The initial technology maturity curve for the development of the Internet of Things is based only on the increased number of deployed and potential sensors. Today, we can look to the future and explore some important success factors. The future trend of the Internet of Things includes the application of Internet of Things and will bring economic benefits to end customers. There is also a tendency for batteries to last longer and last for years. In any wireless Internet of Things monitoring system, data transmission consumes power. Therefore, sensing and processing occur at the edge nodes through intelligent partitioning, and the amount of data is reduced (in a more sporadic or shorter period) by local decision making, thereby bringing significant value-added to the Internet of Things system. Finally, the key element of the future is the ability to operate safely and reliably. Therefore, for a successful IoT system, the focus of IoT design will shift to key performance indicators such as trusted sensors and system uptime. Analysts predict that low-cost development systems are now in the peak of inflated expectations. I predict that the IoT platform will flood the mass market within a year. In the next two to five years, differentiated or specialized high-precision sensors and analog signal chains will become the mainstream, truly pushing the Internet of Things market to the future.
The importance of quality data <br> A key process in the IoT system is the conversion of analog signals to digital signals. Simply put, the better the conversion is done, the more useful the data will be. Silicon technology has revolutionized the conversion and interpretation of the world around it. It bridges the real and digital worlds through detection, measurement, interpretation and connection technologies.
Therefore, the first stage of any IoT system is to detect, measure, and then convert the real-time signal into analytical data. How this phase is completed will lay the foundation for success or failure. If you enter the wrong information data, the results from any Internet of Things analytics cloud platform will also be wrong. Therefore, the most successful IoT system must have measurement and reporting capabilities that cannot be achieved by other systems.
This need to improve measurement and reporting makes quality hardware essential. The recent Gartner report holds the same view. According to the report, the low-cost IoT development board is rapidly entering the trough of disintegration. This may be caused by the excessive number of low-cost development platforms available. However, I think this is more likely because we are more focused on more challenging IoT applications that are more practical and economical. Based on the data results that these applications rely on, rough measurements simply cannot be supported. IoT system nodes and cloud-partitioned cloud technologies support the use of multiple, extended signal chains, including analytics and big data. IoT applications mainly require high intelligence at the edge nodes. This is due to a variety of factors, including insufficient bandwidth to transmit all data to the cloud (or more precisely, data transmission rate limit for error-free transmission), or delays. The problem, which is the speed of the node's required action, means that the system cannot wait for a response from the cloud. Therefore, there are multiple control loops in the nodes, intermediate gateways, and clouds. The cloud can integrate data for a large number of sensors and adjust the edge settings based on these data. McKinsey believes that the actual use of cloud data is only 1%, in addition, the security threat also means that the data will have a local advantage.
Cloud computing derives this insight from the initial readings of a large number of sensors and correlates the various sensor readings based on time, location, and other sensors. This consists of two parts: the ability to detect changes in data (for example, drift in machine performance), and the ability to create a digital model of a software model of a real-world object (such as a motor) or system. These figures can be used to actively repair equipment or plan production processes. This is part of the outlook for the explosive growth of sensors in the coming years and the ability to monetize software and services.
In industrial automation, active machine monitoring can fundamentally improve uptime efficiency, achieve real-time optimization and intervention locally, and at the same time can integrate information across multiple systems across multiple plants in the cloud for analysis and response to increase productivity. .
Therefore, intelligent IoT system partitioning can ensure the effective use of the cloud.
Reliable data is key <br><br><br><br><br> The last part that is critical to the Internet of Things is creating a wireless network. Most networked objects use radio frequency and microwave frequencies to connect back to the cloud wirelessly. There are various operating modes. The operating range is from short to long and the data rate is from low to high. Some devices may not communicate for months or years, while others need to operate across a mission-critical security network. Many sensor nodes will also self-power through batteries or energy harvesters, so efficient operation will be the key. Communication networks are crucial for the transmission of intelligence from sensors to the cloud based on various requirements.
Figure 1a. Technology maturity curve superimposed with low-cost development board data points
Figure 1b. Three global wave of digitization
The importance of quality data <br> A key process in the IoT system is the conversion of analog signals to digital signals. Simply put, the better the conversion is done, the more useful the data will be. Silicon technology has revolutionized the conversion and interpretation of the world around it. It bridges the real and digital worlds through detection, measurement, interpretation and connection technologies.
