A multi-hop path construction method for passive sensor networks

Abstract

The present invention discloses a multi-hop path construction method for a passive sensor network, which relates to the field of Internet of Things communication and routing optimization technology. The method comprises the following steps: S1, initializing a graph structure network composed of multiple passive sensor nodes; S2, each node periodically broadcasting its own state parameters; S3, constructing a load-based edge weight model; S4, generating a weighted graph based on the path node sequence and edge weight model of the graph structure network; using the Dijkstra algorithm to calculate the shortest path based on the weighted graph; S5, using the shortest path as the optimal path between the source node and the target node; S6, the node transmitting data according to the optimal path and recording and updating its own state parameters in real time; S7, each node periodically broadcasting its updated state parameters, and a central scheduler repeating steps S3-S7 based on the received updated node state parameters. The present invention solves the technical problems of low transmission efficiency and low stability of the single-hop model.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things communication and routing optimization, and in particular to a multi-hop path construction method for a passive sensor network. Background Art

[0002] Passive sensor nodes are widely used in scenarios such as structural health monitoring, environmental monitoring, and smart cities. These nodes rely on intermittent recharging from ambient energy (e.g., wireless power transmission) to perform sensing, processing, and communication tasks. Existing research mostly assumes a single-hop communication model. However, in ultra-large-scale IoT deployments, this traditional single-hop model can lead to network delays or interruptions due to overload or failure of a single node, resulting in low transmission efficiency and stability. Furthermore, deploying too many relay nodes incurs cost and maintenance burdens, necessitating the exploration of multi-hop communication strategies involving passive nodes.

[0003] However, unlike traditional active wireless sensor networks, passive nodes, while not constrained by continuous energy consumption, are limited by charging rates and communication loads. Overloading any node can lead to path bottlenecks, increasing overall routing latency. Therefore, a latency-aware distributed routing model is needed to properly schedule data flows between nodes and select the optimal path. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a multi-hop path construction method for a passive sensor network with the shortest delay and taking load balancing into consideration, so as to solve some of the above technical problems.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A multi-hop path construction method for a passive sensor network comprises the following steps:

[0007] S1, initialize a graph structure network consisting of multiple passive sensor nodes;

[0008] S2. Each node periodically broadcasts its own status parameters;

[0009] S3. Based on the node's own state parameters and the graph structure network, a node total delay model and a load-based edge weight model are constructed;

[0010] S4. Generate a weighted graph based on the path node sequence and edge weight model of the graph structure network; and calculate the shortest path using the Dijkstra algorithm based on the weighted graph.

[0011] S5. The shortest path is used as the optimal path between the source node and the target node;

[0012] S6. The node transmits data according to the optimal path and records and updates its own status parameters in real time;

[0013] S7. Each node periodically broadcasts its own updated state parameters. The central scheduler repeats steps S3-S7 based on the received updated node state parameters to dynamically adjust the optimal path.

[0014] A multi-hop path construction method for a passive sensor network, characterized by comprising the following steps:

[0015] S1, initialize a graph structure network consisting of multiple passive sensor nodes;

[0016] S2. Each node periodically broadcasts its own status parameters;

[0017] S3. Based on the node's own state parameters and the graph structure network, a node total delay model and a load-based edge weight model are constructed;

[0018] S4. Generate a weighted graph based on the path node sequence and edge weight model of the graph structure network; and calculate the shortest path using the Dijkstra algorithm based on the weighted graph.

[0019] S5. Taking the shortest path as the optimal path between the source node and the target node;

[0020] S6. The node transmits data according to the optimal path and records and updates its own status parameters in real time;

[0021] S7. Each node periodically broadcasts its own updated state parameters. The central scheduler repeats steps S3-S7 based on the received updated node state parameters to dynamically adjust the optimal path.

[0022] Furthermore, in S1, the graph structure network is , is the set of sensor nodes, is a set of communicable links, each link Represents node i and Direct communication is possible.

[0023] Furthermore, in S2, the state parameters of the node include Charging rate ,node Energy required for unit data communication ,node Current load factor ,in, satisfy .

