Iot software development services: Cloud and edge collaboration to create an intelligent iot ecosystem

Service Overview

The core of the intelligence of the Internet of Things lies in the efficient processing of data and the flexible response of business logic. We offer full-stack iot software development services, focusing on the deep collaboration between the edge and the cloud. We optimize real-time performance and reduce bandwidth costs through edge computing, and combine the cloud platform to achieve device management, data storage and intelligent analysis. Ultimately, we provide users with an intuitive operation interface through mobile /Web applications. Covering the entire process from data processing at the device end to cloud-based decision-making and user interaction, it helps enterprises quickly build efficient, stable and scalable Internet of Things systems.

Core service module

1. Edge software development: Emphasizing both lightweight and real-time performance

· Data preprocessing: Enhance transmission efficiency and reduce cloud load

· Data cleaning: Filter out invalid data collected at the device end (such as abnormal sensor readings and duplicate data) to ensure data quality. For instance, in industrial temperature monitoring, threshold filtering is used to eliminate abnormal values that exceed a reasonable range (such as -50℃ to 200℃) to prevent interference with subsequent analysis.

· Data filtering: Algorithms such as mean filtering and median filtering are adopted to smooth out noisy data and enhance data stability. For instance, in the heart rate monitoring of smart wearable devices, the sliding window mean filtering is used to eliminate movement interference and output a more accurate heart rate value.

· Data compression: Use lightweight compression algorithms (such as LZ4, Huffman coding) to reduce data volume and lower transmission bandwidth occupation. For instance, in video surveillance scenarios, compressing video streams through H.264 video encoding can reduce bandwidth usage by over 80%.

· Local decision-making: Deploy lightweight AI to achieve millisecond-level response

· TinyML model deployment: Quantify the trained AI models (such as speech recognition, image classification, and anomaly detection) into lightweight formats (such as TFLite, ONNX Runtime) and deploy them to edge devices (such as MCU, SoC). For instance, when a local voice wake-up model is deployed in a smart speaker, the device can respond within 100ms after the user utters the wake-up word, without relying on the cloud.

· Real-time rule engine: Triggers local actions (such as starting the fan or closing the valve) based on preset conditions (such as "temperature 50℃ and humidity 80%"). For instance, in industrial ovens, when the temperature sensor detects that the temperature exceeds the limit, the edge device immediately cuts off the heating power supply to prevent damage to the equipment.

· Protocol conversion: Unify data formats and simplify cloud processing

· Multi-protocol adaptation: Supports the conversion of sensor original protocols (such as Modbus, CAN bus) to common formats (such as JSON, Protobuf), facilitating unified parsing in the cloud. For instance, the motor speed data collected by the industrial PLC via Modbus is converted into JSON format and uploaded to the cloud platform.

· Data standardization: Define unified data fields (such as device ID, timestamp, numerical type) to ensure consistent data structures across different devices. For instance, all the data uploaded by the temperature and humidity sensors contain fields such as "device_id", "timestamp", "temperature" and "humidity", which facilitate cloud storage and query.

2. Cloud platform development: Centralized management and intelligent analysis

· Equipment management: Full life cycle control to ensure stable operation of equipment

· Device registration and authentication: Register devices through unique identifiers (such as MAC addresses, IMEI), support certificate (X.509) or Token authentication, and prevent illegal devices from accessing. For instance, smart door locks need to pass cloud authentication before they can be connected to the home network to ensure security.

· Status monitoring: Real-time display of key indicators such as the online status of the device, signal strength, battery power, etc., and support abnormal alarms (such as device offline, low battery). For instance, in a logistics tracking system, the cloud platform can display the real-time location of goods and the status of equipment. If the equipment is offline for more than one hour, an alarm will be automatically triggered.

· Firmware update (OTA) : Supports remote push of firmware updates to fix vulnerabilities or add new features. For instance, smart camera manufacturers can upgrade the facial recognition algorithm for all devices via OTA without the need for manual operation by users.

· Data storage: Efficient storage and rapid query, supporting business analysis

· Time Series Database (TSDB) : It uses InfluxDB, TimescaleDB, etc. to store sensor time series data (such as temperature and humidity changes over time), supporting high concurrent writes and fast aggregated queries. For instance, in energy monitoring, TSDB can store power consumption data and quickly calculate the total electricity consumption within a certain period of time.

· Relational Database Management System (RDBMS) : It uses devices such as MySQL and PostgreSQL to store device metadata (such as device model, installation location, and user information), and supports complex relational queries. For example, query "All temperature and humidity sensors installed in the Beijing area and their affiliated users".

