WO2014198120A1 - 基于覆盖保持和最小生成树的无线移动网络数据传输方法 - Google Patents

基于覆盖保持和最小生成树的无线移动网络数据传输方法 Download PDF

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WO2014198120A1
WO2014198120A1 PCT/CN2014/000188 CN2014000188W WO2014198120A1 WO 2014198120 A1 WO2014198120 A1 WO 2014198120A1 CN 2014000188 W CN2014000188 W CN 2014000188W WO 2014198120 A1 WO2014198120 A1 WO 2014198120A1
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node
nodes
cluster
energy
data
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PCT/CN2014/000188
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English (en)
French (fr)
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罗俊海
蔡济杨
倪静
李涛
葛桐羽
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电子科技大学
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Priority claimed from CN201310236464.8A external-priority patent/CN103298138B/zh
Priority claimed from CN201310258548.1A external-priority patent/CN103327653B/zh
Priority claimed from CN201310380204.8A external-priority patent/CN103414786B/zh
Application filed by 电子科技大学 filed Critical 电子科技大学
Publication of WO2014198120A1 publication Critical patent/WO2014198120A1/zh

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W52/00Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04TPC
    • H04W52/30TPC using constraints in the total amount of available transmission power
    • H04W52/34TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels

Definitions

  • Description of the invention relates to wireless mobile network data transmission method based on coverage retention and minimum spanning tree
  • the present invention belongs to the technical field of wireless mobile networks, and in particular, to a wireless mobile network data transmission method based on coverage preservation and minimum spanning tree. Background technique
  • the topology of the network is automatically generated by topology control, which improves the efficiency of the routing protocol and the MAC (Medium Access Control) protocol, and lays a foundation for data fusion, time synchronization, and target positioning. It is beneficial to save the energy of the node to extend the lifetime of the network.
  • the end of the node lifetime occurs after a period of work. Therefore, the sensor monitoring area becomes an unmonitored area due to the death of the node and may pose a potential threat to the entire monitoring area. In this case, coverage remains a technology that is important to it.
  • sleep scheduling and coverage compensation are the mainstream research directions of coverage-maintaining technology. Sleep scheduling refers to adjusting the working state of a node to save energy consumption. Coverage compensation refers to re-running the area where the monitoring is lost. monitor.
  • the commonly used sleep scheduling algorithms are: a sleep scheduling algorithm for non-hierarchical networks and a sleep scheduling algorithm for hierarchical networks.
  • the sleep scheduling algorithms for non-hierarchical networks mainly include two types: MSNL algorithm and location information that does not require location information.
  • LDAS algorithm the node in MSNL algorithm has three states: active state, sleep state, transition state, when the node is in transition state, if it finds that the monitoring area cannot be covered by other active state or transition state node, it immediately Transition to active state, but the limitation of the MSNL algorithm is that accurate location information is required and multiple adjacent nodes may go to sleep at the same time; LDAS is based on partial redundancy scheduling. But it only applies to the case where the nodes are evenly distributed.
  • the sleep scheduling algorithm for hierarchical networks is mainly proposed by Heinzelman et al of the Massachusetts Institute of Technology.
  • the LEACH algorithm is widely quoted.
  • the node decides whether it is a cluster head node, and the cluster head node determines which cluster it belongs to.
  • the LEACH algorithm cannot guarantee the even distribution of cluster heads.
  • the coverage compensation algorithms commonly used nowadays are mainly the DCM algorithm proposed by Archana et al.
  • the DCM algorithm includes the algorithm of the maximum distance that the node can move and the four heuristic algorithms on how to select the movable neighbor nodes, and further describes the level. Linked DCM algorithm.
  • the DCM algorithm can compensate for the loss of coverage caused by dead nodes, but this compensation is not real-time, but is carried out after the loss of the lead-in area, because there is bound to be incomplete coverage for a certain period of time. It can be seen that the above-mentioned sleep scheduling and coverage compensation algorithms have certain problems, and the versatility of the algorithm is not high, which affects the coverage energy of the network.
  • the energy of the wireless sensor node is very limited. Once the energy is exhausted, it cannot be replenished in real time, and studies have shown that the energy consumed in the data transmission phase accounts for a large proportion. Therefore, in the process of collecting information, a single node is used to separately transmit data to the aggregation node. The method is not suitable, it will waste communication bandwidth and energy and reduce the efficiency of information collection. Data aggregation technology is an effective way to solve this problem. Because a large number of randomly deployed sensor nodes have strong correlation, data aggregation can reduce data redundancy information, reduce data packet transmission, and improve energy utilization. rate. Data aggregation utilizes the computing resources and storage resources of the node.
  • the aggregation node waits for all source nodes to transmit the source data to the aggregation node before data aggregation. The waiting time will inevitably cause a large network delay.
  • the existing data aggregation methods generally have the following types:
  • LEACH Low Energy Adaptive Clustering Hierarchy
  • the clustering protocol of the type by randomly selecting the cluster heads by equal probability, distributes the energy consumption of the entire network to each sensor node in a balanced manner, thereby achieving the purpose of reducing network energy consumption and extending the network life cycle.
  • LEACH assumes that all nodes can communicate directly with the aggregation node, and each node has the computing power to support different MAC protocols, the protocol is not suitable for application in large-scale wireless sensor networks.
  • the protocol does not explain how the number of cluster head nodes can be distributed to the entire network. Therefore, It is very likely that the selected cluster head node is concentrated in a certain area of the network, so that there is no cluster head node around some nodes, resulting in uneven distribution of network energy consumption;
  • PEGASIS Power-Efficient GAthering in Sensor Information Systems
  • the protocol forms a chain with the shortest distance between adjacent nodes according to the geographic location of the node.
  • communication is limited to between adjacent nodes.
  • the node transmits data with minimum power.
  • Each cluster only randomly selects one cluster head to communicate with the base station, which reduces data traffic.
  • the PEGASIS algorithm is based on the fact that the global information of the network can be known by all nodes.
  • flooding In the Flooding protocol, after a node generates or receives data, it broadcasts to all neighbors. The packet does not propagate until it expires or arrives at the destination. However, the protocol has serious flaws: (1) implosion: the node receives multiple copies of the same data from neighboring nodes at the same time; (2), overlap: the nodes receive almost the same transmission sent by multiple nodes monitoring the same area. Data; (3), blind use of resources: Nodes do not consider their own resource constraints, in any case, forward data. These protocols have certain problems in the choice of cluster heads or in the processing of clusters, which makes the energy consumption of the sensors not well controlled.
  • data transmission control methods generally focus on data transmission between mobile nodes, or optimization of transmission paths, and less consideration is based on setting access nodes and data transmission with mobile nodes.
  • the intermediate proxy node transmits large data to the download request, such as the current data transmission method in the on-board self-organizing network.
  • the vehicle network is a self-organized, easy-to-deploy, low-cost, open-structure wireless mobile network that will be built between vehicles and nearby fixed facilities. Vehicles in this wireless mobile network can Exchange each other's information such as speed, position, and data sensed by the vehicle's sensors.
  • the method of transmitting data between vehicles and its existing problems are mainly reflected in the following two points:
  • the traditional wireless sensor network technology has the problems of single coverage and maintenance technology, low network coverage and access node utilization, severe energy waste, short life cycle, slow download speed and poor convenience.
  • the traditional wireless sensor network technology has a single coverage and maintenance technology, low network coverage and access node utilization, severe energy waste, short life cycle, slow download speed and poor convenience. Summary of the invention
  • the purpose of the embodiments of the present invention is to provide a wireless mobile network data transmission method based on coverage retention and minimum spanning tree, which aims to solve the problem that the traditional wireless mobile network technology has a single coverage maintaining technology, and the network coverage and access node utilization rate are low. , energy waste is serious, life cycle is short, download speed is slow, and convenience is poor.
  • Embodiments of the present invention are implemented in this manner, a wireless mobile based on coverage retention and minimum spanning tree
  • the network data transmission method, the wireless mobile network data transmission method based on the coverage retention and the minimum spanning tree includes:
  • An overlay retention method based on sleep scheduling and coverage compensation a data generation method based on a minimum spanning tree between wireless sensor nodes, and a data transmission control method for a wireless mobile network.
  • the coverage keeping method based on sleep scheduling and coverage compensation includes the following steps: Step 1: Determine the number of neighbor nodes: The node broadcasts a HELLO message to the surrounding nodes, and the node records the number of different HELLO messages received to obtain its own neighbor node. Number N;
  • Step 3 estimating the node passing information Remaining energy after the exchange phase: the transmitter consumes energy per bit of information: E ec — te , the receiver consumes energy for each bit of information received: E e ⁇ eC - re, and
  • E e lec-te Eelec-re;
  • E am p the energy transmitted by the transmitting end to send the kb i ts message to the receiving end of distance d is
  • the receiving end receives the k bits information and consumes energy as: E elec _ re *k;
  • the energy consumed by the node with m neighbor nodes in the information exchange process is:
  • Eesti E1 - (E elec - te * k + E amp * k * d 2 ) * m - (E eIec _ re * k) * in, where E is the real-time energy of the node before the information exchange;
  • Step 4 Discover the potential death node: If the node energy satisfies: E estl ⁇ , it is a potential death node, where ⁇ is the average energy consumed in a period of time;
  • Step 5 Node information exchange: Each node will broadcast its own redundancy information and broadcast information of potential dead nodes to all its neighbor nodes; Step 6. The non-potential death node estimates whether it can move to the location of the potential dead node; Estimate the energy consumed by the information exchange: All mobile nodes exchange information before moving. The process consumes energy:
  • E est2 E2 - (E elec _ te * k + E a * k * d 2 ) * L - (E ekc _ re * k) * L - E move * h , where h is the distance moved to the target position , E2 is the real-time energy of the node before the movement;
  • Step seven decide the mobile node:
  • the absolute redundant node with the smallest moving target distance is judged according to the target distance; if there are multiple absolute redundant nodes whose target distances are equal and minimum, then according to the remaining energy Judging the size, selecting the node with the largest remaining energy;
  • the distance is selected according to the moving distance of the relatively redundant node, and the distance moved relative to the redundant node is the maximum movable distance of the redundant node, and the maximum movable distance refers to The maximum distance that can be moved by the lower node of the coverage area, the target position of the relative redundant node movement according to the maximum movable distance; the maximum movable distance of the relatively redundant node, the relative redundancy of the smallest moving maximum distance Node, if there are multiple relative redundant nodes whose maximum movable distances are equal and minimum, then according to the size of the remaining energy, the node with the largest remaining energy is selected.
  • Step 8 Adopt a sleep scheduling mechanism for the remaining absolute redundant nodes: After the node moves to the target location, the absolute redundant node state is changed to sleep.
  • the non-potential death node estimates whether it is possible to move to a potential death festival.
