技术领域Technical Field
本发明涉及一种基于分簇优化的无线传感器网络节点休眠调度方法，属于无线通信技术领域。The invention relates to a wireless sensor network node sleep scheduling method based on clustering optimization, belonging to the technical field of wireless communications.
背景技术Background Art
无线传感器网络(Wireless Sensor Network,WSN)作为一种新兴的信息获取和处理技术，在环境监测、军事侦察和智能交通等多个领域发挥着越来越重要的作用。WSN通过部署在特定区域的大量传感器节点，实现对环境的实时监控与数据收集。然而，这些传感器节点通常由电池供电，其能源有限的特性成为制约WSN广泛应用的关键因素。因此，如何有效管理传感器节点的能源消耗，延长网络的生命周期，是WSN领域中的关键问题。As an emerging information acquisition and processing technology, Wireless Sensor Network (WSN) plays an increasingly important role in many fields such as environmental monitoring, military reconnaissance and intelligent transportation. WSN achieves real-time monitoring and data collection of the environment by deploying a large number of sensor nodes in a specific area. However, these sensor nodes are usually powered by batteries, and their limited energy has become a key factor restricting the widespread application of WSN. Therefore, how to effectively manage the energy consumption of sensor nodes and extend the life cycle of the network is a key issue in the field of WSN.
由于节点随机分布和高密度部署策略，无线传感器网络中通常存在大量冗余节点。这些冗余节点在执行数据采集和传输过程中，不仅增加了不必要的能源消耗，还导致数据重复和信道拥塞等问题，进而降低了网络的整体使用效率。针对上述问题，研究人员正在积极开展对无线传感器网络节点调度策略的探索。基于固定周期的休眠调度，特点在于其简易性和规律性，但缺乏灵活性，不能适应网络条件的动态变化，可能导致资源浪费或响应不及时；其次是基于网络状态的休眠调度，这种方法能够根据网络条件动态调整，优点在于高能效和高适应性，但计算复杂度也较高，可能会增加额外的能源消耗；再次是基于预测的休眠调度，通过预测网络需求来调整休眠和唤醒，优点在于提前响应网络需求，但预测结果的不准确可能导致性能低下，且算法本身复杂、难以实现。因此，基于覆盖的分簇休眠调度算法因其可靠的覆盖能力、高效的数据传输结构及能够有效减少和均衡节点间的能量消耗而显示出其的优越性。Due to the random distribution of nodes and high-density deployment strategy, there are usually a large number of redundant nodes in wireless sensor networks. These redundant nodes not only increase unnecessary energy consumption in the process of data collection and transmission, but also cause problems such as data duplication and channel congestion, thereby reducing the overall efficiency of the network. In response to the above problems, researchers are actively exploring node scheduling strategies for wireless sensor networks. The fixed-cycle sleep scheduling is characterized by its simplicity and regularity, but lacks flexibility and cannot adapt to the dynamic changes of network conditions, which may lead to resource waste or untimely response; the second is the sleep scheduling based on network status. This method can be dynamically adjusted according to network conditions. The advantages are high energy efficiency and high adaptability, but the computational complexity is also high, which may increase additional energy consumption; the third is the sleep scheduling based on prediction, which adjusts sleep and wake-up by predicting network demand. The advantage is to respond to network demand in advance, but inaccurate prediction results may lead to poor performance, and the algorithm itself is complex and difficult to implement. Therefore, the coverage-based clustered sleep scheduling algorithm shows its superiority due to its reliable coverage capability, efficient data transmission structure and ability to effectively reduce and balance energy consumption between nodes.
发明内容Summary of the invention
本发明所要解决的技术问题是：无线传感器网络中由于冗余部署引起的资源浪费以及复杂环境下节点能量使用效率低的问题。本发明的目的在于：提供基于分簇优化的无线传感器网络节点休眠调度方法，解决现有技术中存在的问题。The technical problem to be solved by the present invention is: the waste of resources caused by redundant deployment in wireless sensor networks and the low efficiency of node energy use in complex environments. The purpose of the present invention is to provide a node sleep scheduling method for wireless sensor networks based on clustering optimization to solve the problems existing in the prior art.
优先地，本发明提供一种基于分簇优化的无线传感器网络节点休眠调度方法，包括：根据无线传感器网络节点的部署位置、无线传感器网络节点的统一感知半径、无线传感器网络节点的邻居节点的部署位置和无线传感器网络节点的邻居节点的统一感知半径，计算无线传感器网络节点被无线传感器网络节点的邻居节点覆盖的冗余程度；Preferably, the present invention provides a method for scheduling sleep of wireless sensor network nodes based on clustering optimization, comprising: calculating the redundancy degree of the wireless sensor network node covered by the neighbor nodes of the wireless sensor network node according to the deployment position of the wireless sensor network node, the unified perception radius of the wireless sensor network node, the deployment position of the neighbor nodes of the wireless sensor network node and the unified perception radius of the neighbor nodes of the wireless sensor network node;
将冗余程度与预设阈值比较，判定无线传感器网络节点是否为冗余节点，若判定无线传感器网络节点为冗余节点，将该无线传感器网络节点设置为休眠状态；Compare the redundancy degree with a preset threshold value to determine whether the wireless sensor network node is a redundant node, and if the wireless sensor network node is determined to be a redundant node, set the wireless sensor network node to a dormant state;
基于无线传感器网络节点到簇头的距离、簇头到汇聚节点的距离和剩余能量三个目标，使用改进的多目标浣熊优化算法在剔除休眠节点后的无线传感器网络节点中选举簇头节点；Based on the three objectives of the distance from the wireless sensor network node to the cluster head, the distance from the cluster head to the sink node and the remaining energy, the improved multi-objective raccoon optimization algorithm is used to elect the cluster head node from the wireless sensor network nodes after removing the dormant nodes.
无线传感器网络中剔除簇头节点和休眠节点后的剩余无线传感器网络节点选择距离最近的簇头，获得簇结构。After removing the cluster head nodes and sleeping nodes in the wireless sensor network, the remaining wireless sensor network nodes select the nearest cluster head to obtain the cluster structure.
优先地，无线传感器网络进入数据传输阶段，簇内的无线传感器网络节点向各自簇头节点发送数据，簇头节点接收簇内的无线传感器网络节点发来的数据后，将数据经过数据聚合处理并发送给汇聚节点。Preferably, the wireless sensor network enters the data transmission phase, the wireless sensor network nodes in the cluster send data to their respective cluster head nodes, and after the cluster head nodes receive the data sent by the wireless sensor network nodes in the cluster, they aggregate the data and send it to the sink node.