· Rule engine: Automate business logic and reduce manual intervention

· Conditional trigger action: Trigger preset actions (such as sending alarm notifications or starting the device) based on device data or time conditions (such as "9:00 every Monday" or "Temperature 30℃"). For instance, in an intelligent irrigation system, when the soil moisture drops below the threshold, the rule engine automatically triggers the water pump to start.

· Multi-level rule chain: Supports complex rule combinations (such as "detect temperature first, then determine humidity, and finally control the device"), achieving refined business logic. For instance, in air conditioning control, the rule engine can first determine whether the indoor temperature exceeds the standard, and then decide whether to activate the dehumidification mode in combination with the humidity.

· API openness: Build an ecosystem and support third-party integration

· RESTful API: It provides a standard HTTP interface for third-party applications (such as enterprise ERP systems and mobile apps) to call device data or control devices. For instance, logistics companies can obtain the location data of goods through apis and integrate it into their own management systems.

· WebSocket real-time push: Supports real-time push of device data to the client (such as web-based dashboards), achieving low-latency monitoring. For instance, in financial transaction monitoring, WebSocket can push transaction data to the risk control system in real time to promptly detect abnormal transactions.

3. Mobile /Web application development: Intuitive interaction, enhancing user experience

· User interface: Visual display, clear at a glance

· Dashboard development: Display the trends and distribution of device data through charts (such as line charts, bar charts, heat maps), and support multi-device comparison. For instance, in an environmental monitoring system, the dashboard can simultaneously display the temperature, humidity and PM2.5 data of multiple stations, facilitating users' global monitoring.

· Map integration: Embed Maps (such as Google Maps, Autonavi Maps) on Web/ mobile devices to display the geographical locations of devices (such as smart street lamps, logistics vehicles). For instance, in smart city management, maps can mark the locations of all street lamps and display their online status and brightness levels.

· Interaction design: Remote control and intelligent notification, convenient operation

· Remote control: Supports remote operation of the device through the APP or Web end (such as turning lights on and off, adjusting temperature, and starting the device). For instance, users can turn on the air conditioner at home in advance through a mobile phone APP before leaving work, and by the time they get home, the indoor temperature will have reached a comfortable level.

· Historical data query: It provides the function of querying historical data by time range, device type and other conditions, and supports export in CSV/Excel format. For instance, users can check the electricity consumption of a certain smart meter in the past month and analyze the peak electricity consumption periods.

· Alarm notification push: Real-time notification of device abnormalities (such as temperature exceeding limit, device offline) to users through APP push, SMS, email and other means. For instance, when an intelligent smoke alarm detects smoke, it immediately sends an alarm notification to the user's mobile phone along with the device's location information.

Service advantages

· Edge-cloud collaborative optimization: Reduce cloud load and transmission latency through edge computing, and combine the powerful computing power of the cloud to achieve complex analysis, balancing real-time performance and cost. For instance, in intelligent security, edge devices complete face recognition locally and only upload the recognition results (rather than the original video) to the cloud, reducing bandwidth usage by over 90%.

· High security and reliability: Utilizing technologies such as data encryption (TLS/SSL), device authentication (X.509 certificates), and access control (RBAC) to ensure the security of devices and data. For example, all communication data is transmitted through AES-256 encryption to prevent data leakage.

· Rapid development and flexible expansion: Based on a microservice architecture and modular design, it supports rapid iteration and functional expansion. For instance, when a new type of sensor is added, only the corresponding edge driver and cloud data parsing module need to be developed, without the need to reconstruct the entire system.

· All-industry solutions: Covering multiple scenarios such as smart home, industrial Internet of Things, smart agriculture, and smart city, providing customized development services. For instance, develop edge gateways that support Modbus to JSON conversion for industrial customers, and rule engines that support soil moisture-irrigation linkage for agricultural customers.

Application scenarios

· Smart home: Develop edge-end software for smart door locks, temperature and humidity sensors, and smart sockets, and combine cloud device management with mobile control to achieve home automation.

· Industrial Internet of Things: Deploy edge computing nodes (such as industrial gateways) to achieve device data preprocessing and local decision-making, and the cloud platform supports device monitoring and predictive maintenance.

· Smart agriculture: Through edge sensor data processing and cloud rule engines, it achieves intelligent irrigation, environmental monitoring, and pest and disease early warning.

· Smart City: Develop an edge-cloud collaborative system for smart street lamps, environmental monitoring stations, and parking sensors to enhance urban management efficiency.