CN108848476A - Power-efficient data assembly algorithms based on communication distance control in sensor network - Google Patents
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Abstract
本发明提供了传感器网络中基于通信距离控制的高能效数据汇聚算法，整体分为簇头选举阶段和成簇阶段；簇头选举阶段包括设置距离阈值、选出候选簇头、计算候选簇头的潜在能量消耗和筛选正式簇头；在成簇阶段，节点根据距离阈值选择直接与基站节点通信；当与基站的距离超过距离阈值时，选择与离它最近的簇头节点通信，此时，涉及到根据距离选择距离它最近的簇头节点；在成簇阶段，为了控制距离，又选择了控制分簇大小，以此来间接控制距离。该算法超过距离阈值的节点采用分簇的方式来进行数据汇聚。采取限制分簇大小的策略，保证节点能量消耗的均衡性，在能量效率方面明显高于LEACH和SEP协议。
The invention provides an energy-efficient data aggregation algorithm based on communication distance control in a sensor network, which is generally divided into a cluster head election stage and a clustering stage; the cluster head election stage includes setting a distance threshold, selecting candidate cluster heads, and calculating candidate cluster heads. Potential energy consumption and screening of official cluster heads; in the clustering stage, nodes choose to communicate directly with the base station node according to the distance threshold; when the distance from the base station exceeds the distance threshold, choose to communicate with the nearest cluster head node, at this time, involves To select the cluster head node closest to it according to the distance; in the clustering stage, in order to control the distance, it also chooses to control the size of the cluster, so as to indirectly control the distance. In this algorithm, the nodes whose distance exceeds the threshold value are clustered for data aggregation. The strategy of limiting the size of clusters is adopted to ensure the balance of energy consumption of nodes, and the energy efficiency is significantly higher than that of LEACH and SEP protocols.
Description
技术领域technical field
本发明涉及无线传感器网络技术领域，具体的说是传感器网络中基于通信距离控制的高能效数据汇聚算法。The invention relates to the technical field of wireless sensor networks, in particular to an energy-efficient data aggregation algorithm based on communication distance control in the sensor network.
背景技术Background technique
无线传感器网络的低能耗、无线传输和大规模部署等特点决定了其能够广泛应用于各种需要长时间周期性进行监测和数据采集的场景，例如，文物保护和建筑物结构形变、交通流量监测等。在这类应用场景中，如何降低节点能量消耗，维持无线传感器网络的长时间周期性工作，尽可能地收集目标数据是首要问题。基于分簇的分层次路由算法通过一定的选举策略将无线传感器节点划分为簇首节点和簇内成员节点，簇首节点负责汇集所有簇内成员节点的数据、进行数据融合，并转发至传感器网络数据汇聚节点Sink，避免大多数传感器节点直接与传感器网络的汇聚节点直接进行远距离通信导致能量消耗过多，能够从整体上降低网络能量消耗，从而延长网络寿命。然而，在传感器网络中，由于传感器节点的部署位置不同，其与基站节点的距离也存在明显的差别，节点在数据传输上的能量消耗也因此明显不同。对于传感器节点来说，在设计分簇路由算法的时候充分考虑通信距离差异，提高节点能量效率有助于优化网络寿命。The low energy consumption, wireless transmission and large-scale deployment of wireless sensor networks determine that it can be widely used in various scenarios that require long-term periodic monitoring and data collection, such as cultural relics protection and structural deformation of buildings, traffic flow monitoring Wait. In such application scenarios, how to reduce node energy consumption, maintain long-term periodic work of wireless sensor networks, and collect target data as much as possible is the primary issue. The hierarchical routing algorithm based on clustering divides the wireless sensor nodes into cluster head nodes and cluster member nodes through a certain election strategy. The cluster head nodes are responsible for collecting the data of all member nodes in the cluster, performing data fusion, and forwarding to the sensor network. The data aggregation node Sink avoids the excessive energy consumption caused by the long-distance communication between most sensor nodes and the aggregation node of the sensor network directly, and can reduce the energy consumption of the network as a whole, thereby prolonging the life of the network. However, in the sensor network, due to the different deployment locations of the sensor nodes, there are obvious differences in the distances between the sensor nodes and the base station nodes, and the energy consumption of the nodes in data transmission is also obviously different. For sensor nodes, the difference in communication distance should be fully considered when designing the clustering routing algorithm, and improving the energy efficiency of nodes can help optimize network life.
