CN104883718A - Multilayer prediction control method for sensing network data transmission, and system thereof - Google Patents
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Abstract
本发明提供了一种传感网络数据传输的多层预测控制方法和系统。多层预测控制方法包括：将节点分成多个簇；确定每个簇内的控制节点和传输节点；根据构建的预测控制策略对每个簇内的传输节点的数据传输流进行预测，控制节点只向预测的预测结果中出现的传输节点进行广播。由于控制节点只向预测的预测结果中出现的传输节点进行广播，因而，避免了长时间等待的耗电，从而在传输时延存在的前提下，加强了节点的能量控制，其有效的数据流预测提高了数据传输的准确率，延长了整个传感网络使用寿命。
The invention provides a multi-layer predictive control method and system for sensor network data transmission. The multi-layer predictive control method includes: dividing the nodes into multiple clusters; determining the control nodes and transmission nodes in each cluster; predicting the data transmission flow of the transmission nodes in each cluster according to the constructed predictive control strategy, and the control nodes only Broadcast to the transit nodes appearing in the predicted prediction results. Since the control node only broadcasts to the transmission nodes that appear in the predicted prediction results, the power consumption of long-term waiting is avoided, and the energy control of nodes is strengthened under the premise of transmission delay, and its effective data flow Prediction improves the accuracy of data transmission and prolongs the lifetime of the entire sensor network.
Description
技术领域technical field
本发明涉及传感器网络领域，特别涉及一种传感网络数据传输的多层预测控制方法和系统。The invention relates to the field of sensor networks, in particular to a multi-layer predictive control method and system for sensor network data transmission.
背景技术Background technique
无线传感网络由置于监测区域内由大量具有感知、计算和通信能力的微型传感器节点组成，是一种自组织分布式的网络。这些传感节点具有价格低、体积小等特点，监测数据信息通过网络节点间的协作采集周围环境的相关信息，进行简单的数据处理后，运用短距离多跳的通信方式将信息传输到基站作进一步的分析和处理。The wireless sensor network is composed of a large number of micro sensor nodes with perception, computing and communication capabilities placed in the monitoring area, and is a self-organizing distributed network. These sensor nodes have the characteristics of low price and small size. The monitoring data information collects the relevant information of the surrounding environment through the cooperation between network nodes, and after simple data processing, the information is transmitted to the base station by means of short-distance multi-hop communication. further analysis and processing.
通常情况下，无线传感器节点的能量仅由容量有限的电池提供，且能量难以补充。如何节约节点能量、最大化网络寿命是无线传感器网络协议研究中最首要的问题，而能量控制问题主要从两个方面开展研究：扩展电池能源和减少繁琐操作。然而，前者受到较多的物理因素限制。Usually, the energy of wireless sensor nodes is only provided by batteries with limited capacity, and the energy is difficult to replenish. How to save node energy and maximize network life is the most important issue in the research of wireless sensor network protocols, and the energy control problem is mainly researched from two aspects: extending battery energy and reducing cumbersome operations. However, the former is limited by more physical factors.
例如：专利申请号CN201010248181.1公开号CN101895956A的中国发明专利“多层分布式无线传感器网络数据传输方法”提供了通过对无线传感网络进行分簇形成链路结构的多层控制方法，节点能量是制定链路的影响因素之一，但网络传输过程中的时间延迟并没有考虑。For example: Patent Application No. CN201010248181.1 Publication No. CN101895956A Chinese Invention Patent "Data Transmission Method for Multi-layer Distributed Wireless Sensor Networks" provides a multi-layer control method for forming a link structure by clustering wireless sensor networks, node energy It is one of the influencing factors of making the link, but the time delay in the network transmission process is not considered.
