Method for constructing dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc ad hoc network technology

Abstract

The invention relates to the technical field of network monitoring, in particular to a method for constructing a dynamic self-adaptive alarm monitoring network based on LoRa and Ad-hoc ad hoc network technology, which comprises the following steps: (1) integrating a dynamic Ad-hoc ad hoc network routing protocol on each LoRa node, so that each node can automatically discover neighbor nodes, establish and maintain a dynamic routing table, and automatically discover the neighbor nodes; a non-centralized network architecture is realized; (2) through a multi-hop communication mode, the alarm information can be flexibly transmitted among the nodes, the coverage range of the network is expanded, and the system adapts to a complex geographical environment; and (3) by utilizing the characteristics of low power consumption and long-distance transmission of the LoRa technology, stable communication between the nodes in a large range is ensured, meanwhile, the energy consumption is reduced, and the working life of the nodes is prolonged. Through the dynamic self-adaptive Ad-hoc routing protocol, the network can automatically adjust the routing according to the node state and the environment change, the dynamic change of the network topology is adapted, and the reliable transmission of the alarm information is ensured.

Description

Technical Field

[0001] The present invention relates to the technical fields of Internet of Things technology and wireless ad-hoc network technology, and particularly relates to a method for constructing a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc ad-hoc network technology, which is applicable to field environment monitoring and alarm systems. Background Art

[0002] With the rapid development of wireless sensor networks and Internet of Things technology, the demand for environmental monitoring in field applications is increasing day by day, such as forest fire monitoring, geological disaster warning, meteorological monitoring, etc. In these scenarios, due to the complex and changeable environment, the cost of conventional fixed network wiring is high, and it is difficult to achieve long-term and stable low-power communication in large-scale distributed applications. LoRa (Long Range) is a low-power wide area network technology that uses spread spectrum modulation and has the advantages of low power consumption, long-distance communication, and multi-access, and is very suitable for field environment monitoring systems. However, the fixed star topology of LoRa lacks flexibility in some application scenarios. Especially when nodes fail or the channel is blocked, network transmission is prone to interruption and lacks adaptability. On the other hand, Ad-hoc ad-hoc network is a network that does not require pre-established fixed infrastructure. Nodes can automatically discover and maintain communication paths, and is particularly suitable for building flexible network structures in environments where nodes are widely distributed, frequently moving or changing. The Ad-hoc network avoids relying on fixed base stations through multi-hop communication, can automatically form dynamic routes between nodes, and has extremely high adaptability and fault tolerance. Therefore, combining the long-distance transmission characteristics of LoRa with the dynamic routing ability of Ad-hoc ad-hoc network can build an alarm monitoring network that not only has low-power, long-distance communication capabilities, but also has high flexibility and scalability. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for constructing a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc ad-hoc network technology to solve the following main problems in existing field monitoring systems:

[0004] (1) Traditional network structures, such as LoRa star networks, have poor flexibility and are difficult to cope with node failures or dynamic changes in network topologies, resulting in insufficient network robustness;

[0005] (2) The direct communication distance between nodes is limited, and it is impossible to cover a large range of monitoring areas, making it difficult to achieve large-scale and distributed monitoring;

[0006] (3) In existing solutions, the power consumption of nodes is relatively high, which cannot meet the requirements of long-term field deployment, increasing the maintenance cost and energy consumption.

[0007] To solve the above technical problems, the present invention provides a method for constructing a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc self-organizing network technology. The method includes:

[0008] (1) Integrate a dynamic Ad-hoc self-organizing network routing protocol on each LoRa node, enabling each node to automatically discover neighbor nodes, establish and maintain a dynamic routing table, and achieve a decentralized network architecture;

[0009] (2) Through multi-hop communication, the alarm information can be flexibly transmitted between nodes, expanding the network coverage and adapting to complex geographical environments;

[0010] (3) Utilize the low-power and long-distance transmission characteristics of LoRa technology to ensure stable communication between nodes over a large range, while reducing energy consumption and extending the working life of nodes;

[0011] (4) The network can adaptively adjust according to environmental changes and node status, including automatically identifying failed nodes and weak signal links, and bypassing them to re-plan the optimal communication path to ensure the reliability of the network;

[0012] (5) When the alarm node detects abnormal environmental information, trigger the alarm mechanism, start the multi-hop transmission of alarm information, and reliably transmit the data to the central node or control terminal to achieve real-time monitoring and early warning.