分簇算法在能量效率上的优势已经产生了众多研究成果。以分簇算法适用的网络类型分类，大致可以将这些分簇算法分为同构分簇算法和异构分簇算法。考虑到节点能量消耗的不均衡性和网络拓扑结构的动态性、复杂性，设计一种能够最大化网络寿命的分簇算法是非常困难的。The advantage of clustering algorithm in energy efficiency has produced many research results. Based on the classification of network types applicable to clustering algorithms, these clustering algorithms can be roughly divided into homogeneous clustering algorithms and heterogeneous clustering algorithms. Considering the unbalanced energy consumption of nodes and the dynamics and complexity of network topology, it is very difficult to design a clustering algorithm that can maximize the network lifetime.
发明内容Contents of the invention
为了解决现有技术中的不足，本发明提供了传感器网络中基于通信距离控制的高能效数据汇聚算法，该算法通过设置距离阈值的方式，使得与基站节点的距离小于阈值的节点直接与其通信；超过距离阈值的节点采用分簇的方式来进行数据汇聚。在分簇过程中，为了避免簇的平均通信距离过小，采取限制分簇大小的策略，保证节点能量消耗的均衡性，在能量效率方面明显高于LEACH和SEP协议。In order to solve the deficiencies in the prior art, the present invention provides a high-energy-efficiency data aggregation algorithm based on communication distance control in the sensor network. By setting the distance threshold, the algorithm enables the nodes whose distance to the base station node is less than the threshold to directly communicate with it; Nodes that exceed the distance threshold use clustering for data aggregation. In the process of clustering, in order to avoid the average communication distance of clusters being too small, the strategy of limiting the size of clusters is adopted to ensure the balance of energy consumption of nodes, and the energy efficiency is obviously higher than that of LEACH and SEP protocols.
为了实现上述目的，本发明采用的具体方案为：传感器网络中基于通信距离控制的高能效数据汇聚算法，传感器网络包括若干个节点，所述节点分为传感器节点和基站节点两种，该数据汇聚算法具体包括如下步骤：In order to achieve the above object, the specific scheme adopted by the present invention is: an energy-efficient data aggregation algorithm based on communication distance control in the sensor network. The sensor network includes several nodes, and the nodes are divided into two types: sensor nodes and base station nodes. The algorithm specifically includes the following steps:
步骤一、簇头选举阶段：所述簇头选举阶段包括设置距离阈值、选出候选簇头、计算候选簇头的潜在能量消耗和筛选正式簇头，具体实现步骤如下：Step 1. Cluster head election stage: the cluster head election stage includes setting a distance threshold, selecting candidate cluster heads, calculating the potential energy consumption of candidate cluster heads and screening official cluster heads. The specific implementation steps are as follows:
S1、设置阈值，S1, set the threshold,
其中，P为节点成为簇头的概率，r为网络当前运行的轮数，G为最近1/p轮中未当选为簇头的节点集合；Among them, P is the probability of a node becoming a cluster head, r is the number of rounds that the network is currently running, and G is the set of nodes that were not elected as cluster heads in the last 1/p rounds;
S2、在每一轮的开始，每个存活的节点产生一个随机数，将产生的随机数与步骤S1设置的阈值进行比较，选出候选簇头；S2. At the beginning of each round, each surviving node generates a random number, compares the generated random number with the threshold set in step S1, and selects a candidate cluster head;
S3、计算候选簇头的潜在能量消耗，步骤S2选出的候选簇头潜在的能量消耗包括与潜在的簇成员通信的能量消耗数据融合能量消耗EDA以及簇头与基站通信时的能量消耗S4、当候选簇头的剩余能量ER≥Ech时，候选簇头为筛选得到的正式簇头；S3. Calculate the potential energy consumption of the candidate cluster head, the potential energy consumption of the candidate cluster head selected in step S2 includes the energy consumption of communicating with potential cluster members Data fusion energy consumption E DA and energy consumption when the cluster head communicates with the base station S4. When the residual energy E R ≥ E ch of the candidate cluster head, the candidate cluster head is the official cluster head obtained by screening;
步骤二、成簇阶段：先计算分簇大小；之后设定距离阈值，将非簇头节点和基站节点之间的距离与该距离阈值相比较，当非簇头节点和基站节点之间的距离小于距离阈值时，非簇头节点直接和基站节点进行通信；当非簇头节点和基站节点之间的距离超过距离阈值时，选择离非簇头节点最近的簇头节点进行通信。Step 2, clustering stage: first calculate the cluster size; then set the distance threshold, compare the distance between the non-cluster head node and the base station node with the distance threshold, when the distance between the non-cluster head node and the base station node When the distance is less than the threshold, the non-cluster head node communicates directly with the base station node; when the distance between the non-cluster head node and the base station node exceeds the distance threshold, the cluster head node closest to the non-cluster head node is selected for communication.
