CN107911834B - Method for constructing optimal DAG (demand oriented architecture) in lifetime in data-centered wireless sensor network - Google Patents

Method for constructing optimal DAG (demand oriented architecture) in lifetime in data-centered wireless sensor network Download PDF

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CN107911834B
CN107911834B CN201711455034.XA CN201711455034A CN107911834B CN 107911834 B CN107911834 B CN 107911834B CN 201711455034 A CN201711455034 A CN 201711455034A CN 107911834 B CN107911834 B CN 107911834B
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赵闻博
许录平
戴浩
王光敏
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    • H—ELECTRICITY
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    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • 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
    • H04W40/10—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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Abstract

The invention belongs to the technical field of wireless sensor networks, and discloses a method for constructing an optimal DAG (demand oriented architecture) in a data-centric wireless sensor network. The invention constructs data-centric routing, which encapsulates the change of data volume in the network into the design of the routing structure. At each sampling period, which data to send is driven entirely by the data itself and can only be determined after the sensor node senses the data. No matter how the data pattern in the network changes, the DAG structure can balance the data volume in the network, optimizing the network lifetime.

Description

Method for constructing optimal DAG (demand oriented architecture) in lifetime in data-centered wireless sensor network
Technical Field
The invention belongs to the technical field of Wireless Sensor Networks, and particularly relates to a method for constructing a Directed acyclic graph (Directed acyclic graph) DAG (direct acyclic graph) in a Wireless Sensor Network (WSNs) with data as a center.
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. 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. In each sampling period, the data collected by the sensor nodes needs to be transmitted to the central node, so that the user can conveniently further process the data. Such continuous data collection often extracts a large amount of information from the deployment area of the network, but is very power consuming. Once a node in the network consumes all of the power, the entire network is no longer connected, thereby dividing the network into several disconnected regions. It is of interest to save network power in continuous data collection to extend the lifetime of the network. In the data collection process, the energy use efficiency of the nodes is influenced by the routing structure. 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. Here, a definition of a data pattern is given, that is, a distribution of wireless sensor nodes that transmit data to a sink node in each sampling period. Most of the existing routing works mostly ignore the change of data, and are used to process the complete data pattern, that is, each node in the network will send a data packet to the sink node in each sampling period. However, in continuous sensor data collection, the data pattern in the network is dynamically changing and such changes are completely unpredictable. Such as: in adjacent sampling periods, the data collected by each sensor node is often stable or fluctuates 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. In practice, only relatively important data is reported to the sink node in order to save energy, but the selection of reported data is driven entirely by the data itself and can only be determined if and only if the data is sensed by the sensor. This is known as a data-centric sensor network.
The ever-changing network data patterns greatly increase the difficulty of designing energy-efficient routing protocols. 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 and extend the lifetime of a network.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for constructing an optimal DAG (demand-oriented architecture) in a wireless sensor network with data as a center.
The invention has two significant advantages: (a) in a directed acyclic graph DAG network, each node has multiple parent nodes and multiple child nodes, and each intermediate node can select any parent node to send a packet generated locally by itself or received from a child node. Compared with the tree structure in which each node only has a single parent node, the DAG structure can better balance loads and prolong the life span. (b) The amount of data in the network changes dynamically: the set of wireless sensor nodes in the network that generate local data packets changes dynamically at each sampling period, and this change is unpredictable. At each sampling period, which data to send is driven entirely by the data itself and can only be determined after the sensor node senses the data.
Further, the method for constructing the optimal DAG for the lifetime in the data-centric wireless sensor network comprises the following steps:
modeling a problem with an optimal DAG structure in a life period without considering an idle monitoring condition to form a linear programming problem;
step two, incorporating the energy for idle interception into the expression of the life-span optimization problem, and modeling the problem into an integer programming problem;
and step three, transforming the Integer Programming problem into a solvable Mixed Linear Integer Programming MILP (Mixed Linear Integer Programming MILP) problem through mathematical transformation.
And step four, calculating by using the current most advanced general mathematical programming solution device CPLEX, GLP, Gurobi or Mosek to obtain an optimal solution by contrasting the mixed integer linear programming problem.
Specifically, the modeling method of the problem of the lifetime-optimal DAG construction without considering idle listening is as follows:
Problem-LINEAR
maximize T
Figure BDA0001529111790000031
Figure BDA0001529111790000032
Figure BDA0001529111790000033
condition (1) describes the restriction of the flow, where piT is the total number of packets generated at node i during the lifetime T; condition (2) indicates that the total energy cost of node i is given by the initial energy B during the time of lifetime Ti(ii) is limited; condition (3) specifies that the flow rate is positive.