[0024] Furthermore, it is characterized in that S3 includes:

[0025] S31. Build a node total delay model , node total delay model for: ;in, For nodes The time required to charge a communication. ; For nodes The actual communication delay under the influence of communication load, , For nodes arrive Unit data transmission delay;

[0026] S32, based on the total node delay model , build edge weight model , each link The edge weight of is: ,in, is the regulating factor.

[0027] Furthermore, in S32, the adjustment formula of the adjustment factor is: ;in, is the adjustment factor at the current time t, is the initial adjustment factor, To adjust the response of the factor to the load, is the average load factor of the network at the current time t, is the maximum possible value of the load factor in the network, .

[0028] Furthermore, S4 includes:

[0029] S41. Set the path node sequence of the graph structure network to: ,in, is the source node, For the destination node, is the intermediate node;

[0030] S42, based on the edge weight model, generate Weighted graph of ;

[0031] S43, according to Weighted graph of , calculated using Dijkstra algorithm arrive The shortest path is calculated, and the total delay corresponding to the shortest path is calculated to obtain the minimum total delay.

[0032] Furthermore, in S43, the total delay The calculation formula is:

[0033] ;

[0034] in, is the sum of delays of all nodes in the path, is the sum of the transmission delays of all links in the path, is the total load on the current communication link.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) Enhanced network transmission efficiency and stability: The present invention improves the data transmission efficiency of passive sensor networks in ultra-large-scale Internet of Things environments by introducing a multi-hop routing model based on the shortest delay and combining the Dijkstra algorithm with a load balancing mechanism. This reduces the network transmission delay, especially in complex environments. Even if some nodes are restricted or fail, the stability of the entire network and the data transmission success rate can be maintained at a high level, thus avoiding network interruptions caused by overload or failure of a single node in traditional single-hop routing, and thus enhancing the reliability of the network.

[0037] (2) Optimized path selection and load balancing: The present invention adopts the Dijkstra algorithm to ensure the optimal network communication path by dynamically selecting the shortest delay path and reasonably distributing the node load. In particular, the load balancing factor (adjustment factor) included in the edge weight avoids communication delays caused by overload of intermediate nodes. This method not only improves transmission efficiency, but also balances the node load, reduces the pressure on certain nodes, and thus reduces the risk of node failure. This optimization strategy not only ensures the efficient operation of the network, but also improves the network's adaptability to emergencies and unstable factors.

[0038] (3) Improved network adaptability and scalability: The present invention enables the network to automatically adapt to environmental changes and node load fluctuations through a dynamic network status update mechanism and real-time adjustment of the path selection strategy. This mechanism supports adaptive scheduling and path optimization of the network, maintaining good transmission performance regardless of the large number of nodes or complex network topology. At the same time, the method has high scalability and can be flexibly deployed according to different application scenarios and requirements, achieving efficient multi-hop communication without excessive hardware investment.

[0039] (4) Reduced maintenance costs and improved system stability: The present invention effectively reduces network deployment and maintenance costs by reducing dependence on relay nodes. At the same time, the use of load balancing and path optimization technology improves the stability and recoverability of the system. Even if some nodes fail or the communication quality degrades due to environmental changes, the entire network can still adapt quickly and maintain stable communication performance. This adaptive routing and optimization algorithm reduces the maintenance requirements of the network and provides cost-effectiveness for long-term operation and management in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; of course, they may also refer to mechanical connections or electrical connections; in addition, they may refer to direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0044] like Figure 1 As shown, a multi-hop path construction method for a passive sensor network includes the following steps:

[0045] S1. Initialize a graph structure network consisting of multiple passive sensor nodes; this step performs network initialization, that is, the network graph structure definition.

[0046] S2. Each node periodically broadcasts its own status parameters; this step reports node status information.

[0047] S3: Based on the node's own state parameters and the graph structure network, a node total delay model and a load-based edge weight model are constructed; this step performs node delay modeling and edge weight construction.

