Marine depth mapping algorithm based on the edge computing in Internet of things

Highlights

• The method use the intensive data area to set up the weights to delete some of the wrong data.

• The method can efficiently process large amounts of data in a shorter time.

• In local area calculation, the imbalance of data distribution in the region can be solved.

Abstract

In recent years, the research of marine environmental monitoring system has been especially popular. The construction of Internet of things between devices occupies the main position in marine environment detection system. In order to protect and utilize marine resources, it is urgent to realize the collection and treatment of marine information. We have established the Internet of things system for large ocean data collected by sensors. We calculate the ocean data in terminal devices such as sensors. In the process of calculation and arrangement of data, this paper presents a new method for the calculation of data contours. The aim of this method is to quickly describe the contour of data. The distribution of contour lines can be calculated accurately in a short time. This paper introduces the principle and calculation process of the method. The simulation of big data calculation is carried out in practice. The simulation results are also analyzed. Finally, the advantages of this method are illustrated by comparison simulation. In the same set of data and conditions, the method achieves the improvement of computational efficiency and calculation accuracy. It has certain significance in practical application.

Introduction

With the rapid development of modern science and technology, the rise of Internet of things technology will be changed. The situation of marine environmental monitoring is realized. However, the Internet of things lacks a widely recognized architecture. The concept of the Internet of things was first proposed by experts from the MIT [[4], [8]]. It generally provides information sensing devices such as RFID, infrared sensors, GPS, laser scanners and other information sensors to connect anything to the Internet [37]. They can carry out information exchange and communication to realize intelligent identification, positioning, tracking, monitoring and management. The characteristics of Internet of things are the ability to realize comprehensive perception, reliable transmission and intelligent processing of strange things. The Internet of things mainly combines the sensor network with the Internet. The goal is to make objects and objects communicate with each other, achieve higher work efficiency and save operating costs.

In the process of implementing the Cloud-assisted Internet of Things [[5], [29]] technology, we introduce edge computing technology. Edge computing is the realization of intelligent sensing, connection and control of objects and objects. It is similar to traditional cloud computing, which is to calculate and deal with big data. But the difference is that edge computing doesn’t need to transfer the collected data to distant clouds. These collected data can be resolved on the edge side. In this paper, we introduce a variety of edge sensors and a new algorithm to realize the data processing of terminal equipment. In the existing marine environment monitoring area [[36], [19]], mainly through the wireless sensor network. Sensors collaborate to sense, collect and process information about objects in the network coverage area and send information to the observer. The existing marine environmental monitoring technology does not really realize intelligent marine environmental monitoring. Therefore, the Internet of things in marine environment monitoring is proposed in this paper. The sensor network and the Internet are used to monitor the marine environment. In the field of marine information investigation, we often express the distribution and change of various elements of the ocean in time and space. With the development of the marine survey [[16], [26]], we have obtained more and more information. Therefore, traditional data processing methods and collection methods need to be updated.

Edge calculation is a new model of big data processing. In traditional cloud computing, edge devices do not have the ability to process data. In the edge computing model, edge devices cannot only request content and services from the cloud center, but also store data, cache, and privacy protection. In order to ensure the accuracy of ocean information, we use a variety of sensors to collect ocean information [1]. However, these ocean data collected by a variety of sensors require information interaction and communication. In marine information survey, we use sensors to measure the properties of ocean information. The information includes depth of sea water, temperature and salinity, etc. This paper mainly discusses the information interaction between two sensors on the Internet of things. These two sensors are ADCP(Acoustic Doppler Current Profiler) [32] and CTD(Conductance, Temperature and Depth) [[21], [17]]. These two sensors are used to measure the information of the designated area. In Table 1, we can get the marine information, including the depth and temperature of the water, etc.

The marine environment information is collected by wireless sensor network in the monitoring area. Then the data is transmitted over the Internet in real time. The data receiver completes the data receiving and processing to build a marine environment monitoring system based on the Internet of things. As far as we know, the Internet of things is an important part of the new generation of information technology. The Internet of things is still based on the Internet, and it is extending the Internet. The client extends to everything. It can exchange information and communicate with objects. In the Internet of things about ocean information collection, We divide it into three levels. In Fig. 1 , the first level is the collection of multiple sensor information. It includes on-site ADCP and CTD information collection. The second level is the data link process. It mainly includes lot gateway setting. The last level of data collection is the storage of data. It stores big ocean data in a database [[34], [9]].

In this paper, we propose a fast calculation method to deal with big data [[31], [18]]. Considering the validity of the data and ranking the weights. We calculate intensive region data and rough calculation of sparse data areas. We can get more accurate results in less time [[24], [12]]. The main contributions of this paper are as follows.

(1) In this paper, we take advantage of big data when processing big data. The information is close to reality in dense data. We use the intensive data area to set up the weights to delete some of the wrong data. It can improve the accuracy of the data and enable users to obtain accurate information.

(2) In this paper, we propose a new method that can efficiently process large amounts of data in a shorter time. It achieves the calculation of marine depth curve by establishing a grid of rules.

(3) This method also overcomes the inconvenience brought by the closed contour. In local area calculation, the imbalance of data distribution in the region can be solved. When collecting information, the ship cannot collect data from all regions due to driving requirements. Therefore, the data distribution is uneven. The method of this paper cannot only calculate the data dense area, but also predict the data sparse region.

The rest of this paper is organized as follows. Section 2 provides the background and motivation for marine information processing. Section 3 formulates the contour line calculation method and procedures [28]. In Section 4, we show the experimental results and discuss the comparison. Finally, conclusions are drawn in Section 5.