows: (2) Among them, P rx P is the power of the signal received at the receiver. tx It is the transmission power, G tx It is the transmitter's antenna gain, L tx It's the transmitter's loss, L pl It is the loss in the transmission path, L m G represents the margin for other losses and fading. rx It is the receiving antenna gain, L rx It refers to the loss at the receiver end.

5. The method for allocating communication parameters in a LoRa high-capacity networking system according to claim 3, characterized in that, The link budget model for LoRa network wireless links is as follows: (3) in, Represented as at reference distance The path loss is as follows. This represents the actual distance between the gateway and the LoRa node, where n is the path loss exponent, and X is the distance between the gateway and the LoRa node. σ Let P be a Gaussian distributed random variable with zero mean and variance σ. tx That is the transmission power.

6. A method for allocating communication parameters in a LoRa high-capacity networking system according to claim 4 or 5, characterized in that, The gateway receiving model is as follows: In a LoRa networking system, assuming that data from LoRa node a can be successfully received by LoRa gateway b, then the following should be satisfied:

1. The signal strength received when data sent by LoRa node a reaches the LoRa gateway. It must be greater than the receiver sensitivity of the LoRa gateway; 2. The signal-to-interference-plus-noise ratio (SINR) of the uplink signal at the LoRa gateway must be greater than the SINR threshold at which the LoRa gateway can correctly demodulate the data. ; The probability that the uplink data packet of LoRa node a is successfully received by LoRa gateway b is expressed as: (5) in, Represents probability; The value corresponds to the element T in matrix T. ij .

7. A communication parameter allocation system for a LoRa high-capacity networking system, characterized in that, This includes the wireless communication model creation unit and the communication parameter allocation unit for the LoRa capacity networking system; The LoRa capacity networking system wireless communication model creation unit is used to create the wireless communication model of the LoRa capacity networking system and provide a wireless communication model environment for the communication parameter allocation unit. Communication parameter allocation unit: Based on the wireless communication model of the LoRa capacity networking system, communication parameters are allocated. During the allocation process, the number of adjustment times N corresponding to the LoRa node is used. step Simultaneous adjustment of the SF and TP of the LoRa node, i.e., allocation of the spreading factor SF and signal transmit power TP, and the number of adjustment times N for the LoRa node in the communication parameter allocation unit. step = A / 3; A = min(SINR) margin RSSI margain ); SINR margin = SINR max – SINR min – Margin; RSSI margain = RSSI max – RSSI min – Margin; Among them, SINR margin For the dryness margin, RSSI margain SINR is the received signal strength margin. max SINR min For the LoRa gateway receiver, calculate the maximum and minimum SINR among multiple uplink signals received from a single LoRa node; RSSI max RSSI min This represents the maximum and minimum RSSI values ​​among multiple uplink signals received from a single LoRa node at the LoRa gateway receiver; Margin represents the link margin. The specific adjustment process is as follows: (1) When Nstep > 0, If the node does not use the minimum SF, then reduce the SF and decrease Nstep at the same time; If the node has already used the minimum SF and the TP used is greater than the minimum transmission power, then reduce TP and Nstep at the same time. If the minimum SF and minimum transmission power are used, the SF and TP values ​​of the output node are; (2) When Nstep <= 0, If TP is greater than the maximum transmission power, then decrease TP and increase Nstep at the same time; If TP is less than the maximum transmission power, then increase TP and simultaneously increase Nstep. If TP is less than the minimum transmission power, then set it to the minimum transmission power value and increase Nstep at the same time.

8. A communication parameter allocation device for a LoRa high-capacity networking system, characterized in that, The device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement a LoRa high-capacity networking system communication parameter allocation method as described in any one of claims 1 to 6.

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