CN112134799A - Three-stage sensing topology method and system based on Internet of things - Google Patents

Three-stage sensing topology method and system based on Internet of things Download PDF

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CN112134799A
CN112134799A CN201910555651.XA CN201910555651A CN112134799A CN 112134799 A CN112134799 A CN 112134799A CN 201910555651 A CN201910555651 A CN 201910555651A CN 112134799 A CN112134799 A CN 112134799A
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dfs
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CN112134799B (en
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郝志峰
叶维彰
王金海
刘翔宇
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Foshan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/124—Shortest path evaluation using a combination of metrics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/02—Topology update or discovery
    • H04L45/04—Interdomain routing, e.g. hierarchical routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/14—Routing performance; Theoretical aspects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/48—Routing tree calculation
    • 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
    • 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
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

In the first stage of the three-stage sensing topology method based on the Internet of things, DFS is established to search all simple routes. In the second stage, the AHP based on the pair-wise comparison is suitable for analyzing qualitative and quantitative factors to be considered, so as to improve the experience quality of the technology of the internet of things system. In addition, relative weights for these factors are obtained to enable the method of the present disclosure to focus on those factors that are preferentially improved in resource-limited environments. In the third phase, the TOPSIS based on the weight derived from the AHP is used to select the route with the best similarity metric among all simple routes obtained from the proposed DFS, the system effectively reduces power consumption and improves successful signal transmission probability to improve the service quality of the internet of things, reduces the running time consumption cost of the topology to save energy consumption, thereby providing a better environment of the internet of things.

Description

Three-stage sensing topology method and system based on Internet of things
Technical Field
The disclosure relates to the technical field of computer network topology and computer network routing, in particular to a three-stage sensing topology method and system based on the Internet of things.
Background
In recent years, the internet of things realizes simple, intelligent and efficient connection of a plurality of devices used in daily life through a plurality of intelligent sensors which utilize wireless signals to communicate with each other. The rapid development of the internet of things is a recent progress in sensing technology. The internet of things (IoT) provides a simple and ubiquitous network [1,2] between many elements of everyday life and real world applications. It is an intelligent network of interconnected objects, such as equipment, buildings, vehicles and other items. These internet of things objects embed electronics, software, sensors, etc. for sampling, collecting, sensing, analyzing and exchanging data to improve production efficiency and provide more efficient resource consumption [1-3 ]. Low cost, low power consumption, compact size and open standard sensor stacks enable even the smallest objects installed in any environment to be included in the internet of things at a reasonable cost [3-15 ]. Wireless Sensor Networks (WSNs) consisting of a large number of wireless sensors are the core of the internet of things for collecting signals and allowing communication between objects, as they have greater flexibility over wired networks [3,5-7,9-11,13,15-23, 32 ]. Thus, the availability of powerful and inexpensive smart devices allows for optimized information management, measurement sharing and quality of service improvements [3-15 ].
In the topology of a WSN, nodes are sensors and arcs are communication links. Each node is a base unit representing a device having an embedded processor, memory, wireless interface and local autonomous power supply. The nodes can collect signals such as heat, light, sound, location or motion and can use the local wireless interface to transmit this information for further aggregation and processing [3,5-7,9-11,13,15-23 ]. For example, the node may be a smartphone with an accelerometer, gyroscope, magnetometer, GPS, barometer, temperature sensor, proximity sensor, ambient light sensor, and the like. The sensing capabilities of the devices in the wireless sensor network may also be improved from time to time [3,5-7,9-11,13,15-23 ]. From the user's perspective, quality of service refers to whether a response, message or signal from a system or user can reach a destination reliably, accurately, economically, efficiently and effectively anytime, anywhere [3-15 ]. Therefore, it is very important to identify important factors affecting the signal transmission probability and power consumption in order to make the smart sensor network in the internet of things more reliable and longer in life [3-15 ]. Routing is a process of forwarding signals by using Wi-Fi, Bluetooth, NFC, ZigBee, infrared, 4G/LTE, thread or blank television technologies through a series of nodes and arcs from a source node to a sink node in the WSN [3,5-7,9-11,13,15-18 ]. The series of nodes and arcs from the source node to the sink are called routing paths [3,5,7,9-11,13,15-23] and show how the endpoints of the application respond to client requests, e.g. the routing between nodes thus plays an important role in improving the quality of service. The method comprises six factors, reliability [16-23], energy consumption [5-8], transmission time [3,9], signal transmission quality (including strength and accuracy) [10], and coverage rate [11-13], so that the use cost [15] is considered in the method for constructing the integer programming model so as to improve the service quality of the technology of the Internet of things system.
Battery-powered smart sensors or devices, such as BLE beacons, smart wear, parking sensors, phones, laptops, etc., are very convenient, popular and common WSN devices, allowing objects to interact, which is a major target of the internet of things [3-9 ]. Battery life is a major consideration in determining which devices and device combinations and which communication technologies to use in an internet of things wireless sensor network, as battery life can affect the life of nodes (devices) and thus the entire network [3-9 ]. For example, Bluetooth 4.0 provides ultra-low power standby mode operation using a lightweight access method to ensure that very low power consumption can be achieved in both standby and active modes [3 ].
The present disclosure therefore focuses on the above-described multi-objective problem derived from practical applications by proposing a systematic approach to understanding and managing the problem in practice. The problem solving method provided by the disclosure is based on a three-stage sensing topology method based on the internet of things: depth-first search techniques (DFS) [21,22], originally from graph theory, Analytic Hierarchy Process (AHP) [24-31], and approximate Ideal solution ordering method (TOPSIS) [29-31 ].
The "[ ]" symbol of the present disclosure is a reference of the scheme of the present technology, and "-" in "[ - ]" is a plurality of consecutive references below, for example [30-32] are references [30], [31] and [32], and [1,2] are references [1] and [2 ].
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[4]C.L.Hsu；C.C.Judy(2016).An empirical examination of consumer adoption of Internet of Things services:Network externalities and concern for information privacy perspectives. Computers in Human Behavior 62:516–527.
