技术领域technical field
本发明涉及通信领域，尤其是一种无线传感器网络的路由方法。The invention relates to the communication field, in particular to a routing method for a wireless sensor network.
背景技术Background technique
无线传感器网络是新一代的传感器网络，具有非常广泛的应用前景，其发展和应用，将会给人类的生活和生产的各个领域带来深远影响。早在上世纪70年代，就出现了将传统传感器采用点对点传输、连接传感控制器而构成传感器网络雏形，我们把它归之为第一代传感器网络。随着相关学科的不断发展和进步，传感器网络同时还具有了获取多种信息信号的综合处理能力，并通过与传感控制器的相联，组成了有信息综合和处理能力的传感器网络，这是第二代传感器网络。而从上世纪末开始，现场总线技术开始应用于传感器网络，人们用其组建智能化传感器网络，大量多功能传感器被运用，并使用无线技术连接，无线传感器网络逐渐形成。Wireless sensor network is a new generation of sensor network, which has a very broad application prospect. Its development and application will have a profound impact on various fields of human life and production. As early as the 1970s, the prototype of sensor network formed by using point-to-point transmission of traditional sensors and connecting sensor controllers appeared. We attribute it to the first generation sensor network. With the continuous development and progress of related disciplines, the sensor network also has the comprehensive processing ability to obtain a variety of information signals, and through the connection with the sensor controller, a sensor network with information synthesis and processing capabilities is formed. It is the second generation sensor network. Since the end of the last century, fieldbus technology has been applied to sensor networks. People use it to build intelligent sensor networks. A large number of multifunctional sensors are used and connected using wireless technology. Wireless sensor networks are gradually formed.
发达国家如美国，非常重视无线传感器网络的发展，IEEE正在努力推进无线传感器网络的应用和发展，波士顿大学还于最近创办了传感器中国测控网络协会(SensorNetworkConsortium)，期望能促进传感器联网技术开发。美国的《技术评论》杂志在论述未来新兴十大技术时，更是将无线传感器网络列为第一项未来新兴技术，《商业周刊》预测的未来四大新技术中，无线传感器网络也列入其中。可以预计，无线传感器网络的广泛是一种必然趋势，它的出现将会给人类社会带来极大的变革。Developed countries such as the United States attach great importance to the development of wireless sensor networks. IEEE is working hard to promote the application and development of wireless sensor networks. Boston University also recently established the Sensor Network Consortium in China, hoping to promote the development of sensor networking technology. When discussing the top ten emerging technologies in the future, the "Technology Review" magazine in the United States listed wireless sensor networks as the first future emerging technologies. Among the four new technologies predicted by "Business Weekly", wireless sensor networks were also included. in. It can be predicted that the wide spread of wireless sensor networks is an inevitable trend, and its appearance will bring great changes to human society.
无线传感器网络的核心任务是对周边网络环境的信息感知及数据收集。传统的数据收集方式通过传感器节点与网络基站间的无线通讯来完成，但会导致较高的传感器节点能耗，并出现网络中节点能耗不平衡的情况，从而降低网络正常工作时间。另外，传统数据收集方式要求网络必须具有连通性。为了保证连通性，需要向网络中部署大量的传感器节点，从而不可避免的导致节点的冗余并增加了网络成本。The core task of the wireless sensor network is the information perception and data collection of the surrounding network environment. The traditional data collection method is completed through wireless communication between sensor nodes and network base stations, but it will lead to high energy consumption of sensor nodes, and there will be unbalanced energy consumption of nodes in the network, thereby reducing the normal working time of the network. In addition, traditional data collection methods require that the network must have connectivity. In order to ensure connectivity, a large number of sensor nodes need to be deployed in the network, which inevitably leads to node redundancy and increases network costs.
移动元素为同时具有移动和数据收集能力的人员或设备。移动元素通过自身的移动对网络中的传感器节点进行访问，并利用近距离无线通讯完成对传感器节点所产生数据的收集。通过向网络中引入移动元素来完成数据收集，传感器节点的能耗情况被显著改进，同时也降低了数据收集对网络连通性的要求。A mobile element is a person or device that has both mobility and data collection capabilities. The mobile element visits the sensor nodes in the network through its own movement, and uses short-distance wireless communication to complete the collection of data generated by the sensor nodes. By introducing mobile elements into the network to accomplish data collection, the energy consumption of sensor nodes is significantly improved, and the network connectivity requirements for data collection are also reduced.