步骤S3所述计算候选簇头潜在能量消耗具体过程如下：The specific process of calculating the potential energy consumption of candidate cluster heads described in step S3 is as follows:
其中，Ee表示发送和接收数据时电路消耗的能量，do表示距离阈值，εfs和εmp分别表示通信距离小于和大于距离阈值do时采用的不同功率放大损耗模型的功率损耗，d表示通信距离，l表示数据包大小。Among them, E e represents the energy consumed by the circuit when sending and receiving data, d o represents the distance threshold, ε fs and ε mp represent the power loss of different power amplification loss models adopted when the communication distance is less than and greater than the distance threshold d o , d Indicates the communication distance, and l indicates the packet size.
步骤S4筛选正式簇头时，分别计算节点与簇头节点之间的通信距离dsc，节点与基站节点之间的通信距离dss，判断节点的能量消耗。When selecting the official cluster head in step S4, the communication distance d sc between the node and the cluster head node and the communication distance d ss between the node and the base station node are respectively calculated, and the energy consumption of the node is judged.
步骤二所述计算分簇大小的具体过程如下：The specific process of calculating the cluster size described in step 2 is as follows:
T1、在每一轮开始时，计算过去n轮次产生的总簇头数及平均簇头数 T1. At the beginning of each round, calculate the total number of cluster heads generated in the past n rounds and the average number of cluster heads
T2、根据本轮次存活的节点数Nalive，计算平均分簇大小为Nalive/Cavg；T2. According to the number of surviving nodes N alive in this round, calculate the average cluster size as N alive /C avg ;
T3、在节点分簇过程中，将步骤T2计算得到的平均分簇大小为条件，控制簇成员个数。T3. In the node clustering process, the average cluster size calculated in step T2 is used as a condition to control the number of cluster members.
有益效果：Beneficial effect:
(1)本发明提供了传感器网络中基于通信距离控制的高能效数据汇聚算法，该算法通过设置距离阈值的方式，使得与基站节点的距离小于阈值的节点直接与其通信；超过距离阈值的节点采用分簇的方式来进行数据汇聚。在分簇过程中，为了避免簇的平均通信距离过小，采取限制分簇大小的策略，保证节点能量消耗的均衡性，在能量效率方面明显高于LEACH和SEP协议；(1) The present invention provides an energy-efficient data aggregation algorithm based on communication distance control in a sensor network. By setting a distance threshold, the algorithm enables nodes whose distance to a base station node is less than the threshold to directly communicate with it; nodes exceeding the distance threshold use clustering to aggregate data. In the process of clustering, in order to avoid the average communication distance of clusters being too small, a strategy of limiting the size of clusters is adopted to ensure the balance of energy consumption of nodes, and the energy efficiency is significantly higher than that of LEACH and SEP protocols;
(2)本发明提供了传感器网络中基于通信距离控制的高能效数据汇聚算法，能够明显降低节点能量消耗，维持无线传感器网络的长时间周期性工作，尽可能地收集目标数据，有助于优化网络寿命。(2) The present invention provides an energy-efficient data aggregation algorithm based on communication distance control in sensor networks, which can significantly reduce node energy consumption, maintain long-term periodic work of wireless sensor networks, collect target data as much as possible, and help optimize Network longevity.
