The integration of artificial intelligence (AI) and the Internet of Things (IoT) in practical applications has formed AIoT (Intelligent Internet of Things), which is the intelligent interconnection of all things. At present, AIoT has become the mainstream form of future technology recognized by the industry. The “2020 China Intelligent Internet of Things (AIoT) White Paper” recently released by iResearch predicts that in 2025, there will be nearly 20 billion Internet of Things connections in China. fusion. On the other hand, smart IoT systems expose potential problems in cloud computing. Increased intelligence and automation inevitably lead to unpredictable delays in performance- and security-focused applications.
Today, two major challenges threaten the multiplying number of connected devices: the performance of edge devices used for telecommunications, and the battery life of off-grid IoT applications.
The transmission of raw data is very power-hungry for any device. Traditional cellular wide area networks (WANs) consume a lot of power, making them unsuitable for battery-powered IoT devices. IoT Applications LoRaWAN (Long Range, Wide Area Network) is one of the preferred communication protocols in IoT applications, addressing how artificial intelligence can transform IoT architectures through edge applications.
Why use LoRaWAN and edge AI?
With the proliferation of smart devices, both the core network domain and end devices face communication challenges such as congestion, security, service delays, data privacy and lack of interoperability.
For the domain, much of the challenge comes from over-reliance on cloud computing. When data is sent to the cloud, there is a greater amount of energy consumption, bandwidth, storage and latency, resulting in higher costs. Whereas fog computing or edge computing can reduce costs and increase efficiency.
When wireless technology is used for data transmission, communication barriers in end devices arise. In IoT, the advantage of Bluetooth and other wireless standard technologies is low power consumption, but limited coverage is a big obstacle, especially for smart city services. In this case, Low Power Wide Area Networks (LPWANs) have emerged as a reliable alternative between long-range cellular and short-range operating technologies.
LPWAN is a low-power and extended-range communication physical layer operating on the Sub-GHz unlicensed radio frequency band. LPWAN is a standard protocol effective at the link and network layers, offering variable data rates, increasing the possibility of trading throughput for link robustness, coverage, or energy consumption. Both organizational units and individuals can deploy LPWAN networks.
LPWAN and fog computing architectures close to the edge
Edge computing and fog computing look similar when it comes to intelligence and data processing. However, the main difference between them is where computing and intelligence take place.
The environment of fog computing puts intelligent processing on the local area network (LAN), transferring data from endpoints to gateways. Edge computing, on the other hand, puts processing power and intelligence in devices such as embedded automation controllers.
These devices can run algorithms that produce edge intelligence—a product of AI and edge computing.
Advantages of Leveraging LPWAN for Edge Computing
·Reduced data transfer: Edge computing reduces the amount of data transferred and cloud storage. Another advantage is that placing computing power at the edge of the network minimizes latency and cost while alleviating the need for bandwidth.
·Reduced Latency: Edge computing minimizes the time between data transmission, processing, and action based on insights gained from the process. In addition, the speed of analysis and event processing is increased at a lower cost, and the signal-to-noise ratio is reduced. Edge computing provides low-latency capabilities through real-time services due to its location closer to the end user, which reduces bandwidth and power consumption of the core network and connected devices, which is required for smart cities and vehicle-to-vehicle communications and other latency lower than Necessary for tens of millisecond applications. This is lower latency than mainstream cloud services.
·Enhanced security: Data security and privacy are considered top priorities by most users, mainly because these factors pose a security threat to smart city-related applications. Security must be broken down into three layers: user privacy, data security, and network connectivity. Edge computing addresses IoT security challenges through measures such as credential upgrades and security checks on multiple physical devices.
·Expanded applications: LPWAN and edge devices are ubiquitous in healthcare monitoring, such as detecting patient falls. Edge devices can improve accuracy and adaptability where data is sifted for real-time processing. In traditional systems, raw data sequences are transmitted in the cloud, so the latency of alerts increases. Edge systems reduce the computational effort on sensor nodes by shifting the heavy computational load from sensor nodes to edge gateways.
How to use edge artificial intelligence to accelerate application scenarios
While the model building and training phases of edge devices can consume significant resources and add additional complexity, there are high-quality options on the market that offer customization and reduced complexity.
Avnet’s SmartEdge Agile unit simplifies and dramatically reduces this complexity. SmartEdge Agile is an edge computing device equipped with various types of sensors. Brainium is used to build and train models. The unit has LPWAN connectivity to establish a fog computing architecture and uses a gateway to connect to Brainium. Avnet’s SmartEdge Industrial IoT Gateway securely and seamlessly connects Brainium and the cloud.
