The essence of the Internet of Things is the Internet, but the terminal is no longer a computer (PC, server), but an embedded computer system and its supporting sensors. This is the inevitable result of the development of computer technology. The computers that serve humans present various forms, such as wearable devices, environmental monitoring devices, virtual reality devices, and so on. As long as there is hardware or product connected to the Internet, data interaction occurs, called the Internet of Things.
From intelligent thermostats to fitness trackers, IoT devices are commonplace in people's daily lives. These networked devices collect, process and share data around the physical world around people to help people's lives easier and better.
Similarly, many companies are using the Internet of Things to use data to better understand their operations, make more informed decisions, reposition customer engagement, and rethink how value is created. For example, a supply chain management company deploys sensors in its trays, boxes, and containers to track environmental variables such as the geographic location of the cargo, ambient temperature, and pressure. This translates the value that the company provides to its customers, from leasing pallets to optimizing supply chain costs, which will be the data to know the remaining shelf life of the shipment.
With the rapid development of low-cost sensors, elastic computing and data science, many industry observers expect companies to rapidly deploy IoT devices. In fact, according to research firm Gartner, companies will install about 4.1 billion IoT devices in 2018, and eventually reach 7.5 billion by 2020.
Experts expect that during this period, all of these developments will generate approximately 44 trillion megabytes of additional IoT data worldwide. This brings people to the core question: Which best technology architecture is used to address this explosive growth in data trends? There are three broad options here: local deployment, cloud computing, or hybrid architecture. The answer is always dependent on usage.
Locally deployed IoT architectureThe locally deployed IoT architecture uses edge computing, where data is processed at the edge of the network, which is closest to the data source. According to research firm IDC, by 2019, 45% of IoT device data will be stored, processed, and calculated close to the edge. This model provides a smaller performance footprint and helps organizations respond more to real-time data. For example, on oil rigs, sensors can be used to detect if a faulty valve creates a fire hazard. In this case, the company cannot afford any delay. If the data needs to be sent to the satellite, the response time may be too late before the data center returns to the notification to close the valve. However, with faster edge deployment, data does not have to be far from its data source. This reduces time delays and allows for critical decisions.
In addition, locally deployed architectures do not rely on Internet connectivity, such as cloud environments. And the locally deployed architecture is also favored by companies facing serious data security issues. The local architecture that uses edge computing has many implications.
Cloud IoT ArchitectureThe cloud IoT architecture facilitates the organization and management of a large number of connected devices, driving value through a combination of internal and external data. For example, a supply chain application can benefit from understanding a particular view of a portion relative to the overall aggregated view. Only one set of data outside the full view is meaningless. For example, by using a locally deployed architecture alone, it is impossible to attempt to coordinate the supply chain for each component of the asset build.
In addition, cloud computing architectures provide greater interoperability in integration and interaction with other IoT devices and cloud systems. This model provides more architectural flexibility and utilization of external data sources. Cloud applications see more innovation in the ecosystem, in part because software developers focus on large markets. IoT deployments that leverage cloud computing architectures can be more effective because many products with technological innovation and competitiveness are already available. In essence, cloud computing architecture enables organizations to face future return on investment.
Mixed IoT architectureOften the best approach is to efficiently combine the processing of large core datasets for edge calculations and then process a simplified set of aggregated derived data at the core. For example, smart city-deployed parking sensors can process data for all sensors in close proximity to the parking space, providing only aggregated data on the locations and quantities of different garages open, providing drivers who enter the city with the opportunity to intelligently find parking spaces. After all, the cost of transmitting all of this data every few seconds can be expensive, and drivers close to the destination do not necessarily know that those locations in the parking lot are open. In this case, a hybrid architecture is the ideal choice.
Another example of asset optimization is the application of wind turbines, which use sensors to locally collect and analyze data on each turbine and optimize overall performance. Here, a number of data points provide insight into the health of turbine components. The health of each component is aggregated to provide a view of the condition of the wind turbine. Finally, data from all wind turbines is aggregated to provide operational information for the wind farm. In this case, how much data should be processed at the edge of the network and which data is processed in the data center is an important consideration. The real-time response of the on-premise architecture and the combination of system-wide access and scalability for cloud computing will be best served.
Consider business needsUltimately, design considerations can provide an informed choice for the data and processing architecture of an IoT system. To determine what IoT architecture is best for, check out the organization's current and planned equipment, business goals and scenarios, related processes, and the scope of the planned results. Evaluate these business needs with technical considerations for scalability, performance, bandwidth economy, and technology innovation rates.
With the increasing popularity of IoT devices in work and life, companies must consider not only the business model and deployment plan, but also the system architecture to achieve the promise of the IoT in their business.
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