亚洲这里只有精品,91av视频在线播放,中文字幕一区在线,精品国产日韩一区二区三区,欧美一区二区三区久久成人精品,国产在线无码视频,国产成人激情,国产成人精品无码一区二区三区免费,成人精品tv视频在线,国产精品无码一区二区夜夜

  • <del id="sweqc"><tfoot id="sweqc"></tfoot></del>
    
    
  • What's next for the Internet of things?

    2019-12-20 15:07:09 RFstar

    The initial technology maturity curve for iot development is only based on an increase in the number of deployed and potential sensors. Today, we can look to the future and explore some important success factors. Future trends in the Internet of things, including iot applications, will bring economic benefits to end users. There is also a trend towards longer battery life, lasting for years. In any wireless iot monitoring system, data transmission consumes power. Therefore, the perception and processing take place at the edge nodes through intelligent partitioning, and the amount of data is reduced (in a more sporadic or shorter period) through local decisions, thus bringing significant added value to the iot system. Finally, the key element of the future is the ability to operate safely and reliably. Therefore, for successful iot systems, the focus of iot design will shift to key performance indicators such as trusted sensors and system uptime. Analysts estimate that low  -  cost development systems are currently in the Peak of expectation inflation. In the next two to five years, differentiated or specialized high  -  precision sensors and analog signal chains will become the mainstream and truly push the Internet of things market into the future.

    3-20物聯(lián)網(wǎng) (5)
    3-20物聯(lián)網(wǎng) (6)

    >> importance of good data

    A key process in iot systems is the conversion of analog signals into digital signals. Simply put, the better the transformation, the more useful the data. Silicon technology innovates to transform and interpret the world around it, bridging the real and digital worlds through detection, measurement, interpretation, and connectivity.

    3-20物聯(lián)網(wǎng) (1)

    The most effective iot deployment is the ability to use this data to determine change. And best change is the biggest value for end customer, such as higher efficiency and higher security, such as in factories, machine learning is not only able to identify when may need to machines for predictive maintenance in the future, but also can identify the details and reach a higher level of recognition, to determine what action to take (for example, to identify specific ball bearing in motor wear).

    Therefore, the first stage of any iot system is to detect, measure, and then convert real-time signals into analytical data. How well this stage is completed will lay the foundation for future success. If the wrong information data is entered, the results obtained from any iot analysis cloud platform will also be wrong. Therefore, the most successful iot systems have to have measurement and reporting levels that other systems cannot.

    This need to improve measurement and reporting makes good hardware essential. A recent Gartner report said the same. Report that they are low cost iot development look fast into the bubble period of disillusionment (trough of disillusionment. This may be due to the plethora of low-cost development platforms available. But I think it's more likely that we're focusing on more challenging iot applications that have more real economic value. These applications rely on data results that rough measurements simply cannot support.

    >> partition between iot system nodes and cloud

    Cloud technology supports the adoption of extended multiple signal chains, including analytics and big data. Iot applications mainly in edge nodes achieve high intelligence -  this is the result of many factors, including the transmission of all data to the cloud bandwidth (or more precisely: error  -  free transmission of the data transmission rate limit), or delay problem, namely node required action speed means that the system can't waiting for the response returned from the cloud. Therefore, multiple control loops are required on nodes, intermediate gateways, and in the cloud. The cloud is able to consolidate data for a large number of sensors and adjust edge Settings based on that data. McKinsey reckons that only 1% of cloud data is actually used, and that security threats mean it is better to keep data local.

    3-20物聯(lián)網(wǎng) (2)

    The implementation of intelligent partitioning and embedding algorithm in the sensor can interpret the most critical data at the source in real time. Algorithms embedded in smart sensors and the cloud can read data deeper than silicon chips. In fact, this makes it possible to predict future system behavior. Accelerating the adoption of iot solutions in mission  -  critical applications depends on the ability to build secure systems, which smart partitioning can do.

    Cloud computing draws insights from this connection between a large number of preliminary sensor readings and correlates a variety of different sensor readings based on time, location, and other sensors. This consists of two parts: the ability to detect changes in data (for example, the drift of machine performance) and the ability to create a "digital twin" of a software model of a real object (such as a motor) or system. These digital twins can be used to proactively repair equipment or plan production processes. This is part of the outlook for explosive growth in sensors over the next few years, as well as 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 integrate information across multiple factories and systems in the cloud for analysis and response, thus improving productivity.

    So smart iot system partitioning can ensure effective utilization of the cloud.

    > > reliable data is key

    The final piece of what is crucial to the Internet of things is the creation of wireless networks. The vast majority of networked objects are wirelessly connected back to the cloud using radio and microwave frequencies. The operation mode is various, the operation 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 critical business security networks. Many sensor nodes are also powered by batteries or energy collectors, so efficient operation will be key. Communication networks are critical to the transmission of intelligence from sensors to the cloud on demand.

    3-20物聯(lián)網(wǎng) (3)

    But reliable operation will be the most critical element for the successful implementation of the iot system. All of these different requirements put a lot of emphasis on communication networks for sensor to cloud intelligence delivery. Reliable operational capability is particularly challenging in harsh environments, such as factories built of metal and concrete. What customers need most is low  -  cost, low  -  power, low  -  latency technology. They also want the sensor layout to expand unchecked. Creating a reliable network without relying on wireless protocols is to maintain this high reliability by using alternative paths and channels to overcome interference. Article source website, reprint indicate the source.



    91成人精品一区二区在线 | 精品中文字幕一区二区三区四区| 久久三级视频| AV无码成人片在线观看免费| 国产成人精品午夜| 99九九精品视频| 久久手机免费视频| 高潮videossexohd潮喷| 欧美极品少妇×xxxbbb| 丰满无码人妻热妇无码区gay| 亚洲AV优女天堂熟女| 久久综合给合综合久久| 秋霞无码AV一区二区三区| 成年免费A级毛片免费看丶| 日本伦理电影在线观看| 日韩精品无码专区免费播放| TV国产亚洲AV麻豆| 55大东北熟女啪啪嗷嗷叫| 欧美日韩国产va另类| 色综合久久久久久久久久| 无码福利日韩神码福利片| 最近中文字幕mv免费高清在线 | 久久婷婷综合色丁香五月| 国产午夜视频在线观看| 韩日美无码精品无码| 亚洲色无码国产精品网站可下载| 无码av网址| 高潮久久无码精品亚洲日韩| 无码熟妇人妻av在线网站| 里番工口侵犯肉全彩无码| 女人18毛片水真多免费视频| 亚洲AV无码国产精品永久一区| 无码国产精品一区二区免费vr| 精品亚洲一区二区三区在线观看| 亚洲老妈激情一区二区三区| 亚洲av无码成人精品区狼人影院| 亚洲日韩av无码一区二区三区人| 99v久久综合狠狠综合久久| 国产免费久久精品| 日本精品福利| 18禁无遮挡无码国产免费网站 |