That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through...That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.展开更多
The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after a...The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.展开更多
Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespre...Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.展开更多
[目的/意义]无人智慧农场是智慧农业的重要实践模式。本研究以山东德州“吨半粮”无人智慧农场为实验场所,攻克大田智慧农场建设中的核心技术难题,探索其建设模式与服务机制。[方法]运用物联网技术,研发了智慧农场的立体感知网络,能够...[目的/意义]无人智慧农场是智慧农业的重要实践模式。本研究以山东德州“吨半粮”无人智慧农场为实验场所,攻克大田智慧农场建设中的核心技术难题,探索其建设模式与服务机制。[方法]运用物联网技术,研发了智慧农场的立体感知网络,能够高效采集并汇聚传输环境、作物长势和设备状态等关键数据。借助数据分析挖掘技术,精准提取了小麦的物候期、麦穗特征等关键表型信息。进一步结合智能农机与智能决策技术,研发了集云管控平台、智能化设备及智能农机于一体的智能控制系统。此外,依托多源数据融合、分布式计算和地理信息系统(Geographic Information System, GIS)等技术,构建了农业生产全过程智能管控平台。[结果和讨论]“吨半粮”无人智慧农场感知系统不仅提高了数据传输质量,同时可以完成麦穗、物候期等表型特征的本地分析;智能控制系统可帮助农机提升自主作业精度和灌溉、施药效率、质量,通过农业设备的改造升级实现了农场耕作、种植、管理、收获的全链条智能化管控;大数据智慧服务平台为农户提供了气象预测、灾害预警、最佳播期等农事管理服务,极大地提高了农场管理的数字化、智能化水平。实验结果表明,自组网络数据准确率保持在85%以上,无人机施药可节药55%,灌溉模型可节水20%,“济南17”和“济麦44”分别增产10.18%和7%。[结论]研究结果可为智慧农场建设提供参考和借鉴。展开更多
文摘That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.
文摘The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.
文摘Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.
文摘[目的/意义]无人智慧农场是智慧农业的重要实践模式。本研究以山东德州“吨半粮”无人智慧农场为实验场所,攻克大田智慧农场建设中的核心技术难题,探索其建设模式与服务机制。[方法]运用物联网技术,研发了智慧农场的立体感知网络,能够高效采集并汇聚传输环境、作物长势和设备状态等关键数据。借助数据分析挖掘技术,精准提取了小麦的物候期、麦穗特征等关键表型信息。进一步结合智能农机与智能决策技术,研发了集云管控平台、智能化设备及智能农机于一体的智能控制系统。此外,依托多源数据融合、分布式计算和地理信息系统(Geographic Information System, GIS)等技术,构建了农业生产全过程智能管控平台。[结果和讨论]“吨半粮”无人智慧农场感知系统不仅提高了数据传输质量,同时可以完成麦穗、物候期等表型特征的本地分析;智能控制系统可帮助农机提升自主作业精度和灌溉、施药效率、质量,通过农业设备的改造升级实现了农场耕作、种植、管理、收获的全链条智能化管控;大数据智慧服务平台为农户提供了气象预测、灾害预警、最佳播期等农事管理服务,极大地提高了农场管理的数字化、智能化水平。实验结果表明,自组网络数据准确率保持在85%以上,无人机施药可节药55%,灌溉模型可节水20%,“济南17”和“济麦44”分别增产10.18%和7%。[结论]研究结果可为智慧农场建设提供参考和借鉴。
