Ryan introduces the data communication paradigms for low-power IoT wireless networks, from data generation and transmission models between interconnected IoT devices, network devices, and services. Ryan explains the processing paradigms of the control data and sensing data in the IoT networks, as well as the data flow in the example edge and cloud computing scenarios.
- [Instructor] We have introduced a generic IoT system which includes some key elements as network building blocks such as end devices, local gateways, edge routers and other network appliances at edge, core routers, cloud gateways, cloud services, users, etc. However, we need to see how the data communication happen. Following some general paradigms in the three essential tiers of an IoT system over which we usually have controllability.
They are local IoT network, network edge, and the cloud. A local IoT network typically consists of multiple low powered end devices. The IoT network essentially may work as a closed assistant. If there's no need for data transmission to an external network then those can deployed in different networked portages. The objective of the data transmissions on a local IoT network is to ensure the data can be transmitted to the field gateway or bay station or another sensor node.
If we look them more closely from the data generation and the communication perspective we can see that each end device is a source of machine data. End devices can also generate peak sensing data over time. For example, and end device deployed for a manufacturing operations can generate massive amounts of data over time. Each device may also need to handle control data for the management diagnosis and maintenance purposes.
These data traffic needs to be reliably transferred. In the local low-power wireless network end devices can choose to make local processing of the data or they can exchange data and process them collaboratively or they can offer to transmit data directly to a remote cloud service or application server for further processing. When it comes to the data transmissions and the logic stream of the network we can use the concept of edge or fog computing where fog computing coined by Cisco is considered as a new paradigm of IoT network.
It is defined by Cisco as a paradigm that extends cloud computing and services to the edge of the network. In the context of IoT systems the network edge means a set of edge routers and the network appliances as well as some parts of local gateways. As gateways that connect to end devices need to interact with edge routers in terms of data transmissions. Usually, the edge appliances will handle the data from the local IoT network and transfer the data to the backbone network, but processing data at edge can bring some advantages for an IoT system.
For example, it can reduce the data traffic on the backbone network and at the cloud and therefore offload the computing tasks at the cloud and the reduce in processing time consumed on these adamants so they can optimize data traffic for IoT. Let's see a simple example of IoT application where there are hundreds of sensor nodes collecting the environmental sensing data for every millisecond. However, the user only wants to know the average temperature for the last 24 hours.
In this case, we can choose to transmit the data directly to the cloud or we can let edge routers to make the decision intelligently. So it's obvious that if you choose to process the data at edge of the network we don't have to report all the data to the cloud data storage service and therefore save the bandwidth of the data transmissions on the backbone communication network. In addition there are many things we can do at edge following this fog computing concept.
For example, we can implement intelligent decision making process and also make intelligent configurations over time. Nowadays most of IoT applications use cloud services. From the data communication perspective these cloud services can be considered as the receiver of the machine data where the sender can be an end device or a gateway or an edge router. While a cloud service can also send data to the end devices as well.
How do you interact with a cloud service is very application specific.
Ryan Hu begins by introducing wireless networking for IoT, and going over the basics of wireless communication and wireless networking. Next, he presents and compares the underlying wireless networking technologies in terms of system architectures, communication paradigms, performance, and use cases. Then, he explains how to integrate various networking technologies into an IoT system. To wrap up, Ryan discusses the use of low-power wireless networking in a typical lighting control system for both smart home and smart city environments.
- Low-power wireless networking use cases
- Advantages and disadvantages of wireless networking for IoT applications
- Wireless: Communication, networking, topologies, and architectures
- Determining the power consumption on a wireless network for IoT
- Identifying security risks
- Addressing risks in the key architectural elements
- Reviewing low-power IoT data communication paradigms