Idle listening is considered next. The idea of the second step comes from the following idea: in each sampling period, all data packets sent by each sensor node i to each parent node p thereof are continuously distributed in all time slices of the link (i, p) from the first time slice of the link (i, p); if the node i does not send data in the time slice of a certain link (i, p), all the time slices of the following links (i, p) are set to be null, and the node i does not send data to the node p any more; after node p finds an empty link (i, p) slot, p turns off its radio to save energy during all link (i, p) slots; therefore, at most one time slice will be spent by node p for idle listening per sampling period for each directed link (i, p). During the construction phase of the DAG structure, the size and direction of the data flow on each link is uncertain. The invention conservatively assumes that each sampling period node p will necessarily take a time slice to idle listen on each directed link (i, p).
In consideration of idle listening, the modeling method for constructing a DAG with an optimal lifetime described in step two is as follows:
Problem-MIP
maximize T
Figure BDA0001529111790000041
Figure BDA0001529111790000042
Figure BDA0001529111790000043
Figure BDA0001529111790000044
in order to be more uniform in form with the traditional maximum flow problem so as to be convenient to solve, the original problem is transformed: by using
Figure BDA0001529111790000045
Form of (1) in place of dijAnd by another variable
Figure BDA0001529111790000046
Instead of T, OPTDAG-MIP was converted into the following form:
Problem-MIP2
minimize q
Figure BDA0001529111790000047
Figure BDA0001529111790000048
Figure BDA0001529111790000049
Figure BDA00015291117900000410
this description, unlike other maximum flow problems, differs in the model: (a) the amount of data generated by each point is related to the data transmission probability of the node. (b) The cost of idle listening is taken into account. Since the integer programming problem is often an NP-hard problem in practice, with no solution in polynomial time, further transformed, the mixed integer linear programming problem is described as:
Problem-MILP
minimize q
Figure BDA0001529111790000051
Figure BDA0001529111790000052
Figure BDA0001529111790000053
fij≤zij≤Mfij；
new variable fijIt is shown that in each sampling period,the number of packets transmitted from node i to node j; 0/1 variable zijRelaxed to the common variables: f. ofij≤zij≤Mfij；(0<<<1) And M (M)>>1) Representing a very small and a very large number, respectively. And (3) calculating to obtain an optimal solution by using the current most advanced general mathematical programming solution device CPLEX, GLP, Gurobi or Mosek according to the mixed integer linear programming problem.
Drawings
Fig. 1 is a flowchart of a lifetime-optimized DAG construction method in a data-centric wireless sensor network according to an embodiment of the present invention.
FIG. 2 is an example of constructing a lifetime optimal tree provided by an embodiment of the present invention;
in the figure: (a) a network connection graph; (b) is the lifetime optimal DAG; (c) and constructing the life time optimal tree.
Fig. 3 is a network topology diagram of normalized 100 nodes provided by the embodiment of the present invention.
Fig. 4 is a data diagram of the first 5000 temperatures and solar radiation of a certain sensor provided by an embodiment of the present invention.
FIG. 5 is a graph illustrating the percentage of sensor nodes transmitting temperature data and solar radiation data per sampling period as a function of different data fuzzy thresholds, according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, a method for constructing a lifetime-optimized DAG in a data-centric wireless sensor network according to an embodiment of the present invention includes the following steps:
s101, modeling a problem with the optimal DAG structure in the life period without considering the idle monitoring condition into a linear programming problem;
s102, incorporating energy for idle interception into the expression of a life-span optimization problem, and modeling the problem into an integer programming problem;
s103, the Integer Programming problem is transformed into a solvable Mixed Linear Integer Programming MILP (Mixed Linear Integer Programming MILP) problem through mathematical transformation.
S104, calculating by using the current most advanced general mathematical programming solution device CPLEX, GLP, Gurobi or Mosek to obtain an optimal solution by contrasting the mixed integer linear programming problem.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
Such as the DAG routing structure shown in fig. 2. Fig. 2(a) shows an example network, where each link represents a neighbor relation between nodes, and a fraction next to each node represents a packet transfer probability (probability of a node sending a packet to an aggregation node in a sampling period) of the node. A lifetime-optimized DAG structure is shown in fig. 2 (b). The number on each directed link represents the average number of packets sent on that link during each sampling period. In the DAG structure, node a and node B each receive 2 packets and transmit 2.5 packets in each sampling period, so that a and B are the most heavily loaded bottleneck nodes. In contrast, in the lifetime optimal tree shown in fig. 2(c), the bottleneck node B receives 2.4 packets and transmits 2.9 packets per sample period. The amount of B node load is too large for any a and B in the DAG structure. The DAG structure shown in fig. 2(b) is therefore more efficient at balancing network load and extending lifetime than the optimal tree shown in fig. 2 (c).
The implementation of the method for constructing the optimal DAG in the lifetime in the wireless sensor network with data as the center provided by the embodiment of the invention specifically comprises the following steps:
in a first step, a network is arranged, and referring to fig. 3, 100 sensor nodes are randomly placed in a 1 × 1 square area, and in order to keep the whole network connected, the transmission radius of each node is set to 0.25 in a topological graph.
And secondly, selecting a data sequence and setting a threshold value. Referring to fig. 4, the method was tested 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 3,000,000 sensor data, with two consecutive sensor data samples spaced 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. When the value of e is increased, the threshold range is correspondingly increased, and the data volume reported by each node is reduced accordingly.
And thirdly, setting node energy. 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 and is 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.
And 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.
And fifthly, constructing an optimal DAG structure in the life cycle by using the open source toolkit Mosek or CPLEX, and recording the time consumed for solving the optimal DAG under each group of error e values.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (4)