附图说明Description of drawings
图1为平均通信距离与存活节点数对比图；Figure 1 is a comparison chart of the average communication distance and the number of surviving nodes;
图2为平均通信距离与簇头数对比图；Figure 2 is a comparison chart of the average communication distance and the number of cluster heads;
图3为平均通信距离与网络总体能量消耗对比图；Figure 3 is a comparison chart of the average communication distance and the overall energy consumption of the network;
图4为相同网络参数配置下不同算法的网络寿命图；Figure 4 is a network lifetime diagram of different algorithms under the same network parameter configuration;
图5为相同网络参数配置下不同算法的通信距离图；Figure 5 is a communication distance diagram of different algorithms under the same network parameter configuration;
图6为相同网络参数配置下不同算法的能量消耗图。Fig. 6 is a graph of energy consumption of different algorithms under the same network parameter configuration.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
本发明整体分为簇头选举阶段和成簇阶段；簇头选举阶段根据阈值T(n)和节点产生的随机数进行比较，选出候选簇头，然后根据潜在的能量消耗情况，从候选簇头中选择出正式簇头；在成簇阶段，节点根据距离阈值选择直接与基站节点通信；当与基站的距离超过距离阈值时，选择与离它最近的簇头节点通信，此时，涉及到根据距离选择距离它最近的簇头节点；在成簇阶段，为了控制距离，又选择了控制分簇大小，以此来间接控制距离。The present invention is generally divided into a cluster head election stage and a clustering stage; the cluster head election stage compares the threshold T(n) with the random number generated by the node, selects a candidate cluster head, and then selects the candidate cluster head according to the potential energy consumption. The official cluster head is selected from the head; in the clustering stage, the node chooses to communicate directly with the base station node according to the distance threshold; when the distance from the base station exceeds the distance threshold, it chooses to communicate with the nearest cluster head node. At this time, it involves Select the closest cluster head node according to the distance; in the clustering stage, in order to control the distance, it also chooses to control the size of the cluster, so as to indirectly control the distance.
传感器网络中基于通信距离控制的高能效数据汇聚算法，传感器网络包括若干个节点，所述节点分为传感器节点和基站节点两种，该数据汇聚算法具体包括如下步骤：An energy-efficient data aggregation algorithm based on communication distance control in a sensor network. The sensor network includes several nodes, and the nodes are divided into two types: sensor nodes and base station nodes. The data aggregation algorithm specifically includes the following steps:
步骤一、簇头选举阶段：所述簇头选举阶段包括设置距离阈值、选出候选簇头、计算候选簇头的潜在能量消耗和筛选正式簇头，具体实现步骤如下：Step 1. Cluster head election stage: the cluster head election stage includes setting a distance threshold, selecting candidate cluster heads, calculating the potential energy consumption of candidate cluster heads and screening official cluster heads. The specific implementation steps are as follows:
S1、设置阈值，S1, set the threshold,
其中，P为节点成为簇头的概率，r为网络当前运行的轮数，G为最近1/p轮中未当选为簇头的节点集合。Among them, P is the probability of a node becoming a cluster head, r is the number of rounds that the network is currently running, and G is the set of nodes that have not been elected as cluster heads in the last 1/p rounds.
S2、在每一轮的开始，每个存活的节点产生一个随机数，将产生的随机数与步骤S1设置的阈值进行比较，选出候选簇头；采用基于节点能量消耗预测的方法来避免剩余能量过低的节点被选为簇头节点。S2. At the beginning of each round, each surviving node generates a random number, compares the generated random number with the threshold set in step S1, and selects a candidate cluster head; adopts a method based on node energy consumption prediction to avoid remaining Nodes with too low energy are selected as cluster heads.