Daniele Bernardini等运用能量意识的鲁棒模型预测控制方法进行带传输噪音的无线传感器的传输控制，利用控制器直接对传感器进行控制与反馈，因此对复杂的无线传感网络存在一定的局限性（详见Automatica.vol.48,no.1,pp:36-44,2012）。Daniele Bernardini et al. used the energy-aware robust model predictive control method to control the transmission of wireless sensors with transmission noise, and used the controller to directly control and feed back the sensors. Therefore, there are certain limitations for complex wireless sensor networks (detailed See Automatica.vol.48, no.1, pp:36-44, 2012).
由于传输过程中的网络延时不只是简单的传输滞后，而是将控制问题复杂化，所以多数研究理想化网络节点间的传输延迟。因此，急需一种有效的无线传感网络数据传输控制方法，解决传输延迟引发的网络传输的失效，以及传输节点的能量浪费问题。Since the network delay in the transmission process is not just a simple transmission delay, but complicates the control problem, most studies have idealized the transmission delay between network nodes. Therefore, there is an urgent need for an effective wireless sensor network data transmission control method to solve the failure of network transmission caused by transmission delay and the energy waste of transmission nodes.
发明内容Contents of the invention
本发明的目的是提供一种有助于解决传输延迟引发的网络传输的失效，以及传输节点的能量浪费问题的传感网络数据传输的多层预测控制方法和系统。The purpose of the present invention is to provide a multi-layer predictive control method and system for sensor network data transmission that helps to solve the failure of network transmission caused by transmission delay and the energy waste of transmission nodes.
作为本发明的第一方面，提供了一种传感网络数据传输的多层预测控制方法，包括：将节点分成多个簇；确定每个簇内的控制节点和传输节点；根据构建的预测控制策略对每个簇内的传输节点的数据传输流进行预测，控制节点只向预测的预测结果中出现的传输节点进行广播。As a first aspect of the present invention, a multi-layer predictive control method for sensor network data transmission is provided, including: dividing nodes into multiple clusters; determining control nodes and transmission nodes in each cluster; The strategy predicts the data transmission flow of the transmission nodes in each cluster, and the control node only broadcasts to the transmission nodes that appear in the predicted prediction results.
进一步地，预测控制策略是通过下述方式构建的：每个采样时刻，将每个传输节点的传输量作为系统状态变量，将传感器采样数据量作为系统输入，以建立传输网络的状态空间描述；通过求解在状态空间描述下求解最小目标函数获得最优传输途径在未来一段时域内每个传输节点的数据传输流的预测值。Furthermore, the predictive control strategy is constructed in the following way: at each sampling moment, the transmission volume of each transmission node is used as the system state variable, and the sensor sampling data volume is used as the system input to establish a state space description of the transmission network; By solving the minimum objective function under the description of the state space, the predicted value of the data transmission flow of each transmission node in the future time domain of the optimal transmission path is obtained.
进一步地，方法还包括：根据数据传输流的预测误差，对预测进行误差补偿。Further, the method further includes: performing error compensation on the prediction according to the prediction error of the data transmission stream.
进一步地，预测误差来自于控制节点反馈；将预测控制策略在进行预测时使用的目标函数根据反馈的预测误差在流动时域中进行迭代更新以补偿预测误差。Furthermore, the prediction error comes from the feedback of the control nodes; the objective function used by the prediction control strategy is iteratively updated in the flow time domain according to the feedback prediction error to compensate for the prediction error.
进一步地，簇内的控制节点是剩余电量最多的节点。Further, the control node in the cluster is the node with the most remaining power.
进一步地，如果簇内具有两个或两个以上剩余电量相同的节点，那么选取已作为控制节点次数少的节点作为控制节点。Further, if there are two or more nodes with the same remaining power in the cluster, select the node that has been used as a control node less times as the control node.
进一步地，将节点分成多个簇时，按照位置信息，采用蚁群算法对所有节点进行分簇。Further, when the nodes are divided into multiple clusters, according to the location information, the ant colony algorithm is used to cluster all the nodes.