发明内容Contents of the invention
为了克服现有技术的不足，本发明设计了一种基于移动元素的无线传感器网络的路由协议，该协议能实现网络的数据在得到高效准确接收的同时网络整体的能耗尽可能的降低，而且能适应无线传感器网络大规模、自组织、随机部署、环境复杂等特点，并适合在特殊环境下大量部署和使用，例如人员稀少的野外环境等。并通过移动元素与分簇算法的综合运用，实现高效准确传输传感器数据的同时降低传感器节点的能量消耗，从而使网络的总体存活时间尽可能延长。In order to overcome the deficiencies of the prior art, the present invention designs a routing protocol for wireless sensor networks based on mobile elements. This protocol can realize efficient and accurate reception of network data while reducing overall energy consumption of the network as much as possible, and It can adapt to the characteristics of large-scale, self-organizing, random deployment, and complex environments of wireless sensor networks, and is suitable for large-scale deployment and use in special environments, such as field environments with few people. And through the comprehensive application of mobile elements and clustering algorithms, the energy consumption of sensor nodes can be reduced while efficiently and accurately transmitting sensor data, so that the overall survival time of the network can be extended as much as possible.
本发明解决其技术问题所采用的技术方案是：The technical solution adopted by the present invention to solve its technical problems is:
步骤1：无线传感器节点抛洒及网络构建Step 1: Wireless sensor node spreading and network construction
建立一个由无线传感器节点和基站组成正方形无线传感器网络，基站位于无线传感器网络区域中心，传感器节点由无人机随机抛洒在基站周围，每个传感器节点都有特定的ID编号；Establish a square wireless sensor network composed of wireless sensor nodes and base stations. The base station is located in the center of the wireless sensor network area. The sensor nodes are randomly scattered around the base station by drones. Each sensor node has a specific ID number;
步骤2：传感器节点位置确定及身份确定Step 2: Sensor node location determination and identity determination
各传感器节点通过自身安装的GPS定位系统获得自己的位置信息，并将自己的位置信息通过各传感器节点多跳传输的方式传递给基站，基站根据各节点位置信息的分布密度选取第一级簇头CH(Cluster Heads)，选取簇头的机制如下：设传感器节点的数据收发半径最大为r，确定在每一个传感器节点为中心r为半径的圆内的有效传感器节点的个数，并将有效传感器节点的ID进行记录，按每个传感器节点周围有效传感器节点的个数多少制作路由表，并按照有效传感器节点的个数从大到小排列，选取周围有效传感器节点个数最大的点即该路由表表头的点为CH，之后检查周围有效传感器节点个数排在第二位的点是否已选出并存在于CH点的路由表内，若已在路由表中，则排在第二位的节点定位为普通节点；若不在路由表中，则定为CH，以此类推，直到路由表内所有节点筛选完毕；Each sensor node obtains its own location information through its own GPS positioning system, and transmits its own location information to the base station through multi-hop transmission of each sensor node. The base station selects the first-level cluster head according to the distribution density of each node's location information. CH (Cluster Heads), the mechanism for selecting cluster heads is as follows: set the sensor node’s maximum data sending and receiving radius to r, determine the number of effective sensor nodes in a circle with each sensor node as the center and r as the radius, and set the effective sensor nodes The ID of the node is recorded, and the routing table is made according to the number of effective sensor nodes around each sensor node, and the number of effective sensor nodes is arranged from large to small, and the point with the largest number of effective sensor nodes around is selected as the route The point at the head of the table is CH, and then check whether the second point in the number of effective sensor nodes around has been selected and exists in the routing table of CH point, if it is already in the routing table, then the second The node is positioned as a normal node; if it is not in the routing table, it is defined as CH, and so on, until all nodes in the routing table are screened;
步骤3：移动元素行走路线规划Step 3: Planning the walking route of moving elements
本发明将移动元素ME(Mobile Element)行走路线问题看作旅行商问题，利用最近邻算法的优化算法来进行ME行走路线的规划：选取距行走路线的直线距离小于ME的数据收集覆盖半径R的传感器节点为次级簇头VH(Virtual Heads)，即ME的行走路线确定后，计算各传感器节点到该路线的垂直距离，如果垂直距离小于ME的覆盖半径，也就是该传感器节点在ME的覆盖半径之内，即ME能够接收到该点传来的信息，否则该点被选为VH；The present invention regards the mobile element ME (Mobile Element) walking route problem as a traveling salesman problem, uses the optimization algorithm of the nearest neighbor algorithm to carry out the planning of the ME walking route: select the line distance from the walking route that is less than the data collection coverage radius R of the ME The sensor node is the secondary cluster head VH (Virtual Heads), that is, after the ME's walking route is determined, the vertical distance from each sensor node to the route is calculated. If the vertical distance is less than the coverage radius of ME, that is, the coverage of the sensor node in ME Within the radius, that is, ME can receive the information from this point, otherwise this point is selected as VH;