[5]T.Mekonnen；P.A.Porambage,P.；E.Harjula；M.Ylianttila(2017)Energy Consumption Analysis of High Quality Multi-Tier Wireless Multimedia Sensor Network.IEEE Access 5:15848–15858.
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[7]N.Kushalnagar；G.Montenegro；C.Schumacher(2007).IPv6over Low-Power Wireless Personal Area Networks(6LoWPANs):Overview,Assumptions,Problem Statement,and Goals.IETF. doi:10.17487/RFC4919.RFC 4919.
[8]Jussi Karlgren；Lennart Fahlén；Anders Wallberg；
Figure BDA0002106796130000021
Hansson；Olov
Figure BDA0002106796130000022
Jonas
Figure BDA0002106796130000023
Karl-Petter
Figure BDA0002106796130000024
(2008)Socially Intelligent Interfaces for Increased Energy Awareness in the Home”The Internet of Things.Lecture Notes in Computer Science.Springer 4952:263–275.
[9]P.T.A.Quang；D.S.Kim(2012)Enhancing Real-time Delivery of Gradient Routing for Industrial Wireless Sensor Networks.IEEE Transactions on Industrial Informatics,8(1):61-68.
[10]Chu,Z.；Zhou,F.；Zhu,Z.；Hu,R.Q.；Xiao,P.(2018)Wireless Powered Sensor Networks for Internet of Things:Maximum Throughput and Optimal Power Allocation.IEEE Internet of Things Journal 5(1):310–321.
[11]Lersteau,C.；Rossi,A.；Sevaux,M.(2018)Minimum energy target tracking with coverage guarantee in wireless sensor networks.European Journal of Operational Research 265:882–894.
[12]Vermesan,Ovidiu；Friess,Peter(2013).Internet of Things:Converging Technologies for Smart Environments and Integrated Ecosystems,Aalborg,Denmark:River Publishers.ISBN 978-87-92982-96-4.
[13]C.-C.Lin；D.-J.Deng；C.-C.Kuo；Y.-L.Liang(2018)Optimal Charging Control of Energy Storage and Electric Vehicle of an Individual in the Internet of Energy with Energy Trading. IEEE Transactions on Industrial Informatics 14(6):2570-2578.
[14]J.Wang；N.N.Xiong；J.H.Wang；W.C.Yeh(2018)A Compact Ciphertext-Policy Attribute-Based Encryption Scheme for the Information-Centric Internet of Things,IEEE Access 6:63513-63526.
[15]C.-C.Lin；J.-W.Yang(2018)Cost-efficient Deployment of Fog Computing Systems at Logistics Centers in Industry 4.0.IEEE Transactions on Industrial Informatics 14(10): 4603-4611.
[16]M.Sajwan；K.Gosain；D.A.Sharma(2018)Hybrid energy-efficient multi-path routing for wireless sensor networks.Computers and Electrical Engineering 67:96–113.
[17]Huang,H.；Zhang,J.；Zhang,X.；Yi,B.；Fan,Q.；Li,F.(2017)EMGR:Energy-efficient multicast geographic routing in wireless sensor networks.Computer Networks 129:51–63.
[18]W.C.Yeh；J-S,Lin(2018)New Parallel Swarm Algorithm for Smart Sensor Systems Redundancy Allocation Problems in the Internet of Things,Journal of Supercomputing 74(9): 4358-4384.
[19]C.Huang(2015)A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems.Reliability Engineering&System Safety 142:221-230.
[20]Y.Feng；G.Wang(2018)Binary moth search algorithm for discounted{0-1}knapsack problem. IEEE Transactions on Evolutionary Computation 6:10708-10719.
[21]H.Pham(2007)Special issue on critical reliability challenges and practices.IEEE Transactions on Systems,Man,and Cybernetics(Part A)37(2)141-142.
[22]Y.Niu；Z.Gao；W.H.K.Lam(2017)A new efficient algorithm for finding all d-minimal cuts in multi-state networks”,Reliability Engineering&System Safety 166:151-163.
[23]W.C.Yeh(2017)A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks,IEEE Transactions on Neural Networks and Learning Systems 28(11):2822-2825.
[24]T.L Saaty；L.G.Vargas(1982)The Logic of Priorities:Applications in Business,Energy, Health,and Transportation.Boston:Kluwer-Nijhoff.ISBN 0-89838-071-5.
[25]T.L Saaty；K.Peniwati(2008).Group Decision Making:Drawing out and Reconciling Differences.Pittsburgh,Pennsylvania:RWS Publications.ISBN 978-1-888603-08-8.
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[27]WC Yeh；M.-C.Chuang(2011)Using multi-objective genetic algorithm for partner selection in green supply chain problems,Expert Systems with applications 38(4):4244-4253
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[30]
Figure BDA0002106796130000043
Ivana
Figure BDA0002106796130000044
(2017)Integrated AHP-TOPSIS Method for the Assessment of Health Management Information Systems Efficiency,Economic Themes 55(1):121– 142.
[31]Multiple Attribute Decision Making:Methods and Applications.New York:Springer-Verlag。
Disclosure of Invention
In order to solve the above problems, the present disclosure provides a three-stage sensing topology method and system based on the internet of things, which improves and optimizes, improves and integrates the concepts of simple routing and two well-known MCDM (multi-criteria decision) technologies in the proposed method for multi-criteria decision: analytic Hierarchy Process (AHP) and near ideal solution ordering process (TOPSIS). First, all simple routes are obtained using the proposed DFS, and the AHP is used to analyze the structure of the problem and to obtain the weights of the various selected criteria in the second phase. In the third stage, TOPSIS is used to order simple routes.
