探索Azure IoT Edge：云端智能计算的延伸

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" Beginning Azure IoT Edge Computing - 一本由 David Jensen 撰写的书籍，深入探讨了如何利用微软 Azure 平台进行物联网边缘计算的技术。本书旨在将云计算扩展到智能边缘，帮助读者理解并应用 Azure IoT Edge 实现物联网解决方案。" 在物联网(IoT)领域，边缘计算已经成为一个重要的概念，它允许数据在靠近生成源的地方处理，而不是全部发送到云端。Azure IoT Edge 是微软提供的一项服务，旨在将云计算的功能扩展到设备边缘，以实现更高效、低延迟的数据处理和分析。 **边缘计算的核心概念** 1. **本地处理**：边缘计算允许数据在设备或本地网关上进行处理，减少了对云端带宽的需求，降低了延迟，对于实时决策和响应至关重要。 2. **安全性**：由于数据在边缘处理，可以减少敏感信息传输到云端的风险，提高了数据的安全性。 3. **离线操作**：在连接不稳定或无互联网的情况下，边缘计算能确保设备继续运行，执行预定义的任务。 4. **效率提升**：通过在边缘处理部分数据，可以减少云端的工作负载，提高整体系统效率。 **Azure IoT Edge服务** 1. **模块部署**：Azure IoT Edge 支持创建可部署到边缘设备的自定义或预建模块，如 Azure Functions、容器化的应用程序或服务。 2. **管理与监控**：通过 Azure IoT Hub，用户可以远程管理和监控 IoT Edge 设备及其部署的模块，确保设备状态正常并及时更新。 3. **集成云服务**：Azure IoT Edge 可以无缝集成 Azure 云服务，如 Azure Stream Analytics、Machine Learning 等，将云端的强大分析能力带到边缘。 4. **跨平台支持**：支持多种操作系统和硬件架构，如 Windows、Linux，适应不同类型的物联网设备。 **物联网开发中的 Azure IoT Edge** 1. **开发环境**：开发者可以使用 Azure IoT Edge SDK 和工具（如 Visual Studio Code 扩展）进行模块开发和调试。 2. **安全框架**：Azure IoT Edge 提供了安全框架，包括设备身份验证、模块间通信加密，以及对固件和软件更新的保护。 3. **实时分析**：结合 IoT Edge 上的流处理服务，可以实现实时数据洞察，用于预测维护、异常检测等场景。 4. **AI on the Edge**：Azure IoT Edge 支持在边缘设备上部署机器学习模型，实现本地化的智能分析。 **应用案例** 1. 工业自动化：在工厂环境中，IoT Edge 可以用于实时监测设备状态，预测故障，优化生产流程。 2. 智能城市：监控摄像头通过边缘计算分析交通流量，快速识别异常事件，提高公共安全。 3. 零售业：利用边缘计算实现个性化推荐，通过分析顾客行为提供定制体验。 4. 农业：监测农田条件，实时调整灌溉和施肥策略，提高作物产量。 "Beginning Azure IoT Edge Computing" 这本书深入浅出地介绍了如何利用 Azure 平台构建和管理物联网边缘计算解决方案，是开发者和企业了解和应用 IoT Edge 的宝贵资源。通过学习 Azure IoT Edge，读者能够构建更高效、安全且具有智能分析能力的物联网系统。

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D. Jensen, Beginning Azure IoT Edge Computing, https://doi.org/10.1007/978-1-4842-4536-1_1

CHAPTER 1

Do INeed anIntelligent

Edge?

Businesses today are faced with an unparalleled number of choices and technology

decisions as they try to intelligently evolve their systems. There are decisions about if,

how, and when they should virtualize their datacenter, or if they should just continue to

leverage the investment in their current physical assets. If they virtualize, should they use

their own data center or a cloud provider, or a hybrid of both? If they decide to leverage

a cloud platform, which of the hundreds of available service options should they use?

