CN106954228B - Method for constructing life-cycle optimization tree based on dynamic data pattern - Google Patents
Method for constructing life-cycle optimization tree based on dynamic data pattern Download PDFInfo
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- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/08—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on transmission power
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- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
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- H04W52/0212—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave
- H04W52/0216—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave using a pre-established activity schedule, e.g. traffic indication frame
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Abstract
The invention relates to a construction method of a life cycle optimization tree based on a dynamic data pattern, which comprises the following operation steps: adopting a TPO scheduling mechanism to quantize energy consumption; calculating an energy consumption model of the node v for receiving, sending and idle interception in a sampling period; and constructing a routing tree with the optimal life cycle by adopting the energy consumption model. In the technical scheme, a mathematical model is designed for the tree structure and used for accurately describing the energy consumption of the sensor nodes, and the designed mathematical model is applied to construct an efficient tree structure to optimize the life cycle of the network.
Description
Technical Field
The invention relates to the field of wireless sensor networks, in particular to a construction method of a life cycle optimization tree based on a dynamic data pattern.
Background
The wireless sensor network is composed of densely deployed wireless sensor nodes. Such networks are generally installed in natural areas, and changes in physical quantities in target environments are monitored through mutual cooperation between nodes. Such a network is generally composed of a base station and a plurality of sensor nodes, as shown in fig. 1. The sensor nodes are powered by a battery, the base station is powered by a power supply, and the sensor nodes and the base station are self-organized into a network in a wireless communication mode.
Data collection of sensor networks faces the challenge of premature exhaustion of node energy. Among all energy consumption, wireless communication takes the greatest weight. Excessive data transmission should be avoided in order to save power. Which data is transmitted is determined by the data itself and can only be determined after the monitored data is collected by the sensor nodes. Therefore, the node that reports data is dynamically changing and unpredictable during each sampling period. For example, in adjacent sampling periods, the data collected by each sensor node often fluctuates relatively smoothly or only within a certain range. In order to save electric quantity, only when the deviation between the new sampling numerical value and the data reported last time is large to a certain degree, the node needs to send the data collected this time to the base station. Before a node samples the environment, each sensor node cannot calculate in advance what the deviation of its two reported data is. As another example, in condition-triggered sensor monitoring applications, such as volcano monitoring, data transmission is only required when shock and acoustic signals produce sudden changes. However, before the node samples the data, it does not know whether its future data will satisfy the previously set conditions. The above two application scenarios have the same characteristics: the sensor nodes reporting data to the base station dynamically change over time during each sampling period, and the changes are unpredictable. The distribution of sensor nodes reporting data to a base station in a network is defined as a data pattern.
In the case of dynamic changes in the data pattern, it is important to reduce the energy consumption associated with data collection. The wireless sensor nodes are usually battery-driven, and it is very important to save power to the maximum extent to extend the lifetime of the network (the time that the first node in the network consumes energy). Dynamic data patterns present a challenge to power saving.
On the other hand, the energy utilization efficiency of the nodes is affected by the routing structure during the data collection process. Different routing structures will affect the number of packets received and required to be sent by each node, thereby affecting the energy utilization of the node. All existing routing protocol aspects work on the complete data pattern. The complete data pattern refers to: all nodes in the network generate a data packet in each sampling period to be sent to the aggregation node. These routing protocols are inefficient for handling periodic, dynamic data pattern-accompanied data collection. This is because different data patterns in the network may cause each node to spend different percentages of energy in receiving, transmitting, and idle listening for data packets. Therefore, different data patterns need to be matched with different routing structures to prolong the lifetime of the network. For example, when there are many nodes sending data in the network, receiving and transmitting data packets usually take a considerable weight in the energy consumption of the sensor nodes. In this case, it is very important to balance the data amount of each node. On the other hand, when there are fewer nodes in the network sending data, the energy consumption of the sensor nodes is usually dominated by idle listening. At this time, the sensor nodes around the sink node are no longer the bottleneck of energy consumption. Making the energy spent by each node in idle sensing as small as possible becomes a considerable consideration in designing routing structures. Therefore, in order to cope with dynamic data patterns, it is very important to design a routing structure capable of balancing energy costs of different nodes to extend the lifetime of a network.
Disclosure of Invention
The invention aims to provide a construction method of a lifetime optimization tree based on a dynamic data pattern, which can be applied to construct an efficient tree structure for optimizing the lifetime of a network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a construction method of a life-time optimization tree based on a dynamic data pattern is characterized in that: the method comprises the following operation steps:
s1: adopting a TPO scheduling mechanism to quantize energy consumption;
s2: calculating an energy consumption model of the node v for receiving, sending and idle interception in a sampling period;
s3: and constructing a routing tree with the optimal life cycle by adopting the energy consumption model.