In order to achieve the above object, according to an aspect of the present disclosure, there is provided a three-stage sensing topology method based on the internet of things, the method including the steps of:
inputting: the WSN network topological graph in the Internet of things with the source node 0 and the sink node;
and (3) outputting: best route from node 0 to the sink node with the highest similarity metric value;
step 1, search all simple routes using DFS and let NaAs the number of simple routes;
step 2, k is 1,2, …, NeEach expert of (2) constructing a pairwise comparison matrix
Figure BDA0002106796130000041
Wherein N iscAnd NeNumber of standards and experts respectively;
step 3, adding CkAggregate into pairwise comparison matrices
Figure BDA0002106796130000042
Step 4, according to
Figure BDA0002106796130000051
Normalizing C to C';
step 5, based on
Figure BDA0002106796130000052
Determining the relative weight w of the jth attributejWhere j is 1,2, …, Nc；
Step 6, constructing a decision matrix
Figure BDA0002106796130000053
Wherein each alternative is a simple route obtained from step 1；
Step 7, based on
Figure BDA0002106796130000054
Construction of normalized New decision matrix from X
Figure BDA0002106796130000055
Step 8, according to zi,j＝wj·yi,jEstablishing a weighted normalized decision matrix
Figure BDA0002106796130000056
Step 9, determining PIS
Figure BDA0002106796130000057
And NIS
Figure BDA0002106796130000058
Step 10, based on
Figure BDA0002106796130000059
And
Figure BDA00021067961300000510
calculating i-1, 2, …, NaIs/are as follows
Figure BDA00021067961300000511
And
Figure BDA00021067961300000512
step 11, based on
Figure BDA00021067961300000513
Calculating i-1, 2, …, NaS similarity measure ofi；
And step 12, finding the route (simple route) with the maximum similarity metric as the optimal route output.
Further, in step 1, the method for searching all simple routes using DFS is: generating a DFS tree with all layers in the DFS tree numbered from 1 through a topological graph of initial routes of the nodes of the WSN, in a conventional DFS, the DFS tree starting at the source node, performing the following 3 steps for each branch during the search:
step 1.1, offspring branching step:
step 1.1a. selecting and adding the unvisited descendant node to the last node in the current path;
step 1.1b. if no node can be selected that is not visited in this step, go to step 1.2 brother branch step;
step 1.1c. if the descendant nodes are sink nodes, finding a simple route; saving all ancestor nodes in the sequence, which is the simple route found, and then going to step 1.3 parent branch step;
step 1.2, brother branch step:
step 1.2a, replacing the last node in the current path with one of the sibling nodes which is not visited by the last node, and returning to the step 1.1 to perform descendant branch;
step 1.2b. if no unvisited sibling node can be selected in this step, go to step 1.3 parent branch step;
step 1.3, a father branch step:
step 1.3a. returning to the parent node of the current node and continuing to step 1.2 brother branch step;
step 1.3b. stop if there is no parent node, i.e. the current node is the source node.
Further, in step 1, the method for searching all simple routes using DFS is: and obtaining the shortest path of the arcs between the nodes as a simple route by a Dijkstra method.
Further, in step 3, C is addedkAggregate into pairwise comparison matrices
Figure BDA0002106796130000061
The method comprises the following steps: normalizing the aggregated contrast matrix C to
Figure BDA0002106796130000062
Figure BDA0002106796130000063
Wherein the content of the first and second substances,
Figure BDA0002106796130000064
further, in step 5, the relative weight of the jth attribute is determined by computing a weight vector
Figure BDA0002106796130000065
Figure BDA0002106796130000066
From standard CjWeight w ofjComposition wherein i ═ 1,2, …, NcWherein:
Figure BDA0002106796130000067
further, in step 6, the decision matrix X is composed of alternative schemes and standard schemes, specifically, the decision matrix X is composed of alternative schemes and standard schemes
Figure BDA0002106796130000068
Wherein the element xi,jIs standard CjAlternative A of (1)i1,2, …, Na，j＝1, 2,…,Nc。
The present disclosure also provides a three-stage sensing topology system based on the internet of things, the system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in the following units of the system:
a simple route searching unit for searching all simple routes using DFS and searching NaAs the number of simple routes;
a comparison matrix construction unit for constructing k as 1,2, …, NeEach expert of (2) constructing a pairwise comparison matrix
Figure BDA0002106796130000069
Wherein N iscAnd NeNumber of standards and experts respectively;
a comparison matrix aggregation unit for aggregating CkAggregate into pairwise comparison matrices
Figure BDA00021067961300000610
A comparison matrix normalization unit for normalizing the data based on
Figure BDA00021067961300000611
Normalizing C to C';
a relative weight calculation unit for calculating a relative weight based on
Figure BDA0002106796130000071
Determining the relative weight w of the jth attributejWhere j is 1,2, …, Nc；
A decision matrix construction unit for constructing a decision matrix
Figure BDA0002106796130000072
Wherein each alternative is a simple route obtained from a simple route search unit;
a new decision matrix reconstruction unit for being based on
Figure BDA0002106796130000073
Construction of normalized New decision matrix from X
Figure BDA0002106796130000074
Figure BDA0002106796130000075
A decision matrix normalization unit for normalizing the matrix according to zi,j＝wj·yi,jEstablishing a weighted normalized decision matrix
Figure BDA0002106796130000076
A positive and negative ideal value determination unit for determining PIS
Figure BDA0002106796130000077
And NIS
Figure BDA0002106796130000078
Euclidean distance calculating unit for calculating distance based on
Figure BDA0002106796130000079
And
Figure BDA00021067961300000710
calculating i-1, 2, …, NaIs/are as follows
Figure BDA00021067961300000711
And
Figure BDA00021067961300000712
a similarity measure calculation unit for calculating a similarity measure based on
Figure BDA00021067961300000713
Calculating i-1, 2, …, NaS similarity measure ofi；
And the optimal route acquisition unit is used for finding the route with the maximum similarity metric as the optimal route output.
The beneficial effect of this disclosure does: the invention provides a three-stage sensing topological method and system based on the Internet of things, and aims to effectively reduce power consumption and improve successful signal transmission probability through the system to improve the service quality of the Internet of things, reduce the operating time consumption cost of topology and save energy consumption, thereby providing a better environment of the Internet of things and improving the experience quality of the technology of the Internet of things system.
