A I The companion of the edge gateway — Wireless sensor

I. Functions of the AI Edge Gateway

1. Cloud Computing Capabilities

In recent years, the number of IoT device connections has shown a steady linear growth trend. According to Gartner’s forecast, by 2020, the global count of IoT devices will exceed 20 billion. At the same time, the devices themselves are becoming increasingly intelligent.

The practical implementation and integration of artificial intelligence and the Internet of Things will usher human society into an era of “intelligent connectivity of all things,” while the data generated in its wake will experience explosive growth.

Over the past decade, cloud computing has successfully addressed the growing challenges of storing and managing ever‑increasing volumes of data. However, the growth rate of network bandwidth currently lags far behind the pace of data expansion, and the decline in network‑bandwidth costs is much slower than that of hardware resources such as CPUs and memory. Meanwhile, complex network environments make it difficult to achieve breakthrough improvements in latency. As a result, traditional cloud computing can no longer meet the stringent demands for low latency and high security. Take autonomous vehicles as an example: a car traveling at high speed must respond within milliseconds; if system response times are prolonged due to issues like data transmission or network congestion, the consequences could be severe.

In addition, cloud computing faces the challenge of insufficient bandwidth. If all the massive data generated by edge devices were transmitted to a cloud computing center, it would place enormous strain on network capacity. For example, a Boeing 787 generates more than 5 GB of data per second, yet the bandwidth between the aircraft and satellites is inadequate to support real-time transmission. In short, relying solely on cloud computing’s centralized processing model is insufficient to support IoT‑driven applications and handle the resulting deluge of data. Against this backdrop, edge computing has emerged. By integrating with the existing centralized cloud‑computing paradigm, it effectively addresses the challenges of big‑data processing at both the cloud center and the network edge.

2. Edge Computing Capabilities

If cloud computing is centralized big‑data processing performed “in the cloud,” then edge computing can be understood as big‑data processing at the edge, close to the endpoint—such as a smartphone or an intelligent voice‑interaction device. In many scenarios, edge computing and cloud computing exist in a symbiotic relationship. An apt analogy likens cloud and edge computing to the various “organs” of an octopus, making the concept easier to grasp. As the most intelligent invertebrate in nature, the octopus possesses “conceptual thinking,” which is inseparable from its two powerful memory systems: one is the brain, with 500 million neurons, and the other is the suction cups on its eight arms. In other words, the octopus’s eight limbs can think and solve problems. Cloud computing is like the octopus’s brain, while edge computing resembles those smaller arms—each arm functioning as a miniature data center, situated close to the physical devices themselves. Edge computing is thus closer to the device side and nearer to the end user.

3. AI Edge Gateway Functionality

AI (artificial intelligence) edge gateway, embedded with multiple AI algorithms, performs intelligent analysis on incoming video streams and triggers corresponding actions based on the analysis results. AI algorithms are continuously advancing, yet most of them still face certain accuracy challenges.

II. AI Integration of Edge Gateways with Our Company’s Products

Sensor Data Acquisition

1. Sensor

A sensor (English term: transducer/sensor) is a detection device that can sense the information of the measured quantity and convert it into an electrical signal or other required forms of output according to a specific rule, thereby meeting the requirements for information transmission, processing, storage, display, recording, and control.

The existence and advancement of sensors have endowed objects with sensory capabilities such as touch, taste, and smell, bringing them to life; sensors serve as an extension of the human senses. Characterized by miniaturization, digitization, intelligence, multifunctionality, systematization, and networking, sensors constitute the essential first step in achieving automated sensing and automatic control.

2. Wireless Sensor Acquisition System (LoRa/LoRaWAN Sensor Gateway)

There is a wide variety of sensors, and numerous manufacturers are engaged in their research, development, and production; moreover, no universal standard exists among them. In response to this need, our company has developed multiple wireless gateways and wireless data collectors to acquire data from various sensors, while also introducing a range of wireless sensors tailored to these specific applications.

Our company’s LoRa/LoRaWAN sensor gateway can, internally, collect data from LoRa/LoRaWAN‑compatible sensors. It can also interface with wired‑signal‑to‑LoRa/LoRaWAN converters to gather data from third‑party sensors, converting the wired signals into LoRa/LoRaWAN‑based transmissions and forwarding them to the gateway.

The LoRa/LoRaWAN sensor gateway supports RJ45 and 4G interfaces, enabling connectivity via MQTT to edge gateways or cloud platforms. It forwards received alarm messages to the edge gateway using the MQTT protocol.

3. Application Scenarios of AI Edge Gateways

Based on current industry applications, AI edge gateways are deployed in sectors such as campus security, scenic‑area safety, hospital security, construction‑site worker‑behavior monitoring, and fire‑detection systems.

AI (artificial intelligence) employs AI algorithms to extract and analyze key information from video footage. In many applications, insufficient training data leads to limited accuracy.

4. Integration of AI Edge Gateways with Wireless Sensors

Sensors can be integrated with AI, enhancing AI’s recognition accuracy, reducing false alarm rates, and alleviating the computational load on AI edge gateways. Below is a block diagram illustrating the integration of a sensor with an AI edge gateway:

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5. Some application scenarios combining AI edge gateways with wireless sensors

1. Fire protection field

AI is widely applied in the fire‑fighting sector, using video analytics to detect flames, identify smoke, and recognize behaviors that may lead to fires. Before a fire breaks out, several warning signs typically emerge: rising object temperatures, the presence of unusual odors, and an increase in airborne particulates. Data from these sensors is transmitted via gateways to an edge gateway, which employs AI algorithms to determine whether a fire has occurred and, if so, triggers an alarm.

 

2. Environmental protection field

AI is widely applied in environmental protection, including monitoring air pollution, detecting water quality contamination, and identifying whether enterprises are engaging in illegal or accidental discharges. By integrating AI-powered image analysis with sensors, the accuracy of alarm systems is significantly enhanced.

3. Elevator Safety Field

When the elevator sensor detects an anomaly, the Zhi AI edge box can determine whether there is anyone inside the elevator.

That concludes our overview of the integrated applications of AI edge gateways and sensors. For more information on AI edge gateways, please stay tuned. Shenzhen Hengyi Technology Co., Ltd.

Shenzhen Hengyi Technology Co., Ltd. is an IoT solutions provider that integrates modules, devices, software, and end-to-end solutions. Tailored to the unique characteristics of various industry segments, the company develops customized industry-specific solutions and offers customers a one-stop service encompassing needs analysis, smart hardware, software systems, and data analytics. By doing so, it helps enterprises forge new business models in the IoT era and drive the digital transformation of their industries.