  • the location of the point, the specific process is as follows: Decide whether it is necessary to take compensation action for the loss of the coverage area caused by the dead node: If the potential dead node is an absolute redundant node, no action is required; if all neighbor nodes of the potentially dead node If they are non-redundant nodes, no action can be taken; in other cases, the mobile node reduces the coverage loss caused by the potential dead node; the non-potential death node judges whether it has the energy to move to the location of the potential dead node: in all non-potential deaths Remove non-redundant nodes from the node; Estimate the energy consumed by the movement: The distance from the node to the dead node is h, then the energy to be moved is: E ' «. v ⁇ h , where £ 1 ⁇ 1 ⁇ 2 is the energy consumed by moving the unit distance.
  • the data aggregation method based on the minimum spanning tree specifically includes:
  • Step 2 Select the cluster head: uniformly divide the entire detection area according to the grid, so that the size and shape of each grid are the same, and select the sensor node closest to the center of the grid as the cluster head in each grid, and the detection area According to the square grid evenly divided, select the node closest to the center in the square as the cluster head;
  • the header indicates that it joins the cluster, where, indicating the remaining energy of the node at this time, d, representing the distance between the two nodes, t, indicating the size of the packet that the node can monitor; if a node receives multiple clusters Information, the node selects the value of N to join the cluster. If N is equal, the node randomly selects a cluster and joins the cluster. If the node does not receive the cluster information, the node sends a Help message and joins the nearest one.
  • the LEACH energy consumption model is the consumption model of the energy consumption of the sensor when transmitting and receiving data proposed by the LEACH protocol. The specific expression is:
  • E represents the energy consumption of the wireless transceiver circuit
  • marsRe represents the free space model and the multiplexed amplifier energy consumption respectively
  • is a constant
  • is the distance between the communication nodes
  • E, x and E rx ⁇ k) respectively represent the sensor Energy consumption when transmitting and receiving data; the remaining energy of the node can be obtained by the LEACH energy consumption model;
  • Step 4 The nodes in the cluster form a simple graph model: Step 3 is used to obtain the position of all nodes in the cluster in the cluster, and each node is regarded as a vertex of the graph, and each two adjacent nodes are connected by edges; V. Calculation of weights in the cluster: Through step 3, the cluster head obtains the d, and k of the member nodes in the cluster, and calculates the weight between the adjacent two nodes i, j. The calculation formula of the weight is:
  • W y a, ( ⁇ side. + E jr )+ a 2 d 0 + a 3 (k, + k ⁇ )
  • E jr respectively represents the residual energy of node j and the size of the data that node j can monitor , and + ⁇ + ⁇ ⁇ , so that the system can adjust the value of ⁇ according to the system, or the required specific gravity to obtain the weights that meet different needs;
  • Step 6 construct a minimum spanning tree in the cluster node: according to the simple graph model formed by the nodes in the cluster obtained in step 4 and the weight obtained in step 5, construct a minimum spanning tree in the cluster according to the definition of the Prim minimum spanning tree algorithm;
  • Step 7 Data aggregation within the cluster: After the minimum spanning tree structure of the nodes in the cluster is completed, the sensor node PT/CN2014/000188 starts normal operation. From the lowest level sensor node, the collected data is transmitted to the parent node. The parent node aggregates the data collected by itself and the data transmitted by the child node and then transmits it to its parent node. Transfer aggregated data to the cluster head;
  • the parent node is a parent node that aggregates data according to the data transmission direction in the minimum spanning tree, and the node that transmits data to the parent node is a child node;
  • Step 8 Calculating the weight of the cluster head: After the third cluster is completed, the cluster head obtains the position of the node in the entire cluster, the remaining energy of the node, and the size information of the data that the sensor node may monitor, wherein, + represents the entire cluster.
  • the remaining energy value indicates the data size of the cluster head aggregation, indicating the distance between adjacent cluster heads, and calculates the weight between the adjacent two cluster heads i, j.
  • the formula of the weight is defined as:
  • Step 9 The cluster head node constitutes a simple graph model: each cluster head is regarded as a vertex of the graph, and adjacent cluster heads are connected by edges, and the weight of each edge is obtained by the weight calculation formula in step 8;
  • Step 10 The cluster head node constructs a minimum spanning tree: After the simple graph model formed by the cluster head node given in step 8, the minimum spanning tree is constructed according to the definition of the Prim minimum spanning tree algorithm;
  • Step 11 Cluster head data aggregation: After the minimum spanning tree construction of the cluster head node is completed, the collected data is transmitted to the parent node from the lowest level cluster head, and the parent node transmits the data of the aggregated data and the child node. After being aggregated, it is transmitted to its own parent node, and finally the aggregated data is transmitted to the base station;
  • Step 12 Balance the energy consumption of the node: In order to balance the energy consumption of the node, prevent the node from dying too fast, and maintain the normal operation of the cluster. After each M round, the cluster head is reselected, and then the previous steps are performed again, where the node Energy consumption can be estimated by LEACH energy type;
  • V is a node in the sensor
  • Step 1 The access node uploads the mobile information of the mobile node entering the communication range to the intermediate proxy node;
  • Step 2 When downloading the mobile node to enter the communication range of the access node, sending a download request to the access node; the access node forwarding the download request to the intermediate proxy node; and the intermediate proxy node transmitting data to the download mobile node based on the current access node Access the node and record the current transmission progress to upload to the intermediate proxy node;
  • Step 3 The intermediate proxy node selects the access node as the forwarding access node within a preset range based on the received download request and the mobile node's mobile information; and transmits to the forwarding access node based on the latest transmission progress of the downloaded mobile node. Partially downloading data, and the communication range of the forwarding access node exists between the mobile node and the downloading mobile node;
  • Step 4 The intermediate proxy node selects the mobile node as the carrying and forwarding mobile node in the communication range of the forwarding access node, and transmits the download data to the carrying and forwarding mobile node based on the forwarding access node; when carrying the forwarding mobile node and downloading the mobile node to enter each other In the communication range, the carrying and forwarding mobile node transmits the downloaded data carried to the downloading mobile node.
  • the invention provides a wireless mobile network data transmission method based on coverage retention and minimum spanning tree, Two methods of sleep scheduling and coverage compensation are adopted to achieve the dual goals of energy efficient use and coverage maintenance.
  • the energy prediction method is used to accurately determine the mobility of nodes, and the goal of maintaining coverage is achieved.
  • the sleep scheduling mechanism is adopted to avoid the waste of redundant node energy and achieve the goal of high energy utilization.
  • the data transmission is completed by carrying the forwarding mode, so as to effectively improve the utilization rate of the access node, improve the download speed, and improve the convenience of the wireless mobile network.
  • the data aggregation transmission method based on coverage retention and minimum spanning tree achieves the dual goals of energy efficient utilization and maintaining coverage, saves energy, has a long life cycle, and has fast downloading speed and convenient convenience.
  • FIG. 1 is a flow chart of steps of a data aggregation transmission method based on coverage retention and minimum spanning tree according to an embodiment of the present invention
  • FIG. 2 is a schematic flow chart of a coverage maintaining step using sleep scheduling and coverage compensation according to an embodiment of the present invention
  • FIG. 3 is a flow chart of data aggregation using a minimum spanning tree according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram of a process of dividing and clustering a network node in data aggregation using a minimum spanning tree according to an embodiment of the present invention
  • Figure 5 is a diagrammatic illustration of PRIM in data aggregation using a minimum spanning tree in accordance with an embodiment of the present invention. detailed description
  • the wireless mobile network data transmission method based on coverage retention and minimum spanning tree includes the following steps:
  • S101 Maintaining coverage by using sleep scheduling and coverage compensation
  • S103 Data transmission control for a wireless mobile network
  • the method for maintaining the coverage based on the sleep scheduling and the coverage compensation in step S101, as shown in FIG. 2, specifically includes the following steps:
  • Step 1 Determine the number of neighbor nodes: The node broadcasts a HELLO message to the surrounding nodes, and the node records the number of different HELLO messages received to obtain its own neighbor node number N;
  • Step 2 Estimating Node Redundancy: Using the Number of Neighbor Nodes N to obtain the expected value of node redundancy:
  • ⁇ ( ⁇ ⁇ ) 1 - ⁇ ⁇ 'When ⁇ ( ⁇ ⁇ ) ⁇ ⁇ , it is considered to be an absolute redundant node.
  • is a preset threshold
  • Step 3 estimating the remaining energy after the node passes through the information exchange phase: the transmitter consumes energy per bit of information: Eekc- te , the receiver receives lbit information Energy consumption: E e ec, and there is The energy required to transmit the lbit information per unit distance through the transmitter:
  • the receiving end receives the k bitS information and consumes energy: E elec — re *k;
  • the energy consumed by the node with m neighbor nodes in the information exchange process is:
  • Eesti E1-(E elec — te * k + E amp * k * d 2 ) * m - (E e3ec — re * k) "n , where E j is the real-time energy of the node before the information exchange;
  • Step 4 discover potential death nodes: If the node energy satisfies: ⁇ " ⁇ , then potential a dead node, where, is the average energy consumed over a period of time;
  • Step 5 Node information exchange: Each node will include its own redundancy information and whether to broadcast the information of the potential dead node to all its neighbor nodes;
  • Step 6 The non-potential death node estimates whether it can move to the location of the potential dead node.
  • the specific process is as follows: Decide whether it is necessary to take compensation action for the loss of the coverage area caused by the dead node: If the potential dead node is an absolute redundant node , no action is required; if all neighbor nodes of the potential dead node are non-redundant nodes, no action can be taken; in other cases, the coverage loss caused by the potential dead node is reduced by the mobile node; Whether there is energy moving to the location of the potential dead node: remove the non-redundant node in all non-potential dead nodes; estimate the energy consumed by the mobile: the distance from the node to the dead node is h, then the energy to be moved is: E m . Ve * h, where E m . Ve is the energy consumed by moving the unit distance;
  • E es t2 E2 - (E elec _ te * k + E amp * k * d 2 ) * L - (E eiec _ re * k) * L - E move * , where h is the distance moved to the target position , E2 is the real-time energy of the node before the movement;
  • Step seven decide the mobile node:
  • the absolute redundant node with the smallest moving target distance is judged according to the target distance; if there are multiple absolute redundant nodes whose target distances are equal and minimum, Then, according to the size of the remaining energy, the node with the largest remaining energy is selected;
  • the distance is selected according to the moving distance of the relatively redundant node, and the distance moved relative to the redundant node is the maximum movable distance of the redundant node, and the maximum movable distance refers to The maximum distance that can be moved by the lower node of the coverage area, the target position of the relative redundant node movement according to the maximum movable distance; the maximum movable distance of the relatively redundant node, the relative redundancy of the smallest moving maximum distance Node, if there are multiple relative redundant nodes whose maximum movable distances are equal and minimum, then according to the size of the remaining energy, the node with the largest remaining energy is selected.
  • Step 8 Adopt a sleep scheduling mechanism for the remaining absolute redundant nodes: After the node moves to the target location, the absolute redundant node state is changed to sleep.
  • both sleep scheduling and coverage compensation are adopted, thereby achieving the dual goals of efficient energy utilization and maintaining coverage.