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The foregoing and other features of the present disclosure will become more apparent from the detailed description of the embodiments shown in conjunction with the drawings in which like reference characters designate the same or similar elements throughout the several views, and it is apparent that the drawings in the following description are merely exemplary of the present disclosure from which other drawings may be derived without inventive effort to those skilled in the art, and in which:
FIG. 1 illustrates a WSN network topology;
FIG. 2 illustrates a main flow of a three-stage sensing topology method;
FIG. 3 illustrates an example path and routing topology;
FIG. 4 illustrates a partial DFS tree generated by FIG. 1;
FIG. 5 illustrates an example AHP hierarchy;
FIG. 6 is a flow chart of a three-stage sensing topology method based on the Internet of things;
FIG. 7 illustrates a DFS tree generated by FIG. 1;
fig. 8 is a diagram illustrating a three-stage sensing topology system based on the internet of things according to the present disclosure.
Detailed Description
The conception, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, aspects and effects of the present disclosure. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The embodiment of the disclosure considers a multi-objective problem to improve the service quality of the internet of things [4], and focuses on six important factors: reliability [16-23], energy consumption [5-8], transmission time [3,9], signal transmission quality (including strength and accuracy) [10], coverage [11-13] and cost of use [15 ].
Assuming that C, E, Q, R, S and T are functions of the cost of use, energy consumption, transmission quality, reliability, signal strength and transmission time of arc E, respectively, the method of the present disclosure has six corresponding objectives:
Figure BDA0002106796130000081
Figure BDA0002106796130000082
Figure BDA0002106796130000083
Figure BDA0002106796130000084
Figure BDA0002106796130000085
Figure BDA0002106796130000086
where p is the route from the source node to the sink node. As shown in fig. 1, which is a network topology of a WSN, signals may be routed from nodes 0 to 1 to 3 in fig. 1 as a first route and from nodes 0 to 2 to 3 as a second route. The better of the two routes must be determined using the six factors described above simultaneously.
Equations (1), (2) and (6) are shortest path problems in this multi-objective problem, let c (e), e (e) or t (e) be the distance of arc e, and they can solve the problem using shortest path methods, e.g., Dijkstra's method, in polynomial time if such equations [19,20] are considered separately. However, equations (3) and (5) are both NP-Hard problems [19,21], and are even considered independent, since the longest path is the NP-Hard problem and cannot solve [19] in polynomial time, and therefore, conventional shortest path methods cannot solve this problem. If all paths from the source nodes to the sink nodes are known [19,21] in the above mathematical model, this multi-objective problem can be solved by substituting each path into equations (1) to (6). Unfortunately, there are many paths in the mathematical model described above, the number of which increases with the size of the problem [19,21 ]. Therefore, there is also a need for a new, more efficient approach to address this important multi-objective problem in IoT networks. Furthermore, there may be many non-dominant solutions in a multi-objective problem, and it is always inconvenient and difficult for the decision-maker himself to select one of all non-dominant solutions as the answer to the problem [22-31 ].
Therefore, the present disclosure provides a new three-stage method based on a multi-criterion decision method, which overcomes the above obstacles in the multi-objective problem and improves the service quality of the internet of things system technology.
The present disclosure proposes a method for selecting the best route to improve the quality of service of the internet of things, as shown in fig. 2, fig. 2 shows the main flow of a three-stage sensing topology method, which includes three stages, namely, improving and optimizing and integrating the conventional DFS [19,20], AHP [22-29] and TOPSIS [29-31 ]. This section will discuss the details of these three phases. First, all simple routes must be found using DFS. Secondly, the AHP is implemented to obtain the weight of qualitative and quantitative factors in the internet of things. Finally, TOPSIS is used to analyze and optimize the weights obtained by the AHP in order to find the most suitable route from all the simple routes found in the DFS, which is created in the first stage to search for all simple routes. In the second stage, the AHP based on the pair-wise comparison is suitable for analyzing qualitative and quantitative factors to be considered, so as to improve the experience quality of the technology of the internet of things system. Further, relative weights for these factors are obtained to enable the method of the present disclosure to focus on those factors that are preferentially improved in resource-limited environments. In the third phase, TOPSIS based on weights derived from AHP is used to select the route with the best similarity metric among all simple routes obtained from the proposed DFS.
Simple routing path and DFS:
in graph theory, a routing path is a sequence of arcs connected by a series of vertices. For example, there are at least four paths from node 0 to node 3, and fig. 3 shows an example of a path and routing topology. In this disclosure, routing refers to a special path used to represent the transfer of data traffic in a WSN.
For example, in (a) of fig. 3, a signal may be transmitted from node 0 to node 1 to node 3, and the transmission path is a route. In some routing cases, there may be redundant arcs, but these arcs may be removed without preventing transmission from the source node to the sink node, such as arc e in fig. 3 (b)0,2Loop { e) in FIG. 3 (c)1,2,e2,1And arc e2,1Loop { e) in FIG. 3 (d)1,2,e 2,11 of, all these can be deleted while still leaving a path connecting nodes 0 and 3. To facilitate distinguishing between routes without redundant arcs and routes containing redundant arcs, routes without redundant arcs are referred to as simple routes.
It is clear that simple routing is more reliable and results in lower power consumption than non-simple routing, since more arcs require more power and reduces the probability of successful signal transmission [16-23 ]. For example, let the power and reliability of all arcs be 1 unit time and 0.9, respectively. Table I lists the required power and the final reliability for the following four routing paths in fig. 3:
TABLE I Power and reliability of edges in FIG. 3 before and after removal of redundant edges
Figure BDA0002106796130000101
Therefore, for the above problem, the first stage must be to find all simple routes. These simple routes can be found by DFS (depth first search) or BFS (breadth first search). Let all layers in the DFS tree number from 1. In conventional DFS, the DFS tree employed in the method starts at the source node and the following three steps are performed for each branch during the search:
(1) a descendant branch step:
a. the next-to-next node in the current path is selected and added to the last node in the current path, shown in fig. 4 as the partial DFS tree generated by fig. 1, e.g., node 1, node 2, and node 3 in fig. 4 (a), fig. 4 (b), and fig. 4(c), respectively.
b. If no unvisited node can be selected in this step, a sibling branching step is entered, e.g., the second node 3 in (d) of FIG. 4 (in level 4 of the DFS tree).
c. If the descendant node is a sink node, finding a simple route; all ancestors in the sequence are saved (this is a simple route found) and then go to the parent branch step, e.g., the path from node 0 to 1 to 2 to 3 in FIG. 4(c) and the path from node 0 to 1 to 3 in FIG. 4 (d).