优先地，判断是否存在活跃节点，若存在活跃节点则在将数据经过数据聚合处理并发送给汇聚节点之后，判断是否符合动态簇更新条件；Prioritize, determine whether there is an active node, if there is an active node, after the data is aggregated and sent to the sink node, determine whether it meets the dynamic cluster update conditions;
若符合动态簇更新条件则执行将冗余程度与预设阈值比较，判定无线传感器网络节点是否为冗余节点，若判定无线传感器网络节点为冗余节点，则将该无线传感器网络节点设置为休眠状态。If the dynamic cluster update condition is met, the redundancy degree is compared with a preset threshold to determine whether the wireless sensor network node is a redundant node. If the wireless sensor network node is determined to be a redundant node, the wireless sensor network node is set to a dormant state.
优先地，动态簇更新条件包括条件一和条件二，条件一是指存在新增的死亡节点；条件二是指任一簇头节点剩余能量小于当前无线传感器网络中存活的无线传感器网络节点的平均能量。Preferably, the dynamic cluster update condition includes condition one and condition two, condition one refers to the existence of a newly added dead node; condition two refers to the remaining energy of any cluster head node being less than the average energy of the surviving wireless sensor network nodes in the current wireless sensor network.
优先地，根据无线传感器网络节点的部署位置、无线传感器网络节点的统一感知半径、无线传感器网络节点的邻居节点的部署位置和无线传感器网络节点的邻居节点的统一感知半径，计算无线传感器网络节点被无线传感器网络节点的邻居节点覆盖的冗余程度，包括：Preferably, the redundancy degree of the wireless sensor network node covered by the neighbor nodes of the wireless sensor network node is calculated according to the deployment position of the wireless sensor network node, the unified perception radius of the wireless sensor network node, the deployment position of the neighbor nodes of the wireless sensor network node, and the unified perception radius of the neighbor nodes of the wireless sensor network node, including:
S201、在L×L方形监测范围区域内随机部署N个静态节点和1个汇聚节点，N个静态节点构成的节点集合为：，设定各节点的感知半径为，各节点的通信半径为，且；d(i,j)表示节点i与节点j之间的距离；表示传感器i的感知区域；S201. Randomly deploy N static nodes and 1 aggregation node in the L×L square monitoring range. The node set composed of the N static nodes is: , set the perception radius of each node to , the communication radius of each node is ,and ;d(i,j) represents the distance between nodei and nodej ; represents the sensing area of sensori ;
S202、计算节点i的邻居节点集合：S202, calculate the neighbor node set of nodei :
, ,
S203、计算节点的冗余程度S：S203, calculate the redundancy level S of the node:
。 .
优先地，将冗余程度与预设阈值比较，判定无线传感器网络节点是否为冗余节点，若判定无线传感器网络节点为冗余节点，将该无线传感器网络节点设置为休眠状态,包括：Preferably, comparing the redundancy degree with a preset threshold value to determine whether the wireless sensor network node is a redundant node, and if the wireless sensor network node is determined to be a redundant node, setting the wireless sensor network node to a dormant state, including:
将冗余程度S与预设阈值α比较，若冗余程度S不小于预设阈值α，则判定无线传感器网络节点i为冗余节点，并将该无线传感器网络节点设置为休眠状态。The redundancy level S is compared with a preset thresholdα . If the redundancy level S is not less than the preset thresholdα , the wireless sensor network nodei is determined to be a redundant node, and the wireless sensor network node is set to a dormant state.
优先地，基于无线传感器网络节点到簇头的距离、簇头到汇聚节点的距离和剩余能量三个目标，使用改进的多目标浣熊优化算法在剔除休眠节点后的无线传感器网络节点中选举簇头节点,包括：Prioritize, based on the distance from the wireless sensor network node to the cluster head, the distance from the cluster head to the sink node and the remaining energy, the improved multi-objective raccoon optimization algorithm is used to elect the cluster head node from the wireless sensor network nodes after removing the dormant nodes, including:
初始化所有浣熊在搜索空间中的位置：Initialize the positions of all raccoons in the search space:
， ,
其中Xp是第p个浣熊在搜索空间中的新位置，xp,q是第q个决策变量的值，NCOA是浣熊的数量，k是决策变量的数量,r是区间[0,1]中的随机实数，lbq和ubq分别是第q个决策变量的下界和上界；whereXp is the new position of thepth raccoon in the search space, xp,q is the value of theqth decision variable,NCOA is the number of raccoons,k is the number of decision variables,r is a random real number in the interval [0,1],lbq andubq are the lower and upper bounds of theqth decision variable, respectively;
浣熊种群使用种群矩阵X表示：The raccoon population is represented by the population matrix X:
， ,
计算目标函数的矢量F：Calculate the objective function vectorF :
, ,
式中，Fp是基于第p个浣熊获得的目标函数值，是基于第个浣熊获得的目标函数值；WhereFp is the objective function value obtained based on thep -th raccoon, It is based on The objective function value obtained by each raccoon;
基于预设迭代次数，更新浣熊种群位置。Update the raccoon population location based on a preset number of iterations.