  • the node judges whether it needs the neighboring neighbor nodes to move to it by estimating its own redundancy and residual energy; in this case, the concept of absolute redundant nodes and relative redundant nodes is first proposed; then the neighbor nodes determine whether they have moved to the target. The ability to position; the energy prediction method is used to accurately determine the mobility of the node, thereby achieving the goal of maintaining coverage, and then adopting a sleep scheduling mechanism to avoid waste of redundant node energy and achieve high efficiency utilization of energy. The goal.
  • the flow block diagram of the data aggregation method based on the minimum spanning tree in step S102, as shown in FIG. 3, specifically includes:
  • all wireless sensor nodes have the same composition in the entire detection area, that is, have the same initial energy, perceptual radius, communication radius, etc.; on the two-dimensional plane, the coverage of the sensor node is a node centered on a radius R
  • the circular area, which is the perceived radius of the sensor node, and the perceived radius R is determined by the physical characteristics of the node sensing unit; the communication radius is the node can send
  • the area of the circle formed by the maximum range of messages requires a communication radius of at least 2 times the perceived radius in embodiments of the invention.
  • Step 2 Select the cluster head: uniformly divide the entire detection area according to the grid, so that the size and shape of each grid are the same, and select the sensor node closest to the center of the grid as the cluster head in each grid, and the detection area According to the square grid evenly divided, select the node closest to the center in the square as the cluster head.
  • d v represents the distance between the two nodes, indicating that the node can monitor the size of the obtained data packet; if a node receives multiple cluster information, the node selects If the N value is small, the node is randomly selected and added to the cluster. If the node does not receive the cluster information, the node sends a Help message to join the nearest one. Cluster;
  • the LEACH energy consumption model is a consumption model of the energy consumption of the sensor when transmitting and receiving data proposed by the LEACH protocol. The specific expression is:
  • E ete represents the energy consumption of the wireless transceiver circuit
  • ⁇ / is the distance between the communication nodes
  • k the number of data bits to be transmitted or received
  • E, and E réelleW respectively indicate the sensor sends
  • the energy consumption when receiving data the remaining energy of the node can be obtained by the LEACH energy consumption model.
  • Step 4 The nodes in the cluster form a single graph model: Through step 3, the positions of all the nodes in the cluster are obtained, and each node is regarded as a vertex of the graph, and each two adjacent nodes are connected by edges.
  • Step 5 Calculating the weights in the cluster: Through step 3, the cluster head obtains the sum of the member nodes in the cluster and k t , and calculates the weight between the adjacent nodes i and j.
  • the calculation formula of the weight is:
  • W v a, (£, r + E jr ) + a 2 dy + a 3 (k i + k j ) ( 1 )
  • E jr respectively represents the residual energy of node j and the data that node j can monitor
  • the size, and a] + (3 2 + a 3 1, so the system can adjust the value of ⁇ according to the system pair, or the required specific gravity to get the weights to meet different needs.
  • Step 6 construct a minimum spanning tree in the cluster node: according to the simple graph model formed by the nodes in the cluster obtained in step 4 and the weight obtained in step 5, construct a minimum spanning tree in the cluster according to the definition of the Prim minimum spanning tree algorithm;
  • FIG. 4 is a schematic diagram of the PRIM in the data aggregation method based on the minimum spanning tree, in this 8
  • the node whose node is not in f/ is selected.
  • Step 7 Data aggregation in the cluster: After the minimum spanning tree structure of the nodes in the cluster is completed, the sensor node starts to work normally. Starting from the lowest level sensor node, the collected data is transmitted to the parent node, and the parent node collects the data and the child collected by itself. The data from the node is aggregated and then passed to its parent node, and finally the aggregated data is transmitted to the cluster head;
  • the parent node is a parent node that aggregates data according to the data transmission direction in the minimum spanning tree, and the node that transmits data to the parent node is a child node.
  • Step 8 Calculating the weight of the cluster head: After the third cluster is completed, the cluster head obtains the position of the node in the entire cluster, the remaining energy of the node, and the size information of the data that the sensor node may monitor, wherein
  • Step IX the cluster head node constitutes a simple graph model: each cluster head is regarded as a vertex of the graph, adjacent 14 000188 The cluster heads are connected by edges, and the weight of each edge is obtained by the weight calculation formula (2) in step 8.
  • step eight and step nine can also be exchanged, that is, a simple graph model is formed between the cluster head nodes, and the cluster head node information obtained in step three is calculated according to the cluster head node information obtained in step three.
  • Step 10 The cluster head node constructs a minimum spanning tree: After the simple graph model composed of the cluster head nodes given in step 8, the minimum spanning tree is constructed according to the definition of the Prim minimum spanning tree algorithm;
  • Step 11 Cluster head data aggregation: After the minimum spanning tree construction of the cluster head node is completed, the collected data is transmitted to the parent node from the lowest level cluster head, and the parent node transmits the data of the aggregated data and the child node. After being aggregated, it is transmitted to its own parent node, and finally the aggregated data is transmitted to the base station;
  • the entire wireless sensor network starts to work normally, until after the M round is run or when the node dies, the cluster or The minimum spanning tree of the cluster head is reconstructed, the node death is the node battery energy exhaustion, and the node no longer works.
  • Step 12 Balance the energy consumption of the node: In order to balance the energy consumption of the node, prevent the node from dying too fast, and maintain the normal operation of the cluster. After each M round, the cluster head is reselected, and then the previous steps are performed again, where the node Energy consumption can be estimated by LEACH.
  • Step 13 Maintenance of the cluster: After the node in the cluster dies, the minimum spanning tree path in the cluster may be invalidated. Therefore, before the node is about to die, the node sends a Die message to the cluster head, indicating that it is about to die. After receiving this information, the cluster head begins to reconstruct the minimum spanning tree for the nodes in the cluster.
  • the invention simulates the result of the minimum spanning tree based data aggregation method for the wireless mobile network according to the present invention, and randomly selects 100 sensor nodes in a given experimental region, and the distance between the base station and the nearest node is not less than 75 meters.
  • the channel bandwidth is set to 1M BPS
  • the average transmission and reception delay of each packet is 25 S
  • the average data length is 500 BYTES
  • the energy consumption of the transmitter sending information and the receiver receiving information is 50 NJ/BIT.
  • the energy consumed by transmitting 1BIT information through the unit distance transmitter is IOO PJ/BIT/M 2
  • the simulation experiment is used to evaluate the effect of the algorithm.
  • the test shows that the algorithm can evenly distribute the energy consumption of the sensor nodes, maximally prolong the life cycle of the whole network, and finally make the node energy be used efficiently.
  • the data transmission control method for the wireless mobile network of the present invention in step S103 includes the following steps:
  • the access node uploads mobile information of the mobile node entering its communication range to the intermediate proxy node; when downloading the mobile node to enter the communication range of the access node, sending a download request to the access node; the access node forwards the download request to An intermediate proxy node; the intermediate proxy node transmits data to the downloading mobile node based on the current access node; the access node records the current transmission progress and uploads to the intermediate proxy node; and the intermediate proxy node is based on the received download request and the mobile node's mobile information.
  • the intermediate proxy node selects the mobile node as the carry-and-forward mobile node within the communication range of the forwarding access node, and transmits the download data to the carrying and forwarding mobile node based on the forwarding access node; when carrying the forwarding mobile node and downloading the mobile node to enter each other's communication Fan When carrying forward tilted downward and crashed to the mobile node the mobile node transmits the download data carried.
  • the transmission control method of the present invention utilizes an access node in an idle state in a wireless mobile wireless network, and selects a different carrying and forwarding mobile node to carry the forwarding mode according to the possibility that the mobile node meets the downloading mobile node within the coverage of the access node.
  • the data transmission is completed to effectively improve the utilization of the access node and increase the download speed.
  • the present invention further includes: the access node records the transmission status information between the access node and the mobile node and uploads it to the intermediate proxy node; when the mobile node carrying the forwarding mobile node and the download mobile node leave each other's communication In the range, the carrying and forwarding mobile node sends the transmission status information between the mobile node and the downloading mobile node to the intermediate proxy node based on the transmission node; the transmission status information includes a transmission start time and a transmission duration; and the intermediate proxy node is based on the forwarding access node.
  • the historical transmission state information of each mobile node in the communication range and the mobile information of the mobile node determine the transmission potential of each forwarding access node: determining the number of predicted encounters between the mobile node and the currently downloaded mobile node based on the mobile node's mobile information; and according to the mobile node Historical transfer status information for each pre-pre 0188 Measure the encounters to match, determine the actual number of encounters, meet for each prediction, if it is satisfied:
  • the current active phase of the downloading mobile node has a forwarding phase corresponding to it, and the corresponding forwarding phase ends after the last active phase ends;
  • the forwarding phase also has a carry-and-forward phase, and the carry-forward phase ends after the last active phase ends.
  • the current corresponding forwarding phase ends within the time T after the end of the corresponding bearer-transfer phase.
  • the match is successful, and the current prediction encounter is defined as actual.
  • the ratio of the actual number of encounters of the mobile node to the number of predicted encounters is the probability of encountering the mobile node;
  • the phase of the action is to download the data transmission process between the mobile node and the access node;
  • the forwarding phase is to download the mobile node and carry the forwarding mobile node
  • the data transmission process is carried out;
  • the carrying and forwarding phase is a data transmission process between the carrying forwarding mobile node and the forwarding access point;
  • the intermediate proxy node determines the transmission potential of the forwarding access node based on the predicted number of encounters and the actual number of encounters, and transmits Forwarding the greatest potential transmission section download data access node, forwarding the potential to meet transmission probability of each mobile node within communication range of the access node and.
  • the beneficial effects of the present invention are: using an access node in an idle state, selecting different carry and forward mobiles according to the possibility that the mobile node meets the downloading mobile node within the coverage of the access node.
  • the node completes data transmission by carrying the forwarding mode to effectively improve the utilization of the access node, improve the download speed, and improve the convenience of the wireless mobile network.