(2) The steps of brother branching:
a. replace the last node in the current path with one of its unvisited sibling nodes and return to the descendant branch step. For example, if there is still an unvisited node, e.g., node 4, after reaching the last node, e.g., node 3, then node 3 is replaced with node 4 and the descendant branch step is performed.
b. If no unvisited sibling can be selected in this step, the parent branch step is entered, e.g., node 3 (in level 4 of the DFS tree) returns to node 2 (in level 3 of the DFS tree) in FIG. 4 (d).
(3) Parent branch step:
a. returning to the parent of the current node and proceeding with the sibling branching step, e.g., node 3 (in level 4 of the DFS tree) returns to node 2 (in level 3 of the DFS tree) and goes to node 3 (in level 3 of the tree).
b. And stopping if there is no parent node, i.e. the current node is the source node. For example, if the current node is node 0 and there are no descendants from node 0, the DFS process is stopped.
Analytic Hierarchy Process (AHP):
saaty's Analytic Hierarchy Process (AHP) is one of the most practical and useful analytical multi-criteria decision method (MCDM) because it is simple, dynamic, systematic and efficient [22 ]. AHP can solve complex and/or unstructured problems by decomposing complex and unstructured environments into hierarchical structures to fully present the relationships between standards [22-29 ].
AHP is used here to obtain relative weights to distinguish between different degrees of importance in the factors considered and to reflect the decision maker's preference for the factors by giving weights. The main procedure for obtaining relative weighted AHP based on geometric averaging is as follows [22 ].
Step A1, building a hierarchical structure with target solution, standard and alternative solutions at a first level, a second level and a third level, respectively. For example, FIG. 5 shows an example AHP hierarchy, FIG. 5 is an AHP hierarchy with four criteria, c in the second level1,c2,c3And c4And three alternatives a in the third layer1,a2And a3；
Step A2, comparing each expert in pairs to construct a comparison matrix of pairs of experts k
Figure BDA0002106796130000111
Figure BDA0002106796130000112
Figure BDA0002106796130000113
Wherein
Figure BDA0002106796130000114
Is a comparison of criteria i according to criteria j of expert k. Note that the nature of the pairwise comparison is to determine the preference expressed by the decision maker through the relative importance expression of saay.
Step A3, collecting data from expert's pairwise comparison matrix to form an aggregate pairwise comparison matrix for expert k
Figure RE-GDA0002158301100000115
Figure BDA0002106796130000116
Wherein the content of the first and second substances,
Figure BDA0002106796130000117
step A4, normalizing the aggregated pairwise comparison matrix C to
Figure BDA0002106796130000118
Figure BDA0002106796130000119
Wherein the content of the first and second substances,
Figure BDA0002106796130000121
step a5, determining i ═ 1,2, …, N according to the following equationcRelative weight w of the ith attribute of (2)i：
Figure BDA0002106796130000122
In the present disclosure, the hierarchy of AHPs contains only two levels (no alternate levels are used), and AHPs are used to determine the weight coefficients, while priorities are set, i.e., the TOPSIS method is used to perform ranking of alternatives.
Approximate ideal solution ordering method (TOPSIS):
the approximate ideal solution ordering method (TOPSIS), first proposed by Hwang and Yoon in 1981, is an MCDM widely used in the presence of multiple and often conflicting criteria to evaluate and rank the performance of alternatives, multiple criteria similar to the ideal solution [30 ].
There are two types of standards: revenue and cost [29-31 ]. Lower values are better for cost criteria and the opposite for benefit criteria. TOPSIS is based on the following concept: the alternative chosen should be closest to the Positive Ideal Solution (PIS) and at the maximum geometrical distance from the Negative Ideal Solution (NIS). The PIS contains all the best standard values, maximizing the benefit criteria and minimizing the cost criteria. On the other hand, the NIS, which consists of all the worst criteria values, minimizes the revenue criteria and maximizes the cost criteria. Extensive investigations on TOPSIS can be found in [29-31 ].
TOPSIS has been used to address many practical topics and has been expanded by many studies for uncertain cases due to its structural integrity, simplicity and ease of operation. Thus, the present disclosure employs selecting the best alternative (solution) among the non-dominant solutions.
At the beginning of TOPSIS, the decision matrix must be known
Figure BDA0002106796130000126
And a weight vector W, the decision matrix X is composed of alternative schemes and criteria and is described by equation (13):
Figure BDA0002106796130000123
wherein the element xi,jIs standard CjAlternative A of (1)iRating of (i) 1,2, …, NaAnd j ═ 1,2, …, NcWeight vector
Figure BDA0002106796130000124
From standard CjWeight w ofjComposition wherein i ═ 1,2, …, NcWherein:
Figure BDA0002106796130000125
the main process of toposis based on the geometric distance method representing "near ideal" is described in the following steps [30 ]:
step T1, constructing a normalized new decision matrix by X
Figure BDA0002106796130000131
xi,jAll values of (A) come from different sourcesAnd needs to be normalized to transform it into a new decision matrix Z, which is a normalized and dimensionless matrix based on the following equation:
Figure BDA0002106796130000132
step T2, establishing a weighted normalized decision matrix
Figure BDA0002106796130000133
zi,j＝wj·yi,j， (16)
Wherein, i is 1,2, …, NaAnd j ═ 1,2, …, Nc.。
Step T3, determining PIS
Figure BDA0002106796130000134
And NIS
Figure BDA0002106796130000135
Wherein
Figure BDA0002106796130000136
And
Figure BDA0002106796130000137
respectively from criterion CjObtained j-1, 2, …, NcThe best and worst solution of, i.e., if C is requiredjIn order to maximize the number of the channels,
Figure BDA0002106796130000138
is the maximum (minimum) solution (min) sum
Figure BDA0002106796130000139
Is the maximum (minimum) solution, C if requiredjTo minimize (maximize).
Step T4, calculating
Figure BDA00021067961300001310
And
Figure BDA00021067961300001311
they are selected from
Figure BDA00021067961300001312
And
Figure BDA00021067961300001313
1,2, …, NaEach weighted normalized substitution of
Figure BDA00021067961300001314
As shown in equations (17) and (18), respectively.
