CN-121985347-A - Wireless sensor network target fence repairing method

Abstract

A wireless sensor network target fence repairing method relates to a network sensor target optimizing method, and aims at the situation that a target fence monitoring requirement exists in a domain and a plurality of targets exist, a sensor node is adopted to deploy the target fence. And searching for a shortest path to repair the fence when the sensor node on the target fence fails due to energy exhaustion. Firstly, a kepler algorithm (SCOKOA) for solving the optimization problem and based on S elite center of gravity reverse learning is provided, then, the target fence restoration problem is abstracted into a combined optimization problem, and a discretized kepler algorithm (DSCOKOA) based on S elite center of gravity reverse learning is adopted to search the shortest restoration path. Finally, on the classical combination optimization problem traveller test problem, the efficiency and stability of DSCOKOA in solving the shortest path problem are verified, and a solution is provided for the situation that the target fence repairing problem is combined with an actual scene and the safety range set by the target is regarded as an obstacle in the repairing path.

Inventors

• Peng Funan
• WANG XINYI
• Tong Xitong

Assignees

• 沈阳化工大学

Dates

Publication Date

20260505

Application Date

20260115

Claims (2)

1. 1. A wireless sensor network target fence repairing method is characterized in that a sensor node is adopted to deploy a target fence when a plurality of targets exist in a region with target fence monitoring requirements, and when the sensor node on the target fence is out of order due to energy consumption, the shortest path repairing fence is searched; Firstly, a kepler algorithm SCOKOA for solving the optimization problem and based on S elite center of gravity reverse learning is improved, then, the target fence restoration problem is abstracted into a combined optimization problem, and a discretized kepler algorithm DSCOKOA based on S elite center of gravity reverse learning is adopted to search the shortest restoration path; Finally, on the classical combination optimization problem traveller test problem, verifying DSCOKOA the efficiency and stability in solving the shortest path problem, and aiming at the target fence repairing problem, combining the actual scene, and giving a solution under the condition that the safety range set by the target is regarded as an obstacle in the repairing path; The method comprises the following specific repairing processes: (1) TSPLIB dataset experiments for tourist questions On the TSPLIB data set of the traveller problem, initializing a population by adopting a greedy algorithm according to the result of the distance between the preprocessed cities, and comparing the shortest path values found by each algorithm along with the change of iteration times, on the TSPLIB data sets att48, rand50, eil, berlin52, eil76, pr76, rate 99, kroA100 and tsp225 of the traveller problem, comparing the optimal values and average values of the shortest paths found by the algorithms, knowing that the DSCOKOA algorithm finds the optimal paths on the small data sets rand50, eil and berlin in the optimizing result of the 9 data sets, and on the two data sets att48 and rate 99, although the optimal path values are slightly higher than the DPSO algorithm, the average values are better than the algorithm, so that the optimizing trend is good, and the optimizing trend is illustrated through the comparison analysis and the discretization method is effective; (2) Target fence repair instance When the target fence is repaired, a certain safety range is required to be set for the monitored or protected target when the sensor node is replaced according to the shortest path, so that the target is required to bypass the safety range set by the target by combining with an actual scene; (3) Repair route when meeting obstacle When encountering an obstacle in the repairing process, setting all path points at the vertexes of the obstacle, namely, green vertexes, and selecting the shortest path in all possible paths to bypass the obstacle when bypassing the obstacle, namely, purple dotted lines; Before searching the optimal path, the DSCOKOA algorithm preprocesses the distance value of any two points according to whether the distance value passes through an obstacle or not, and finally optimizing to obtain the shortest path of the repairing target fence.
2. 2. The method for repairing the target fence of the wireless sensor network according to claim 1, wherein the TSPLIB dataset experiments of the problem of the traveling business (1) are run 30 times each, the population scale is 100, and the iteration number is 300 generations.