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Abstract

本发明公开了一种基于覆盖保持和最小生成树的无线移动网络数据传输方法,该方法包括基于睡眠调度和覆盖补偿的覆盖保持方法、无线传感器节点之间基于最小生成树的数据聚合方法和用于无线移动网络的数据传输控制方法。本发明达到了能量高效利用和保持覆盖率的双重目标,提高了高效率利用能量的目标;延长了整个无线移动网络的生命周期,使得所有节点的能量能够得到高效的利用;有效的提高了接入节点的利用率,提高下载速度,提升无线移动网络的便捷性。本发明基于覆盖保持和最小生成树的数据聚合传输方法达到了能量高效利用和保持覆盖率的双重目标,节省能量,生命周期长,下载速度快和便捷性好。

Description

说 明 书 基于覆盖保持和最小生成树的无线移动网络数据传输方法 技术领域
本发明属于无线移动网络的技术领域, 尤其涉及一种基于覆盖保持和最小 生成树的无线移动网络数据传输方法。 背景技术
无线移动网络中, 通过拓朴控制自动生成的良好的网络拓朴结构, 能够提 高路由协议和 MAC (Medium Access Control)协议的效率, 可为数据融合、 时间 同步和目标定位等多方面奠定基础, 有利于节省节点的能量来延长网络的生存 期。 但是在实际中, 由于传感器节点的能量有限, 工作一段时间后就会出现节 点生命期的结束。 因此传感器监控区域会因为节点的死亡而成为不被监测的区 域, 并且可能会对整个监测区域构成潜在的威胁。 在这种情况下, 覆盖保持成 为及其重要的技术。 在现有的覆盖保持技术中, 睡眠调度和覆盖补偿是覆盖保 持技术的主流研究方向, 其中睡眠调度是指调整节点的工作状态从而节省能量 的消耗, 覆盖补偿是指对于失去监测的区域重新进行监测。 但目前已有的算法 中还没有一种可以同时实现以上两方面的技术。
现在通常使用的睡眠调度算法有: 非层次型网络的睡眠调度算法和层次型 网络的睡眠调度算法, 其中, 非层次型网络的睡眠调度算法主要又包括两类: MSNL算法和不需要位置信息的 LDAS算法, MSNL算法中的节点有 3种状态: 活动状态、 睡眠状态、 过渡状态, 当节点处于过渡状态时, 如果它发现的监测 区域不能被其他活动状态或过渡状态的节点覆盖, 它就立即转为活动状态, 但 是 MSNL算法的局限在于需要精确的位置信息并且多个相邻的节点可能同时进 入睡眠状态; LDAS基于部分冗余调度。但是其只适用于节点均匀分布的情况。 层次型网络的睡眠调度算法主要是麻省理工学院的 Heinzelman等人提出了一 直被广泛引用的 LEACH算法。 LEACH算法中节点自决定是否为簇头节点,而 非簇头节点决定自己属于哪一簇。但是 LEACH算法不能保证簇头的均匀分配。 现在通常使用的覆盖补偿算法主要是 Archana等人提出的 DCM算法, DCM算 法中包含了节点可移动最大距离的算法和关于如何选择可移动的邻居节点的四 种启发式算法, 并且进一步描述了级联的 DCM算法。 DCM算法能够弥补死亡 节点引起的覆盖区域损失, 但是这种补偿不是实时的, 而是在引 盖区域损 失后进行, 因为必然会存在一定时间的覆盖不完全。 可以看出, 上述睡眠调度 和覆盖补偿算法都存在一定的问题, 算法通用性不高, 影响网络的覆盖能量。
无线传感器节点的能量十分有限, 一旦能量耗尽将无法实时补充, 并且有 研究表明, 数据传输阶段消耗的能量所占比重较大, 因此在收集信息的过程中 采用单个节点单独传送数据到汇聚节点的方法是不合适的, 会浪费通信带宽和 能量并降低信息收集的效率。 数据聚合技术是解决这一问题的有效途径, 由于 大量随机部署的传感器节点所感知的数据具有较强的相关性, 通过数据聚合, 降低数据冗余信息, 减少数据包的传输, 提高能量的利用率。 数据聚合利用的 是节点的计算资源和存储资源, 只要将增加计算量的能量消耗控制在小于降低 通信量的能量消耗, 就可以达到减少节点能量损耗、 减少网络通信带宽, 延长 网络生命周期的目的。 但是, 数据聚合过程中, 聚合节点要等待所有源节点将 源数据传输到聚合节点后再进行数据聚合, 等待时间必然会造成很大的网络延 迟。
现有的数据聚合方式一般有以下几种:
1、 LEACH: LEACH ( Low Energy Adaptive Clustering Hierarchy )
型的分簇协议, 通过等概率地随机循环选择簇头, 将整个网络的能耗负载均衡 地分配到每个传感器节点,从而达到降低网络能耗,延长网络生命周期的目的。 但是由于 LEACH假定所有节点能够与汇聚节点直接通信, 并且每个节点都具 备支持不同 MAC协议的计算能力, 因此该协议不适合在大规模的无线传感器 网络中应用。协议没有说明簇头节点的数目怎么分布才能及于整个网络, 因此, 很可能出现被选的簇头节点集中在网络某一区域的现象, 这样就会使得一些节 点的周围没有任何簇头节点, 从而导致网络能耗分布不均匀;
2、 PEGASIS: PEGASIS(Power-Efficient GAthering in Sensor Information Systems)中的簇是一条基于地理位置的链, 协议根据节点的地理位置形成一条 相邻节点之间距离最短的链。 PEGASIS中通信只限于相邻节点之间, 节点以最 小功率发送数据, 每轮只随机选择一个簇首与基站通信,减少了数据通信量。 PEGASIS算法建立在网络全局信息能够被所有节点知道的基础上,在实际应用 中存在以下问题: 由于节点能力有限, 单个节点 4 保存网络; 当节点意外死 亡时, 需要重新广播全局信息; 容错性不佳, 若 PEGASIS链上的任一节点意外 死亡则从链端到该节点的所有数据将丟失。
3、 Flooding: Flooding协议中, 节点产生或收到数据后向所有邻节点广播, 数据包直到过期或到达目的地才停止传播。但该协议具有严重缺陷: ( 1 )、内爆: 节点几乎同时从邻节点收到多份相同数据; (2 )、 交叠: 节点先后收到监控同一 区域的多个节点发送的几乎相同的数据; (3 )、 资源利用盲目: 节点不考虑自身 资源限制, 在任何情况下都转发数据。 这些协议在簇头的选择或者簇内的处理 上都存在一定的问题, 这就使得传感器的能耗不能得到很好的控制。 在当前的 无线移动网络中, 对数据传输控制方法通常专注于移动节点间的数据传输, 或 者传输路径的优化等, 而较少考虑基于对接入节点设置及其与移动节点的数据 传输来实现中间代理节点对下载请求大数据的传输 , 如车载自组织网络中现行 的数据传输方式。
车载网络是将在一定通信范围内的车辆, 车辆与附近固定设施之间构建成 一个自组织的、 部署方便、 费用低廉、 结构开放的无线移动网络, 处在这一无 线移动网络中的车辆可以相互交换各自的车速、 位置等信息以及车载传感器感 知的数据。 当前, 车辆间数据的传输方法及其存在的问题, 主要体现在以下两 点:
( 1 )利用道路交通流量统计信息, 计算路径探测消息在路口的不同方向上 的可传递概率, 以及消息通过每个路段的延迟, 然后在此基础上, 评估通过不 同路径将消息传递到目的地的延迟,最后,选择延迟最小的路径进行数据传输, 但是没有考虑个体车辆在消息传输时的性能差异, 所以对数据传输性能存在一 定的影响;
( 2 )数据传输时,假设了在高速公路环境下所有行驶车辆都下载相同的内 容, 但是由于高速公路道路拓朴十分简单, 并且不同车辆下载内 艮难一致, 因此主要用于车载环境下的文件检索和共享等小数据的传输, 而不适用于诸如 音视频等的大数据传输。
因此, 当某个移动节点需要从中间代理节点请求下载数据时, 若仅依赖于 现行的移动节点之间的数据传输方式, 则无法实现对于大数据的传输; 若仅依 赖于接入节点实现对所下载数据的传输, 由于接入节点的覆盖范围有限, 大数 据的下载时间较长, 对移动节点而言, 很难在移动的过程中直接从某一个接入 节点完全下载所请求的数据; 另外, 在大多数情况下接入节点都是处于空闲状 态, 直接从某一个接入节点完全下载所请求的数据的方式使得其接入节点未能 得到有效利用。
目前, 传统的无线传感器网络技术存在覆盖保持技术单一, 网络覆盖率和接入 节点利用率低, 能量浪费严重, 生命周期短, 下载速度慢, 便捷性差等问题。 传统的无线传感器网络技术存在覆盖保持技术单一, 网络覆盖率和接入节点利 用率低, 能量浪费严重, 生命周期短, 下载速度慢, 便捷性差。 发明内容
本发明实施例的目的在于提供一种基于覆盖保持和最小生成树的无线移动 网络数据传输方法,旨在解决传统的无线移动网络技术存在覆盖保持技术单一, 网络覆盖率和接入节点利用率低, 能量浪费严重, 生命周期短, 下载速度慢, 便捷性差的问题。
本发明实施例是这样实现的, 一种基于覆盖保持和最小生成树的无线移动 网络数据传输方法 , 该基于覆盖保持和最小生成树的无线移动网络数据传输方 法包括:
基于睡眠调度和覆盖补偿的覆盖保持方法、 无线传感器节点之间基于最小 生成树的数据聚合方法和用于无线移动网络的数据传输控制方法。
进一步, 基于睡眠调度和覆盖补偿的覆盖保持方法包括以下步骤: 步骤一, 确定邻居节点数: 节点广播 HELLO 消息给周围节点, 节点记录 接受到的不同的 HELLO 消息的数目从而得到其本身的邻居节点数 N;
步骤二,估计节点冗余度:利用邻居节点数 N得到节点冗余度的期望值为: e('1N) = 1 - ΦΝ'当 Ε(ηΝ)≥ α时认为是绝对冗余节点, 当 1— « < E(¾) < «时为 相对冗余节点, O S E IN) 1-"1时为非冗余节点, 其中, α为预先设定的阁值; 步骤三,估计节点经过信息交换阶段之后的剩余能量:发送机每传 lbit信息 消耗能量: E ecte, 接收机每接收 lbit信息消耗能量: EeeC- re, 且有
Eelec-te=Eelec-re; 每传输 lbit信息通过单位距离发送端放大器需消耗的能量: Eamp, 发送端发送 kbits信息到距离 d 的接收端需消耗的能量为
Eeiec-te *k+Eamp*k*d2 ? 接收端接收 k bits信息消耗能量为: Eelec_re*k; 具有 m 个邻居节点的节点需要在信息交换过程中消耗的能量为:
(Eelec-te * k + Eamp * k * d2) * 111 + (Eelecre * k) * 1Ή 在信息交换过程之后具有 m 个邻居节点的节点的剩余能量为:
Eesti =E1 - (Eelecte * k + Eamp *k*d2) * m - (EeIec_re * k) * in, 其中, E 为信息交换 前的节点的实时能量;
步骤四, 发现潜在的死亡节点: 如果节点能量满足: Eestl < , 则为潜在的 死亡节点, 其中, έ为一个时间段内消耗的平均能量;