Figure 2. From sensor to cloud
The most effective IoT deployment scenario is the ability to use this data to determine changes. And the best change is to bring the ultimate value to the end customer, such as higher efficiency or higher security - for example, in the factory, machine learning can not only identify when the future may need to predict the maintenance of the machine, but also The details can be identified to achieve a higher level of identification to determine what action needs to be taken (for example, to identify the wear of a particular ball bearing in a motor).Therefore, the first stage of any IoT system is to detect, measure, and then convert the real-time signal into analytical data. How this phase is completed will lay the foundation for success or failure. If you enter the wrong information data, the results from any Internet of Things analytics cloud platform will also be wrong. Therefore, the most successful IoT system must have measurement and reporting capabilities that cannot be achieved by other systems.
This need to improve measurement and reporting makes quality hardware essential. The recent Gartner report holds the same view. According to the report, the low-cost IoT development board is rapidly entering the trough of disintegration. This may be caused by the excessive number of low-cost development platforms available. However, I think this is more likely because we are more focused on more challenging IoT applications that are more practical and economical. Based on the data results that these applications rely on, rough measurements simply cannot be supported. IoT system nodes and cloud-partitioned cloud technologies support the use of multiple, extended signal chains, including analytics and big data. IoT applications mainly require high intelligence at the edge nodes. This is due to a variety of factors, including insufficient bandwidth to transmit all data to the cloud (or more precisely, data transmission rate limit for error-free transmission), or delays. The problem, which is the speed of the node's required action, means that the system cannot wait for a response from the cloud. Therefore, there are multiple control loops in the nodes, intermediate gateways, and clouds. The cloud can integrate data for a large number of sensors and adjust the edge settings based on these data. McKinsey believes that the actual use of cloud data is only 1%, in addition, the security threat also means that the data will have a local advantage.
Figure 3. Edge node intelligence, current and future
Intelligent partitioning and embedding algorithms implemented in the sensor can interpret the most critical data in real time at the source. Embedded smart sensors and cloud-based algorithms can make deeper data interpretation beyond silicon chips. In fact, this makes it possible to predict future system behavior. The ability to accelerate the adoption of IoT solutions in mission-critical applications depends on the ability to build a security system, and intelligent partitioning can be done.Cloud computing derives this insight from the initial readings of a large number of sensors and correlates the various sensor readings based on time, location, and other sensors. This consists of two parts: the ability to detect changes in data (for example, drift in machine performance), and the ability to create a digital model of a software model of a real-world object (such as a motor) or system. These figures can be used to actively repair equipment or plan production processes. This is part of the outlook for the explosive growth of sensors in the coming years and the ability to monetize software and services.
In industrial automation, active machine monitoring can fundamentally improve uptime efficiency, achieve real-time optimization and intervention locally, and at the same time can integrate information across multiple systems across multiple plants in the cloud for analysis and response to increase productivity. .
Therefore, intelligent IoT system partitioning can ensure the effective use of the cloud.
Reliable data is key <br><br><br><br><br> The last part that is critical to the Internet of Things is creating a wireless network. Most networked objects use radio frequency and microwave frequencies to connect back to the cloud wirelessly. There are various operating modes. The operating range is from short to long and the data rate is from low to high. Some devices may not communicate for months or years, while others need to operate across a mission-critical security network. Many sensor nodes will also self-power through batteries or energy harvesters, so efficient operation will be the key. Communication networks are crucial for the transmission of intelligence from sensors to the cloud based on various requirements.
Figure 4. Reliable IoT network
But reliable operation will be the most critical element for the successful implementation of IoT systems. All these different requirements will attach great importance to communication networks in order to realize sensor-to-cloud intelligence transmission. In harsh environments (such as those made of metal and concrete), reliable operation is particularly challenging. What customers need most is low-cost, low-power, low-latency technology. They also hope that the sensor layout can be expanded without restrictions. Creating a reliable network without relying on wireless protocols is to use alternative paths and channels to overcome interference and maintain this high reliability.Overmolding the Connectors offers significant opportunities for cable improvements with higher pull strength and waterproof issue for those parts, which without these characteristic by conventional types.Such as jst jwpf connector. Just be free to contact us if you need any wire-harness solutions or partner for your products. Our professional and experienced team would support you by satisfied skill and service.
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