Figure BDA00021067961300001315
Figure BDA00021067961300001316
Step T5, calculating each weighted normalized substitution ZiS similarity measure ofiAs shown in equation (19), where i is 1,2, …, Na。
Figure BDA00021067961300001317
At step T6, the highest value of the similarity measure is the best choice.
A three-stage sensing topology method based on the Internet of things comprises the following steps:
this section discusses a proposed three-stage sensing topology method for improving service quality of the internet of things, which describes how to find all simple routes between node 0 and sink nodes using DFS, calculate weights of all factors listed by experts using AHP, and allocate the optimal simple route using TOPSIS, as shown in fig. 6, which is a flowchart of the three-stage sensing topology method based on the internet of things, and the flow of the three-stage sensing topology method based on the internet of things includes the following steps: .
Inputting: the WSN network topological graph in the Internet of things with the source node 0 and the sink node;
and (3) outputting: best route from node 0 to the sink node with the highest similarity metric value;
step 1, search all simple routes using DFS and let NaAs the number of simple routes;
step 2, k is 1,2, …, NeEach expert of (2) constructing a pairwise comparison matrix
Figure BDA0002106796130000141
Wherein N iscAnd NeNumber of standards and experts respectively;
step 3, adding CkAggregate into pairwise comparison matrices
Figure BDA0002106796130000142
Step 4, normalizing C to C' according to equation (11);
step 5, determine the relative weight w of the jth attribute based on equation (12)jI.e. calculating a weight vector W, where j is 1,2, …, Nc；
Step 6, constructing a decision matrix
Figure BDA0002106796130000143
Wherein each alternative is a simple route obtained from step 1;
step 7, constructing a normalized new decision matrix from X based on equation (15)
Figure BDA0002106796130000144
Step 8, establishing a weighted normalized decision matrix according to equation (16)
Figure BDA0002106796130000145
Step 9, determining PIS
Figure BDA0002106796130000146
And NIS
Figure BDA0002106796130000147
Step 10, calculate i ═ 1,2, …, N based on equations (17) and (18)aIs/are as follows
Figure BDA0002106796130000148
And
Figure BDA0002106796130000149
step 11, calculate i ═ 1,2, …, N based on equation (19)aS similarity measure ofi；
And step 12, finding the route (simple route) with the maximum similarity metric as the optimal route output.
Wherein, the first stage is step 1; the second stage is step 2 to step 5; the third stage is step 6 to step 12. One embodiment of the disclosed method:
the general procedure of the proposed three-phase sensing topology method based on the internet of things can be best demonstrated by embodiments. For convenience, the example shown in FIG. 1 was chosen to illustrate the stepwise process of finding the best route between nodes 1 and 4.
Table II lists information for six factors per arc, where C, E, Q, R, S, and T represent cost of use, energy consumption, transmission quality, reliability, signal strength, and transmission time.
TABLE II information contained by six factors in each arc
Figure BDA00021067961300001410
Assume that five experts are assigned to analyze the system, find the factors that cause the greatest quality of service problem, and construct their own pairwise comparison matrix in step 1.
The entire process of the three-phase sensing topology method of the disclosed method embodiment is described below. Fig. 7 shows the DFS tree generated from fig. 1, and the following steps are to perform a three-stage sensing topology method on the DFS tree shown in fig. 7.
Step 1, four simple arcs, i.e. simple routes (see fig. 4), are identified in fig. 1 using DFS (see fig. 7): in a first stage p1＝{e0,1,e1,2,e2,3},p2＝{e0,1,e1,3},p3＝{e0,2,e2,1,e1,3And p4＝{e0,2,e1,3}。
Step 2, establishing a pair comparison matrix for each expert
Figure BDA0002106796130000151
As shown in table III.
Table III expert paired comparison matrix
Figure BDA0002106796130000152
Step 3, obtaining a pair-wise comparison matrix by aggregating expert's pair-wise comparison matrix based on equation (9)
Figure BDA0002106796130000153
As shown in table IV.
TABLE IV pairwise comparison matrix of aggregate values for five experts
Figure BDA0002106796130000154
Figure BDA0002106796130000161
Step 4, normalize C to C according to equation (11)
Figure RE-GDA0002158301100000154
As shown in table V.
TABLE V normalized aggregate dual comparison matrix values
Figure BDA0002106796130000163
Step 5, the average of the six factors in each row is the weight of each factor, i.e., the weights of C, E, Q, R, S, and T are 0.40,0.13,0.15,0.10,0.13, and 0.10 (see last column in Table V).
Step 6, decision matrix
Figure BDA0002106796130000164
As shown in table VI. Note: each alternative is a simple route obtained from step I.
Elements of Table VI X
Figure BDA0002106796130000165
Step 7, establishing a normalized new decision matrix according to equation (15)
Figure BDA0002106796130000166
As shown in table VII.
Elements of Table VII Y
Figure BDA0002106796130000167
Step 8, establishing a weighted normalized decision matrix
Figure BDA0002106796130000168
As shown in table VIII.
Elements of Table VIII Z
Figure BDA0002106796130000169
STEP 9, available from Table VII, PIS
Figure BDA0002106796130000171
Figure BDA0002106796130000172
(see Table VIII) and NIS
Figure BDA0002106796130000173
Figure BDA0002106796130000174
(see Table VIII).
Step 10, calculating i as 1,2, …, NaIs/are as follows
Figure BDA0002106796130000175
And
Figure BDA0002106796130000176
as shown in table IX.
Table IX for i ═ 1,2, …, Na,
Figure BDA0002106796130000177
And siValue of (A)
Figure BDA0002106796130000178
Step 11, calculating i as 1,2, …, NaS similarity measure ofiLast line(s) in Table IXiRows).
Step 12, from Table IX, p2The highest similarity metric value 0.801209 is obtained, so that the internet of things selects the routing path for topology communication.
In the same way, all the best simple routes are found by the three-stage sensing topological method based on the internet of things, as shown in table X.