步骤五, 节点信息交换: 每个节点将包含本身的冗余度信息和是否为潜在 的死亡节点的信息广播给其所有的邻居节点; 步骤六, 非潜在死亡节点估计是否可以移动到潜在的死亡节点的位置; 估计信息交换消耗的能量: 所有可移动节点移动前要进行信息交换, 此过 程消耗能量为:
(Eelec-te * k + Eamp * k * d2) * L + (Eelec_re * k) * L , L 为进行信息交 换的节点的数目, k为信息的 bit, d 为信息传送的距离;
若节点移动, 估计节点在移动后的剩余能量:
Eest2 = E2 - (Eelec_te * k + Ea * k * d2) * L - (Eekc_re * k) * L - Emove * h , 其中, h为移动到目标位置的距离, E2 为移动前的节点的实时能量;
判断节点是否具有移动的能量: 要求移动节点到底新位置后至少工作 X 个 时间段, 若节点能量满足: Eest2 - X * E > 0 , 则此节点具有移动到目标位置的 能量, 否则, 不具有此能力, 其中, X 为预先设定的阈值;
步骤七, 决定移动节点:
根据如下规则在所有可移动的节点中选择最佳节点:
若在可移动节点中存在绝对冗余节点, 根据目标距离判断, 移动目标距离 最小的绝对冗余节点; 若存在多个绝对冗余节点的目标距离相等且均为最小, 则再根据剩余能量 的大小判断, 选择剩余能量最大的节点;
若在可移动节点中只有相对冗余节点, 则根据相对冗余节点的移动距离进 行选择, 相对冗余节点移动的距离为相对冗余节点的最大可移动距离, 最大可 移动距离是指在不影响覆盖区域的 ^牛下节点可移动的最大距离, 根据最大可 移动距离确定相对冗余节点移动的目标位置; 比较相对冗余节点的最大可移动 距离, 移动最大可移动距离最小的相对冗余节点, 若存在多个相对冗余节点的 最大可移动距离相等且均为最小, 则再根据剩余能量 的大小判断, 选择剩 余能量最大的节点。
步骤八, 对剩余绝对冗余节点采用睡眠调度机制: 在节点移动到目标位置 后, 将绝对冗余节点状态改变为睡眠。
进一步, 在步骤六中, 非潜在死亡节点估计是否可以移动到潜在的死亡节 点的位置, 具体过程如下: 决定是否需要对将死亡节点引起的覆盖面积的丟失 采取补偿动作: 如果潜在死亡节点是绝对冗余节点, 则不需采取任何行动; 如 果潜在死亡节点的所有邻居节点均为非冗余节点, 则无法采取任何行动; 其他 情况下通过移动节点减少潜在死亡节点引起的覆盖损失; 非潜在死亡节点自判 断是否具有移动到潜在死亡节点位置的能量: 在所有非潜在死亡节点中去掉非 冗余节点; 估计移动消耗的能量: 节点距离将死亡节点的距离为 h, 则移动要消 耗的能量为: E'«。v^ h , 其中, £1∞½为移动单位距离消耗的能量。
进一步, 基于最小生成树的数据聚合方法, 具体包括:
步骤一, 部署无线传感器节点: 在面积为 5 = ^^<£的检测区域内, 将无线 传感器节点部署在检测区域, 基站部署在检测区域外, 基站用于接收和处理整 个无线传感网络收集到的数据信息;
步骤二, 选择簇头: 将整个检测区域按网格进行均匀划分, 使每个网格的 大小形状相同, 在每个网格中选择位置距离网格中心最近的传感器节点作为簇 头, 检测区域按照方形网格均匀划分, 选取方格中距离中心最近的节点作为簇 头;
步骤三,分簇: 簇头选择完成后, 簇头广播 Cluster{ID, N, Hop}信息,其 中, ID为节点的编号, N为 Cluster信息转发的跳数, 且 N的初值为 0, Hop 为系统设定的跳数; 处于簇头附近的邻居节点收到 Cluster信息后 N增加 1再 转发这一信息, 直到 N=Hop就不再转发 Cluster信息; 簇头的邻居节点转发 Cluster信息后再向将 Cluster信息转发给自己的邻居节点,然后发送一个反馈信 息 Join{ID, N, E,r , dy , t, }给将 Cluster信息转发给自己的节点, 最终将 Join 信息转发给簇头表示自己加入该簇,其中, 表示该节点此时的剩余能量, d,, 表示两节点间的距离, t,表示该节点能够监测得到的数据包的大小; 如果一个 节点收到了多个 Cluster信息, 节点就选择 N值小的加入该簇, 若 N相等节点 就随便选择一个簇并加入到该簇; 如果节点没有收到 Cluster信息, 则节点发送 Help信息, 加入离自己最近的一个簇; 其中, 得到每个节点初始的剩余能量 E,f后, 就可以通过 LEACH能耗模型 来估算节点能量的剩余值, 例如进行了 M轮后, 一轮为传感器节点得到监测数 据然后将数据逐层上传, 最终将数据传输给基站的这一过程为一轮, 节点的剩 余能量可以估算为: E = E,rM(£" + £«)= E- - M、2kEe!ec + ks — spaceampd2、 , E"即为节 点反馈给簇头的剩余能量, LEACH能耗模型是 LEACH协议提出的传感器在发 送和接收数据时能量消耗的消耗模型, 具体表达形式为:
Γ Γ "、, Γ η I
El (k, d) = Ea_elec (k) + Etx_amp {k,d) = , , '
[kEelec +
Figure imgf000010_0001
,d≥d0
EJk) = Ereelec(k) = kEelec ; 其中, E 表示无线收发电路能耗, ^, mp和^。„分别表示自由空 间模型和多路消 莫型的放大器能耗, ^是常数, ί是通信节点相隔距离, 为 要发送或接收的数据位数 , E,x 和 Erx {k)分别表示传感器发送和接收数据时 的能耗; 通过 LEACH能耗模型即可得到节点的剩余能量;
步骤四, 簇内节点构成简单图模型: 通过步骤三得到簇内所有节点在簇内 所处的位置, 将每个节点当做图的一个顶点, 每两个相邻节点间用边相连接; 步骤五,簇内权值的计算:通过步骤三,簇头获取簇内成员节点的 、 d„和 k, , 计算相邻两节点 i, j之间的权值, 权值的计算公式为:
Wy = a, (Ε„. + Ejr )+ a2d0 + a3 (k, + k} ) 其中, Ejr、 分别表示节点 j的剩余能量和节点 j能够监测得的数据的大 小, 且 +^ +^ ^ , 这样系统就可以根据系统对 、 或 所要求的比重不同 调整 ^的值而得到满足不同需要的权值;
步骤六, 簇内节点构建最小生成树: 根据步骤四得到的簇内节点构成的简 单图模型和步骤五得到的权值,根据 Prim最小生成树算法的定义构建簇内节点 最小生成树;
步驟七,簇内数据聚合: 簇内节点的最小生成树构造完成后, 传感器节点 P T/CN2014/000188 开始正常工作, 从最低一级传感器节点开始, 将收集的数据传给父节点, 父节 点将自己收集的数据和子节点传来的数据聚合后再传给自己的父节点, 最终将 聚合数据传输给簇头;
其中, 父节点为在最小生成树中按照数据的传输方向汇聚数据的节点称为 父节点, 将数据传输给父节点的节点为子节点;
步骤八, 簇头权值的计算: 通过步骤三分簇完成后, 簇头获得整个簇内节 点的位置、 节点剩余能量和传感器节点可能监测得到数据的大小信息, 其中 ,. + 表示整个簇的剩余能量值, 表示簇头聚合的数据大小, 表示相邻簇头间的距离, 对相邻两簇头 i, j之间权值进行计算, 权值的公式 定义为:
Figure imgf000011_0001
其中, E ,.和 分别表示簇头 j的剩余能量值和簇头 j聚合的数据大小,且 b、 = i , 系统根据系统对 EOT、 ^或 A:„要求的比重不同调整 6,的值而得到 满足不同需要的权值;
步骤九, 簇头节点构成简单图模型: 将每个簇头当做图的一个顶点, 相邻 簇头之间用边相连接, 每条边的权值由步骤八中的权值计算公式得到;
步骤十, 簇头节点构建最小生成树: 由步骤八给出的簇头节点 成的简单 图模型后, 根据 Prim最小生成树算法的定义来构建最小生成树;
步骤十一, 簇头数据聚合: 簇头节点的最小生成树构造完成后, 从最低一 级簇头开始, 将收集的数据传给父节点, 父节点将自己聚合的数据和子节点传 来的数据聚合后再传给自己的父节点, 最终将聚合数据传输给基站;
步骤十二, 均衡节点能耗: 为了平衡节点能量的消耗, 防止节点过快死亡, 维持簇正常运行, 每进行 M轮以后, 就重新选择簇头, 然后重新进行前面的步 骤, 其中, 节点的能耗可由 LEACH能«莫型进行估算;
步骤十三,簇的维持: 簇内节点死亡后, 就可能会造成簇内的最小生成树 路径失效, 所以在节点即将死亡前, 节点发送一个 Die信息给簇头, 表示自己 即将死亡,簇头接收这一信息后,簇头就开始对簇内节点重新构建最小生成树。 进一步,在步骤六中 Prim最小生成树算法的定义为:假设 E是连通图 G=(V,
E)上最小生成树中边的集合, 其中 V为传感器中的节点;
( 1 )初始化: U={u0}(uoGV), 其中 u0表示开始时选择的顶点, U是他们 的集合, Ε={ Φ }, 其中 Ε表示选择的边的集合;
( 2 )对于任意的 ueu, veV-U所构成的边 (u, v)GE, 寻找一条权值最 小的边 (uo, vo), 并加到 E, 同时将 vo并入 U;
( 3 )假如 U=V, 则转 (4), 否则转到 (2);
( 4 )因此, 在生成树丁 , E)中, 具有 n-1条边构成边的集合 E, 则 T为 连通图 G的最小生成树。
进一步, 用于无线移动网络的数据传输控制方法, 包括以下步骤: 步骤一, 接入节点将进入其通信范围内的移动节点的移动信息上传至中间 代理节点;
步骤二, 当下载移动节点进入接入节点的通信范围时, 向接入节点发送下 载请求; 接入节点将下载请求转发至中间代理节点; 中间代理节点基于当前接 入节点向下载移动节点传输数据; 接入节点并记录当前传输进度上传至中间代 理节点;
步骤三, 中间代理节点基于收到的下载请求、 移动节点的移动信息, 在预 设范围内选择接入节点作为转发接入节点; 并基于下载移动节点的最近传输进 度, 向转发接入节点传输部分下载数据, 转发接入节点的通信范围内存在移动 节点与下载移动节点相遇;
步骤四, 中间代理节点在转发接入节点的通信范围内选择移动节点作为携 带转发移动节点, 并基于转发接入节点向携带转发移动节点传输下载数据; 当 携带转发移动节点与下载移动节点进入彼此的通信范围时, 携带转发移动节点 向下载移动节点传输所携带的下载数据。