[0013] Preferably, the dynamic Ad-hoc self-organizing network routing protocol adopts an improved AODV or OLSR protocol, which considers factors such as node signal strength, geographical location, remaining battery power, and link quality, dynamically adjusts the routing path, and selects the optimal path for alarm information transmission.

[0014] Preferably, the network supports decentralized management, and nodes communicate equally with each other. It uses a distributed algorithm to autonomously manage and maintain the routing table, has high scalability, can adapt to the needs of large-scale distributed networks, and ensures the robustness of the network.

[0015] Preferably, the alarm monitoring network is suitable for long-term monitoring in extreme natural environments. The nodes are designed with low power consumption and have self-energy management functions, can work stably for a long time under unattended conditions in the wild, and can provide real-time feedback of alarm information.

[0016] This method integrates a dynamic adaptive Ad-hoc routing protocol on LoRa nodes, enabling nodes to achieve dynamic routing adjustment without central control, ensuring stable transmission of alarm information. Especially when nodes or channels fail, the network can still operate effectively.

[0017] The specific steps are as follows:

[0018] (1) LoRa Node Hardware Design:

[0019] Each LoRa node consists of the following modules:

[0020] Sensor module, used to monitor environmental data such as temperature, humidity, smoke, seismic vibration or other sensible environmental data, and select appropriate sensors according to application requirements;

[0021] LoRa communication module, using LoRa radio frequency chips such as SX1276 of Semtech, responsible for realizing low-power, long-distance wireless communication;

[0022] Dynamic routing module, based on microcontrollers such as the STM32 series, running improved Ad-hoc routing protocols such as AODV or OLSR, to achieve route discovery, selection and maintenance;

[0023] Power management module: including battery power supply, power conversion and management circuits, supporting solar energy, battery or other power supply methods, with low-power design and energy management strategies.

[0024] (2) Dynamic Routing Implementation:

[0025] Neighbor discovery: Each node automatically discovers neighboring nodes and obtains a neighbor list by periodically broadcasting Hello messages and taking advantage of the wide coverage of LoRa;

[0026] Route selection: Nodes select routes based on multiple metrics, including signal strength, link quality, remaining energy of nodes, hop count, and geographical location information, and use a weighted function for comprehensive evaluation to select the optimal path;

[0027] Route maintenance: When the network topology changes, such as node movement, failure or new node addition, the node can update the routing table in a timely manner, recalculate the optimal path, and ensure network connectivity.

[0028] (3) Alarm Information Transmission:

[0029] Alarm trigger: When a certain node monitors an abnormal event, such as temperature exceeding the threshold, detecting smoke or geological vibration, the alarm mechanism is immediately triggered;

[0030] Multi-hop transmission: Alarm information is transmitted step by step to the target node such as the central node or control terminal through the multi-hop routing of the Ad-hoc network; during the transmission process, the node caches and forwards the data to ensure the integrity and timeliness of the information.

[0031] Reliable transmission: An acknowledgement mechanism and retransmission strategy are adopted to ensure the reliable transmission of alarm information in a complex environment.

[0032] (4) Power Consumption Management:

[0033] Node sleep: When there are no communication and monitoring tasks, the node enters a low-power sleep mode to reduce energy consumption;

[0034] Dynamic power consumption adjustment: According to the battery status, network load, and task requirements of the node, the working mode and transmission power are dynamically adjusted to extend the working life of the node;

[0035] Energy harvesting: Solar and electromagnetic energy harvesting modules can be optionally configured to power the node and further extend the working time.