优先地，基于预设迭代次数，更新浣熊种群位置，包括：Preferably, based on a preset number of iterations, the raccoon population location is updated, including:
假设种群中最好成员的位置是鬣蜥的位置，对浣熊从树上爬起的位置进行数学模拟：Assuming that the position of the best member of the population is that of the iguana, mathematically simulate the position of the raccoon climbing up the tree:
， ,
式中，为第p个浣熊的新位置，为第p个浣熊新位置的第q个维度，r是区间[0,1]中的随机实数，Iguana表示鬣蜥在搜索空间中的位置，Iguanaq为Iguana的第j维，I为一个整数，是底函数；In the formula, is the new position of the pth raccoon, is the qth dimension of the new position of the pth raccoon, r is a random real number in the interval [0,1], Iguana represents the position of the iguana in the search space, Iguanaq is the jth dimension of Iguana,I is an integer, is the base function;
鬣蜥落地后被放置在搜索空间中的一个随机位置；After landing, the iguana was placed in a random position in the search space;
基于鬣蜥的位置，地面上的浣熊在搜索空间中移动：Based on the iguana's position, the raccoon on the ground moves through the search space:
， ,
， ,
， ,
式中，IguanaG为鬣蜥在地面上的位置；为IguanaG的第q个维；Where IguanaG is the position of the iguana on the ground; is the qth dimension of IguanaG ;
如果没有为每个浣熊的新位置提高目标函数值，则浣熊保持在先前的位置：If the objective function value is not improved for each new position of the raccoon, the raccoon remains in its previous position:
， ,
式中，是的目标函数值，是在目标函数中的值；In the formula, yes The objective function value of yes The value in the objective function;
在每个浣熊在搜索空间中的新位置附近生成随机位置：Generate random positions near each raccoon's new position in the search space:
， ,
， ,
式中，是第q个决策变量的局部下界，是第q个决策变量的局部上界，是第q个决策变量的下界，是第q个决策变量的上界；t是迭代计数器，T是预设的最大迭代数值；是基于COA的第二阶段的第p个浣熊的新位置，是第p个浣熊的新位置的第q个维度；In the formula, is the local lower bound of the qth decision variable, is the local upper bound of the qth decision variable, is the lower bound of the qth decision variable, is the upper bound of the qth decision variable; t is the iteration counter, and T is the preset maximum iteration value; is the new position of the pth raccoon in the second phase based on COA, is the qth dimension of the new position of the pth raccoon;
如果第p个浣熊在搜索空间中的新位置提高了目标函数值，则第p个浣熊在搜索空间中的新位置为：If the new position of the pth raccoon in the search space improves the objective function value, then the new position of the pth raccoon in the search space is:
， ,
式中，是第p个浣熊新位置的第q个维度的目标函数值，r是区间[0,1]中的随机数；In the formula, is the objective function value of the qth dimension of the pth raccoon’s new position, and r is a random number in the interval [0,1];
利用Tent混沌产生的序列来初始化所有浣熊在搜索空间中的位置：Use the sequence generated by Tent chaos to initialize the positions of all raccoons in the search space:
， ,
其中，Zt为Tent映射生成的混沌序列，β为随机数；Among them, Zt is the chaotic sequence generated by Tent mapping, and β is a random number;
进一步生成浣熊初始个体初始位置Xp,q：Further generate the initial positionXp,q of the initial individual raccoon:
， ,
式中，Xmin,q为序列的最小值，Xmax,q为序列的最大值。Where Xmin,q is The minimum value of the sequence, Xmax,q is The maximum value of the sequence.
优先地，基于无线传感器网络节点到簇头的距离、簇头到汇聚节点的距离和剩余能量三个目标，使用改进的多目标浣熊优化算法在剔除休眠节点后的无线传感器网络节点中选举簇头节点，包括：Prioritize, based on the three objectives of the distance from the wireless sensor network node to the cluster head, the distance from the cluster head to the sink node and the remaining energy, the improved multi-objective raccoon optimization algorithm is used to elect the cluster head node from the wireless sensor network nodes after removing the dormant nodes, including:
计算簇头节点f(H)：Calculate the cluster head node f(H):
， ,
， ,
， ,
， ,
式中，f1(H)为第一目标函数，f2(H)为第二目标函数，f3(H)为第三目标函数，表示成员节点ni与簇头hj间距离，是选举出的簇头节点集合，表示集合H内元素个数，节点ni所属簇的成员节点集为，sink表示无线传感器网络中的汇聚节点，E(hj)表示簇头hj当前的剩余能量。Where, f1 (H) is the first objective function, f2 (H) is the second objective function, and f3 (H) is the third objective function. represents the distance between member nodeni and cluster headhj , is the set of elected cluster head nodes, represents the number of elements in the set H, and the member node set of the cluster to which nodeni belongs is , sink represents the sink node in the wireless sensor network, and E(hj ) represents the current residual energy of cluster head hj .
优先地，无线传感器网络中剔除簇头节点和休眠节点后的剩余无线传感器网络节点选择距离最近的簇头，获得簇结构，包括：Prioritize, after removing the cluster head nodes and the dormant nodes in the wireless sensor network, the remaining wireless sensor network nodes select the nearest cluster head to obtain a cluster structure, including:
簇头节点使用广播的方式宣布获选消息，剔除簇头节点和冗余节点后的剩余无线传感器网络节点就近选择簇头节点并发送入簇请求；The cluster head node announces the selected message by broadcasting. After removing the cluster head node and redundant nodes, the remaining wireless sensor network nodes select the cluster head node nearby and send a cluster entry request.
簇头节点接收剩余节点入簇请求后将入簇后的剩余无线传感器网络节点作为成员节点，为所有该簇结构中的剩余无线传感器网络节点发送TDMA时隙表。After receiving the cluster entry request of the remaining nodes, the cluster head node regards the remaining wireless sensor network nodes after entering the cluster as member nodes, and sends a TDMA time slot table to all the remaining wireless sensor network nodes in the cluster structure.
优先地，本发明提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现第一方面任一项所述方法的步骤。Preferably, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the program.
优先地，本发明提供一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现第一方面中任一项所述方法的步骤。Preferably, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.
本发明所达到的有益效果：The beneficial effects achieved by the present invention are:
本发明在仿真实验方面，测试了冗余判定机制中预设阈值α对于休眠节点个数的影响，验证了随着α值大小的减小，判定为冗余节点进而转为休眠状态的节点就越多，符合理论预期效果；然后本发明将改进的浣熊优化算法应用于无线传感网络节点休眠调度中的簇头选举阶段，与原算法和其他三种不同智能优化算法以及无线传感网络中广泛应用的LEACH协议和PEGASIS协议进行对比，使用改进浣熊优化算法的无线传感网络节点休眠调度方法在每轮存活节点数、网络剩余能量和每轮消耗能量方面表现最好，本发明不仅有效均衡无线传感网络节点间的能量消耗，显著延长了网路寿命。In terms of simulation experiments, the present invention tests the influence of a preset threshold value α in a redundant judgment mechanism on the number of dormant nodes, and verifies that as the value of α decreases, more nodes are judged as redundant nodes and then turned to a dormant state, which is in line with theoretical expected effects; then the present invention applies the improved raccoon optimization algorithm to the cluster head election stage in the dormant scheduling of wireless sensor network nodes, and compares them with the original algorithm and three other different intelligent optimization algorithms as well as the LEACH protocol and PEGASIS protocol widely used in wireless sensor networks. The wireless sensor network node dormant scheduling method using the improved raccoon optimization algorithm performs best in terms of the number of surviving nodes in each round, the network remaining energy and the energy consumed in each round. The present invention not only effectively balances the energy consumption between wireless sensor network nodes, but also significantly extends the network life.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本申请的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
图1为本发明的流程图。FIG. 1 is a flow chart of the present invention.
图2为本发明的模型原理图。Fig. 2 is a schematic diagram of a model of the present invention.
图3为本发明中改进后浣熊优化算法的流程图。FIG3 is a flow chart of the improved raccoon optimization algorithm of the present invention.