S3、计算候选簇头的潜在能量消耗，计算候选簇头潜在能量消耗具体过程如下：首先，传感器节点采用LEACH协议的能量消耗模型。节点的能量消耗主要分为三个部分：发送数据时的能量消耗ETx，接收数据时的能量消耗ERx以及融合数据时的能量消耗EDA。结合通信距离d和数据包大小l，可得公式(1)：S3. Calculate the potential energy consumption of the candidate cluster heads. The specific process of calculating the potential energy consumption of the candidate cluster heads is as follows: First, the sensor nodes adopt the energy consumption model of the LEACH protocol. The energy consumption of a node is mainly divided into three parts: energy consumption E Tx when sending data, energy consumption E Rx when receiving data, and energy consumption E DA when fusing data. Combining the communication distance d and the data packet size l, the formula (1) can be obtained:
其中，Ee表示发送和接收数据时电路消耗的能量，do表示距离阈值，εfs和εmp分别表示通信距离小于和大于距离阈值do时采用的不同功率放大损耗模型的功率损耗，d表示通信距离，l表示数据包大小；Among them, E e represents the energy consumed by the circuit when sending and receiving data, d o represents the distance threshold, ε fs and ε mp represent the power loss of different power amplification loss models adopted when the communication distance is less than and greater than the distance threshold d o , d Indicates the communication distance, l indicates the packet size;
其次，当节点成为簇头后，其潜在的能量消耗可以划分为三部分：与潜在的簇成员通信的能量消耗数据融合能量消耗EDA以及簇头与基站通信时的能量消耗 Second, when a node becomes a cluster head, its potential energy consumption can be divided into three parts: the energy consumption of communicating with potential cluster members Data fusion energy consumption E DA and energy consumption when the cluster head communicates with the base station
其中，表示潜在簇成员i与簇头通信时簇头接收数据时的能量消耗，表示簇头将数据发送至基站时的能量消耗。两者都采用公式(1)进行计算。in, Indicates the energy consumption of the cluster head receiving data when the potential cluster member i communicates with the cluster head, Indicates the energy consumption when the cluster head sends data to the base station. Both are calculated using formula (1).
S4、当候选簇头的剩余能量ER≥Ech时，候选簇头为筛选得到的正式簇头；为了实现对通信距离的控制，筛选正式簇头时，分别计算节点与簇头节点之间的通信距离dsc，节点与基站节点之间的通信距离dss，判断节点的能量消耗。从中选择出符合距离控制条件的节点，并让其直接与基站通信，从而保持整个网络的平均通信距离能够维持在能量最优的位置；S4. When the remaining energy of the candidate cluster head E R ≥ E ch , the candidate cluster head is the official cluster head obtained by screening; in order to realize the control of the communication distance, when screening the official cluster head, calculate the distance between the node and the cluster head node The communication distance d sc of the node and the communication distance d ss between the node and the base station node are used to judge the energy consumption of the node. Select the nodes that meet the distance control conditions and let them communicate directly with the base station, so as to maintain the average communication distance of the entire network at an energy-optimized position;
对于每个节点而言，其数据传输可采用两种方式完成：(1)直接长距离转发至基站；(2)与簇头节点通信，簇头节点执行数据融合后再转发至基站。在这两种方式中，节点的能量消耗分别如下，(本发明仅考虑短距离通信情况，即假设所涉及的节点间的通信距离均小于do)：For each node, its data transmission can be completed in two ways: (1) direct long-distance forwarding to the base station; (2) communicating with the cluster head node, the cluster head node performs data fusion and then forwards to the base station. In these two ways, the energy consumption of the nodes is as follows respectively (the present invention only considers the short-distance communication situation, that is, it is assumed that the communication distances between the involved nodes are all less than d o ):
方式一：节点的能量消耗主要为发送数据所耗费的能量(不考虑基站的能量消耗)： Method 1: The energy consumption of the node is mainly the energy consumed by sending data (without considering the energy consumption of the base station):
方式二：能量消耗包括发送数据，簇头节点接收数据和数据融合。考虑到数据融合后，簇头节点仅发送一个数据包至基站，对于每个簇成员节点，本发明不考虑簇头节点与基站通信的能量消耗。因此， Method 2: Energy consumption includes sending data, cluster head node receiving data and data fusion. Considering that after data fusion, the cluster head node only sends one data packet to the base station, and for each cluster member node, the present invention does not consider the energy consumption of the cluster head node communicating with the base station. therefore,
基于上面两种能量消耗计算方法，最优化节点能量消耗的问题转化为可得公式(2)：Based on the above two energy consumption calculation methods, the problem of optimizing node energy consumption is transformed into Formula (2) can be obtained:
当且仅当公式(2)小于0时，节点选择的能量最优传输方式为直接与基站通信。基于公式(2)可得：最终dss与dsc的关系转化为：考虑到dsc＜do，且数据包长度l＝4000，则有由此可见，对于节点来说，当其与基站的距离小于do时，应选择直接与基站通信以降低整网的能量消耗。经实验证明，当节点与基站距离小于0.8do时选择直接与基站通信，能量节省效果最好。If and only if formula (2) is less than 0, the optimal energy transmission mode selected by the node is to communicate directly with the base station. Based on formula (2), we can get: Finally, the relationship between d ss and d sc is transformed into: Considering d sc <d o , and the data packet length l=4000, then we have It can be seen that for a node, when its distance from the base station is less than d o , it should choose to communicate directly with the base station to reduce the energy consumption of the entire network. It is proved by experiments that when the distance between the node and the base station is less than 0.8d o , the energy saving effect is the best if the node chooses to communicate directly with the base station.