步骤4：传感器节点与移动元素之间的信息传输Step 4: Information transfer between sensor nodes and mobile elements
网络中各传感器节点接收到基站传来的其他传感器节点的身份信息以及数据传递信息后，每个传感器节点收集该传感器节点能收集到的所有信息，包括温度、湿度、压力、磁场、声音、气体和放射线信息，并把收集到的信息传输给离该传感器节点最近的CH或VH节点，最近的CH或VH节点将信息暂存，等待ME经过时将信息传输给ME，ME是从整个正方形区域的中心出发沿着基站规划好的ME的行走路线行走；After each sensor node in the network receives the identity information and data transmission information of other sensor nodes from the base station, each sensor node collects all the information that the sensor node can collect, including temperature, humidity, pressure, magnetic field, sound, gas and radiation information, and transmit the collected information to the nearest CH or VH node to the sensor node, the nearest CH or VH node temporarily stores the information, and transmits the information to ME when the ME passes by. ME is from the entire square area Starting from the center of the base station, walk along the route of the ME planned by the base station;
步骤5：移动元素ME返回基站后，并将ME行走期间从CH或VH节点收集来的所有信息传给基站，移动元素ME按照规划路线重复行走；从ME刚开始行走直至有一个节点死亡的时间记为该节点在网络中的存活时间，当网络中有一个节点死亡时，视为该网络死亡，基站重新执行步骤2，即根据各节点位置信息的分布密度，重新进行位置确定及身份确定，并把最新的路线规划告知ME，在下一轮的ME行走时，ME将按照新的路线收集数据。Step 5: After the mobile element ME returns to the base station, it transmits all the information collected from CH or VH nodes to the base station during the walking of the ME, and the mobile element ME walks repeatedly according to the planned route; the time from when the ME starts walking until a node dies It is recorded as the survival time of the node in the network. When a node in the network dies, it is regarded as the death of the network, and the base station re-executes step 2, that is, re-determines the location and identity according to the distribution density of the location information of each node. And inform the ME of the latest route planning, and when the ME walks in the next round, the ME will collect data according to the new route.
本发明的有益效果是是本发明能适应无线传感器网络大规模、自组织、随机部署、环境复杂等特点，并适合在人员稀少的野外环境等特殊环境下大量部署和使用，本发明将移动元素与传统的分簇算法进行结合，可高效准确的传输各传感器节点所收集的数据，并尽可能降低了各传感器节点的能量消耗，使网络的总体存活时间尽可能延长。本发明跟随节点死亡变换拓扑结构，重新选取簇首节点与二级簇头，能够以最大程度降低簇头节点的耗能，从而增加网络总体的存活时间，通过与其他有益方法的网络存活时间的数据进行对比，本方法体现出了明显的优势。The beneficial effect of the present invention is that the present invention can adapt to the large-scale, self-organized, random deployment, complex environment and other characteristics of wireless sensor networks, and is suitable for large-scale deployment and use in special environments such as field environments with few people. Combined with the traditional clustering algorithm, it can efficiently and accurately transmit the data collected by each sensor node, and reduce the energy consumption of each sensor node as much as possible, so that the overall survival time of the network can be extended as much as possible. The present invention changes the topology structure following the node death, reselects the cluster head node and the second-level cluster head, and can reduce the energy consumption of the cluster head node to the greatest extent, thereby increasing the overall survival time of the network. Compared with the data, this method shows obvious advantages.
附图说明Description of drawings
图1是基于移动元素的无线传感器路由机制实现流程图。Figure 1 is a flow chart of the implementation of wireless sensor routing mechanism based on mobile elements.
图2是ME行走路线规划图。Fig. 2 is a planning map of the ME walking route.
图3是20-35节点网络存活时间图，其中纵坐标为网络存活时间，横坐标为网络节点个数。Fig. 3 is a diagram of the network survival time of 20-35 nodes, wherein the ordinate is the network survival time, and the abscissa is the number of network nodes.
图4是100-400节点网络存活时间图，其中纵坐标为网络存活时间，横坐标为网络节点个数。Fig. 4 is a diagram of the network survival time of 100-400 nodes, where the ordinate is the network survival time, and the abscissa is the number of network nodes.