作为本发明的第二方面，提供了一种传感网络数据传输的多层预测控制系统，包括：多个节点，多个节点分成多个簇，每个簇内的节点分为控制节点和传输节点；控制器，与控制节点通讯连接，控制器根据构建的预测控制策略对每个簇内的传输节点的数据传输流进行预测；控制节点只向预测的预测结果中出现的传输节点进行广播。As a second aspect of the present invention, a multi-layer predictive control system for sensor network data transmission is provided, including: multiple nodes, multiple nodes are divided into multiple clusters, and nodes in each cluster are divided into control nodes and transmission The node; the controller, communicates with the control node. The controller predicts the data transmission flow of the transmission nodes in each cluster according to the constructed prediction control strategy; the control node only broadcasts to the transmission nodes that appear in the predicted prediction results.
进一步地，控制器在每个采样时刻，将每个传输节点的传输量作为系统状态变量，将传感器采样数据量作为系统输入，以建立传输网络的状态空间描述，通过求解状态空间描述的目标函数获得最优传输途径下，未来一段时域内，每个传输节点的数据传输流的预测值。Furthermore, at each sampling moment, the controller takes the transmission amount of each transmission node as the system state variable, and the sensor sampling data volume as the system input to establish the state space description of the transmission network, and solve the objective function described by the state space Obtain the predicted value of the data transmission flow of each transmission node in the future time domain under the optimal transmission path.
进一步地，控制节点将数据传输流的预测误差反馈给控制器；控制器根据预测误差，对目标函数在流动时域中进行迭代更新以补偿预测误差。Further, the control node feeds back the prediction error of the data transmission flow to the controller; the controller iteratively updates the objective function in the flow time domain according to the prediction error to compensate for the prediction error.
由于控制节点只向预测的预测结果中出现的传输节点进行广播，因而，避免了长时间等待的耗电，从而在传输时延存在的前提下，加强了节点的能量控制，其有效的数据流预测提高了数据传输的准确率，延长了整个传感网络使用寿命。附图说明Since the control node only broadcasts to the transmission nodes that appear in the predicted prediction results, the power consumption of long-term waiting is avoided, and the energy control of nodes is strengthened under the premise of transmission delay, and its effective data flow Prediction improves the accuracy of data transmission and prolongs the lifetime of the entire sensor network. Description of drawings
图1是无线传感网络传输节点的分簇示例图；Fig. 1 is a clustering example diagram of wireless sensor network transmission nodes;
图2是无线传感网络的分层控制架构图；Fig. 2 is a hierarchical control architecture diagram of a wireless sensor network;
图3是控制节点A在50s内的传输仿真图；Fig. 3 is a transmission simulation diagram of control node A within 50s;
图4是控制节点B在50s内的传输仿真图；Fig. 4 is a transmission simulation diagram of the control node B within 50s;
图5是控制节点C在50s内的传输仿真图；Fig. 5 is a transmission simulation diagram of control node C within 50s;
图6是控制节点D在50s内的传输仿真图。Fig. 6 is a transmission simulation diagram of control node D within 50s.
具体实施方式Detailed ways
以下是本发明优选实施例的详细描述，应当理解，优选实施例仅为了说明本发明，而不是为了限制本发明的保护范围。The following is a detailed description of the preferred embodiments of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.
作为本发明的第一方面，请参考图1，本发明提供了一种传感网络（例如无线传感网络等）数据传输的多层预测控制方法，包括：将节点分成多个簇；确定每个簇内的控制节点和传输节点；根据构建的预测控制策略对每个簇内的传输节点的数据传输流进行预测，控制节点只向预测的预测结果中出现的传输节点进行广播（即与传输节点进行数据传输）。As the first aspect of the present invention, please refer to FIG. 1. The present invention provides a multi-layer predictive control method for data transmission in a sensor network (such as a wireless sensor network, etc.), including: dividing nodes into multiple clusters; determining each Control nodes and transmission nodes in a cluster; predict the data transmission flow of transmission nodes in each cluster according to the constructed predictive control strategy, and the control node only broadcasts to the transmission nodes that appear in the predicted prediction results (that is, the same as the transmission node node for data transmission).