The specific scheme is as follows:
step S2 includes the following operations:
s21, calculating the mathematical expectation of the node v for generating the data packet in one sampling period and the energy consumption for sending the data packet;
s22, calculating all data packets generated in the subtree taking the child node of the node v as the root and the energy consumption of the node v for receiving the data packets;
s23, the total energy cost for node v to use on idle sensing per sample period is calculated.
In step S3, a greedy policy is used to construct a routing tree with an optimal lifetime.
In the technical scheme, a mathematical model is designed for the tree structure and used for accurately describing the energy consumption of the sensor nodes, and the designed mathematical model is applied to construct an efficient tree structure to optimize the life cycle of the network.
Drawings
FIG. 1 is a block diagram of a typical wireless sensor network;
FIG. 2a is a network topology diagram of a tree constructed using the present invention;
FIG. 2b is a graph of packet generation probability for a tree constructed using the present invention;
FIG. 2c is a tree with an optimal life span tree constructed using the present invention;
FIG. 3 is a network topology diagram of normalized 100 nodes;
FIG. 4a is a graph of data for the first 5000 temperatures and solar radiation of a certain sensor;
FIG. 4b is a graph of the percentage of sensor nodes transmitting temperature data per sampling period versus different data fuzzy thresholds
FIG. 4c is a graph of the percentage of sensor nodes transmitting solar radiation data per sampling period as a function of different data fuzzy thresholds.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the following description is given in conjunction with the accompanying examples. It is to be understood that the following text is merely illustrative of one or more specific embodiments of the invention and does not strictly limit the scope of the invention as specifically claimed.
The technical scheme adopted by the invention comprises the following steps:
s1: first, a TPO scheduling mechanism is introduced to quantify energy consumption. A tpo (traffic Pattern overhead) scheduling algorithm is used to construct a transmission schedule according to which the nodes transmit and receive data without collision. TPO is a Time Division Multiple Access (TDMA) algorithm that has proven to be energy efficient. The introduction of the TPO scheduling mechanism makes it possible to quantify the energy consumption of the nodes.
To quantify the energy overhead of each node v in the TPO schedule, we assume that within each sampling period, the node v data generation probability is pi(i probability of sending data to the base station). The energy used by a node v to send a data packet in the network is etThe energy used for sensing a primary channel is er. The generation of data packets at different nodes conforms to the independent same distribution at different sampling periods.
S2: the energy used by the computing node v to receive, transmit, and idle listen in one of its sample periods.
S21: the computing node v is at one sampling period (T) thereofv) The mathematical expectation of the generated data packet and the energy cost of transmitting the data packet;
s22: calculating all data packets generated in a subtree taking the child nodes of the node v as the root, and the energy consumption of the node v for receiving the data packets;
s23: in the TPO scheduling method, node v does not need to idle listen to child node k if and only if child node k transmits data in each of its time slices. The total energy used by node v on idle sensing during each sampling period is calculated.
The total energy consumption of the node v in one sampling period is formed by the superposition of the three parts.
S3: the energy cost model designed as above is used to construct a routing tree with optimal lifetime. And adopting a greedy strategy to add nodes in the network into the routing tree T to be constructed one by one. Initially, T only includes the sink node s, and in each iteration of the step, a set E is constructed that includes all nodes adjacent to the tree T (at least one node in the tree T is a neighbor node). At each iteration, only the nodes in set E are considered for addition to the current tree T. For each node v E, the method checks neighbor nodes of all nodes v in the current T, and selects a proper neighbor node to connect with the node v. If the node v is connected with the neighboring node u in the T, only the energy consumption rate of the node on the path from the node u to the sink node s will change. Therefore, the nodes recalculate their energy consumption rates and record the shortest lifetime on the path. For all nodes v E and possibly the parent node u of node v, the method adds to T a link (u, v) that maximizes the lifetime of the current T until T covers all nodes in the network, as shown in fig. 2a, 2b, 2 c.
The invention is illustrated in detail below by means of a specific example:
a first step of arranging a network;
referring to fig. 3, 100 sensor nodes are randomly placed in a square area, and in order to keep the whole network connected, the transmission radius of each node is set to be 0.25 in the topological graph.
Secondly, selecting a data sequence and setting a threshold;
referring to fig. 4a, a test was performed using a sensor data sequence of temperature and solar radiation collected by the open source LEM project at washington university. Each data sequence contains more than 3000000 sensor data, where two consecutive sensor data sample time intervals are 1 second apart. In order to control the deviation between the data collected by the base station and the real sensor data within e (which can be regarded as an error range), each node sets a threshold value [ u-e, u + e ] by taking the data reported last time as the center. In each sampling period, only when the data collected by each node exceeds the range of the threshold set by the node, the node needs to transmit the data to the base station and update the range of the threshold. Otherwise, the node does not need to submit anything. Referring to fig. 4b and 4c, when the value of e becomes larger, the threshold range becomes larger correspondingly, and the amount of data reported by each node decreases accordingly.
