农业工程学报
農業工程學報
농업공정학보
2014年
23期
224-231
,共8页
杨劲松%虞丽娟%凌培亮%陈成明%夏俊
楊勁鬆%虞麗娟%凌培亮%陳成明%夏俊
양경송%우려연%릉배량%진성명%하준
渔船%数据处理%传感器%雾计算%云计算%物联网%远洋渔船作业系统
漁船%數據處理%傳感器%霧計算%雲計算%物聯網%遠洋漁船作業繫統
어선%수거처리%전감기%무계산%운계산%물련망%원양어선작업계통
fishing vessels%data processing%sensors%fog computing%cloud computing%internet of things%operating system of pelagic fishing vessel
远洋渔船上不断增加的船载智能设备和传感器增大了对网络通讯带宽和流量的要求，目前船岸间通讯的高昂费用和带宽限制成为整个系统的瓶颈。该文在远洋渔船作业系统中引入云雾混合计算架构。研究以云计算为基本架构，采用面向服务的构架，提供可靠和安全的数据存储中心，降低了对用户端的设备要求，利于不同设备间的数据与应用共享；同时，利用船载设备的计算和存储能力，采用雾计算架构的设备固件更新分发机制获取最少必要固件资源后在船载网络内部进行推送和更新，并利用船载传感网络雾计算模型在船载网络内部存储和压缩传感器环境数据，以降低船岸之间的数据通讯量。研究证明，云雾混合计算架构在保证船岸数据信息交互一体化的同时明显降低了对数据通讯带宽的要求，减少了网络流量。在测试期内，开富号各计算节点及传感设备固件更新文件数据量降低为传统架构的15.13%，传感器发往云端的报文数据量降低为传统架构的4.75%。参考卫星宽带流量套餐费用计算（1 MB费用约为10美元，2014年标准），在该时间段内，仅“开富号”一条船舶约节约7500美元，每年能节约通讯费36000美元，具有一定的经济效益。研究实践证明云雾混合计算有利于改善远洋渔船物联网系统通讯质量。
遠洋漁船上不斷增加的船載智能設備和傳感器增大瞭對網絡通訊帶寬和流量的要求，目前船岸間通訊的高昂費用和帶寬限製成為整箇繫統的瓶頸。該文在遠洋漁船作業繫統中引入雲霧混閤計算架構。研究以雲計算為基本架構，採用麵嚮服務的構架，提供可靠和安全的數據存儲中心，降低瞭對用戶耑的設備要求，利于不同設備間的數據與應用共享；同時，利用船載設備的計算和存儲能力，採用霧計算架構的設備固件更新分髮機製穫取最少必要固件資源後在船載網絡內部進行推送和更新，併利用船載傳感網絡霧計算模型在船載網絡內部存儲和壓縮傳感器環境數據，以降低船岸之間的數據通訊量。研究證明，雲霧混閤計算架構在保證船岸數據信息交互一體化的同時明顯降低瞭對數據通訊帶寬的要求，減少瞭網絡流量。在測試期內，開富號各計算節點及傳感設備固件更新文件數據量降低為傳統架構的15.13%，傳感器髮往雲耑的報文數據量降低為傳統架構的4.75%。參攷衛星寬帶流量套餐費用計算（1 MB費用約為10美元，2014年標準），在該時間段內，僅“開富號”一條船舶約節約7500美元，每年能節約通訊費36000美元，具有一定的經濟效益。研究實踐證明雲霧混閤計算有利于改善遠洋漁船物聯網繫統通訊質量。
원양어선상불단증가적선재지능설비화전감기증대료대망락통신대관화류량적요구，목전선안간통신적고앙비용화대관한제성위정개계통적병경。해문재원양어선작업계통중인입운무혼합계산가구。연구이운계산위기본가구，채용면향복무적구가，제공가고화안전적수거존저중심，강저료대용호단적설비요구，리우불동설비간적수거여응용공향；동시，이용선재설비적계산화존저능력，채용무계산가구적설비고건경신분발궤제획취최소필요고건자원후재선재망락내부진행추송화경신，병이용선재전감망락무계산모형재선재망락내부존저화압축전감기배경수거，이강저선안지간적수거통신량。연구증명，운무혼합계산가구재보증선안수거신식교호일체화적동시명현강저료대수거통신대관적요구，감소료망락류량。재측시기내，개부호각계산절점급전감설비고건경신문건수거량강저위전통가구적15.13%，전감기발왕운단적보문수거량강저위전통가구적4.75%。삼고위성관대류량투찬비용계산（1 MB비용약위10미원，2014년표준），재해시간단내，부“개부호”일조선박약절약7500미원，매년능절약통신비36000미원，구유일정적경제효익。연구실천증명운무혼합계산유리우개선원양어선물련망계통통신질량。
The communication bandwidth from pelagic fishing vessel to shore was unable to cope with the increasing needs of smart devices and sensors on vessel. Fog computing and cloud computing were combined to solve this problem effectively in operating system of pelagic fishing vessel. Cloud computing could be used as the basic framework of system. Fog computing could be used as model in the local area network and sensor network on vessel. This mixed computing architectures could effectively play the advantages of each other and have a complementary effect. In research, cloud computing was taken as basic framework of operating system of pelagic fishing vessel, and service-oriented architecture service component was used to provide a reliable and safe data storage center, which could reduce the equipment requirements for client and share data and applications between different devices. There were two sources for service components. One part came from the encapsulation of original system function and was released through the network via the standards such as simple object access protocol, web services description language, universal description discovery and integration, et al. . It could effectively protect the investment. Other parts were new service components facing new requirements, which were packaged by extensible web service and supported open, dynamic interoperability model. The standard cloud computing framework increased the demand for network bandwidth. For the data volume of firmware update and message sent by sensors occupied larger proportion of communications from vessel to shore, this paper also studied equipment firmware distribution mechanism and shipboard sensor network computation model based on fog computing in order to reduce ship shore data traffic. With the aid of computing and storage capacity of shipboard equipment, the firmware distribution mechanism changed the traditional way that each device got update files from the cloud directly, pushed and updated the firmware between smart devices and sensors in local area network of vessel. This paper also studied shipboard sensor network computation model based on fog computing. For shore-based command center just was focused on changes or change tendency of message sent by sensors, the system could reduce the amount of data transmission by send filtered date only. Smart devices with computing and storage capacity could be used to select and calculate message data before they were sent to cloud by sensors in vessel, which reduced the amount of communication with high monitoring accuracy. Practice has proved that cloud-fog mixed computing architectures could not only ensure interactive integration of information from vessel to shore, but also significantly reduce the requirement for data communication bandwidth and the network traffic. During the practice period, from March 15 to May 31 in 2014, the data volume of firmware update file of smart devices and sensors was 250.905 MB, the actual firmware update communication flow was 37.175 MB. Firmware update data decreased to 14.81%of the traditional architecture. At the same time, the data volume of cumulative message produced by 27 temperature sensors was 571.63 MB, and the actual data volume of message sent from vessel to shore was 27.16 MB after selection and calculation, so message data volume reduced to 4.75%. In this period, about $7500 were saved. According to this calculation, each boat can save communication cost by$36 000 each year and the costs of communication were markedly reduced. The empirical research obtained obvious effect on a pelagic fishing vessel named Kaifu.