优选地，簇的数量与节点的传输频率和/或传输距离成反比例函数，例如，当传输节点的传输频率高或传输节点的传输距离较远时，降低簇的数量以减少控制节点的能耗。特别地，对传感网络中的所有节点进行分簇。优选地，预测控制策略是建立滚动时域内的，其用于对未来一段时域的传感网络的数据传输流进行预测。Preferably, the number of clusters is an inversely proportional function to the transmission frequency and/or transmission distance of the nodes, for example, when the transmission frequency of the transmission node is high or the transmission distance of the transmission node is long, the number of clusters is reduced to reduce the energy consumption of the control node . In particular, all nodes in the sensor network are clustered. Preferably, the predictive control strategy is established in a rolling time domain, which is used to predict the data transmission flow of the sensor network in a future time domain.
特别地，只有那些在预测结果中，可能会发生数据传输的传输节点才会将其采集到的数据发送给控制器。这可以理解为，控制器将其预测的结果发送给相关的控制节点，控制节点再根据控制器的预测结果，向预测结果中的会发生数据传输的传输节点发出广播，当这些被预测中的传输节点收到广播后，将其数据直接（不通过控制节点）传输给控制器。In particular, only those transmission nodes that may have data transmission in the predicted results will send the collected data to the controller. It can be understood that the controller sends its predicted results to the relevant control nodes, and the control nodes broadcast to the transmission nodes in the predicted results where data transmission will occur according to the predicted results of the controller. When these predicted After the transmission node receives the broadcast, it transmits its data directly (not through the control node) to the controller.
请参考图1，在图1所示的一个示意性的实施例中，所有的节点被分成四个簇，每个簇中设置有一个控制节点A、B、C和D，这些控制节点分别与控制器通讯连接。显然，簇的个数并不限于图1所示的实施例中的情形，而是可以根据上述的原则确定。Please refer to Fig. 1, in a schematic embodiment shown in Fig. 1, all nodes are divided into four clusters, each cluster is provided with a control node A, B, C and D, and these control nodes are respectively connected with Controller communication connection. Apparently, the number of clusters is not limited to the situation in the embodiment shown in FIG. 1 , but can be determined according to the above principles.
由于控制节点只向预测的预测结果中出现的传输节点进行广播，因而，避免了传感节点的长时间监听数据传输引发的能量消耗，从而在传输时延存在的前提下，加强了传输节点的能量控制，有效的数据流预测避免了因节点能量不足带来的传输中断，缩短了整体的传输路径，高效的节点能量控制延长了整个传感网络使用寿命。Since the control node only broadcasts to the transmission nodes that appear in the predicted prediction results, it avoids the energy consumption caused by the long-time monitoring data transmission of the sensor nodes, thereby strengthening the transmission node's performance under the premise of transmission delay. Energy control, effective data flow prediction avoids transmission interruption caused by insufficient node energy, shortens the overall transmission path, and efficient node energy control prolongs the service life of the entire sensor network.
优选地，预测控制策略是通过下述方式构建的：每个采样时刻，将每个传输节点的传输量作为系统状态变量，将传感器采样数据量作为系统输入，以建立传输网络的状态空间描述；通过在状态空间描述下求解最小目标函数获得最优传输途径在未来一段时域内每个传输节点的数据传输流的预测值。显然，预测控制策略也可以通过本领域的其他技术手段，例如ARMA(自回归移动平均模型)等来构建，并不建于本发明中所列举的方式，只要能够起到预测的作用即可。Preferably, the predictive control strategy is constructed in the following manner: at each sampling moment, the transmission amount of each transmission node is used as a system state variable, and the sensor sampling data volume is used as a system input to establish a state space description of the transmission network; The predicted value of the data transmission flow of each transmission node in the future time domain of the optimal transmission path is obtained by solving the minimum objective function under the description of the state space. Obviously, the predictive control strategy can also be constructed by other technical means in the field, such as ARMA (autoregressive moving average model), etc., not in the way listed in the present invention, as long as it can play a predictive role.