本发明提供的基于覆盖保持和最小生成树的无线移动网络数据传输方法, 采用了睡眠调度和覆盖补偿两种方法, 从而达到了能量高效利用和保持覆盖率 的双重目标, 通过采用能量模型进行能量预测的方式达到准确判断节点的移动 能力, 进而达到了保持覆盖率的目标, 之后又采取睡眠调度机制, 避免冗余节 点能量的浪费, 达到高效率利用能量的目标; 通过合理布置传感器节点和对节 点进行分簇, 使传感器节点的能耗均匀分布, 延长了整个无线移动网络的生命 周期, 使得所有节点的能量能够得到高效的利用; 利用处于空闲状态的接入节 点, 根据接入节点覆盖范围内移动节点与下载移动节点相遇的可能性, 选择不 同的携带转发移动节点通过携带转发方式完成数据传输, 以有效提高接入节点 的利用率, 提高下载速度, 提升无线移动网络的便捷性。 本发明基于覆盖保持 和最小生成树的数据聚合传输方法达到了能量高效利用和保持覆盖率的双重目 标, 节省能量, 生命周期长, 下载速度快, 便捷性好。 附图说明
图 1是本发明实施例提供的基于覆盖保持和最小生成树的数据聚合传输方 法的步骤流程图;
图 2是本发明实施例的采用睡眠调度和覆盖补偿的覆盖保持步骤流程示意 图;
图 3是本发明实施例的利用最小生成树的数据聚合的流程框图;
图 4是本发明实施例的利用最小生成树的数据聚合中网络节点的划分和簇 头选取过程示意图;
图 5是本发明实施例的利用最小生成树的数据聚合中 PRIM的附图说明。 具体实施方式
为了使本发明的目的、 技术方案及优点更加清楚明白, 以下结合实施例, 对本发明进行进一步详细说明。 应当理解, 此处所描述的具体实施例仅用以解 释本发明, 并不用于限定本发明。 下面结合附图及具体实施例对本发明的应用原理作进一步描述。 如图 1所示, 本发明实施例的基于覆盖保持和最小生成树的无线移动网络 数据传输方法包括以下步骤:
S101: 釆用睡眠调度和覆盖补偿的覆盖保持;
S102: 利用最小生成树的数据聚合;
S103: 用于无线移动网络的数据传输控制;
在步骤 S101基于睡眠调度和覆盖补偿的覆盖保持方法,如图 2所示,具体包 括如下步骤:
步骤一, 确定邻居节点数: 节点广播 HELLO 消息给周围节点, 节点记录 接受到的不同的 HELLO 消息的数目从而得到其本身的邻居节点数 N;
步骤二,估计节点冗余度:利用邻居节点数 N得到节点冗余度的期望值为:
Ε(ηΝ) = 1Ν'当 Ε(ηΝ)≥α时认为是绝对冗余节点, 当1 - <£(¾)<"时为 相对冗余节点, o E N) ^1- 时为非冗余节点, 其中, α为预先设定的阈值; 步骤三,估计节点经过信息交换阶段之后的剩余能量:发送机每传 lbit信息 消耗能量: Eekc- te , 接收机每接收 lbit信息消耗能量: Ee〗ec, 且有
Figure imgf000014_0001
每传输 lbit信息通过单位距离发送端放大器需消耗的能量:
EamP, 发送端发送 kbits信息到距离 d 的接收端需消耗的能量为
Erfec-te *k+ Eamp,k*d2 ? 接收端接收 k bitS信息消耗能量为: Eelecre*k; 具有 m 个邻居节点的节点需要在信息交换过程中消耗的能量为:
(Eelec-te * k + Eamp * k * d2) * in + (Eelec_re * k) * m 在信息交换过程之后具有 m个邻居节点的节点的剩余能量为:
Eesti = E1- (Eelecte * k + Eamp * k * d2) * m - (Ee3ecre * k) "n , 其中, E j 为信息交换 前的节点的实时能量;
步骤四, 发现潜在的死亡节点: 如果节点能量满足: Ε "<έ, 则为潜在的 死亡节点, 其中, 为一个时间段内消耗的平均能量;
步骤五, 节点信息交换: 每个节点将包含其本身的冗余度信息和是否为潜 在的死亡节点的信息广播给其所有的邻居节点;
步骤六, 非潜在死亡节点估计其是否可以移动到潜在的死亡节点的位置, 具体过程如下: 决定是否需要对将死亡节点引起的覆盖面积的丢失采取补偿动 作: 如果潜在死亡节点是绝对冗余节点, 则不需采取任何行动; 如果潜在死亡 节点的所有邻居节点均为非冗余节点, 则无法采取任何行动; 其他情况下通过 移动节点减少潜在死亡节点引起的覆盖损失; 非潜在死亡节点自判断是否具有 移动到潜在死亡节点位置的能量: 在所有非潜在死亡节点中去掉非冗余节点; 估计移动消耗的能量:节点距离将死亡节点的距离为 h,则移动要消耗的能量为: Emve * h, 其中, Emve为移动单位距离消耗的能量;
估计信息交换消耗的能量: 所有可移动节点移动前要进行信息交换, 此过 程消耗能量为:
(Eeiec-te * k + Eamp * k * d2) * L + (Eelec_re * k) * L, L 为进行信息交 换的节点的数目, k为信息的 bit, d 为信息传送的距离;
若节点移动, 估计节点在移动后的剩余能量:
Eest2 = E2 - (Eelec_te * k + Eamp * k * d2) * L - (Eeiec_re * k) * L - Emove * , 其中, h为移动到目标位置的距离, E2 为移动前的节点的实时能量;
判断节点是否具有移动的能量: 要求移动节点到底新位置后至少工作 X个 时间段, 若节点能量满足: Eest2 - x * ≥0 , 则此节点具有移动到目标位置的 能量, 否则, 不具有此能力, 其中, X 为预先设定的阈值;
步骤七, 决定移动节点:
根据如下规则在所有可移动的节点中选择最佳节点:
若在可移动节点中存在绝对冗余节点, 才艮据目标距离判断, 移动目标距离 最小的绝对冗余节点; 若存在多个绝对冗余节点的目标距离相等且均为最小, 则再根据剩余能量 《的大小判断, 选择剩余能量最大的节点;
若在可移动节点中只有相对冗余节点, 则根据相对冗余节点的移动距离进 行选择, 相对冗余节点移动的距离为相对冗余节点的最大可移动距离, 最大可 移动距离是指在不影响覆盖区域的 ^牛下节点可移动的最大距离, 根据最大可 移动距离确定相对冗余节点移动的目标位置; 比较相对冗余节点的最大可移动 距离, 移动最大可移动距离最小的相对冗余节点, 若存在多个相对冗余节点的 最大可移动距离相等且均为最小, 则再根据剩余能量 的大小判断, 选择剩 余能量最大的节点。
步骤八, 对剩余绝对冗余节点采用睡眠调度机制: 在节点移动到目标位置 后, 将绝对冗余节点状态改变为睡眠。
本发明的在调度机制中同时采用了睡眠调度和覆盖补偿两种方法, 从而达 到了能量高效利用和保持覆盖率的双重目标。 首先节点通过估计自身冗余度和 剩余能量判断是否需要周围的邻居节点向其移动; 在这其中首次提出了绝对冗 余节点和相对冗余节点的概念; 之后邻居节点判断自身是否具有移动到目标位 置的能力; 通过采用能量模型进行能量预测的方式达到准确判断节点的移动能 力, 进而达到了保持覆盖率的目标, 之后又采取睡眠调度机制, 避免冗余节点 能量的浪费, 达到高效率利用能量的目标。
在步骤 S102中基于最小生成树的数据聚合方法的流程框图, 如图 3所示, 具体包括:
步骤一,部署无线传感器节点: 在面积为 5 = χ 的检测区域内, 将无线 传感器节点部署在检测区域, 基站部署在检测区域外, 基站用于接收和处理整 个无线传感网络收集到的数据信息;
其中, 在整个检测区域内所有无线传感器节点的构成相同, 即具有相同的 初始能量、 感知半径、 通信半径等; 在二维平面上, 传感器节点的覆盖范围是 一个以节点为圆心, 半径为 R的圆形区域, 该圆形区域即为传感器节点的感知 半径, 感知半径 R由节点感知单元的物理特性决定; 通信半径为节点可以发送 消息的最大范围构成的圓的区域, 在本发明实施方案中要求通信半径至少为感 知半径的 2倍。
步骤二,选择簇头: 将整个检测区域按网格进行均匀划分, 使每个网格的 大小形状相同, 在每个网格中选择位置距离网格中心最近的传感器节点作为簇 头, 检测区域按照方形网格均匀划分, 选取方格中距离中心最近的节点作为簇 头。
步骤三,分簇: 簇头选择完成后, 簇头广播 Cluster{ID, N, Hop}信息,其 中, ID为节点的编号, N为 Cluster信息转发的跳数, 且 N的初值为 0, Hop 为系统设定的跳数; 处于簇头附近的邻居节点收到 Cluster信息后 N增加 1再 转发这一信息, 直到 N=Hop就不再转发 Cluster信息; 簇头的邻居节点转发 Cluster信息后再向将 Cluster信息转发给自己的邻居节点,然后发送一个反馈信 息 Join{ID, N, E,r , dv , }给将 Cluster信息转发给自己的节点, 最终将 Join 信息转发给簇头表示自己加入该簇,其中, 表示该节点此时的剩余能量, dv 表示两节点间的距离, 表示该节点能够监测得到的数据包的大小; 如果一个 节点收到了多个 Cluster信息, 节点就选择 N值小的加入该簇, 若 N相等节点 就随便选择一个簇并加入到该簇; 如果节点没有收到 Cluster信息, 则节点发送 Help信息, 加入离自己最近的一个簇;
其中, 得到每个节点初始的剩余能量 后, 就可以通过 LEACH能耗模型 来估算节点能量的剩余值,例如进行了 M轮后,一轮为传感器节点得到监测数 据然后将数据逐层上传, 最终将数据传输给基站的这一过程为一轮, 节点的剩 余能量可以估算为: E = E'r -M(Eix + Ej = Elr—A 2kEekc + ksfKespaceampd1 ) , £,即为节 点反馈给簇头的剩余能量。 LEACH能耗模型是 LEACH协议提出的传感器在发 送和接收数据时能量消耗的消耗模型, 其具体表达形式为:
E k, d)
Figure imgf000017_0001
(k) = kEt 其中, Eete表示无线收发电路能耗, ^ pa mp和^。„分别表示自由空 间模型和多路消^^莫型的放大器能耗, 是常数, ί /是通信节点相隔距离, k 要发送或接收的数据位数, E , 和 E„W分别表示传感器发送和接收数据时 的能耗; 通过 LEACH能耗模型即可得到节点的剩余能量。
步骤四, 簇内节点构成筒单图模型: 通过步骤三得到簇内所有节点在簇内 所处的位置, 将每个节点当做图的一个顶点, 每两个相邻节点间用边相连接。
步骤五,簇内权值的计算:通过步骤三,簇头获取簇内成员节点的 、 和 kt , 计算相邻两节点 i, j之间的权值, 权值的计算公式为:
Wv = a, (£,r + Ejr )+ a2dy +a3(ki+kj) ( 1 ) 其中, Ejr、 分别表示节点 j的剩余能量和节点 j能够监测得的数据的大 小, 且 a】 +(32 +a3 = 1, 这样系统就可以根据系统对 、 或 所要求的比重不同 调整 ^的值而得到满足不同需要的权值。