图4为本发明在不同阈值α下每轮休眠节点个数对比图。FIG4 is a comparison diagram of the number of sleep nodes in each round under different thresholds α according to the present invention.
图5为本发明的与其他算法每轮存活节点个数对比图。FIG5 is a comparison chart of the number of surviving nodes in each round between the present invention and other algorithms.
图6为本发明的与其他算法网络剩余能量对比图。FIG6 is a graph comparing the residual energy of networks of the present invention and other algorithms.
图7为本发明的与其他算法每轮消耗能量对比图。FIG7 is a comparison diagram of energy consumption per round between the present invention and other algorithms.
具体实施方式DETAILED DESCRIPTION
为便于对申请的技术方案进行，以下首先在对本申请所涉及到的一些概念进行说明。To facilitate the technical solution of the application, some concepts involved in the application are first explained below.
本发明构建了一种基于分簇优化的无线传感器网络节点休眠调度方法（Energy-efficient Sleep Scheduling based Clusters and Improved Coati OptimizationAlgorithm,ESSC-ICOA），首先根据冗余判定机制选择冗余节点进入休眠状态；然后结合Tent混沌映射和种群自适应划分机制对多目标浣熊优化算法（COA）进行改进，使用改进后多目标浣熊优化算法（ICOA）对网络中活跃节点实施簇头节点选举操作后，剩余无线传感器网络节点就近入簇，簇状数据传输结构形成；其次，在数据传输阶段，簇内无线传感器网络节点向簇头传输数据，各簇头将收集到的数据聚合后发送给网络中的汇聚节点；最后，搭配动态簇更新机制分析本轮节点情况，判断是否需要重新构建全新的簇结构。The present invention constructs an energy-efficient sleep scheduling method for wireless sensor network nodes based on cluster optimization (Energy-efficient Sleep Scheduling based Clusters and Improved Coati Optimization Algorithm, ESSC-ICOA). Firstly, redundant nodes are selected to enter a sleep state according to a redundancy judgment mechanism. Then, a multi-objective raccoon optimization algorithm (COA) is improved by combining Tent chaotic mapping and a population adaptive partitioning mechanism. After the improved multi-objective raccoon optimization algorithm (ICOA) is used to perform cluster head node election operation on active nodes in the network, the remaining wireless sensor network nodes are clustered nearby, and a cluster data transmission structure is formed. Secondly, in the data transmission stage, the wireless sensor network nodes in the cluster transmit data to the cluster head, and each cluster head aggregates the collected data and sends it to a sink node in the network. Finally, a dynamic cluster update mechanism is used to analyze the node situation in this round to determine whether a new cluster structure needs to be rebuilt.
参见图1，本申请公开了基于分簇优化的无线传感器网络节点休眠调度方法，包括以下步骤：步骤S1,根据监测范围区域内的无线传感器网络节点的部署位置、无线传感器网络节点的统一感知半径、无线传感器网络节点的邻居节点的部署位置和无线传感器网络节点的邻居节点的统一感知半径，计算无线传感网络中的无线传感器网络节点被无线传感器网络节点的邻居节点覆盖的冗余程度；将冗余程度与预设阈值比较，判定无线传感器网络节点是否为冗余节点；若判定无线传感器网络节点为冗余节点，将该无线传感器网络节点设置为休眠状态；Referring to FIG. 1 , the present application discloses a method for scheduling sleep of wireless sensor network nodes based on clustering optimization, comprising the following steps: Step S1, calculating the redundancy degree of wireless sensor network nodes in a wireless sensor network covered by neighboring nodes of the wireless sensor network nodes according to the deployment positions of wireless sensor network nodes in a monitoring range, the unified perception radius of the wireless sensor network nodes, the deployment positions of neighboring nodes of the wireless sensor network nodes, and the unified perception radius of neighboring nodes of the wireless sensor network nodes; comparing the redundancy degree with a preset threshold to determine whether the wireless sensor network node is a redundant node; if the wireless sensor network node is determined to be a redundant node, setting the wireless sensor network node to a sleep state;
步骤S2,基于无线传感器网络节点到簇头的距离、簇头到汇聚节点的距离和剩余能量三个目标，使用改进的多目标浣熊优化算法在排除休眠节点的无线传感器网络节点中选举簇头节点；Step S2, based on the distance from the wireless sensor network node to the cluster head, the distance from the cluster head to the sink node and the remaining energy, an improved multi-objective raccoon optimization algorithm is used to select a cluster head node from the wireless sensor network nodes excluding the dormant nodes;
步骤S3,无线传感器网络中排除簇头节点和休眠节点后的剩余无线传感器网络节点选择距离最近的簇头，获得簇结构。Step S3: After excluding the cluster head node and the sleep node in the wireless sensor network, the remaining wireless sensor network nodes select the cluster head closest to them to obtain the cluster structure.
步骤S4,无线传感器网络进入数据传输阶段，主要包括簇内的无线传感器网络节点向各自簇头节点发送数据，簇头节点接收簇内的无线传感器网络节点发来的数据后，经过数据聚合处理后发送给汇聚节点，完成一轮的数据传输。Step S4, the wireless sensor network enters the data transmission phase, which mainly includes the wireless sensor network nodes in the cluster sending data to their respective cluster head nodes. After the cluster head node receives the data sent by the wireless sensor network nodes in the cluster, it sends it to the aggregation node after data aggregation processing, completing a round of data transmission.
步骤S5,判断是否存在活跃节点，若存在活跃节点则在将数据经过数据聚合处理并发送给汇聚节点之后，判断是否符合动态簇更新条件；若符合动态簇更新条件则执行步骤S1。Step S5, determine whether there is an active node. If there is an active node, after the data is aggregated and sent to the aggregation node, determine whether it meets the dynamic cluster update conditions; if it meets the dynamic cluster update conditions, execute step S1.
获得簇结构之后，判断无线传感器网络中的节点是否需要重新构建簇结构，包括：After obtaining the cluster structure, it is determined whether the nodes in the wireless sensor network need to rebuild the cluster structure, including:
动态簇更新机制需要综合分析当前轮次节点情况，若符合动态簇更新条件，则重新进行冗余节点判定和基于改进的多目标浣熊优化算法在排除休眠节点的节点中选举簇头，利用剩余节点构建全新的簇结构，进行数据感知和传输。The dynamic cluster update mechanism needs to comprehensively analyze the current round of node conditions. If the dynamic cluster update conditions are met, the redundant nodes are re-determined and the cluster head is elected from the nodes excluding the dormant nodes based on the improved multi-objective raccoon optimization algorithm. A new cluster structure is constructed using the remaining nodes for data perception and transmission.