优选地，请参考图2，本方法还包括：根据数据传输流的预测误差，对预测进行误差补偿。Preferably, referring to FIG. 2 , the method further includes: performing error compensation on the prediction according to the prediction error of the data transmission stream.
优选地，预测误差来自于控制节点反馈；将预测控制策略在进行预测时使用的目标函数根据反馈的预测误差在流动时域中进行迭代更新以补偿预测误差。预测误差的存在促发进行预测行为（例如，可由控制器执行），否则执行最近时刻对网络传输流的预测策略。Preferably, the forecast error comes from the feedback of the control node; the objective function used by the predictive control strategy for forecasting is iteratively updated in the flow time domain according to the fed back forecast error to compensate for the forecast error. The existence of prediction error triggers the prediction behavior (for example, can be executed by the controller), otherwise the prediction strategy for the network transport flow at the most recent moment is executed.
优选地，簇内的控制节点是剩余电量最多的节点。例如，可运用贪婪算法、基于节点ID的链接聚类算法、或者基于信道接入的被动分簇算法优先选择剩余电量最多的传输节点作为控制节点，且控制节点不再作为此次数据传输的传输节点。Preferably, the control node in the cluster is the node with the most remaining power. For example, greedy algorithm, link clustering algorithm based on node ID, or passive clustering algorithm based on channel access can be used to preferentially select the transmission node with the most remaining power as the control node, and the control node is no longer the transmission node for this data transmission. node.
优选地，如果簇内具有两个或两个以上剩余电量相同的节点，那么选取已作为控制节点次数少的节点作为控制节点。例如，控制节点随时间可以是变动的，比如，第一时刻可能是某一个节点，而另一时刻，又可能是另一个节点。可见，在滚动时域内，作为非控制节点的传输节点在下一时刻是否作为传输节点，与该传输节点的位置和剩余电量有关。特别地，每次传输时，都会进行相应的预测，并确定该次传输所确定的控制节点。Preferably, if there are two or more nodes with the same remaining power in the cluster, the node that has been used as a control node for a few times is selected as the control node. For example, the control node may change over time, for example, it may be a certain node at the first moment, and it may be another node at another moment. It can be seen that in the rolling time domain, whether a transmission node as a non-control node will be a transmission node at the next moment is related to the position and remaining power of the transmission node. In particular, for each transmission, a corresponding prediction is made, and the control node determined for this transmission is determined.
优选地，将节点分成多个簇时，按照位置信息，采用蚁群算法、遗传算法、神经网络算法、或者粒子群算法等对所有节点进行分簇，显然，也可以采用本领域其它常规的智能算法对节点进行分簇。Preferably, when the nodes are divided into multiple clusters, according to the location information, ant colony algorithm, genetic algorithm, neural network algorithm, or particle swarm algorithm are used to cluster all nodes. Obviously, other conventional intelligent methods in this field can also be used. The algorithm clusters the nodes.
请参考图3至图6，其给出了传感网络传输节点A-D在50s内的传输仿真图。如图3所示，当传输节点的传输量为零时，说明该节点在此刻传输过程中并没有参与传输工作。每个传输节点的传输量都在一定范围内控制，说明系统具有较强的鲁棒性，以及高效的传输节点能量控制。Please refer to FIG. 3 to FIG. 6, which show the transmission simulation diagrams of the sensor network transmission nodes A-D within 50s. As shown in FIG. 3 , when the transmission amount of the transmission node is zero, it means that the node does not participate in the transmission work at the moment of the transmission process. The transmission volume of each transmission node is controlled within a certain range, which shows that the system has strong robustness and efficient transmission node energy control.