步骤六, 簇内节点构建最小生成树: 根据步骤四得到的簇内节点构成的简 单图模型和步骤五得到的权值,根据 Prim最小生成树算法的定义构建簇内节点 最小生成树;
其中, Prim最小生成树算法的定义为: 假设 E是连通图 G=(V, E)上最小 生成树中边的集合, 其中 V为传感器中的节点;
(1)初始化: U={uo}(uoEV), 其中 uo表示开始时选择的顶点, U是他们 的集合, Ε={Φ}, 其中 Ε表示选择的边的集合;
(2)对于任意的 ueu, vEV-U所构成的边 (u, v)eE, 寻找一条权值最 小的边 (uo, vo), 并将其加到 E, 同时将 vo并入 U;
(3) «oU=V, 则转 (4), 否则转到 (2);
(4) 因此, 在生成树丁= , E)中, 一定具有 n-1条边构成边的集合 E, 则 T为连通图 G的最小生成树。
如图 4所示为基于最小生成树的数据聚合方法中 PRIM的附图说明, 在本 8 发明申请方案中根据 PRIM算法的定义, 釆用的具体算法过程为: V = {Vf 2,...w代 内的节点, V,表示簇头, 边上的值表示权值;
从 开始, 则 i/ = {W, 选择权值最小的边, 即^, v7);
U = {Vl,V7j, 继续选择权值最小的边, 即( 7, V2);
υ = {ν νΊ2}, 继续选择权值最小的边, 即^, V3);
U = {V„V7,V2,V3}, 继续选择权值最小的边, 即( 3, V);
υ = {ν νΊ2,ν„νΑ), 继续选择权值最小的边, 即( 4, V5);
υ = {ν„νΊ2,ν„ν„ν5} , 继续选择权值最小的边, 即^, v6y,
如果 t/中有权值相同的边, 就选择节点不在 f/中的边。
步骤七, 簇内数据聚合: 簇内节点的最小生成树构造完成后, 传感器节点 开始正常工作, 从最低一级传感器节点开始, 将收集的数据传给父节点, 父节 点将自己收集的数据和子节点传来的数据聚合后再传给自己的父节点, 最终将 聚合数据传输给簇头;
其中, 父节点为在最小生成树中按照数据的传输方向汇聚数据的节点称为 父节点, 将数据传输给父节点的节点为子节点。
步骤八, 簇头权值的计算: 通过步骤三分簇完成后, 簇头获得整个簇内节 点的位置、 节点剩余能量和传感器节点可能监测得到数据的大小信息, 其中
= + +.·. 表示整个簇的剩余能量值, 表示簇头聚合的数据大小, 表示相邻簇头间的距离, 对相邻两簇头 i, j之间权值进行计算, 权值的公式 定义为:
WtJ = b (Ear+Ecjr )+ b2Dy + b3 (Kci +KCJ) (2) 其中, 和 分别表示簇头 j的剩余能量值和簇头 j聚合的数据大小,且 bi +b2+b3=\ ,这样系统就可以根据系统对 EOT、 /^或 要求的比重不同调整 ^的 值而得到满足不同需要的权值;
步骤九, 簇头节点构成简单图模型: 将每个簇头当做图的一个顶点, 相邻 14 000188 簇头之间用边相连接, 每条边的权值由步骤八中的权值计算公式(2 )得到。
当然, 步骤八和步骤九的顺序也可以进行交换, 即先在簇头节点之间构成 简单图模型, 在才艮据步骤三中得到的簇头节点信息, 计算相邻两簇头之间的权 值
步骤十, 簇头节点构建最小生成树: 由步骤八给出的簇头节点构成的简单 图模型后, 根据 Prim最小生成树算法的定义来构建最小生成树;
其中 Prim最小生成树算法的定义在步骤六中已经给出, 在此不再累述。 步骤十一, 簇头数据聚合: 簇头节点的最小生成树构造完成后, 从最低一 级簇头开始, 将收集的数据传给父节点, 父节点将自己聚合的数据和子节点传 来的数据聚合后再传给自己的父节点, 最终将聚合数据传输给基站;
其中, 通过前面步骤六和步骤十中的簇内和簇头的最小生成树都构建完成 后, 整个无线传感网络就开始正常工作, 直到运行了 M轮以后或者有节点死亡 才对簇内或者簇头的最小生成树进行重构, 节点死亡为节点电池能量耗尽, 节 点不再进行工作。
步骤十二, 均衡节点能耗: 为了平衡节点能量的消耗, 防止节点过快死亡, 维持簇正常运行, 每进行 M轮以后, 就重新选择簇头, 然后重新进行前面的步 骤, 其中, 节点的能耗可由 LEACH能^^莫型进行估算。
步骤十三, 簇的维持: 簇内节点死亡后, 就可能会造成簇内的最小生成树 路径失效, 所以在节点即将死亡前, 节点发送一个 Die信息给簇头, 表示自己 即将死亡,簇头接收这一信息后,簇头就开始对簇内节点重新构建最小生成树。
本发明利用仿真器对本发明一种基于最小生成树的数据聚合方法用于无线 移动网络的结果进行仿真,随机选取 100个传感器节点在给定实验区域中,基站 与最近节点间距离不小于 75米, 信道带宽设置为 1M BPS , 每个数据包的平均发 送和接受延迟均为 25 S, 平均数据长度为 500 BYTES , 发送机发送信息和接受机 接受信息的能量消耗均为 50 NJ/BIT,每传输 1BIT信息通过单位距离发送端放大器 需消耗的能量为 IOO PJ/BIT/M2, 以此模型进行仿真实验来评估该算法的效果,实 验表明该算法能使传感器节点的能耗均匀分布, 最大的延长整个网络的生存周 期, 最终使得节点能量得到高效利用。
在步骤 S103中本发明的用于无线移动网络的数据传输控制方法, 包括下列 步骤:
接入节点将进入其通信范围内的移动节点的移动信息上传至中间代理节 点; 当下载移动节点进入接入节点的通信范围时, 向接入节点发送下载请求; 接入节点将下载请求转发至中间代理节点; 中间代理节点基于当前接入节点向 下载移动节点传输数据; 接入节点并记录当前传输进度上传至中间代理节点; 中间代理节点基于收到的下载请求、 移动节点的移动信息, 在预设范围内选择 接入节点作为转发接入节点; 并基于下载移动节点的最近传输进度, 向转发接 入节点传输部分下载数据, 转发接入节点的通信范围内存在移动节点与下载移 动节点相遇; 中间代理节点在转发接入节点的通信范围内选择移动节点作为携 带转发移动节点, 并基于转发接入节点向携带转发移动节点传输下载数据; 当 携带转发移动节点与下载移动节点进入彼此的通信范围时, 携带转发移动节点 向下栽移动节点传输所携带的下载数据。
本发明的传输控制方法, 利用无线移动无线网络中处于空闲状态的接入节 点, 根据接入节点覆盖范围内移动节点与下载移动节点相遇的可能性, 选择不 同的携带转发移动节点通过携带转发方式完成数据传输, 以有效提高接入节点 的利用率, 提高下载速度。 为了进一步提升对接入节点的利用率, 本发明还包 括, 接入节点记录其与移动节点之间的传输状态信息并上传至中间代理节点; 当携带转发移动节点与下载移动节点离开彼此的通信范围时, 携带转发移动节 点基于接入节点将其与下载移动节点之间的传输状态信息发送至中间代理节 点; 传输状态信息包括传输起始时间和传输持续时间; 中间代理节点基于转发 接入节点通信范围内各移动节点的历史传输状态信息、 移动节点的移动信息确 定各转发接入节点的传输潜力: 基于移动节点的移动信息确定移动节点与当前 下载移动节点的预测相遇次数; 并根据移动节点的历史传输状态信息对每次预 0188 测相遇进行匹配, 确定实际相遇次数, 对每次预测相遇, 若满足: 当前下载移 动节点的最近作用阶段有一个转发阶段相对应, 且所对应转发阶段在最近作用 阶段结束之后结束; 当前对应转发阶段还有一个携带转发阶段相对应, 且携带 转发阶段在最近作用阶段结束之后结束, 当前对应转发阶段在对应携带转发阶 段结束后的时间 T 内结束, 则匹配成功, 定义当前预测相遇为实际相遇; 取移 动节点的实际相遇次数与预测相遇次数比值为移动节点的相遇概率; 作用阶段 为下载移动节点与接入节点之间的数据传输过程; 转发阶段为下载移动节点与 携带转发移动节点之间的数据传输过程; 携带转发阶段为携带转发移动节点与 转发接入点之间的数据传输过程; 中间代理节点基于预测相遇次数和实际相遇 次数, 确定转发接入节点的传输潜力, 并向传输潜力最大的转发接入节点传输 部分下载数据, 传输潜力为转发接入节点通信范围内各移动节点的相遇概率之 和。
综上, 由于采用了上述技术方案, 本发明的有益效果是: 利用处于空闲状 态的接入节点, 根据接入节点覆盖范围内移动节点与下载移动节点相遇的可能 性, 选择不同的携带转发移动节点通过携带转发方式完成数据传输, 以有效提 高接入节点的利用率, 提高下载速度, 提升无线移动网络的便捷性。
以上所述仅为本发明的较佳实施例而已, 并不用以限制本发明, 凡在本发 明的精神和原则之内所作的任何修改、 等同替换和改进等, 均应包含在本发明 的保护范围之内。

Claims

权 利 要 求 书
1、一种基于覆盖保持和最小生成树的无线移动网络数据传输方法,其特征 在于, 该基于覆盖保持和最小生成树的无线移动网络数据传输方法包括基于睡 眠调度和覆盖补偿的覆盖保持方法、 无线传感器节点之间基于最小生成树的数 据聚合方法和用于无线移动网络的数据传输控制方法。
2、如权利要求 1所述的基于覆盖保持和最小生成树的无线移动网络数据传 输方法,其特征在于,基于睡眠调度和覆盖补偿的覆盖保持方法包括以下步骤: 步骤一, 确定邻居节点数: 节点广播 HELLO 消息给周围节点, 节点记录 接受到的不同的 HELLO 消息的数目从而得到本身的邻居节点数 N;
步骤二,估计节点冗余度:利用邻居节点数 N得到节点冗余度的期望值为:
E(%) = 1 - ,当 Ε(ηΝ)≥ α时认为是绝对冗余节点, 当 1-α<Ε(ηΝ)< ^时为 相对冗余节点, G E N) 1- αΙ时为非冗余节点, 其中, α为预先设定的阈值; 步骤三,估计节点经过信息交换阶段之后的剩余能量:发送机每传 lbit信息 消耗能量: Eelec- te, 接收机每接收 lbit信息消耗能量: re, 且有
Figure imgf000023_0001
每传输 lbit信息通过单位距离发送端放大器需消耗的能量:
Eamp, 发送端发送 kbits信息到距离 d 的接收端需消耗的能量为
EeteC-te*k + Eamp*k*d2 ? 接收端接收 kbitS信息消耗能量为: Eelecre*k; 具有 m 个邻居节点的节点需要在信息交换过程中消耗的能量为:
(Eelec-te Eamp * k * d2) * in + (Eelecre * k) * m 在信息交换过程之后具有 m个邻居节点的节点的剩余能量为:
Eesti = E 1 - (Eelec_te * k + Eamp * k * d2) * m - (Eelecre * k) * m , 其中, El 为信息交换 前的节点的实时能量;
步骤四, 发现潜在的死亡节点: 如果节点能量满足: Eestl < , 则为潜在的 死亡节点, 其中, 为一个时间段内消耗的平均能量; 步骤五, 节点信息交换: 每个节点将包含本身的冗余度信息和是否为潜在 的死亡节点的信息广播给所有的邻居节点;