动态簇更新条件包括条件一和条件二，条件一：存在新增的死亡节点；条件二：任一簇头节点剩余能量小于当前无线传感器网络中存活的无线传感器网络节点的平均能量。The dynamic cluster update conditions include condition one and condition two. Condition one: there are newly added dead nodes; condition two: the remaining energy of any cluster head node is less than the average energy of the surviving wireless sensor network nodes in the current wireless sensor network.
根据无线传感器网络节点的部署位置、无线传感器网络节点的统一感知半径、无线传感器网络节点的邻居节点的部署位置和无线传感器网络节点的邻居节点的统一感知半径，计算无线传感器网络节点被无线传感器网络节点的邻居节点覆盖的冗余程度，包括：According to the deployment position of the wireless sensor network node, the unified perception radius of the wireless sensor network node, the deployment position of the neighbor node of the wireless sensor network node and the unified perception radius of the neighbor node of the wireless sensor network node, the redundancy degree of the wireless sensor network node covered by the neighbor node of the wireless sensor network node is calculated, including:
S201、在L×L方形监测范围区域内随机部署N个静态节点和1个汇聚节点，N个静态节点构成的节点集合为：，设定各节点的感知半径为，各节点的通信半径为，且；d(i,j)表示节点i与节点j之间的距离；表示传感器i的感知区域；S201. Randomly deploy N static nodes and 1 aggregation node in the L×L square monitoring range. The node set composed of the N static nodes is: , set the perception radius of each node to , the communication radius of each node is ,and ; d(i,j) represents the distance between node i and node j; represents the sensing area of sensor i;
S202、计算节点i的邻居节点集合：S202, calculate the neighbor node set of node i:
, ,
S203、计算节点的冗余程度S：S203, calculate the redundancy level S of the node:
； ;
S204、将冗余程度S与预设阈值α比较，若冗余程度S不大于预设阈值α，则判定无线传感器网络节点i为冗余节点，并将该无线传感器网络节点设置为休眠状态。S204, comparing the redundancy level S with a preset thresholdα , if the redundancy level S is not greater than the preset thresholdα , determining that the wireless sensor network nodei is a redundant node, and setting the wireless sensor network node to a dormant state.
基于无线传感器网络节点到簇头的距离、簇头到汇聚节点的距离和剩余能量三个目标，使用改进的多目标浣熊优化算法在剔除休眠节点后的无线传感器网络节点中选举簇头节点，包括：Based on the three objectives of the distance from the wireless sensor network node to the cluster head, the distance from the cluster head to the sink node and the remaining energy, the improved multi-objective raccoon optimization algorithm is used to elect the cluster head node from the wireless sensor network nodes after removing the dormant nodes, including:
S301、初始化所有浣熊在搜索空间中的位置：S301. Initialize the positions of all raccoons in the search space:
， ,
其中Xp是第p个浣熊在搜索空间中的新位置，xp,q是第q个决策变量的值，NCOA是浣熊的数量，k是决策变量的数量,r是区间[0,1]中的随机实数，lbq和ubq分别是第q个决策变量的下界和上界；whereXp is the new position of thepth raccoon in the search space, xp,q is the value of theqth decision variable,NCOA is the number of raccoons,k is the number of decision variables,r is a random real number in the interval [0,1],lbq andubq are the lower and upper bounds of theqth decision variable, respectively;
浣熊种群使用种群矩阵X表示：The raccoon population is represented by the population matrix X:
， ,
计算目标函数的矢量F：Calculate the objective function vectorF :
， ,
式中，Fp是基于第p个浣熊获得的目标函数值，是基于第个浣熊获得的目标函数值；S302、基于预设迭代次数，更新浣熊种群位置，分为两个不同的阶段进行更新。WhereFp is the objective function value obtained based on thep -th raccoon, It is based on The objective function value obtained by each raccoon; S302, based on the preset number of iterations, updating the position of the raccoon population, which is divided into two different stages for updating.
1)第一阶段：对鬣蜥的狩猎和攻击策略（探索阶段）。在搜索空间中更新浣熊种群的第一阶段是基于模拟它们攻击鬣蜥时的策略进行建模。在这一策略中，一群浣熊爬上树去够一只鬣蜥并吓唬它。其他几只浣熊在树下等待，直到鬣蜥摔倒在地。鬣蜥落地后，浣熊攻击并猎杀它。这种策略导致浣熊移动到搜索空间的不同位置，这展示了COA在解决问题空间的全局搜索中的探索能力。在COA设计中，假设种群中最好成员的位置是鬣蜥的位置。对浣熊从树上爬起的位置进行数学模拟，如下式：1) Phase 1: Hunting and attack strategy for iguanas (exploration phase). The first phase of updating the raccoon population in the search space is modeled based on the strategy used to simulate their attacks on iguanas. In this strategy, a group of raccoons climbed up a tree to reach an iguana and scare it. Several other raccoons waited under the tree until the iguana fell to the ground. After the iguana landed, the raccoons attacked and hunted it. This strategy caused the raccoons to move to different locations in the search space, which demonstrated the exploration ability of COA in solving the global search of the problem space. In the COA design, it is assumed that the position of the best member of the population is the position of the iguana. The position of the raccoon climbing up from the tree is mathematically simulated as follows:
， ,
式中，为第p个浣熊的新位置，为第p个浣熊新位置的第q个维度，r是区间[0,1]中的随机实数，Iguana表示鬣蜥在搜索空间中的位置，Iguanaq为Iguana的第j维，I为一个整数，是底函数；In the formula, is the new position of the pth raccoon, is the qth dimension of the new position of the pth raccoon, r is a random real number in the interval [0,1], Iguana represents the position of the iguana in the search space, Iguanaq is the jth dimension of Iguana,I is an integer, is the base function;
鬣蜥落地后被放置在搜索空间中的一个随机位置；After landing, the iguana was placed in a random position in the search space;
基于鬣蜥的位置，地面上的浣熊在搜索空间中移动：Based on the iguana's position, the raccoon on the ground moves through the search space:
， ,
， ,
， ,
式中，IguanaG为鬣蜥在地面上的位置；为IguanaG的第q个维；Where IguanaG is the position of the iguana on the ground; is the qth dimension of IguanaG ;
如果没有为每个浣熊的新位置提高目标函数值，则浣熊保持在先前的位置：If the objective function value is not improved for each new position of the raccoon, the raccoon remains in its previous position:
， ,
式中，是的目标函数值，是在目标函数中的值。In the formula, yes The objective function value of yes The value in the objective function.