Description

Wireless sensor network target fence repairing method Technical Field The invention relates to a network sensor target optimization method, in particular to a wireless sensor network target fence repairing method. Background With the development of wireless sensor network technology, the wireless sensor network has been widely popularized in industrial and scientific research by virtue of the advantages of strong covering capacity, good practicability and the like. Industrial uses of wireless sensor networks are expanding, such as in industrial machinery condition monitoring [1], remote monitoring [2], warehouse management [3], and other industrial operational levels. In addition to this, they are also being used in a large number of applications such as biological habitat monitoring and environmental science. Meanwhile, the method starts to be applied to daily life of people, such as intelligent home [4], intelligent transportation [5], environment monitoring [6] and the like, and is already covered in various industries and fields. The task of wireless sensor network deployment not only can monitor targets and areas, but also can be deployed into fences to prevent intrusion, and the deployment and repair of the target fences which have recently emerged in recent years are gradually becoming the main direction of research. Regarding target fence monitoring problems, 2018, cheng et al defined a new wireless sensor network monitoring coverage problem—target fence monitoring problem [7]. The target fence is a continuous annular fence formed around the target. The target fence has dbound constraints set by the application and the requirements. dbound define a minimum distance of the constructed fence to the target. For target ts, si among the plurality of sensors of the constructed target fence is closest to target ts, it indicates that dist (si, ts) is not less than dbound. Where dist (si, ts) represents the distance between si and target ts in the sensor. From the definition of object fences, it is understood that each object is formed with a sensor as a continuously closed fence at a position not less than the distance constraint, as shown in fig. 1. Target barrier coverage is not an alternative to target coverage, area coverage, and barrier coverage. After the construction of the target fence is completed, the target fence starts to work, a monitoring and protecting task is implemented, and after a period of time, some sensor nodes cannot work normally due to insufficient energy or other factors, so that a notch appears in the target fence, and at the moment, the target fence needs to be repaired. In the intelligent warehouse scenario, in order to prevent people or robots entering the warehouse from accidentally touching or approaching a product or material (referred to as a target) with a certain risk or radiation, a target fence needs to be established beyond a certain range around the targets (a range with a certain distance as a radius around the targets). When a plurality of different types of articles are stored, the articles cannot be put together, a certain distance is kept, and a target fence needs to be established for the situation. When the sensor node on the target fence fails due to energy exhaustion, the target fence is repaired in the shortest time, namely the shortest path for repairing the target fence is found. On the repair path, the area of the target set safety range is regarded as an obstacle, and the shortest path repair scheme is given. Such as the target fence of fig. 1. To complete the repair task, the shortest repair path is found, and then the problem of repairing the target fence is converted into a classical tourist problem. The Problem of traveling staff, also called the salesperson Problem, abbreviated as TSP (TRAVELING SALESMAN Problem) Problem [8], is an NP-difficult Problem in combinatorial optimization, which is to find a traveling scheme of traveling staff that aims to go through all cities and has the shortest path, start from a certain city, go through all cities once and finally return to the starting city. The rules, while simple, increase the complexity of the solution after the number of places increases. Reference is made to: [1] Shu Shuai, shang Baoping, huang Yi, et al mechanical vibration WSN mass data adaptive transmission control method [ J ]. Vibration and shock, 2023, 42 (04): 263-269. [2] Gu Yifeng, zhang Zhenghua, shen Yi, etc. wireless sensor node data acquisition and distributed remote control [ J ]. Radio engineering, 2020, 50 (08): 661-665. [3] Zhang Longjie, zhang Xiaoyu, hu Hui, etc. large-scale warehouse unmanned intelligent monitoring system [ J ] of WSN, SCM and embedded system application, 2019, 19 (09): 82-85+89. [4] Zeng De, xu Jiangchun, zhang Kuangwei, etc. LEACH algorithm improvement research based on smart home networking design [ J ]. Electronic measurement and instrumentation report, 2019, 33 (12): 197-202. [5] Zhang Yuanyuan, zhang Ru