步骤二、成簇阶段：先计算分簇大小；之后设定距离阈值，将非簇头节点和基站节点之间的距离与该距离阈值相比较，当非簇头节点和基站节点之间的距离小于距离阈值时，非簇头节点直接和基站节点进行通信；当非簇头节点和基站节点之间的距离超过距离阈值时，选择离非簇头节点最近的簇头节点进行通信。Step 2, clustering stage: first calculate the cluster size; then set the distance threshold, compare the distance between the non-cluster head node and the base station node with the distance threshold, when the distance between the non-cluster head node and the base station node When the distance is less than the threshold, the non-cluster head node communicates directly with the base station node; when the distance between the non-cluster head node and the base station node exceeds the distance threshold, the cluster head node closest to the non-cluster head node is selected for communication.
步骤二中，所述计算分簇大小的具体过程如下：In step 2, the specific process of calculating the cluster size is as follows:
T1、在每一轮开始时，计算过去n轮次产生的总簇头数及平均簇头数 T1. At the beginning of each round, calculate the total number of cluster heads generated in the past n rounds and the average number of cluster heads
T2、根据本轮次存活的节点数Nalive，计算平均分簇大小为Nalive/Cavg；T2. According to the number of surviving nodes N alive in this round, calculate the average cluster size as N alive /C avg ;
T3、在节点分簇过程中，将步骤T2计算得到的平均分簇大小为条件，控制簇成员个数。T3. In the node clustering process, the average cluster size calculated in step T2 is used as a condition to control the number of cluster members.
实验研究数据experimental research data
一、LEACH算法主要特征的相关性分析1. Correlation Analysis of Main Features of LEACH Algorithm
无线传感器网络采用典型的多对一传输方式时，经典分簇算法LEACH的主要特征间的相关性如图1-3所示。LEACH以轮为单位组织时间，并以周期性等概率的方式随机选择节点为簇头。选出的簇头节点负责组织成簇，并收集、融合所有簇成员的数据，然后将融合后的数据传输至基站节点。由于传感器节点采用随机部署的方式，在监测区域分布的节点并不均匀，并且簇头节点选择具有随机性，因此，对LEACH的主要特征的相关性分析是改善分簇算法性能的第一步。When the wireless sensor network adopts a typical many-to-one transmission mode, the correlation between the main features of the classic clustering algorithm LEACH is shown in Figure 1-3. LEACH organizes time in units of rounds, and randomly selects nodes as cluster heads in a periodic and equiprobable manner. The selected cluster head node is responsible for organizing clusters, collecting and fusing the data of all cluster members, and then transmitting the fused data to the base station node. Since sensor nodes are randomly deployed, the distribution of nodes in the monitoring area is not uniform, and the selection of cluster head nodes is random. Therefore, the correlation analysis of the main characteristics of LEACH is the first step to improve the performance of the clustering algorithm.
本实验以100个无线传感器节点随机部署在200m x 200m的区域为实验环境，研究了网络的平均通信距离、网络总体能量消耗、簇头数、存活的节点数四个因素的相关性，并从中提取影响LEACH性能的关键因素，以此为基础优化分簇算法的性能。实验过程中，随机生成10个实验网络拓扑，每个实验拓扑的预定运行时间为2000轮。In this experiment, 100 wireless sensor nodes are randomly deployed in a 200m x 200m area as the experimental environment, and the correlation between the average communication distance of the network, the overall energy consumption of the network, the number of cluster heads, and the number of surviving nodes is studied. The key factors affecting the performance of LEACH are extracted, and the performance of the clustering algorithm is optimized on this basis. During the experiment, 10 experimental network topologies are randomly generated, and the scheduled running time of each experimental topology is 2000 rounds.