作为本发明的第二方面，请参考图1和图2，提供了一种传感网络数据传输的多层预测控制系统，包括：多个节点，多个节点分成多个簇，每个簇内的节点分为控制节点和传输节点；控制器，与控制节点通讯连接，控制器根据构建的预测控制策略对每个簇内的传输节点的数据传输流进行预测；控制节点只向预测的预测结果中出现的传输节点进行广播。As the second aspect of the present invention, please refer to Fig. 1 and Fig. 2, a kind of multi-layer predictive control system of sensor network data transmission is provided, including: a plurality of nodes, a plurality of nodes are divided into a plurality of clusters, each cluster The nodes are divided into control nodes and transmission nodes; the controller communicates with the control node, and the controller predicts the data transmission flow of the transmission nodes in each cluster according to the constructed predictive control strategy; the control node only reports the predicted prediction results The transmission nodes that appear in the broadcast.
优选地，控制器在每个采样时刻，将每个传输节点的传输量作为系统状态变量，将传感器采样数据量作为系统输入，以建立传输网络的状态空间描述，通过求解状态空间描述的目标函数获得最优传输途径下，未来一段时域内，每个传输节点的数据传输流的预测值。Preferably, at each sampling moment, the controller takes the transmission amount of each transmission node as the system state variable, and the sensor sampling data volume as the system input to establish a state space description of the transmission network, and solve the objective function described by the state space Obtain the predicted value of the data transmission flow of each transmission node in the future time domain under the optimal transmission path.
优选地，控制节点将数据传输流的预测误差反馈给控制器；控制器根据预测误差，对目标函数在流动时域中进行迭代更新以补偿预测误差。预测误差的存在促发控制器进行预测行为，否则执行最近时刻对网络传输流的预测策略。Preferably, the control node feeds back the prediction error of the data transmission flow to the controller; the controller iteratively updates the objective function in the flow time domain according to the prediction error to compensate for the prediction error. The existence of prediction error prompts the controller to perform prediction behavior, otherwise it executes the prediction strategy for the network transmission flow at the latest moment.
请参考图2，每个控制节点将预测误差反馈回控制器，以形成闭环反馈回路。通过在控制器的目标函数中加强对系统状态变量预测误差的约束，目标函数在滚动时域中的迭代更新以补偿预测误差。Referring to Figure 2, each control node feeds the prediction error back to the controller to form a closed-loop feedback loop. By strengthening the constraint on the prediction error of the system state variables in the controller's objective function, the objective function is iteratively updated in the rolling time domain to compensate for the prediction error.
请参考图3至图6，其给出了传感网络传输节点A-D在50s内的传输仿真图。如图3所示，当传输节点的传输量为零时，说明该节点在此刻传输过程中并没有参与传输工作。每个传输节点的传输量都在一定范围内控制，说明系统具有较强的鲁棒性，以及高效的传输节点能量控制。Please refer to FIG. 3 to FIG. 6, which show the transmission simulation diagrams of the sensor network transmission nodes A-D within 50s. As shown in FIG. 3 , when the transmission amount of the transmission node is zero, it means that the node does not participate in the transmission work at the moment of the transmission process. The transmission volume of each transmission node is controlled within a certain range, which shows that the system has strong robustness and efficient transmission node energy control.
综上所述，本发明从全局传感信息传输的角度出发，建立了多层控制架构，并对所有节点进行分簇，且在每簇中选取一个节点作为控制节点，其它的作为传输节点。在此基础上，本发明考虑到传输延迟构建了预测控制策略，并根据该预测控制策略对传输网络的下一个时刻的最优传输数据流进行预测。当预测误差存在时，还可通过控制节点将该预测误差反馈给控制器，以便进行预测误差补偿并更新预测的策略，因而具有较好的鲁棒性，有效地解决了时延引发的控制方法的失效性，可在传输时延存在的前提下，加强无线节点的能量控制，以便有效地预测数据流，提高了数据传输的准确率，延长整个无线传感网络的使用寿命。To sum up, the present invention establishes a multi-layer control architecture from the perspective of global sensor information transmission, and clusters all nodes, and selects one node in each cluster as the control node, and the others as transmission nodes. On this basis, the present invention constructs a predictive control strategy considering the transmission delay, and predicts the optimal transmission data flow of the transmission network at the next moment according to the predictive control strategy. When the prediction error exists, the prediction error can also be fed back to the controller through the control node, so as to compensate the prediction error and update the prediction strategy, so it has better robustness and effectively solves the control method caused by time delay. In the premise of the existence of transmission delay, the energy control of wireless nodes can be strengthened, so as to effectively predict the data flow, improve the accuracy of data transmission, and prolong the service life of the entire wireless sensor network.