2) 第二阶段：逃离捕食者的过程（剥削阶段）2) Stage 2: The process of escaping from predators (exploitation stage)
更新浣熊在搜索空间中的位置的第二阶段是基于浣熊遇到捕食者和逃离捕食者时的自然行为进行建模。当捕食者攻击一只浣熊时，浣熊就会逃离原始位置。浣熊在这一战略中的举措使其处于接近当前位置的安全位置，这表明了COA在本地搜索中的利用能力高。The second phase of updating the raccoon's position in the search space is modeled based on the raccoon's natural behavior when encountering and fleeing from predators. When a predator attacks a raccoon, the raccoon flees from its original position. The raccoon's moves in this strategy put it in a safe position close to its current position, which shows the high utilization of COA in local search.
为了模拟这种行为，在每个浣熊在搜索空间中的新位置附近生成随机位置：To simulate this behavior, generate random positions near each raccoon's new position in the search space:
， ,
， ,
式中，是第q个决策变量的局部下界，是第q个决策变量的局部上界，是第q个决策变量的下界，是第q个决策变量的上界；t是迭代计数器，T是预设的最大迭代数值；是基于COA的第二阶段的第p个浣熊的新位置，是第p个浣熊的新位置的第q个维度；In the formula, is the local lower bound of the qth decision variable, is the local upper bound of the qth decision variable, is the lower bound of the qth decision variable, is the upper bound of the qth decision variable; t is the iteration counter, and T is the preset maximum iteration value; is the new position of the pth raccoon in the second phase based on COA, is the qth dimension of the new position of the pth raccoon;
如果浣熊的新位置提高了目标函数值，则该条件使用下式模拟第p个浣熊在搜索空间中的新位置：If the new position of the raccoon improves the objective function value, then this condition simulates the new position of thepth raccoon in the search space using the following formula:
， ,
式中，是第p个浣熊新位置的第q个维度的目标函数值，r是区间[0,1]中的随机数。In the formula, is the objective function value of the qth dimension of the pth raccoon’s new position, and r is a random number in the interval [0,1].
步骤S301中，包括利用Tent混沌产生的序列来初始化所有浣熊在搜索空间中的位置，如下式：In step S301, the sequence generated by Tent chaos is used to initialize the positions of all raccoons in the search space, as shown in the following formula:
其中，Zt为Tent映射生成的混沌序列，β为随机数。为保持算法初始化信息的随机性，β取值为(0,1)。进一步生成浣熊初始个体初始位置Xp,q，如下式：Among them,Zt is the chaotic sequence generated by Tent mapping, and β is a random number. To maintain the randomness of the algorithm initialization information,β is taken as (0,1). The initial positionXp,q of the initial individual of the raccoon is further generated as follows:
， ,
式中，Xmin,q为序列的最小值，Xmax,q为序列的最大值。Where Xmin,q is The minimum value of the sequence, Xmax,q is The maximum value of the sequence.
在步骤S302中，第一阶段位置浣熊位置更新引入种群自适应划分机制。在第一阶段中树上浣熊数量为NCOA×V，树下浣熊数量为NCOA×(1-V)，V表示比例因子，其定义如下式：In step S302, the first stage of the raccoon position update introduces a population adaptive division mechanism. In the first stage, the number of raccoons on the tree is NCOA ×V, and the number of raccoons under the tree is NCOA ×(1-V), where V represents a proportional factor, which is defined as follows:
， ,
其中Vi表示比例初值，Vf表示比例终值，表示调节系数，>0。WhereVi represents the initial value of the ratio,Vf represents the final value of the ratio, represents the adjustment coefficient, >0.
在步骤S3中，改进后多目标浣熊优化算法选举簇头节点的目标函数，定义是选举出的簇头节点集合，表示集合H内元素个数，节点ni所属簇的成员节点集为；In step S3, the objective function of the improved multi-objective raccoon optimization algorithm for selecting cluster head nodes is defined as is the set of elected cluster head nodes, represents the number of elements in the set H, and the member node set of the cluster to which nodeni belongs is ;
设定的第一目标函数f1(H)即为成员节点到其各自簇头的平均传输距离，表示成员节点nj与簇头hj间距离：The first objective function f1 (H) is set to be the average transmission distance from the member node to its respective cluster head. Represents the distance between member nodenj and cluster headhj :
， ,
设定的第二目标函数f2(H)是各簇头到汇聚节点的平均传输距离：The second objective function f2 (H) is set to be the average transmission distance from each cluster head to the sink node:
， ,
第三目标函数f3(H)为所有簇头的平均剩余能量，如下式：The third objective function f3 (H) is the average residual energy of all cluster heads, as follows:
， ,
最后，多目标簇头节点f(H)选择问题为：Finally, the multi-target cluster head node f(H) selection problem is:
, ,
利用上式，计算获得簇头节点。Using the above formula, the cluster head node is calculated.
在步骤S4中，无线传感器网络中排除簇头节点和休眠节点后的剩余无线传感器网络节点选择距离最近的簇头孔，获得簇结构，包括：In step S4, the remaining wireless sensor network nodes after excluding the cluster head node and the sleep node in the wireless sensor network select the cluster head hole closest to them to obtain a cluster structure, including:
S401、簇头节点使用广播的方式宣布获选消息，除簇头节点和冗余节点后的剩余无线传感器网络节点就近选择簇头节点并发送入簇请求；S401, the cluster head node announces the selected message by broadcasting, and the remaining wireless sensor network nodes except the cluster head node and the redundant node select the cluster head node nearby and send a cluster entry request;
S402、簇头节点接收剩余节点入簇请求后将入簇后的剩余无线传感器网络节点作为成员节点，为所有该簇结构中的剩余无线传感器网络节点发送TDMA时隙表。S402: After receiving the cluster joining request from the remaining nodes, the cluster head node takes the remaining wireless sensor network nodes that have joined the cluster as member nodes, and sends a TDMA time slot table to all the remaining wireless sensor network nodes in the cluster structure.
无线传感器网络进入数据传输阶段，簇内的无线传感器网络节点向各自簇头节点发送数据，簇头节点接收簇内的无线传感器网络节点发来的数据后，将数据经过数据聚合处理并发送给汇聚节点，包括：The wireless sensor network enters the data transmission stage. The wireless sensor network nodes in the cluster send data to their respective cluster head nodes. After the cluster head node receives the data sent by the wireless sensor network nodes in the cluster, it aggregates the data and sends it to the sink node, including:
成员节点接收TDMA时隙表后，需要在自身时隙内将数据发送给各自对应的簇头节点；After receiving the TDMA time slot table, the member nodes need to send data to their corresponding cluster head nodes within their own time slots;
簇头节点接收数据后，经过数据聚合处理后发送给汇聚节点，完成一轮数据传输。After the cluster head node receives the data, it sends it to the sink node after data aggregation processing, completing a round of data transmission.