如图1-3所示，其中，图1为平均通信距离与存活节点数对比图，两因素相关性系数为0.88，图中，平滑线条表示每一轮存活的节点数，点状曲折线条表示每一轮的平均通信距离；图2为平均通信距离与簇头数对比图，两因素相关性系数为0.86，图中，平滑线条表示每一轮的簇头数，点状曲折线条表示每一轮的平均通信距离；图3为平均通信距离与网络总体能量消耗对比图，两因素相关性系数为0.88，图中，平滑线条表示每一轮的总能量消耗，点状曲折线条表示每一轮的平均通信距离。从图1-3的相关性分析可得，网络平均通信距离与网络存活的节点数、簇头数以及网络总体能量消耗具有高度相关性，其相关性均大于0.8。由图2和图3可得，网络总体能量消耗与簇头数也同样具有高度相关性，可见，平均通信距离是影响LEACH性能表现的关键因素，也是设计面向传感器网络的能量高效分簇算法的关键所在。本发明在此实验研究基础上提出传感器网络中基于通信距离控制的高能效数据汇聚算法。As shown in Figure 1-3, among them, Figure 1 is a comparison chart of the average communication distance and the number of surviving nodes, and the correlation coefficient of the two factors is 0.88. The average communication distance of each round; Figure 2 is a comparison chart of the average communication distance and the number of cluster heads, and the correlation coefficient of the two factors is 0.86. The average communication distance of rounds; Figure 3 is a comparison chart of the average communication distance and the overall energy consumption of the network. average communication distance. From the correlation analysis in Figure 1-3, it can be seen that the average communication distance of the network is highly correlated with the number of surviving nodes, the number of cluster heads and the overall energy consumption of the network, and the correlations are all greater than 0.8. From Figure 2 and Figure 3, it can be seen that the overall energy consumption of the network is also highly correlated with the number of cluster heads. It can be seen that the average communication distance is the key factor affecting the performance of LEACH, and it is also the basis for designing an energy-efficient clustering algorithm for sensor networks. The key. On the basis of the experimental research, the present invention proposes an energy-efficient data aggregation algorithm based on communication distance control in the sensor network.
二、仿真与分析2. Simulation and analysis
本发明以Matlab为实验平台，将100个节点随机部署在200m x 200m的实验区域中，基站节点的坐标为(100，100)。所有实验结果都基于10个随机网络拓扑，每个网络拓扑运行2000轮的平均值。算法假设采用的是理想MAC协议，数据传输过程中不存在丢包与传输冲突。实验参数设置如表1所示。本发明选取的对比算法为LEACH和SEP。In the present invention, Matlab is used as an experimental platform, and 100 nodes are randomly deployed in an experimental area of 200m x 200m, and the coordinates of the base station nodes are (100, 100). All experimental results are based on 10 random network topologies, and each network topology is averaged over 2000 rounds. The algorithm assumes that the ideal MAC protocol is used, and there is no packet loss and transmission conflict during data transmission. The experimental parameter settings are shown in Table 1. The comparison algorithms selected by the present invention are LEACH and SEP.
表1实验参数Table 1 Experimental parameters
将本发明算法的网络寿命、通信距离和能量消耗情况分别与LEACH和SEP算法进行对比，对比结果如下：The network life, communication distance and energy consumption of the algorithm of the present invention are compared with the LEACH and SEP algorithms respectively, and the comparison results are as follows:
(1)网络寿命(1) Network Lifespan
在本发明中，网络寿命定义为从网络开始运行到全网中所有节点死亡所持续的时间。本发明算法的网络寿命与LEACH和SEP算法进行对比。如图4所示，由图可知，在网络运行2000轮以后，采用本发明算法的网络依然存活的节点数为40个左右，采用LEACH的网络中已经没有存活的节点。由于SEP算法的能量异构特性，在2000轮时，网络中虽有存活的节点，但其数量仅为1个。由于本发明采用了节点能量消耗预测机制，避免了低能量节点被选择为簇头节点，而通信距离控制策略又有效地避免了靠近基站节点的传感器节点能量浪费。通过控制分簇大小，有效的对簇头及簇成员的能量消耗进行了限制，保持了节点的能量消耗均衡。可见，本发明算法能够明显提高网络寿命。In the present invention, the network lifetime is defined as the duration from the start of network operation to the death of all nodes in the entire network. The network lifetime of the algorithm of the present invention is compared with that of the LEACH and SEP algorithms. As shown in Figure 4, it can be seen from the figure that after 2000 rounds of network operation, the number of surviving nodes in the network using the algorithm of the present invention is about 40, and there are no surviving nodes in the network using LEACH. Due to the energy heterogeneity of the SEP algorithm, at round 2000, although there are surviving nodes in the network, the number is only one. Because the present invention adopts the node energy consumption prediction mechanism, low-energy nodes are avoided from being selected as cluster head nodes, and the communication distance control strategy effectively avoids energy waste of sensor nodes close to base station nodes. By controlling the size of the cluster, the energy consumption of the cluster head and cluster members is effectively limited, and the energy consumption of the nodes is kept balanced. It can be seen that the algorithm of the present invention can obviously improve the network lifetime.