图5是100节点网络存活时间随CH半径的变化关系，其中纵坐标为网络存活时间，横坐标为CH半径大小。Figure 5 shows the relationship between the 100-node network survival time and the CH radius, where the ordinate is the network survival time, and the abscissa is the CH radius.
具体实施方式Detailed ways
下面结合附图和实施例对本发明进一步说明。The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
步骤1：无线传感器节点抛洒及网络构建Step 1: Wireless sensor node spreading and network construction
建立一个由无线传感器节点和基站组成正方形无线传感器网络，基站位于无线传感器网络区域中心，传感器节点由无人机随机抛洒在基站周围，每个传感器节点都有特定的ID编号；Establish a square wireless sensor network composed of wireless sensor nodes and base stations. The base station is located in the center of the wireless sensor network area. The sensor nodes are randomly scattered around the base station by drones. Each sensor node has a specific ID number;
如图1为整个发明方法的结构步骤框图，将n个传感器节点随机均匀分布在一个100*100正方形区域内，移动元素可以在正方形区域内随意走动，每一个节点都有一个数据传输距离的限制r。假设每个节点都有ID编号并且均装有GPS，可以获得他们的地理位置信息。并且当ME行走到传感器节点周围时可以进行数据传输。Figure 1 is a block diagram of the structural steps of the entire inventive method, where n sensor nodes are randomly and evenly distributed in a 100*100 square area, mobile elements can move freely in the square area, and each node has a data transmission distance limit r. Assuming that each node has an ID number and is equipped with GPS, their geographic location information can be obtained. And when the ME walks around the sensor node, data transmission can be performed.
步骤2：传感器节点位置确定及身份确定Step 2: Sensor node location determination and identity determination
各传感器节点通过自身安装的GPS定位系统获得自己的位置信息，并将自己的位置信息通过各传感器节点多跳传输的方式传递给基站，基站根据各节点位置信息的分布密度选取第一级簇头CH(Cluster Heads)，选取簇头的机制如下：设传感器节点的数据收发半径最大为r，确定在每一个传感器节点为中心r为半径的圆内的有效传感器节点的个数，并将有效传感器节点的ID进行记录，按每个传感器节点周围有效传感器节点的个数多少制作路由表，并按照有效传感器节点的个数从大到小排列，选取周围有效传感器节点个数最大的点即该路由表表头的点为CH，之后检查周围有效传感器节点个数排在第二位的点是否已选出并存在于CH点的路由表内，若已在路由表中，则排在第二位的节点定位为普通节点；若不在路由表中，则定为CH，以此类推，直到路由表内所有节点筛选完毕；Each sensor node obtains its own location information through its own GPS positioning system, and transmits its own location information to the base station through multi-hop transmission of each sensor node. The base station selects the first-level cluster head according to the distribution density of each node's location information. CH (Cluster Heads), the mechanism for selecting cluster heads is as follows: set the sensor node’s maximum data sending and receiving radius to r, determine the number of effective sensor nodes in a circle with each sensor node as the center and r as the radius, and set the effective sensor nodes The ID of the node is recorded, and the routing table is made according to the number of effective sensor nodes around each sensor node, and the number of effective sensor nodes is arranged from large to small, and the point with the largest number of effective sensor nodes around is selected as the route The point at the head of the table is CH, and then check whether the second point in the number of effective sensor nodes around has been selected and exists in the routing table of CH point, if it is already in the routing table, then the second The node is positioned as a normal node; if it is not in the routing table, it is defined as CH, and so on, until all nodes in the routing table are screened;
步骤3：移动元素行走路线规划Step 3: Planning the walking route of moving elements
本发明将移动元素ME(Mobile Element)行走路线问题看作旅行商问题，利用最近邻算法的优化算法来进行ME行走路线的规划：选取距行走路线的直线距离小于ME的数据收集覆盖半径R的传感器节点为次级簇头VH(Virtual Heads)；即ME的行走路线确定后，计算各传感器节点到该路线的垂直距离，如果垂直距离小于ME的覆盖半径，也就是该传感器节点在ME的覆盖半径之内，即ME能够接收到该点传来的信息，否则该点被选为VH；The present invention regards the mobile element ME (Mobile Element) walking route problem as a traveling salesman problem, uses the optimization algorithm of the nearest neighbor algorithm to carry out the planning of the ME walking route: select the line distance from the walking route that is less than the data collection coverage radius R of the ME The sensor node is the secondary cluster head VH (Virtual Heads); that is, after the walking route of the ME is determined, the vertical distance from each sensor node to the route is calculated. If the vertical distance is less than the coverage radius of the ME, that is, the coverage of the sensor node in the ME Within the radius, that is, ME can receive the information from this point, otherwise this point is selected as VH;
在ME行走路线已经确定的情况下，选取距路线的直线距离小于ME的数据收集覆盖半径R的传感器节点为VH。待基站选取好所有的CH与VH之后，通过广播使各个节点获取自己的身份信息以及周围节点的身份信息。In the case that the ME walking route has been determined, the sensor node whose straight-line distance from the route is less than the data collection coverage radius R of ME is selected as VH. After the base station has selected all CHs and VHs, each node obtains its own identity information and the identity information of surrounding nodes through broadcasting.