[0048] S4. Generate a weighted graph based on the path node sequence and edge weight model of the graph structure network; calculate the shortest path using the Dijkstra algorithm based on the weighted graph; this step implements the Dijkstra routing calculation method with delay and load weighting.

[0049] S5. The shortest path is used as the optimal path between the source node and the target node. This step allocates the shortest delay multi-hop path.

[0050] S6. The node transmits data according to the optimal path and records and updates its own state parameters in real time. In this step, the node performs the communication task.

[0051] S7, each node periodically broadcasts its own state parameters after updating, and the central scheduler repeats steps S3-S7 according to the received updated node state parameters to dynamically adjust the optimal path. In this embodiment, the edge weight model is updated in real time after the node state parameters are updated. This step performs periodic routing updates and returns to the node delay modeling step if the communication task is not completed.

[0052] This embodiment improves the data transmission efficiency of passive sensor networks in ultra-large-scale Internet of Things environments and reduces network transmission delays through a multi-hop routing model combined with the Dijkstra algorithm and load balancing mechanism. In particular, in complex environments, even if some nodes are restricted or fail, the stability of the entire network and the data transmission success rate can be maintained at a high level, thereby avoiding network interruptions caused by overload or failure of a single node in traditional single-hop routing, and enhancing network reliability.

[0053] Preferably, in S1, the graph structure network is , is the set of sensor nodes, is a set of communicable links, each link Represents node i and Direct communication is possible.

[0054] Preferably, in S2, the state parameters of the node include Charging rate (Amount of energy obtained per unit time, unit: W), node Energy required for unit data communication (Unit: J), node Current load factor (node The ratio of the amount of data processed per unit time to the maximum data processing capacity), where satisfy .

[0055] The present invention is based on a multi-hop path construction method for a passive sensor network. In one embodiment, S3 includes:

[0056] S31. Build a node total delay model , node total delay model for: ;in, For nodes The time required to charge a communication. ; For nodes The actual communication delay under the influence of communication load, , For nodes arrive The unit data transmission delay (unit: s), that is, the node and The communication delay between Related to communication distance, medium, and rate;

[0057] S32, based on the total node delay model , build edge weight model , each link The edge weight of is: ,in, is the adjustment factor, the weight is the current point The total delay plus and The communication delay between nodes, plus the load factor of the current node, constitutes the weight of the edge. , can determine the impact of load factor on routing selection; dynamically adjust according to the load of the network Generally speaking, when the network load is low, the path with the shortest delay is given priority, which can reduce ; When the network load is high, load balancing is given priority. Should be increased.

[0058] In S32, the adjustment formula of the regulation factor is: ;in, is the adjustment factor at the current time t, is the initial adjustment factor, which can be set to a constant, such as 1; To adjust the degree of response of the factor to the load, it can be determined by experiment or simulation (usually a small positive number); is the average load factor of the network at the current time t, is the maximum possible value of the load factor in the network, (usually set to 1). This adjustment formula shows that as the network load increases, will increase proportionally, making load balancing the driving force for more path selection; and when the network load is low, It will tend to the initial value, thus focusing more on minimizing the delay. In the adjustment formula, the adjustment factor It will be dynamically adjusted according to the real-time load of the network. This formula can not only adapt to high load situations and achieve load balancing by increasing the influence of the load factor, but also maintain the goal of minimizing latency when the load is low, thereby achieving the optimal routing effect.

[0059] The present invention is based on a multi-hop path construction method for a passive sensor network. In one embodiment, S4 includes:

[0060] S41. Set the path node sequence of the graph structure network to: ,in, is the source node, For the destination node, is the intermediate node;

[0061] S42, based on the edge weight model, generate Weighted graph of ;

[0062] S43, according to Weighted graph of , calculated using Dijkstra algorithm arrive The shortest path is calculated, and the total delay corresponding to the shortest path is calculated to obtain the minimum total delay.

[0063] Preferably, in S43, the total delay The calculation formula is:

[0064] ;

[0065] in, is the sum of delays of all nodes in the path, is the sum of the transmission delays of all links in the path, is the total load on the current communication link.