[0036] The present invention provides a method for constructing a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc self-organizing network technology. Compared with the prior art, it has the following improvements and advantages:

[0037] 1. High flexibility and self-adaptability: Through a dynamic adaptive Ad-hoc routing protocol, the network can automatically adjust the routing according to the node status and environmental changes, adapt to the dynamic changes of the network topology, and ensure the reliable transmission of alarm information.

[0038] 2. Low-power design: Using LoRa technology and energy management strategies, the node can operate for a long time in a low-power mode, which is especially suitable for unattended monitoring scenarios in the wild and reduces the maintenance cost.

[0039] 3. Wide coverage and multi-hop communication: Through multi-hop routing technology, the network can cover areas beyond the single-node communication distance, adapt to complex wild terrains, and achieve large-scale monitoring and early warning.

[0040] 4. High fault tolerance and reliability: When a node fails or the link quality deteriorates, the network can automatically reconstruct the routing and select an alternative path to ensure the stability and reliability of the overall system.

[0041] 5. Scalability: The network supports decentralized management, and nodes can be smoothly added or removed, with high scalability, meeting the requirements of large-scale distributed networks. Description of the Drawings

[0042] The following further explains the present invention in conjunction with the drawings and embodiments:

[0043] Figure 1 is a schematic diagram of the structure of a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc self-organizing network technology;

[0044] Figure 2 is a block diagram of the hardware composition of a LoRa node;

[0045] Figure 3 is a flow chart of dynamic route selection;

[0046] Figure 4 This is a schematic diagram of multi-hop transmission of alarm information.

[0047] Description of reference numerals:

[0048] 1. Instrument body; 2. Main support ring plate; 3. Main support leg; 4. Support foot; 6. Positioning bolt; 7. Pedal; 8. Fixed bottom block; 9. Instrument support plate; 10. Toggle gear; 12. Connecting block; 13. Fixing nail; 14. Dual-purpose fixing rod; 15. Suspension handle; 16. Calibration spherical plate; 17. High-strength rolling ball; 18. Passive gear ring; 19. Inner support ring; 20. Selective sliding card; 21. Lifting ring; 22. Transmission ring; 23. Bearing ring; 24. Threaded top ring; 25. Inner Fixed fan plate; 26, spring one; 90, calibration hole; 111, bubble indicator; 112, instrument chassis; 113, foot screw; 120, rotation slot; 121, connecting slide; 122, spring two; 151, hook; 161, disk extension; 200, tooth groove; 201, connection slot; 211, slotted extension plate; 230, rolling groove; 231, inner circle card; 240, passive groove; 2001, slide card toggle handle; 2010, slide groove; 2002, convex surface. DETAILED DESCRIPTION

[0049] The present invention is described in detail below, and the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The present invention provides a method for constructing a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc self-organizing network technology. The technical solution of the present invention is:

[0051] Embodiment 1: Safety monitoring and warning system for workers in outdoor areas without signal.

[0052] In construction work in the wild uninhabited area, it is difficult to obtain the safety information of personnel in a timely manner due to the lack of operator network coverage. The network method provided by the present invention builds a self-organizing local area network through the LoRa node device worn by the operator to achieve interconnection communication. The specific implementation includes the following steps:

[0053] (1) Each worker wears a LoRa node with built-in motion sensors, such as accelerometers and gyroscopes, and physiological parameter sensors, such as heart rate and body temperature;

[0054] (2) Communication links are automatically formed between nodes through an Ad-hoc network, and the collected data is transmitted to the central monitoring station in a multi-hop manner;

[0055] (3) When abnormal situations are detected, such as a person falling or abnormal vital signs, an alarm is immediately triggered to ensure the safety of the person;

[0056] (4) The network can be automatically adjusted according to the movement of people to ensure the stability of the communication link.

[0057] Example 2: Forest fire real-time monitoring and early warning system.