本发明专利的移动元素行走路线的规划，应用最近邻算法计算，其步骤如下：The planning of the walking route of the mobile element in the patent of the present invention is calculated by using the nearest neighbor algorithm, and the steps are as follows:
(1)任选一个传感器节点V1作起点，找一条与V1关联权值最小的一条边e1，e1的另一端点记为V2得一条路V1V2；(1) Choose a sensor node V1 as the starting point, find an edge e1 with the smallest weight associated with V1 , record the other end of e1 as V2 to get a road V1 V2 ;
(2)设已选出路V1，V2，…Vi，在点集V(G)-{V1，V2，...，Vi}中取一个与Vi最近的相邻顶点Vi+1,得V1，V2，...Vi，Vi+1；(2) Assuming that the selected paths V1 , V2 ,...Vi , take the nearest neighbor to Vi in the point set V(G)-{V1 , V2 ,...,Vi } Vertex Vi+1 , get V1 , V2 ,...Vi , Vi+1 ;
(3)若i+1<P(G)，用i代i+1返回(2)，否则记P＝V1V2...VPV1，其中P(G)是G里面的点的总个数；(3) If i+1<P(G), replace i+1 with i and return to (2), otherwise record P=V1 V2 ... VP V1 , where P(G) is a point in G the total number of
(4)若存在i，j且1<i+1<j<p，并且W(Vi，Vj)+W(Vi+1，Vj+1)＜W(Vi，Vi+1)+W(Vj，Vj+1)，则Cij＝V1，V2，...ViVj，Vj-1，...Vi+1Vj+1，V2，...，VpV1，其中Cij是近似最优的回路，W(Vi，Vj)为Vi，Vj两点间的权值，W(Vi+1，Vj+1)为Vi+1，Vj+1两点间的权值，W(Vi，Vi+1)为Vi，Vi+1两点间的权值，W(Vj，Vj+1)为Vj，Vj+1两点间的权值，p是仍旧存活点的总个数，经过上述路径规划可得图2所示的路径规划图。(4) If there are i, j and 1<i+1<j<p, and W(Vi , Vj )+W(Vi+1 , Vj+1 )<W(Vi ,Vi+ 1 )+W(Vj , Vj+1 ), then Cij=V1 , V2 ,...Vi Vj , Vj-1 ,...Vi+1 Vj+1 , V2 ,..., Vp V1 , where Cij is an approximate optimal circuit, W(Vi , Vj ) is the weight between Vi and Vj , W(Vi+1 , Vj+ 1 ) is the weight between Vi+1 and Vj+1 , W(Vi , Vi+1 ) is the weight between Vi and Vi+1 , W(Vj , Vj+1 ) is the weight between Vj and Vj+1 , and p is the total number of surviving points. After the above path planning, the path planning graph shown in Figure 2 can be obtained.
步骤4：传感器节点与移动元素之间的信息传输Step 4: Information transfer between sensor nodes and mobile elements
网络中各传感器节点接收到基站传来的其他传感器节点的身份信息以及数据传递信息后，每个传感器节点收集该传感器节点能收集到的所有信息，包括温度、湿度、压力、磁场、声音、气体和放射线信息，并把收集到的信息传输给离该传感器节点最近的CH或VH节点，最近的CH或VH节点将信息暂存，等待ME经过时将信息传输给ME，ME是从整个正方形区域的中心出发沿着基站规划好的ME的行走路线行走；After each sensor node in the network receives the identity information and data transmission information of other sensor nodes from the base station, each sensor node collects all the information that the sensor node can collect, including temperature, humidity, pressure, magnetic field, sound, gas and radiation information, and transmit the collected information to the nearest CH or VH node to the sensor node, the nearest CH or VH node temporarily stores the information, and transmits the information to ME when the ME passes by. ME is from the entire square area Starting from the center of the base station, walk along the route of the ME planned by the base station;
ME在行走的过程中会发送广播告诉节点自己将要到达的信息，各节点在平时已经将需要传输的信息传送给了CH或者VH,二者在检测到ME经过时会将所缓存的信息传输给ME。In the process of walking, ME will send a broadcast to tell the nodes that they are about to arrive. Each node has sent the information to be transmitted to CH or VH in normal times. When they detect that ME passes by, they will transmit the cached information to CH or VH. ME.