以上所述仅为本发明的优选实施例，并不用于限制本发明，显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109963262A (en) * | 2019-01-28 | 2019-07-02 | 华南理工大学 | A wireless sensor scheduling optimization method in wireless sensor networks |
| CN111935667A (en) * | 2020-08-18 | 2020-11-13 | 电子科技大学 | Power distribution method for packet predictive control system |
| CN115397044A (en) * | 2022-08-22 | 2022-11-25 | 山东华迪智能技术有限公司 | Intelligent wireless sensor group coordination method and system |
| CN115397043A (en) * | 2022-08-22 | 2022-11-25 | 山东华迪智能技术有限公司 | Intelligent wireless sensor networking control method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101895956A (en) * | 2010-08-05 | 2010-11-24 | 中国兵器工业第二〇五研究所 | Data transmission method of multilayer distributed wireless sensor network |
| US20110111701A1 (en) * | 2009-11-06 | 2011-05-12 | Samsung Electronics Co., Ltd. | Apparatus and method for avoiding channel interference in a single channel sensor network |
| CN103401804A (en) * | 2013-06-06 | 2013-11-20 | 中国人民解放军理工大学 | Control system and method for node data caching and forwarding of wireless sensor network |
-
2014
- 2014-03-01 CN CN201410071818.2A patent/CN104883718B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110111701A1 (en) * | 2009-11-06 | 2011-05-12 | Samsung Electronics Co., Ltd. | Apparatus and method for avoiding channel interference in a single channel sensor network |
| CN101895956A (en) * | 2010-08-05 | 2010-11-24 | 中国兵器工业第二〇五研究所 | Data transmission method of multilayer distributed wireless sensor network |
| CN103401804A (en) * | 2013-06-06 | 2013-11-20 | 中国人民解放军理工大学 | Control system and method for node data caching and forwarding of wireless sensor network |
Non-Patent Citations (1)
| Title |
|---|
| 付彬等: "容迟移动传感器网络预测辅助的数据传输机制", 《小型微型计算机系统》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109963262A (en) * | 2019-01-28 | 2019-07-02 | 华南理工大学 | A wireless sensor scheduling optimization method in wireless sensor networks |
| CN109963262B (en) * | 2019-01-28 | 2020-08-18 | 华南理工大学 | Wireless sensor scheduling optimization method in wireless sensor network |
| CN111935667A (en) * | 2020-08-18 | 2020-11-13 | 电子科技大学 | Power distribution method for packet predictive control system |
| CN111935667B (en) * | 2020-08-18 | 2022-04-12 | 电子科技大学 | Power Allocation Method for Group Predictive Control System |
| CN115397044A (en) * | 2022-08-22 | 2022-11-25 | 山东华迪智能技术有限公司 | Intelligent wireless sensor group coordination method and system |
| CN115397043A (en) * | 2022-08-22 | 2022-11-25 | 山东华迪智能技术有限公司 | Intelligent wireless sensor networking control method and system |
| CN115397043B (en) * | 2022-08-22 | 2024-12-17 | 山东华迪智能技术有限公司 | Intelligent wireless sensor network control method and system |
| CN115397044B (en) * | 2022-08-22 | 2025-01-24 | 山东华迪智能技术有限公司 | Intelligent wireless sensor group collaboration method and system |
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