How do these services fit together? What is the most cost-efficient option? The decision

points and options can be very overwhelming. Cloud platforms have created a culture

where the question is not “Is that possible?” but rather “Is that the most efficient way to

do that?” In this environment, where so many tools and advanced capabilities are a click

away, being innovative is not about having the capabilities but about correctly applying

the capabilities to most accurately match the business need. It can be quite confusing

if the right decision factors are not identified. Busyness does not equal productivity. In

technical terms, having a fault-tolerant, highly available, cost-optimized, cloud-native

architecture is still the wrong architecture if it does not correctly meet the needs of the

business.

In this chapter, we will discuss the aspects of edge computing that need to be

considered when you are determining whether it satisfies the needs of your business and

discuss a few real-world scenarios to examine an architecture that properly includes an

intelligent edge.

Edge Computing

Much like the technology options mentioned earlier, edge computing can be

inaccurately identified as the solution when the needs of the business are poorly

defined, or the benefits of the intelligent edge pattern are poorly understood. Edge

computing must be correctly applied to reap the benefits. In fact, it may even be more

susceptible to causing additional problems when it is misapplied. In order to avoid this

pitfall, edge computing must be correctly defined and understood. So, what is edge

computing? The term edge is a relative term. It is helpful (but not complete) to think of it

as “not centralized” computing. The centralized computing model equates to computing

in one (central) location, whether that is in the cloud or in your own datacenter. So,

one aspect of edge computing is that it is much different than the centralized approach

where all data is pushed to a single ingest or storage endpoint. Edge computing is very

decentralized.

But, what nuance about the word “edge” makes this an accurate description? “Edge”

implies that something is located as close as possible next to a reference point. Think

about the two worlds we operate in– one world is our physical world and the second

is the digital world. The physical world is the natural world we live in that contains

everything we can observe with our five senses. The digital world is a world in which

we are merely visitors. We can’t touch, feel, or smell data or digital signals. And for the

longest time, the digital world was kept very separated from our physical world. It lived

in a far-off land known as the Data Center. Then it moved, seemingly, even farther away

to a further-off land known as the Cloud.

However, now, the digital world is moving closer. It’s becoming very integrated

into our physical world– so integrated in fact that the lines between the two worlds are

becoming blurred. The things in our physical world have become the gateway through

which we encounter the digital world. We used to go to specified locations to access

the digital world. But now, we encounter the digital world continuously. This border of

hybrid things that are part physical and part digital is the edge that is referred to in the

term edge computing or intelligent edge.

CHAPTER 1 DO INEED ANINTELLIGENT EDGE?

understand what concerns to address when discussing edge computing with your

organization’s decision-makers who may not be ready (yet) to make the necessary

changes to their practices and policies.

Security

Security is probably the most common objection whenever a change in computing

patterns begins to emerge. Rightfully so. If any new approach is going to endure, it must

be secure. The IoT pattern and the related security risks have been scrutinized over the

past several years and have been discussed at length. So, I will not repeat that discussion

here, but I would like to list some of the most common concerns relating to the IoT Edge

pattern to establish a baseline understanding of the objections.

Direct Access

Direct access to the device is one of the most commonly stated concerns about any

IoT solution. The concern stems from the fact IoT devices are not deployed to a secure

location like a data center. Rather, they are deployed to unsecured, remote locations that

are susceptible to various forms of tampering. Tampering can include but is not limited to:

• Device manipulation: Someone holds a lighter up to a heat sensor to

set off an alarm

• Device rmware hacking: Someone connects to the device and

accesses the rmware to modify the code or replace the code

altogether

• Device secret hacking: Someone gains access to security secrets like

access tokens, certificates, device identity keys and more

Once the security layer of restricted physical access is removed, other security

mechanisms must be leveraged that address the additional security risks introduced.

Untrusted Execution

Another common security concern is the inability to trust the code that is running on

the device. It’s the same issue as the device firmware hacking concern listed above.

Essentially, the problem statement is: how can I guarantee that the code I provisioned

to a device is the same code that is running now? How do I know that someone has not

CHAPTER 1 DO INEED ANINTELLIGENT EDGE?

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探索Azure IoT Edge：云端智能计算的延伸

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