How LoRaWAN and AI at the Edge Revolutionize the IoT
Smarter Internet of Things (IoT) systems have exposed fault lines in cloud-based computing. The inevitable rise of intelligence and automation has led to unpredicted latency in applications where performance and safety issues hold prominence.
Now, two key challenges threaten the exponential growth of connected devices: the capability of edge devices for long-range communication, and battery life catering to off-grid IoT applications.
Raw data transmissions are power gluttons for any device. Conventional cellular wide area networks (WAN) consume power in massive amounts and thus incompatible with battery-run IoT devices. The IoT application LoRaWAN (low range, wide area network) is one of the preferred communication protocols in IoT applications capable of addressing how artificial intelligence (AI) will transform IoT architectures through edge applications.
Why LoRaWAN and AI at the edge?
As smart devices proliferate, both core network domains and end devices encounter communication challenges like congestion, security, service delay, data privacy and the absence of interoperability.
For network domains, the bulk of the challenges stem from leaning disproportionately on cloud computing. Greater energy consumption, bandwidth, storage, and latency lead to higher costs when data is sent to the cloud. Fog or edge computing can reduce costs and improve efficiency.
Communication roadblocks in end devices surface when equipment funnels data using wireless technologies. An advantage of Bluetooth and other standards in IoT is low power consumption, but their limited coverage can be an obstacle — especially for smart city services, for example. In cases like this, low-power wide-area networks (LPWANs) present a promising alternative between long-range cellular-based and short-range operating technologies.
LPWAN describes a physical layer for low power and more extended range communication, working on sub-gigahertz unlicensed radio bands. The LPWAN is standard protocol valid for a link and network layers. It offers variable data rates, stoking the possibility to exchange throughput for link robustness, coverage range, or energy consumption. Both organizations and individuals can deploy LPWAN networks.
LPWAN and edge form fog computing architecture
In terms of intelligence and processing, edge computing and fog computing may appear similar. However, the key difference between them is the location of computing power and intelligence.
A fog environment places intelligence at the local area network (LAN), where the architecture transmits data from the endpoints to a gateway. Edge computing, on the other hand, places processing power and intelligence in devices like embedded automation controllers.
These devices can run algorithms, yielding edge intelligence — the product of AI and edge computing.
Benefits of edge computing using LPWAN
Reduced data transmission: Edge computing reduces the amount of transmitted data and cloud storage. Another advantage: Piping computing power to the edge of the network minimizes latency and cost while easing the demand for bandwidth.
Reduced latency: Moreover, edge computing minimizes the lag between data transmission, processing and taking an action based on insights drawn during the process. Analysis and event processing accelerates with reduced cost. The signal to noise ratio is lessened. Because user proximity reduces bandwidth and power intake of core networks and connecting devices, edge computing provides low latency capabilities with real-time services — a must for smart-city applications. Vehicle-to-vehicle communications and other applications need latency below tens of milliseconds — lower than mainstream cloud services can provide.
Improved security options: Most users cite data security and privacy as principal concerns, mainly because these factors pose security threats to bigger smart city applications. Security must be shored up in three layers: user privacy, data security, and network connection. Edge computing solves IoT security challenges like up-gradation of credentials and security checks on multiple physical devices.
Expanded applications: LPWAN and edge devices are omnipresent in healthcare monitoring, such as for detecting patient falls. In cases where the data is screened for real-time processing, edge devices can improve accuracy and adaptability. Alert latency rises in a traditional system — as opposed to an edge system — where raw data sequences are transmitted in the cloud. Edge systems reduce the computational load on sensor nodes by switching the heavy computational jobs from the sensor nodes to the edge gateways.
How to leverage edge AI in your application
While the model-building and training phases for edge devices require substantial consumption of resources, which leads to additional complexity, there are high-quality options that offer both customization and reduced complexity.
Avnet’s SmartEdge Agile device can streamline and substantially reduce such complexity. Since SmartEdge Agile is a proprietary edge computing device with different sensor pimples. Brainium portal is used to build and train models. The device has LPWAN connectivity to create fog architecture and uses a gateway to connect to Brainium. The Avnet SmartEdge Industrial IoT Gateway allows secure and seamless connectivity to Brainium and the cloud.