(2)通信距离(2) Communication distance
通信距离指的是网络的平均通信距离，包括簇内通信距离与簇头和汇聚节点通信距离之和。通信距离与节点能量消耗和网络总体能量消耗密切相关。如图5所示，由图可知，在网络运行2000轮后，本发明的平均通信距离有所下降，但依然能够稳定在50m左右，从而能够平衡各节点能量消耗，延长网络寿命。而随着死亡节点数的增加，在1200轮之后，LEACH与SEP算法的平均通信距离出现了明显的下降，这说明虽然两种算法能够在一定程度上平衡节点的能量消耗，但是若干轮后，由于节点的剩余能量相当，最终导致大部分节点在很短的时间内快速死亡。The communication distance refers to the average communication distance of the network, including the sum of the intra-cluster communication distance and the communication distance between the cluster head and the sink node. Communication distance is closely related to node energy consumption and overall network energy consumption. As shown in Figure 5, it can be seen from the figure that after 2000 rounds of network operation, the average communication distance of the present invention has decreased, but it can still be stabilized at about 50m, thereby balancing the energy consumption of each node and prolonging the network life. With the increase of the number of dead nodes, after 1200 rounds, the average communication distance between LEACH and SEP algorithms has dropped significantly, which shows that although the two algorithms can balance the energy consumption of nodes to a certain extent, after several rounds, Because the remaining energy of the nodes is equal, most of the nodes will eventually die quickly in a short period of time.
(3)能量消耗(3) Energy consumption
如图6所示，由图可知，通信距离相对较长，本发明虽然在网络刚开始运行阶段的能量消耗高于LEACH和SEP，但由于对每个分簇大小实行了控制，一方面保证了其通信距离的稳定性，另一方面可以控制簇头节点的能量消耗量，从而比LEACH和SEP算法的能量消耗更加均衡，有效延长了网络的寿命。As shown in Figure 6, it can be seen from the figure that the communication distance is relatively long. Although the energy consumption of the present invention is higher than that of LEACH and SEP at the initial stage of network operation, due to the control of the size of each cluster, on the one hand, it ensures The stability of its communication distance, on the other hand, can control the energy consumption of the cluster head nodes, so that the energy consumption of the LEACH and SEP algorithms is more balanced, effectively extending the life of the network.
以上所述，仅是本发明的较佳实施例而已，并非对本发明作任何形式上的限制，虽然本发明已以较佳实施例描述如上，然而并非用以限定本发明，任何熟悉本专业的技术人员，在不脱离本发明技术方案范围内，当可利用上述所述技术内容作出的些许更动或修饰均为等同变化的等效实施例，但凡是未脱离本发明技术方案内容，依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化与修饰，均仍属于本发明技术方案的范围内。The above descriptions are only preferred embodiments of the present invention, and do not limit the present invention in any form. Although the present invention has been described above with preferred embodiments, it is not intended to limit the present invention. Anyone familiar with this field Those skilled in the art, without departing from the scope of the technical solution of the present invention, may use the above-mentioned technical content to make some changes or modifications that are equivalent embodiments of equivalent changes, but if they do not depart from the content of the technical solution of the present invention, according to this Technical Essence of the Invention Any simple modifications, equivalent changes and modifications made to the above embodiments still fall within the scope of the technical solutions of the present invention.
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