Thirdly, node energy is set;
setting the energy supply of the base station to infinity, while the energy of the sensor nodes is limited, and sending one packet by the sensor consumes 1 unit of energy, while listening to the channel once would take 0.75 units of energy. The initial energy of the nodes is the same, set to 50,000 energy units. The lifetime of a network is defined as the time from the very beginning until the first node in the network is exhausted.
Fourthly, estimating the data reporting probability of each node;
and respectively setting different values e for the two data sequences of temperature and solar radiation, observing the data reporting condition of each node in a period of time, and normalizing the data reporting times to the period of time to be used as the reporting probability corresponding to the error range e.
Fifthly, constructing a life period optimal tree;
and adding the nodes into the routing tree T to be constructed one by one through iteration by using a greedy strategy.
Devices, mechanisms, components, and methods of operation not specifically described herein are optional and may be readily adapted by those of ordinary skill in the art to perform the same functions and practice as the present invention. Or the same devices, mechanisms, components and methods of operation selected for use and implementation in accordance with common general knowledge of life.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art, after learning the present disclosure, can make several equivalent changes and substitutions without departing from the principle of the present invention, and these equivalent changes and substitutions should also be considered as belonging to the protection scope of the present invention.
Claims (1)
1. A construction method of a life-time optimization tree based on a dynamic data pattern is characterized in that: the method comprises the following operation steps:
s1: adopting a TPO scheduling mechanism to quantize energy consumption; TPO scheduling algorithm is used for constructing a transmission schedule, and the nodes receive and transmit data without collision according to the transmission schedule; TPO is a time division multiple access, TDMA, algorithm that has proven to be energy efficient; the introduction of a TPO scheduling mechanism enables the energy consumption of a quantization node to be possible; in order to quantify the energy overhead of each node v in a TPO scheduling table, in each sampling period, the data generation probability of the node v is pi, i is the probability of sending data to a base station; the energy used by a node v in the network for sending a data packet is et, and the energy used for intercepting a primary channel is er; in different sampling periods, the generation of data packets on different nodes conforms to independent same distribution;
s2: calculating an energy consumption model of the node v for receiving, sending and idle interception in a sampling period;
s21: the computing node v is in one of its sampling periods TvThe mathematical expectation of the generated data packet and the energy cost of transmitting the data packet;
s22: calculating all data packets generated in a subtree taking the child nodes of the node v as the root, and the energy consumption of the node v for receiving the data packets;
s23: in the TPO scheduling method, a node v does not need to carry out idle monitoring on a child node k, and if and only if the child node k sends data in each time slice of the child node k; calculating the total energy of the node v used on idle interception in each sampling period; the total energy consumption of the node v in one sampling period is formed by the superposition of the three parts;
s3: constructing a routing tree with an optimal life cycle by adopting the energy consumption model; adding nodes in the network into a routing tree T to be constructed one by adopting a greedy strategy; at the beginning, T only comprises a sink node s, and in each step of iteration, a set E is constructed, wherein the set E comprises all nodes adjacent to the tree T, and at least one node in the tree T is a neighbor node; during each iteration, only the nodes in the set E are considered to be added into the current tree T; for each node v E, checking neighbor nodes of all nodes v in the current T, and selecting a proper neighbor node to connect with the node v; if the node v is connected with a neighbor node u in the T, only the energy consumption rate of the node on the path from the node u to the sink node s can be changed; therefore, the nodes recalculate their energy consumption rates and record the shortest lifetime on the path; for all nodes v E and possibly the parent node u of node v, a link (u, v) that maximizes the lifetime of the current T is added to T until T covers all nodes in the network.
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| CN109982357B (en) * | 2019-04-09 | 2021-08-20 | 合肥工业大学 | Sampling period optimization method based on multi-hop wireless sensor network |
| CN112333729B (en) * | 2020-10-12 | 2023-03-28 | 深圳市华奥通通信技术有限公司 | Communication power consumption calculation method, system, device and storage medium |
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| CN103945508A (en) * | 2014-02-24 | 2014-07-23 | 浙江理工大学 | Wireless-sensing-network topology construction method based on probability comparison |
| CN104539542A (en) * | 2014-12-03 | 2015-04-22 | 南京邮电大学 | Low-energy-consumption routing tree pruning method based on mobile Sink data collection |
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| CN106507424A (en) * | 2016-10-25 | 2017-03-15 | 南京大学 | Based on the tree construction building method for giving optimization data fusion reliability after network lifecycle in wireless sensor network |
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| CN102026331A (en) * | 2010-12-23 | 2011-04-20 | 重庆邮电大学 | Distributed multi-jump energy-saving communication method in wireless sensor network |
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