步骤5：移动元素ME返回基站后，并将ME行走期间从CH或VH节点收集来的所有信息传给基站，移动元素ME按照规划路线重复行走；从ME刚开始行走直至有一个节点死亡的时间记为该节点在网络中的存活时间，当网络中有一个节点死亡时，视为该网络死亡，基站重新执行步骤2，即根据各节点位置信息的分布密度，重新进行位置确定及身份确定，并把最新的路线规划告知ME，在下一轮的ME行走时，ME将按照新的路线收集数据。Step 5: After the mobile element ME returns to the base station, it transmits all the information collected from CH or VH nodes to the base station during the walking of the ME, and the mobile element ME walks repeatedly according to the planned route; the time from when the ME starts walking until a node dies It is recorded as the survival time of the node in the network. When a node in the network dies, it is regarded as the death of the network, and the base station re-executes step 2, that is, re-determines the location and identity according to the distribution density of the location information of each node. And inform the ME of the latest route planning, and when the ME walks in the next round, the ME will collect data according to the new route.
本发明还进行了CH覆盖半径优化，由于本发明所获得的数据都是在CH覆盖半径取最大值25，以及只考虑单跳信息传输的况下进行的，改变CH半径势必会对网络存活时间以及数据收发时延造成影响，故本发明又对上述问题进行了进一步的研究，改变CH的半径，对应网络存活时间的改变，找出了网络存活时间最长时的CH半径值，相当于对CH半径值做了优化。The present invention has also carried out CH coverage radius optimization, because the data that the present invention obtains is all taken maximum value 25 in CH coverage radius, and carries out under the situation that only considers single-hop information transmission, changes CH radius certainly will influence network survival time And the time delay of data sending and receiving causes influence, so the present invention has carried out further research to above-mentioned problem again, changes the radius of CH, corresponding to the change of network survival time, has found out the CH radius value when the network survival time is the longest, is equivalent to The CH radius value has been optimized.
本发明仿真出了在100节点的情况下网络存活时间随着CH半径的变化关系，见图5，通过改变CH的半径，仿真得出不同的CH半径下网络的总体存活时间，并通过分析数据得出当CH的半径取在10左右时，既可以使网络存活时间加长又不至于使ME的行走路程过长而增加时延，故在选取CH覆盖半径时以10为最佳，即可以保证网络存活时间，又能使ME的行走路程不会太长，从而降低时延。The present invention simulates the relationship between the network survival time and the change of CH radius in the case of 100 nodes, as shown in Figure 5, by changing the radius of CH, the simulation obtains the overall survival time of the network under different CH radii, and by analyzing the data It is concluded that when the radius of CH is set at about 10, the network survival time can be lengthened without causing the ME to travel too long and increase the time delay. Therefore, 10 is the best choice for CH coverage radius, which can guarantee The network survival time can also make the walking distance of the ME not too long, thereby reducing the delay.
经过仿真得出本发明的结果符合理论实际分析，具体成果如下：Obtain the result of the present invention through emulation and accord with theoretical actual analysis, and concrete achievement is as follows:
本发明进行了在不同节点个数的情况下网络总体存活时间长短的对比。每个数据均通过连续测量100次取平均值得到。通过对比数据发现在网络所取节点个数位于20-35之间时，本文的网络存活时间比目前现有方法要提高1-2倍；节点所取个数位于100-400时，本文的网络存活时间比现有方法要提高4-5倍。网络存活时间是检验无线传感器网络路由设计好坏的重要指标，由此可以看出本提供的方法对于无线传感器网络的路由设计具有重要的价值。The present invention compares the overall survival time of the network under the condition of different numbers of nodes. Each data is obtained by taking the average value of 100 consecutive measurements. By comparing the data, it is found that when the number of nodes in the network is between 20-35, the survival time of the network in this paper is 1-2 times higher than that of the current existing methods; when the number of nodes is in the range of 100-400, the network survival time of this paper is The survival time is 4-5 times higher than that of existing methods. Network survival time is an important index to test the quality of wireless sensor network routing design, so it can be seen that the method provided in this paper has important value for wireless sensor network routing design.