[0066] This embodiment utilizes the Dijkstra algorithm to ensure optimal network communication paths by dynamically selecting the shortest-latency path and rationally distributing node loads. In particular, the load balancing factor (adjustment factor) included in the edge weights avoids communication delays caused by overloaded intermediate nodes. This method not only improves transmission efficiency but also balances node loads, reducing the pressure on certain nodes and thereby lowering the risk of node failure. This optimization strategy not only ensures efficient network operation but also enhances the network's adaptability to emergencies and unstable factors. It also improves network adaptability and scalability: Through a dynamic network state update mechanism and real-time adjustment of path selection strategies, the present invention enables the network to automatically adapt to environmental changes and node load fluctuations. This mechanism supports adaptive scheduling and path optimization, maintaining excellent transmission performance regardless of the number of nodes or complex network topology. Furthermore, this method is highly scalable and can be flexibly deployed according to different application scenarios and requirements, achieving efficient multi-hop communication without excessive hardware investment. Reduced maintenance costs and improved system stability: This invention effectively reduces network deployment and maintenance costs by reducing reliance on relay nodes. At the same time, the use of load balancing and path optimization technologies improves system stability and recoverability. Even if some nodes fail or communication quality degrades due to environmental changes, the entire network can still adapt quickly and maintain stable communication performance. This adaptive routing and optimization algorithm reduces network maintenance requirements and provides cost-effectiveness for long-term operation and management in practical applications.

[0067] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention and are intended to illustrate the technical solutions of the present invention, rather than limiting them, and certainly not limiting the patent scope of the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features therein may be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention. In other words, any changes or refinements made to the main design concept and spirit of the present invention that have no substantive significance, provided that the technical problems they solve are still consistent with those of the present invention, should be included in the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention to other related technical fields should also be included in the patent protection scope of the present invention.

Claims

1. A multi-hop path construction method for a passive sensor network, characterized in that: The steps include: S1, initialize a graph structure network consisting of multiple passive sensor nodes; S2. Each node periodically broadcasts its own status parameters; S3. Based on the node's own state parameters and the graph structure network, a node total delay model and a load-based edge weight model are constructed; S4. Generate a weighted graph based on the path node sequence and edge weight model of the graph structure network; and calculate the shortest path using the Dijkstra algorithm based on the weighted graph. S5. Taking the shortest path as the optimal path between the source node and the target node; S6. The node transmits data according to the optimal path and records and updates its own status parameters in real time; S7. Each node periodically broadcasts its updated state parameters. The central scheduler repeats steps S3-S7 based on the received updated state parameters to dynamically adjust the optimal path. In S1, the graph structure network is , is the set of sensor nodes, is a set of communicable links, each link Represents node i and Direct communication is possible; In S2, its own state parameters include node Charging rate ,node Energy required for unit data communication ,node Current load factor ,in, satisfy ; S3 includes: S31. Build a node total delay model , node total delay model for: ;in, For nodes The time required to charge a communication. ; For nodes The actual communication delay under the influence of communication load, , For nodes arrive Unit data transmission delay; S32, based on the total node delay model , build edge weight model , each link The edge weight of is: ,in, is the regulating factor; In S32, the adjustment formula of the regulation factor is: ;in, is the adjustment factor at the current time t, is the initial adjustment factor, To adjust the response of the factor to the load, is the average load factor of the network at the current time t, is the maximum possible value of the load factor in the network, ; S4 includes: S41. Set the path node sequence of the graph structure network to: ,in, is the source node, For the destination node, is the intermediate node; S42, based on the edge weight model, generate Weighted graph of ; S43, according to Weighted graph of , calculated using Dijkstra algorithm arrive The shortest path, calculate the total delay corresponding to the shortest path, and get the minimum total delay; In S43, total delay The calculation formula is: ； in, is the sum of delays of all nodes in the path, is the sum of the transmission delays of all links in the path, is the total load on the current communication link.

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