在本申请实施例中，本发明提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现上述任一项所述方法的步骤。In an embodiment of the present application, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the program.
在本申请实施例中，本发明提供一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现上述任一项所述方法的步骤。In an embodiment of the present application, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.
图4展示了在不同预设阈值α下，冗余判定机制对网络中每轮休眠节点数量的影响。分析表明，当α值设定较低时，即网络对覆盖率的要求较低时，会判定出更多的部分冗余节点并将其置于休眠状态。这反映了阈值α在平衡能量消耗与网络覆盖率之间的关键作用：较低的α值倾向于优先考虑能量节约，通过增加休眠节点的数量来减少能量消耗，而对网络覆盖率的影响相对次要。因此，阈值α的设定是一个重要的参数，需要根据具体应用场景中对能效和覆盖率的要求来综合考量和调整。Figure 4 shows the impact of the redundancy determination mechanism on the number of dormant nodes in each round in the network under different preset thresholds α. Analysis shows that when the α value is set low, that is, when the network has a low coverage requirement, more partially redundant nodes will be determined and put into a dormant state. This reflects the key role of the threshold α in balancing energy consumption and network coverage: lower α values tend to prioritize energy conservation and reduce energy consumption by increasing the number of dormant nodes, while the impact on network coverage is relatively minor. Therefore, the setting of the threshold α is an important parameter that needs to be comprehensively considered and adjusted according to the requirements for energy efficiency and coverage in specific application scenarios.
图5给出LEACH、PEGASIS、ESSC-ICOA（改进后浣熊优化算法）、ESSC-COA（原始浣熊优化算法）、ESSC-MOALO（蚁狮优化算法）、ESSC-MSSA（樽海鞘优化算法）和ESSC-NSWOA（鲸鱼优化算法）在不同轮次下的活跃节点数，使用半数节点死亡(HND,half node die)时网络进行的轮数作为考量算法效能的重要指标，具体数值见下表。可知，ESSC-ICOA相较于LEACH和PEGASIS，显著延长了网络的工作时间，分别提升了68.34%和28.32%。主要原因是ESSC-ICOA确保了网络中适合的节点成为簇头，负责复杂的数据收集与传输任务。此外，动态簇更新机制允许簇头在节点间适时轮换，避免了个别节点过早耗尽能量，从而显著延长了网络的生命周期；相较于ESSC-COA、ESSC-MOALO、ESSC-MSSA和ESSC-NSWOA，ESSC-ICOA在网络工作时间上的提升分别达到了7.67%、30.55%、34.50%和19.25%，体现了改进后的浣熊优化算法相较于其他智能优化算法在能效提升方面的显著优势。Figure 5 shows the number of active nodes in different rounds for LEACH, PEGASIS, ESSC-ICOA (improved raccoon optimization algorithm), ESSC-COA (original raccoon optimization algorithm), ESSC-MOALO (antlion optimization algorithm), ESSC-MSSA (salp sea squirt optimization algorithm) and ESSC-NSWOA (whale optimization algorithm). The number of rounds performed by the network when half of the nodes die (HND, half node die) is used as an important indicator to consider the performance of the algorithm. The specific values are shown in the table below. It can be seen that ESSC-ICOA significantly extends the working time of the network compared with LEACH and PEGASIS, increasing by 68.34% and 28.32% respectively. The main reason is that ESSC-ICOA ensures that suitable nodes in the network become cluster heads, responsible for complex data collection and transmission tasks. In addition, the dynamic cluster update mechanism allows cluster heads to rotate among nodes in a timely manner, avoiding the premature exhaustion of energy in individual nodes, thereby significantly extending the life cycle of the network; compared with ESSC-COA, ESSC-MOALO, ESSC-MSSA and ESSC-NSWOA, the improvement in network working time of ESSC-ICOA reached 7.67%, 30.55%, 34.50% and 19.25% respectively, reflecting the significant advantages of the improved Raccoon optimization algorithm in improving energy efficiency compared with other intelligent optimization algorithms.
图6给出LEACH、PEGASIS、ESSC-ICOA、ESSC-COA、ESSC-MOALO、ESSC-MSSA和ESSC-NSWOA在不同轮次下的网络剩余能量。从结果可以明显观察到，随着运行轮数的增加，与PEGASIS和LEACH相比，ESSC算法能够始终保持更高的剩余能量水平。这一现象主要归因于ESSC中包含的休眠调度机制，避免了冗余节点非必要的能量消耗，提升了节点能量的使用效率。此外，ESSC算法在簇头选举过程中，考虑到了成员节点至簇头的距离和簇头至汇聚节点的距离，这种选举策略有效减少了因长距离数据传输而导致的高节点能耗。其次，相较于其他智能优化算法，ICOA基于原始浣熊算法的优越性和本文提出的改进措施的有效性使得其构建的簇结构节能高效，减少了数据传输阶段的能量消耗。Figure 6 shows the network residual energy of LEACH, PEGASIS, ESSC-ICOA, ESSC-COA, ESSC-MOALO, ESSC-MSSA and ESSC-NSWOA in different rounds. It can be clearly observed from the results that with the increase of the number of running rounds, the ESSC algorithm can always maintain a higher residual energy level compared with PEGASIS and LEACH. This phenomenon is mainly attributed to the sleep scheduling mechanism included in ESSC, which avoids unnecessary energy consumption of redundant nodes and improves the efficiency of node energy use. In addition, the ESSC algorithm takes into account the distance from the member node to the cluster head and the distance from the cluster head to the sink node during the cluster head election process. This election strategy effectively reduces the high node energy consumption caused by long-distance data transmission. Secondly, compared with other intelligent optimization algorithms, the superiority of ICOA based on the original raccoon algorithm and the effectiveness of the improvement measures proposed in this paper make the cluster structure it constructs energy-saving and efficient, reducing the energy consumption in the data transmission stage.