图3与图4为本发明方法与该论文结果的网络存活时间对比图，其中图3为20-35节点网络存活时间图，图4为100-400节点网络存活时间图。每个图的数据均通过连续测量100次取平均值得到。通对比发现在20-35节点时本文的网络存活时间比该论文所提方法要提高1-2倍，在100-400节点时本文的网络存活时间比该论文所提方法要提高4-5倍，初步分析这是利用了VH从而降低了CH的负担，从而使得网络的总能耗降低。Figure 3 and Figure 4 are comparison diagrams of the network survival time between the method of the present invention and the results of the paper, wherein Figure 3 is a diagram of the network survival time of 20-35 nodes, and Figure 4 is a diagram of the network survival time of 100-400 nodes. The data in each figure are obtained by taking the average value of 100 continuous measurements. Through comparison, it is found that the network survival time of this paper is 1-2 times higher than the method proposed in this paper at 20-35 nodes, and the network survival time of this paper is 4-5 times higher than the method proposed in this paper at 100-400 nodes , the preliminary analysis is that the VH is used to reduce the burden of the CH, thereby reducing the total energy consumption of the network.
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| Country | Link |
|---|---|
| CN (1) | CN104754683B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203697A (en)* | 2016-07-08 | 2016-12-07 | 西北大学 | A kind of paths planning method during Unmanned Aerial Vehicle Data collection |
| CN106559757A (en)* | 2016-11-23 | 2017-04-05 | 宁波大红鹰学院 | A kind of UAV Communication method |
| CN107212860A (en)* | 2017-06-28 | 2017-09-29 | 深圳源广安智能科技有限公司 | A kind of physiological signal intelligent monitor system |
| CN107613480A (en)* | 2017-09-24 | 2018-01-19 | 中南大学 | A kind of method of data capture based on tree-shaped cluster and mobile element |
| CN108200056A (en)* | 2017-12-29 | 2018-06-22 | 广东工业大学 | A kind of proof of identity method and system of unmanned device |
| CN110519820A (en)* | 2019-08-27 | 2019-11-29 | 中科芯(苏州)微电子科技有限公司 | A kind of method for routing applied in cluster UAV Communication |
| CN110972230A (en)* | 2019-12-23 | 2020-04-07 | 杭州电子科技大学 | A method of LEACH secondary clustering routing protocol based on cuckoo algorithm |
| CN111212455A (en)* | 2020-01-16 | 2020-05-29 | 中国人民解放军陆军工程大学 | Dynamic clustering system and method for wireless self-organizing network |
| CN113311779A (en)* | 2021-05-27 | 2021-08-27 | 湖南国天电子科技有限公司 | Buoy data acquisition processing control system |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120057526A1 (en)* | 2010-09-02 | 2012-03-08 | Weixiang Chen | Soft State Framework for Mobile WSN Routing |
| CN102740395A (en)* | 2012-07-12 | 2012-10-17 | 南京邮电大学 | Self-organizing routing method facing mobile sensor network |
| CN103152792A (en)* | 2013-03-28 | 2013-06-12 | 东南大学 | Mobile data collecting method based on rail assistance in wireless sensor network |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120057526A1 (en)* | 2010-09-02 | 2012-03-08 | Weixiang Chen | Soft State Framework for Mobile WSN Routing |
| CN102740395A (en)* | 2012-07-12 | 2012-10-17 | 南京邮电大学 | Self-organizing routing method facing mobile sensor network |
| CN103152792A (en)* | 2013-03-28 | 2013-06-12 | 东南大学 | Mobile data collecting method based on rail assistance in wireless sensor network |
| Title |
|---|
| RUONAN ZHANG ETC: "A Hybrid Approach Using Mobile Element and Hierarchical Clustering for Data Collection in WSNs", 《2015 IEEE WIRELESS COMMUNICATION AND NETWORKING CONFERENCE》* |
| 张蕾等: "无线传感器网络中一种基于移动sink的数据收集算法", 《传感技术学报》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203697A (en)* | 2016-07-08 | 2016-12-07 | 西北大学 | A kind of paths planning method during Unmanned Aerial Vehicle Data collection |
| CN106559757A (en)* | 2016-11-23 | 2017-04-05 | 宁波大红鹰学院 | A kind of UAV Communication method |
| CN107212860A (en)* | 2017-06-28 | 2017-09-29 | 深圳源广安智能科技有限公司 | A kind of physiological signal intelligent monitor system |
| CN107613480B (en)* | 2017-09-24 | 2020-04-17 | 中南大学 | A Data Collection Method Based on Tree Clusters and Moving Elements |
| CN107613480A (en)* | 2017-09-24 | 2018-01-19 | 中南大学 | A kind of method of data capture based on tree-shaped cluster and mobile element |