[0058] In forest fire prevention, it is crucial to detect a fire in a timely manner. Using the method of the present invention:

[0059] (1) A large number of LoRa nodes are arranged in the forest area, and each node is equipped with a temperature sensor, a smoke sensor, and a humidity sensor;

[0060] (2) The nodes form a self-organizing monitoring network through an Ad-hoc network, covering the entire forest area;

[0061] (3) When a certain node detects an abnormal increase in temperature or an excessive smoke concentration, an alarm message is immediately triggered;

[0062] (4) The alarm message is transmitted through multi-hop, bypassing the nodes that may fail, and quickly transmitted to the fire prevention command center;

[0063] (5) The system can dynamically adjust the key monitoring areas according to the development of the fire, improving the accuracy of early warning.

[0064] Example 3: Geological disaster monitoring and early warning system.

[0065] In mountainous areas or areas prone to geological disasters, the method of the present invention can be used to monitor geological changes in real time:

[0066] (1) LoRa nodes are arranged in areas prone to landslides and debris flows, and are equipped with seismic sensors, tilt sensors, and soil humidity sensors;

[0067] (2) The nodes build a monitoring network through an Ad-hoc network to collect geological data in real time;

[0068] (3) When abnormal seismic fluctuations, surface tilting, or a sharp change in soil humidity are detected, an alarm message is triggered;

[0069] (4) The alarm message is transmitted to the monitoring center through a multi-hop network, and relevant departments can take preventive measures in a timely manner.

[0070] Example 4: Flood warning monitoring system.

[0071] In rivers, lakes or other water areas, use the method of the present invention:

[0072] (1) Arrange LoRa nodes equipped with water level sensors, rain gauges and current meters;

[0073] (2) The nodes form a self-organizing network to monitor hydrological data in real time;

[0074] (3) When the water level exceeds the warning line or the rainfall reaches the threshold, trigger a flood warning;

[0075] (4) The warning information is transmitted through multi-hop to notify downstream residents and flood control departments to take precautions in advance.

[0076] Through the above specific implementation manners, the network method of the present invention has broad application prospects in various field monitoring and warning systems, and can effectively improve the timeliness and reliability of environmental monitoring.

[0077] The above description enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a dynamic adaptive alarm monitoring network based on LoRa and Ad-hoc self-organizing network technology, characterized in that: The following steps are involved: (1) Integrate a dynamic Ad-hoc self-organizing network routing protocol on each LoRa node, so that each node can automatically discover neighboring nodes, establish and maintain a dynamic routing table, and realize a decentralized network architecture; (2) Through multi-hop communication, alarm information can be flexibly transmitted between nodes, expanding the coverage of the network and adapting to complex geographical environments; (3) Utilize the low power consumption and long-distance transmission characteristics of LoRa technology to ensure stable communication between nodes over a large range, while reducing energy consumption and extending the working life of nodes; (4) The network can make adaptive adjustments based on environmental changes and node status, including automatically identifying failed nodes and weak signal links, bypassing them and replanning the optimal communication path to ensure network reliability; (5) When the alarm node detects abnormal environmental information, it triggers the alarm mechanism, starts the multi-hop transmission of the alarm information, and reliably transmits the data to the central node or control terminal to achieve real-time monitoring and early warning.

2. The method according to claim 1, characterized in that: The dynamic Ad-hoc network routing protocol adopts an improved AODV or OLSR protocol, takes into account the signal strength, geographical location, remaining power, and link quality of the node, dynamically adjusts the routing path, and selects the optimal path for transmission of alarm information.

3. The method according to claim 1 or 2, characterized in that: The network supports decentralized management, with equal communication between nodes, and uses distributed algorithms to autonomously manage and maintain routing tables. It has high scalability, can adapt to the needs of large-scale distributed networks, and ensure the robustness of the network.

4. The method according to claim 1, characterized in that The alarm monitoring network is suitable for long-term monitoring in extreme natural environments. The nodes are designed with low power consumption and have self-energy management functions. They can work stably for a long time in the wild without human supervision and provide real-time feedback of alarm information.

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