图7给出LEACH、PEGASIS、ESSC-ICOA、ESSC-COA、ESSC-MOALO、ESSC-MSSA和ESSC-NSWOA在不同轮次下的网络剩余能量。可以看出，PEGASIS和LEACH相较于ESSC，每轮能量消耗处在较高水平。使用不同智能优化算法的ESSC，每轮能量消耗可以保持较低的均匀水平，得益于节点休眠调度、多目标簇头选举策略和动态簇更新机制三者的协同作用。通过这种方式优化每轮的能量使用，有效提升能量管理效率。Figure 7 shows the network residual energy of LEACH, PEGASIS, ESSC-ICOA, ESSC-COA, ESSC-MOALO, ESSC-MSSA and ESSC-NSWOA in different rounds. It can be seen that PEGASIS and LEACH have a higher energy consumption per round than ESSC. Using ESSC with different intelligent optimization algorithms, the energy consumption per round can be kept at a low uniform level, thanks to the synergy of node sleep scheduling, multi-target cluster head election strategy and dynamic cluster update mechanism. In this way, the energy use of each round is optimized and the energy management efficiency is effectively improved.
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
本领域技术人员在考虑说明书及实践这里发明的发明后，将容易想到本发明的其它实施方案。本申请旨在涵盖本发明的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本发明的一般性原理并包括本发明未发明的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本发明的真正范围和精神由下面的权利要求指出。Those skilled in the art will readily appreciate other embodiments of the invention after considering the specification and practicing the invention invented herein. This application is intended to cover any variations, uses or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not invented by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.
以上的具体实施方式，对本申请的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上仅为本申请的具体实施方式而已，并不用于限定本申请的保护范围，凡在本申请的技术方案的基础之上，所做的任何修改、等同替换、改进等，均应包括在本申请的保护范围之内。The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above are only specific implementation methods of the present application and are not used to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present application should be included in the scope of protection of the present application.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119342486B (en)* | 2024-10-16 | 2025-09-02 | 南昌理工学院 | A wireless sensor network node deployment method for facility agriculture |
| CN119789177A (en)* | 2024-11-26 | 2025-04-08 | 汕头大学 | Wireless sensor network dynamic clustering method, device, electronic equipment and medium |
| CN119212080A (en)* | 2024-11-29 | 2024-12-27 | 南京信息工程大学 | A distributed opportunity array radar wireless sensor network positioning method and system |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104135752A (en)* | 2014-07-31 | 2014-11-05 | 南京邮电大学 | Cluster head node selection method and clustering method of wireless sensor network |
| CN110149608A (en)* | 2019-04-04 | 2019-08-20 | 江苏大学 | A kind of resource allocation methods of the optical-wireless sensor network based on DAI |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024814B (en)* | 2013-01-09 | 2015-06-24 | 中国人民解放军理工大学 | Wireless sensor network energy conservation method based on redundancy controlling and clustering routing |
| US9148849B2 (en)* | 2013-06-03 | 2015-09-29 | King Fahd University Of Petroleum And Minerals | Coverage, connectivity and communication (C3) protocol method for wireless sensor networks |
| CN108055683B (en)* | 2017-12-29 | 2021-02-05 | 东北林业大学 | Method for balancing energy consumption and keeping coverage of underwater wireless sensor network |
| CN111698705B (en)* | 2020-05-29 | 2021-12-21 | 华南理工大学 | A non-uniform cluster routing method for wireless sensor networks based on energy optimization |
| CN112492661B (en)* | 2020-12-10 | 2022-04-15 | 中南民族大学 | Wireless sensor network clustering routing method based on improved sparrow search algorithm |
| CN112954763B (en)* | 2021-02-07 | 2022-12-23 | 中山大学 | WSN clustering routing method based on goblet sea squirt algorithm optimization |
| CN112822747B (en)* | 2021-03-02 | 2022-09-30 | 吉林大学 | A Routing Strategy Method Based on Genetic Algorithm and Ant Colony Algorithm |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104135752A (en)* | 2014-07-31 | 2014-11-05 | 南京邮电大学 | Cluster head node selection method and clustering method of wireless sensor network |
| CN110149608A (en)* | 2019-04-04 | 2019-08-20 | 江苏大学 | A kind of resource allocation methods of the optical-wireless sensor network based on DAI |
| Publication number | Publication date |
|---|---|
| CN118250766A (en) | 2024-06-25 |
| Publication | Publication Date | Title |
|---|---|---|
| CN118250766B (en) | Node sleep scheduling method for wireless sensor networks based on clustering optimization | |
| Pinto et al. | An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms | |
| CN111867139A (en) | Implementation method and system of deep neural network adaptive backoff strategy based on Q-learning | |
| CN107277830A (en) | A kind of wireless sensor network node dispositions method based on particle group optimizing and mutation operator | |
| CN104093180B (en) | Wireless sensing network route method based on multi-gateway data transmisison | |
| CN113905389A (en) | Wireless sensor network coverage method based on particle swarm optimization imperial butterfly algorithm | |
| CN110049526A (en) | Based on the method for data capture and system for improving cluster algorithm in WSN | |
| CN113573333A (en) | A Particle Swarm Heterogeneous WSNs Coverage Optimization Algorithm Based on Virtual Force | |
| CN118714612A (en) | A load balancing method and system for edge gateway based on improved gold rush optimization algorithm | |
| CN114531665B (en) | A wireless sensor network node clustering method and system based on Levy flight | |
| Zhang et al. | Clustering model based on node local density load balancing of wireless sensor network | |
| Nabavi | An optimal routing protocol using multi-objective whale optimization algorithm for wireless sensor networks | |
| CN119313115B (en) | Resource pool scheduling strategy optimization method based on improved block chain | |
| Shen et al. | Energy-efficient task assignment based on entropy theory and particle swarm optimization algorithm for wireless sensor networks | |
| Saleem et al. | Ant based self-organized routing protocol for wireless sensor networks | |
| Devi et al. | Jarrot butterfly optimized flamingo search algorithm for optimal routing in WSN | |
| Cui | Research and improvement of LEACH protocol in wireless sensor networks | |
| Boonma et al. | Bisnet: A biologically-inspired architecture forwireless sensor networks | |
| CN114727415B (en) | A sensor scheduling method for large-scale WSN multi-target tracking | |
| Liu et al. | Modeling and performance optimization of wireless sensor network based on Markov chain | |
| Meghanathan | A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks. | |
| Yuvaraja et al. | Lifetime enhancement of WSN using energy-balanced distributed clustering algorithm with honey bee optimization | |
| CN104702497A (en) | Sarsa algorithm and ant colony optimization-based route control algorithm | |
| Amrieen | Particle swarm optimization based load balancing clustering technique for wireless sensor networks | |
| CN116939626A (en) | Wireless sensor network coverage optimization method based on improved fruit fly optimization algorithm |
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