1. A method for constructing an optimal DAG (demand oriented architecture) in a data-centric wireless sensor network is characterized by comprising the following steps of:
modeling a problem with an optimal DAG structure in a life period without considering an idle monitoring condition to form a linear programming problem;
step two, incorporating the energy for idle interception into the expression of the life-span optimization problem, and modeling the problem into an integer programming problem;
step three, transforming the integer programming problem into a solvable mixed linear integer programming problem through mathematical transformation;
step four, calculating by using the current most advanced general mathematical programming solution device CPLEX, GLP, Gurobi or Mosek to obtain an optimal solution by contrasting the mixed integer linear programming problem;
when the energy of idle interception is considered, the integer programming modeling method of the structure of the DAG with the optimal lifetime comprises the following steps:
Problem-MIP
maximize T
Figure FDA0002614630730000011
Figure FDA0002614630730000012
Figure FDA0002614630730000013
Figure FDA0002614630730000014
transforming the original problem: by using
Figure FDA0002614630730000015
Form of (1) in place of dijAnd by another variable
Figure FDA0002614630730000016
Instead of T, OPTDAG-MIP was converted into the following form:
Problem-MIP2
minimize q
Figure FDA0002614630730000021
Figure FDA0002614630730000022
Figure FDA0002614630730000023
Figure FDA0002614630730000024
the modeling method of the mixed integer linear programming problem comprises the following steps:
Problem-MILP
minimize q
Figure FDA0002614630730000025
Figure FDA0002614630730000026
Figure FDA0002614630730000027
fij≤zij≤Mfij；
new variable fijIndicating the transfer from node i to node during each sampling periodThe number of packets of j; 0/1 variable zijRelaxed to the common variables: f. ofij≤zij≤Mfij；(0<<<1) And M (M)>>1) Representing a very small and a very large number, respectively; and (3) calculating to obtain an optimal solution by using the current most advanced general mathematical programming solution device CPLEX, GLP, Gurobi or Mosek according to the mixed integer linear programming problem.
2. The method of constructing a lifetime-optimal DAG in a data-centric wireless sensor network of claim 1, wherein the method of modeling the problem without considering idle listening:
Problem-LINEAR
maximizeT
Figure FDA0002614630730000028
Figure FDA0002614630730000029
Figure FDA00026146307300000210
condition (1) describes the restriction of the flow, where piT is the total number of packets generated at node i during the lifetime T; condition (2) indicates that the total energy cost of node i is given by the initial energy B during the time of lifetime Ti(ii) is limited; condition (3) specifies that the flow rate is positive.
3. The method for constructing a lifetime-optimized DAG in a data-centric wireless sensor network as recited in claim 1, wherein the second step specifically comprises: in each sampling period, all data packets sent by each sensor node i to each parent node p thereof are continuously distributed in all time slices of the link (i, p) from the first time slice of the link (i, p); if the node i does not send data in the time slice of a certain link (i, p), all the time slices of the following links (i, p) are set to be null, and the node i does not send data to the node p any more; after node p finds an empty link (i, p) slot, p turns off its radio to save energy during all link (i, p) slots; therefore, in each sampling period, the node p spends at most one time slice, and performs idle listening on each directed link (i, p); each sampling period node p will necessarily take a time slice for idle listening to each directed link (i, p).
4. A wireless sensor network applying the method for constructing the optimal DAG for the lifetime in the data-centric wireless sensor network according to any one of claims 1 to 3.
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