| CN108200056A (en)* | 2017-12-29 | 2018-06-22 | 广东工业大学 | A kind of proof of identity method and system of unmanned device |
| CN108200056B (en)* | 2017-12-29 | 2021-03-16 | 广东工业大学 | Method and system for identity verification of unmanned device |
| WO2021036055A1 (en)* | 2019-08-27 | 2021-03-04 | 中科芯(苏州)微电子科技有限公司 | Routing method applied to unmanned aerial vehicle swarm communication |
| CN110519820A (en)* | 2019-08-27 | 2019-11-29 | 中科芯(苏州)微电子科技有限公司 | A kind of method for routing applied in cluster UAV Communication |
| CN110972230A (en)* | 2019-12-23 | 2020-04-07 | 杭州电子科技大学 | A method of LEACH secondary clustering routing protocol based on cuckoo algorithm |
| CN111212455A (en)* | 2020-01-16 | 2020-05-29 | 中国人民解放军陆军工程大学 | Dynamic clustering system and method for wireless self-organizing network |
| CN111212455B (en)* | 2020-01-16 | 2022-02-22 | 中国人民解放军陆军工程大学 | Dynamic clustering system and method for wireless self-organizing network |
| CN113311779A (en)* | 2021-05-27 | 2021-08-27 | 湖南国天电子科技有限公司 | Buoy data acquisition processing control system |
| CN113311779B (en)* | 2021-05-27 | 2022-03-01 | 湖南国天电子科技有限公司 | A buoy data acquisition and processing control system |
| Publication number | Publication date |
|---|---|
| CN104754683B (en) | 2019-01-29 |
| Publication | Publication Date | Title |
|---|---|---|
| CN104754683A (en) | Wireless sensor network data acquisition method based on multi-hop routing and mobile elements | |
| CN103200616B (en) | A kind of energy-efficient deployment method setting up Internet of Things network model | |
| Jiang et al. | Routing protocols for unmanned aerial vehicles | |
| CN101493518B (en) | Wireless sensor network node positioning method and device | |
| Park et al. | DroneNetX: Network reconstruction through connectivity probing and relay deployment by multiple UAVs in ad hoc networks | |
| CN102288192B (en) | Multi-robot path planning method based on Ad Hoc network | |
| Zheng et al. | Energy-efficient localization and tracking of mobile devices in wireless sensor networks | |
| Xie et al. | Utilizing active sensor nodes in smart environments for optimal communication coverage | |
| Rani et al. | Multi-hop routing in wireless sensor networks: an overview, taxonomy, and research challenges | |
| CN103716803B (en) | A wireless sensor network relay node deployment method | |
| Kurt et al. | Distributed connectivity maintenance in swarm of drones during post-disaster transportation applications | |
| Lee | Classification of node degree based on deep learning and routing method applied for virtual route assignment | |
| CN109673034A (en) | A kind of wireless sensor network cluster routing method that must be searched for based on longicorn | |
| CN105611502A (en) | Indoor positioning system and method based on WiFi Mesh network | |
| CN103347288B (en) | A kind of wireless sensor network does not wait width hierarchical routing protocol method | |
| CN104135750B (en) | The more mobile beacon group mobile route planing methods of wireless sensor network based on network density sub-clustering | |
| JP2009206560A (en) | Sensor data collection system and method, sensor network constructing device and method, program, and recording medium | |
| CN102740395A (en) | Self-organizing routing method facing mobile sensor network | |
| CN107205254B (en) | Mobile sink node access and interference rejection method and system based on tight delay constraint | |
| CN103249110A (en) | Dynamic-tree-based wireless sensor network target tracking method | |
| CN108055634A (en) | A method for wireless sensor network node positioning and clock synchronization based on mobile anchor nodes | |
| Chai et al. | Wireless sensor networks | |
| Rashed et al. | Effects of UAV mobility patterns on data collection in wireless sensor networks | |
| Singh et al. | Optimization techniques in wireless sensor networks | |
| CN102413471B (en) | Networking method of wireless grid sensor based on link stability estimation |
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