步骤六, 非潜在死亡节点估计是否可以移动到潜在的死亡节点的位置; 估计信息交换消耗的能量: 所有可移动节点移动前要进行信息交换, 此过 程消耗能量为:
(Eelec-te * k + Eamp * k * d2) * L + (EeIec_re * k) * L , L 为进行信息交 换的节点的数目, k为信息的 bit, d 为信息传送的距离;
若节点移动, 估计节点在移动后的剩余能量:
Eest2 = E2 - (Eelec_te * k + Eamp * k * d2) * L - (EeIec_re * k) * L - Emove * , 其中, h为移动到目标位置的距离, E2 为移动前的节点的实时能量;
判断节点是否具有移动的能量: 要求移动节点到底新位置后至少工作 X个 时间段, 若节点能量满足: Eest2 - χ * έ≥0, 则此节点具有移动到目标位置的 能量, 否则, 不具有此能力, 其中, X为预先设定的阈值;
步骤七, 决定移动节点:
根据如下规则在所有可移动的节点中选择最佳节点:
若在可移动节点中存在绝对冗余节点, 根据目标距离判断, 移动目标距离 最小的绝对冗余节点; 若存在多个绝对冗余节点的目标距离相等且均为最小, 则再根据剩余能量 Eest2的大小判断, 选择剩余能量最大的节点;
若在可移动节点中只有相对冗余节点, 则才艮据相对冗余节点的移动距离进 行选择, 相对冗余节点移动的距离为相对冗余节点的最大可移动距离, 最大可 移动距离是指在不影响覆盖区域的条件下节点可移动的最大距离, 根据最大可 移动距离确定相对冗余节点移动的目标位置; 比较相对冗余节点的最大可移动 距离, 移动最大可移动距离最小的相对冗余节点, 若存在多个相对冗余节点的 最大可移动距离相等且均为最小, 则再 据剩余能量 Ewe的大小判断, 选择剩 余能量最大的节点;
步骤八, 对剩余绝对冗余节点采用睡眠调度机制: 在节点移动到目标位置 后, 将绝对冗余节点状态改变为睡眠。
3、 如权利要求 2所述的基于覆盖保持和最小生成树的无线移动网络数据传 输方法, 其特征在于, 在步骤六中, 非潜在死亡节点估计是否可以移动到潜在 的死亡节点的位置, 具体过程如下: 决定是否需要对将死亡节点引起的覆盖面 积的丟失采取补偿动作: 如果潜在死亡节点是绝对冗余节点, 则不需采取任何 行动; 如果潜在死亡节点的所有邻居节点均为非冗余节点, 则无法采取任何行 动; 其他情况下通过移动节点减少潜在死亡节点引起的覆盖损失; 非潜在死亡 节点自判断是否具有移动到潜在死亡节点位置的能量: 在所有非潜在死亡节点 中去掉非冗余节点; 估计移动消耗的能量: 节点距离将死亡节点的距离为 h,则 移动要消耗的能量为: Em。v^ h , 其中, Emw为移动单位距离消耗的能量。
4、如权利要求 1所述的基于覆盖保持和最小生成树的无线移动网络数据传 输方法, 其特征在于, 基于最小生成树的数据聚合方法, 具体包括:
步骤一, 部署无线传感器节点: 在面积为 S = x 的检测区域内, 将无线 传感器节点部署在检测区域, 基站部署在检测区域外, 基站用于接收和处理整 个无线传感网络收集到的数据信息;
步骤二, 选择簇头: 将整个检测区域按网格进行均匀划分, 使每个网格的 大小形状相同, 在每个网格中选择位置距离网格中心最近的传感器节点作为簇 头, 检测区域按照方形网格均匀划分, 选取方格中距离中心最近的节点作为簇 头;
步骤三, 分簇: 簇头选择完成后, 簇头广播 Cluster{ID, N, Hop}信息,其 中, ID为节点的编号, N为 Cluster信息转发的跳数, 且 N的初值为 0, Hop 为系统设定的跳数; 处于簇头附近的邻居节点收到 Cluster信息后 N增加 1再 转发这一信息, 直到 N=Hop就不再转发 Cluster信息; 簇头的邻居节点转发 Cluster信息后再向将 Cluster信息转发给自己的邻居节点,然后发送一个反馈信 息 Join{ID, N, E,r , dtJ , / 给将 Cluster信息转发给自己的节点, 最终将 Join 信息转发给簇头表示自己加入该簇,其中, 表示该节点此时的剩余能量, 表示两节点间的距离, >t,表示该节点能够监测得到的数据包的大小; 如果一个 节点收到了多个 Cluster信息, 节点就选择 N值小的加入该簇, 若 N相等节点 就随便选择一个簇并加入到该簇; 如果节点没有收到 Cluster信息, 则节点发送 Help信息, 加入离自己最近的一个簇;
其中, 得到每个节点初始的剩余能量 后, 就可以通过 LEACH能耗模型 来估算节点能量的剩余值, 例如进行了 M轮后, 一轮为传感器节点得到监测数 据然后将数据逐层上传, 最终将数据传输给基站的这一过程为一轮, 节点的剩 余能量可以估算为: + £ = E-― k£free_space_ampd2 ), 即为节 点反馈给簇头的剩余能量, LEACH能耗模型是 LEACH协议提出的传感器在发 送和接收数据时能量消耗的消耗模型, 具体表达形式为:
kEelec + ke d ,d≤ d
Ε,Λ ά) = EL,_elec(k) + Etx_am (k,d) =
Erx (k) = E, (k) = kEt 其中, Ee/∞表示无线收发电路能耗, ε 和 s 分别、表示, 自》 由空 间模型和多路消^^莫型的放大器能耗, 是常数, c /是通信节点相隔距离, k为 要发送或接收的数据位数, Ettfc ^和 E„w分别表示传感器发送和接收数据时 的能耗; 通过 LEACH能耗模型即可得到节点的剩余能量;
步骤四, 簇内节点构成简单图模型: 通过步骤三得到簇内所有节点在簇内 所处的位置, 将每个节点当做图的一个顶点, 每两个相邻节点间用边相连接; 步骤五,簇内权值的计算:通过步骤三,簇头获取簇内成员节点的 E„.、 d„和 k, , 计算相邻两节点 i, j之间的权值, 权值的计算公式为:
Figure imgf000026_0001
其中, Ejr、 分别表示节点 j的剩余能量和节点 j能够监测得的数据的大 小, 且 】 + "2 + "3 =1, 这样系统就可以根据系统对 r、 或 所要求的比重不同 调整 的值而得到满足不同需要的权值; 步骤六,簇内节点构建最小生成树: 根据步骤四得到的簇内节点构成的简 单图模型和步骤五得到的权值,根据 Prim最小生成树算法的定义构建簇内节点 最小生成树;
步骤七, 簇内数据聚合: 簇内节点的最小生成树构造完成后, 传感器节点 开始正常工作, 从最低一级传感器节点开始, 将收集的数据传给父节点, 父节 点将自己收集的数据和子节点传来的数据聚合后再传给自己的父节点, 最终将 聚合数据传输给簇头;
其中, 父节点为在最小生成树中按照数据的传输方向汇聚数据的节点称为 父节点, 将数据传输给父节点的节点为子节点;
步骤八,簇头权值的计算: 通过步骤三分簇完成后, 簇头获得整个簇内节 点的位置、 节点剩余能量和传感器节点可能监测得到数据的大小信息, 其中
+ 表示整个簇的剩余能量值, 表示簇头聚合的数据大小, 表示相邻簇头间的距离, 对相邻两簇头 i, j之间权值进行计算, 权值的公式 定义为:
Figure imgf000027_0001
其中, 和 分别表示簇头 j的剩余能量值和簇头 j聚合的数据大小,且 b +b2 +b3 = l , 系统根据系统对 EOT、 或 ,要求的比重不同调整 6,的值而得到 满足不同需要的权值;
步骤九, 簇头节点构成简单图模型: 将每个簇头当做图的一个顶点, 相邻 簇头之间用边相连接, 每条边的权值由步骤八中的权值计算公式得到;
步骤十, 簇头节点构建最小生成树: 由步骤八给出的簇头节点构成的简单 图模型后, 根据 Prim最小生成树算法的定义来构建最小生成树;
步骤十一, 簇头数据聚合: 簇头节点的最小生成树构造完成后, 从最低一 级簇头开始, 将收集的数据传给父节点, 父节点将自己聚合的数据和子节点传 来的数据聚合后再传给自己的父节点, 最终将聚合数据传输给基站;
步骤十二, 均衡节点能耗: 为了平衡节点能量的消耗, 防止节点过快死亡, 维持簇正常运行, 每进行 M轮以后, 就重新选择簇头, 然后重新进行前面的步 骤, 其中, 节点的能耗可由 LEACH能耗模型进行估算;
步骤十三,簇的维持: 簇内节点死亡后, 就可能会造成簇内的最小生成树 路径失效, 所以在节点即将死亡前, 节点发送一个 Die信息给簇头, 表示自己 即将死亡,簇头接收这一信息后,簇头就开始对簇内节点重新构建最小生成树。
5、如权利要求 4所述的基于覆盖保持和最小生成树的无线移动网络数据传 输方法, 其特征在于,在步驟六中 Prim最小生成树算法的定义为: 假设 E是连 通图 G=(V, E)上最小生成树中边的集合, 其中 V为传感器中的节点;
( 1 )、 初始化: U={u0}(u。ev), 其中 u。表示开始时选择的顶点, U是他们 的集合, Ε={ Φ }, 其中 Ε表示选择的边的集合;
( 2 )、 对于任意的 uEU, vEV-U所构成的边 (u, v)EE, 寻找一条权值最 小的边 (u0, vo), 并加到 E, 同时将 v0并入 U;
( 3 )、 假如 U=V, 则转 (4), 否则转到 (2);
( 4 )、 因此, 在生成树丁=(¥, E)中, 具有 n-1条边构成边的集合 E, 则 T 为连通图 G的最小生成树。
6、如权利要求 1所述的基于覆盖保持和最小生成树的无线移动网络数据传 输方法, 其特征在于, 用于无线移动网络的数据传输控制方法, 包括以下步骤: 步骤一, 接入节点将进入通信范围内的移动节点的移动信息上传至中间代 理节点;
步骤二, 当下载移动节点进入接入节点的通信范围时, 向接入节点发送下 载请求; 接入节点将下载请求转发至中间代理节点; 中间代理节点基于当前接 入节点向下载移动节点传输数据; 接入节点并记录当前传输进度上传至中间代 理节点;
步骤三, 中间代理节点基于收到的下载请求、 移动节点的移动信息, 在预 设范围内选择接入节点作为转发接入节点; 并基于下载移动节点的最近传输进 度, 向转发接入节点传输部分下载数据, 转发接入节点的通信范围内存在移动 节点与下载移动节点相遇;
步骤四, 中间代理节点在转发接入节点的通信范围内选择移动节点作为携 带转发移动节点, 并基于转发接入节点向携带转发移动节点传输下载数据; 当 携带转发移动节点与下载移动节点进入彼此的通信范围时, 携带转发移动节点 向下载移动节点传输所携带的下载数据。
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