Table X all simple routes
Figure BDA0002106796130000179
As can be seen from table X, even though two nodes are located on either side of an arc, it can be observed that it is not necessary to transmit signals directly along the arc. For example, nodes 0 and 2 are located on both sides of arc e2, and the optimal route between the two nodes is not along e2, but is from node 0 to node 2 through node 1. therefore, from this simple example, it follows that a three-stage sensing topology method based on the internet of things of the present disclosure is very useful in improving the quality of service of the wireless sensor network of the internet of things over the existing methods.
Conclusion of the examples:
five sets of numerical experiments were conducted to verify the performance of the method performed on the medium scale network. In these experiments, the number of nodes n was 10,20,30,40,50, respectively, and was uniformly randomly generated in an area of 100m × 100 m. Thus, all network structures are randomly modeled.
The method proposed by the present disclosure is implemented in the C programming language, running on Windows 10 with Intel Core i7-5960X CPU and 16GB RAM.
Table XI lists the run times for each test question. For the NP-hard problem, it is unexpected that runtime increases as the number of nodes increases, i.e., the size of the problem. Most of the run-time consumption is used to search all simple paths, i.e. the run-time in phase 1, phase 2 is minimal, as five experts have only six factors. The run time of phase 3 is less than the run time of phase 1 because any path is not a simple path. Further, a general network always includes the period in phase 1. In phase 3, only these simple paths found in phase 1 have their values of cost of use, energy consumption, transmission quality, reliability, signal strength and transmission time calculated by multiplying the weight of each factor obtained in phase 2.
Thus, from table XI, the method proposed by the present disclosure is able to solve the problem of medium-scale WSNs.
TABLE XI average run time for five sets of numerical experiments processed by the method of the present disclosure
Figure BDA0002106796130000181
The method aims to effectively improve the experience quality of the technology of the Internet of things system, and comprises the step of determining main factors influencing the experience quality. In order to achieve the aim, a three-stage sensing topology method based on the Internet of things is provided. In the first stage of the three-stage sensing topology method based on the internet of things, a DFS is created to search all simple routes. In the second stage, the AHP based on the pair-wise comparison is suitable for analyzing qualitative and quantitative factors to be considered, so as to improve the experience quality of the internet of things system technology. In addition, relative weights for these factors are obtained to enable the method of the present disclosure to focus on those factors that are preferentially improved in resource-limited environments. In the third phase, TOPSIS based on weights derived from AHP is used to select the route with the best similarity metric among all simple routes obtained from the proposed DFS.
The performance and the applicability of the proposed three-stage sensing topology method based on the internet of things are illustrated through an application example of case research. In addition, the simulation result proves the performance of the method, and the problem of 50 nodes can be solved. Therefore, with such a three-stage sensing topology method based on the internet of things, it is indeed possible to generate reasonable results and the best route available for making a decision quickly.
An embodiment of the present disclosure provides a three-stage sensing topology system based on the internet of things, as shown in fig. 8, which is a three-stage sensing topology system diagram based on the internet of things, and the three-stage sensing topology system based on the internet of things of the embodiment includes: the system comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps in the embodiment of the three-stage sensing topological system based on the internet of things.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in the following units of the system:
a simple route searching unit for searching all simple routes using DFS and searching NaAs the number of simple routes;
a comparison matrix construction unit for constructing k as 1,2, …, NeEach expert of (2) constructing a pairwise comparison matrix
Figure BDA0002106796130000191
Wherein N iscAnd NeNumber of standards and experts respectively;
a comparison matrix aggregation unit for aggregating CkAggregate into pairwise comparison matrices
Figure BDA0002106796130000192
A comparison matrix normalization unit for normalizing the data based on
Figure BDA0002106796130000193
Normalizing C to C';
a relative weight calculation unit for calculating a relative weight based on
Figure BDA0002106796130000194
Determining the relative weight w of the jth attributejWhere j is 1,2, …, Nc；
A decision matrix construction unit for constructing a decision matrix
Figure BDA0002106796130000195
Wherein each alternative is a simple route obtained from a simple route search unit;
a new decision matrix reconstruction unit for being based on
Figure BDA0002106796130000196
Construction of normalized New decision matrix from X
Figure BDA0002106796130000197
Figure BDA0002106796130000198
A decision matrix normalization unit for normalizing the matrix according to zi,j＝wj·yi,jEstablishing a weighted normalized decision matrix
Figure BDA0002106796130000199
A positive and negative ideal value determination unit for determining PIS
Figure BDA00021067961300001910
And NIS
Figure BDA00021067961300001911
Euclidean distance calculating unit for calculating distance based on
Figure BDA00021067961300001912
And
Figure BDA00021067961300001913
calculating i-1, 2, …, NaIs/are as follows
Figure BDA00021067961300001914
And
Figure BDA00021067961300001915
a similarity measure calculation unit for calculating a similarity measure based on
Figure BDA00021067961300001916
Calculating i-1, 2, …, NaS similarity measure ofi；
And the optimal route acquisition unit is used for finding the route with the maximum similarity metric as the optimal route output.
The three-stage sensing topology system based on the Internet of things can be operated in computing equipment such as desktop computers, notebooks, palmtop computers and cloud servers. The three-stage sensing topological system based on the Internet of things can be operated by a system comprising but not limited to a processor and a memory. Those skilled in the art will appreciate that the example is merely an example of an internet of things based three-stage sensing topology system, and does not constitute a limitation of an internet of things based three-stage sensing topology system, and may include more or less components than the internet of things based three-stage sensing topology system, or some components in combination, or different components, for example, the internet of things based three-stage sensing topology system may further include input and output devices, network access devices, buses, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the three-stage sensing topology system operation system based on the internet of things, and various interfaces and lines are utilized to connect various parts of the whole three-stage sensing topology system operable system based on the internet of things.
The memory may be used for storing the computer program and/or module, and the processor may implement various functions of the three-stage sensor topology system based on the internet of things by operating or executing the computer program and/or module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the present disclosure has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed as effectively covering the intended scope of the disclosure by providing a broad, potential interpretation of such claims in view of the prior art with reference to the appended claims. Furthermore, the foregoing describes the disclosure in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the disclosure, not presently foreseen, may nonetheless represent equivalent modifications thereto.

Claims (7)

1. The three-stage sensing topology method based on the Internet of things is characterized in that the input of the method is a WSN network topology graph in the Internet of things with a source node 0 and a sink node; the method output is the best route from node 0 to the sink node with the highest similarity metric value; the method comprises the following steps:
step 1, search all simple routes using DFS and let NaAs the number of simple routes;
step 2, k is 1,2, …, NeEach expert of (2) constructing a pairwise comparison matrix
Figure FDA0002106796120000011
Wherein N iscAnd NeNumber of standards and experts respectively;
step 3, adding CkAggregate into pairwise comparison matrices
Figure FDA0002106796120000012
Step 4, according to
Figure FDA0002106796120000013
Putting C inIs normalized to C';
step 5, based on
Figure FDA0002106796120000014
Determining the relative weight w of the jth attributejWhere j is 1,2, …, Nc；
Step 6, constructing a decision matrix
Figure FDA0002106796120000015
Wherein each alternative is a simple route obtained from step 1;
step 7, based on
Figure FDA0002106796120000016
Construction of normalized New decision matrix from X
Figure FDA0002106796120000017
Step 8, according to zi,j＝wj·yi,jEstablishing a weighted normalized decision matrix
Figure FDA0002106796120000018
Step 9, determine
Figure FDA0002106796120000019
And
Figure FDA00021067961200000110
step 10, based on
Figure FDA00021067961200000111
And
Figure FDA00021067961200000112
calculating i-1, 2, …, NaIs/are as follows
Figure FDA00021067961200000113
And
Figure FDA00021067961200000114
step 11, based on
Figure FDA00021067961200000115
Calculating i-1, 2, …, NaS similarity measure ofi；
And step 12, finding the route with the maximum similarity metric as the optimal route output.
2. The three-stage sensing topology method based on the internet of things as claimed in claim 1, wherein in step 1, the method for searching all simple routes using DFS is: generating a DFS tree from a topological graph of initial routes of nodes of the WSN, having all layers in the DFS tree numbered from 1, the DFS tree starting at a source node, performing the following three steps for each branch during a search:
step 1.1, offspring branching step:
step 1.1a. selecting and adding the unvisited descendant node to the last node in the current path;
step 1.1b. if no node can be selected that is not visited in this step, go to step 1.2 brother branch step;
step 1.1c. if the descendant nodes are sink nodes, finding a simple route; saving all ancestor nodes in the sequence, which is the simple route found, and then going to step 1.3 parent branch step;
step 1.2, brother branch step:
step 1.2a, replacing the last node in the current path with one of the sibling nodes which is not visited by the last node, and returning to the step 1.1 to perform descendant branch;
step 1.2b. if no unvisited sibling node can be selected in this step, go to step 1.3 parent branch step;
step 1.3, a father branch step:
step 1.3a. returning to the parent node of the current node and continuing to step 1.2 brother branch step;
step 1.3b. stop if there is no parent node, i.e. the current node is the source node.
3. The three-stage sensing topology method based on the internet of things as claimed in claim 1, wherein in step 1, the method for searching all simple routes using DFS is: and obtaining the shortest path of the arcs between the nodes as a simple route by a Dijkstra method.
4. The three-stage sensing topology method based on the Internet of things of claim 1, wherein in step 3, C is usedkAggregate into pairwise comparison matrices
Figure FDA0002106796120000021
The method comprises the following steps: normalizing aggregated pairwise comparison matrix C to
Figure FDA0002106796120000022
Figure FDA0002106796120000023
Figure FDA0002106796120000024
Wherein the content of the first and second substances,
Figure FDA0002106796120000025
5. the three-stage sensing topology method based on the internet of things as claimed in claim 1, wherein in step 5, the relative weight of the jth attribute is determined by calculating a weight vector
Figure FDA0002106796120000026
From standard CjWeight w ofjComposition wherein i ═ 1,2, …, NcWherein:
Figure FDA0002106796120000027
6. the three-stage sensing topology method based on the internet of things as claimed in claim 1, wherein in step 6, the decision matrix X is composed of alternative schemes and standard schemes, specifically, the decision matrix X is composed of alternative schemes and standard schemes
Figure FDA0002106796120000031
Wherein the element xi,jIs standard CjAlternative A of (1)i1,2, …, Na，j＝1,2,…,Nc。
7. A three-stage sensing topology system based on the Internet of things, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the following units of the system:
a simple route searching unit for searching all simple routes using DFS and searching NaAs the number of simple routes;
a comparison matrix construction unit for constructing k as 1,2, …, NeEach expert of (2) constructing a pairwise comparison matrix
Figure FDA0002106796120000032
Wherein N iscAnd NeNumber of standards and experts respectively;
a comparison matrix aggregation unit for aggregating CkAggregate into pairwise comparison matrices
Figure FDA0002106796120000033
A comparison matrix normalization unit for normalizing the data based on
Figure FDA0002106796120000034
Normalizing C to C';
a relative weight calculation unit for calculating a relative weight based on
Figure FDA0002106796120000035
Determining the relative weight w of the jth attributejWhere j is 1,2, …, Nc；
A decision matrix construction unit for constructing a decision matrix
Figure FDA0002106796120000036
Wherein each alternative is a simple route obtained from a simple route search unit;
a new decision matrix reconstruction unit for being based on
Figure FDA0002106796120000037
Construction of normalized New decision matrix from X
Figure FDA0002106796120000038
Figure FDA0002106796120000039
A decision matrix normalization unit for normalizing the matrix according to zi,j＝wj·yi,jEstablishing a weighted normalized decision matrix
Figure FDA00021067961200000310
A positive and negative ideal value determination unit for determining
Figure FDA00021067961200000311
And
Figure FDA00021067961200000312
euclidean distance calculating unit for calculating distance based on
Figure FDA00021067961200000313
And
Figure FDA00021067961200000314
calculating i-1, 2, …, NaIs/are as follows
Figure FDA0002106796120000041
And
Figure FDA0002106796120000042
a similarity measure calculation unit for calculating a similarity measure based on
Figure FDA0002106796120000043
Calculating i-1, 2, …, NaS similarity measure ofi；
And the optimal route acquisition unit is used for finding the route with the maximum similarity metric as the optimal route output.
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