Different cloud service providers for IoT-based onion storage monitoring systems
| Cloud service provider | Services | Description | Key features | Use case |
|---|---|---|---|---|
| AWS | AWS IoT Core | Connects IoT devices to the cloud, enabling secure communication and data processing. | Device management, data analytics, and ML integration | Real-time monitoring, data storage, and analytics |
| Microsoft Azure | Azure IoT Hub | Centralized service for managing IoT devices and ingesting data for processing. | Device provisioning, data routing, and integration with Azure services | Device management, telemetry, and control |
| Google Cloud Platform | Google Cloud IoT Core | Securely connects, manages, and ingests data from globally dispersed IoT devices. | Device manager, protocol bridges, and real-time analytics | Large-scale deployment, data processing, and analytics |
| IBM Cloud | IBM Watson IoT Platform | Provides a managed service for IoT device connectivity and data processing. | Real-time data visualization, device management, and analytics | Industrial IoT applications and predictive maintenance |
| Oracle Cloud | Oracle IoT Cloud Service | Enables connection and management of IoT devices with integrated analytics. | Device virtualization, data analytics, and application integration | Supply chain management and asset tracking |
| SAP | SAP IoT | Integrates IoT data with business processes to provide real-time insights. | Business process integration, analytics, and digital twins | Business process optimization and real-time insights |
| Think Speak | Thing Speak IoT Platform | Collects and stores sensor data in the cloud, enabling real-time data analysis. | Real-time data collection, MATLAB (MathWorks, Natick, Massachusetts, United States) analytics, and visualization | Academic projects and small-scale monitoring |
| Particle | Particle Cloud | Manages IoT devices and provides an integrated platform for data processing. | Device management, real-time updates, and integrations | Prototyping and small to medium-scale deployments |
| Cisco | Cisco IoT Cloud Connect | Provides secure and scalable IoT connectivity and management. | Secure device connectivity, data management, and analytics | Secure communication and enterprise IoT solutions |
| Siemens | Siemens Mind Sphere | Industrial IoT as a service platform connecting devices and enterprise systems. | Industrial analytics, digital twins, and application development | Manufacturing and industrial automation |
| PTC | PTC Thing Worx | Provides a platform for building IoT applications with real-time data integration. | Rapid application development, analytics, and device management | Smart manufacturing and connected products |
Different sensors are used in IoT-based onion storage monitoring systems
| Sensor type | Example | Function | Parameter monitored | Specifications |
|---|---|---|---|---|
| Temperature sensor | DHT22 | Measures ambient temperature | Temperature (°C/°F) | Accuracy: ±0.4°C and range: −41–80°C |
| Humidity sensor | DHT22 | Measures moisture levels in the air | Relative humidity (%) | Accuracy: ±2%–6% and range: 0%–100% |
| Soil moisture sensor | VH400 | Monitors the moisture content of the soil | Soil moisture (VWC, m3/m3) | Output: analog and range: 0%–46% VWC |
| Light sensor | BH1750 | Measures light intensity | Light intensity (lux) | Range: 0–65,545 lux and accuracy: ±20% |
| Gas sensor | MQ135 | Detects gases, such as NH3, CO2, and benzene | Gas concentration (ppm) | Sensitivity: 10–250 ppm for NH3 and CO2 |
| pH sensor | SEN0161 | Measures the acidity or alkalinity of the soil | pH level | Range: 0–14 pH and accuracy: ±0.1 pH |
| Pressure sensor | BMP280 | Monitors air pressure within storage areas | Pressure (Pa, kPa) | Range: 300–1,100 hPa and accuracy: ±1 hPa |
| CO2 sensor | MH-Z19B | Measures carbon dioxide levels | CO2 concentration (ppm) | Range: 0–5,000 ppm and accuracy: ±50 ppm |
| Ethylene sensor | MiCS-5524 | Detects ethylene gas, indicating ripening | Ethylene concentration (ppm) | Sensitivity: 1–100 ppm |
| Proximity sensor | HC-SR04 | Detects the presence of objects or movement | Distance (cm, m) | Range: 2–400 cm and accuracy: ±3 mm |
| Airflow sensor | FS7-15 | Measures the speed and flow of air | Airflow rate (m/s, CFM) | Range: 0–15 m/s and accuracy: ±0.2 m/s |
| Weight sensor | HX711 + load cell | Measures the weight of stored onions | Weight (kg, lbs) | Capacity: 0–50 kg and accuracy: ±0.01 kg |
| UV sensor | GUVA-S12SD | Monitors UV radiation exposure | UV index | Range: 0–10 UV index and sensitivity: 0.1 |
| RFID sensor | MFRC522 | Identifies tagged objects using radio waves | RFID tags | Frequency: 13.56 MHz and range: 2–5 cm |
| Camera/visual sensor | Raspberry Pi Camera V2 | Captures images or video for visual monitoring | Image/video feed | Resolution: 8 MP and frame rate: 30 fps |
Communication protocols used in IoT-based onion storage monitoring systems
| Communication protocol | Frequency band | Range | Data rate | Power consumption | Typical uses |
|---|---|---|---|---|---|
| Wi-Fi (802.11) | 2.4 GHz and 5 GHz | Up to 100 m (indoor) | Up to 600 Mbps (802.11n) | High | Real-time monitoring and control |
| Zigbee (IEEE 802.15.4) | 2.4 GHz and 900 MHz | Up to 100 m | 20–250 kbps | Low | Sensor networks and low-power applications |
| BLE | 2.4 GHz | Up to 100 m | 125 kbps to 2 Mbps | ery low | Short-range communication and mobile integration |
| LoRaWAN | 433 MHz, 868 MHz, and 915 MHz | Up to 15 km (rural) and 5 km (urban) | 0.3–50 kbps | Very low | Long-range communication and rural areas |
| NB-IoT | Licensed LTE spectrum (varies by region) | Up to 35 km | Up to 250 kbps | Low | Cellular connectivity and urban and rural areas |
| Sigfox | 868 MHz (EU) and 902 MHz (US) | Up to 50 km (rural) and 10 km (urban) | 100 bps | Very low | Ultra-narrowband, long-range communication |
| RFID | 125 kHz, 13.56 MHz, and 860–960 MHz | Up to several meters | Up to 640 kbps | Passive (no battery) or low (active tags) | Asset tracking and inventory management |
| Cellular (3G/4G/5G) | Licensed bands (varies by region) | Up to several km | Up to 10 Gbps (5G) | High | Wide-area connectivity and real-time data |
| Z-Wave | 868.42 MHz (EU) and 908.42 MHz (US) | Up to 100 m | Up to 100 kbps | Low | Home automation and low-power applications |
| Ethernet | Wired | Up to 100 m (cable length) | Up to 10 Gbps | N/A (wired power) | High-speed, reliable communication |
Summarizing current technologies available in the market vs_ emerging technologies (future scope) for IoT-based onion storage monitoring system
| Sr. No. | Technology | Current technologies | Emerging technologies (future scope) |
|---|---|---|---|
| 1. | Sensing technology | Temperature sensors (e.g., DS18B20) and humidity sensors (e.g., DHT11) | Advanced sensors (e.g., Li DAR and hyper spectral imaging) for real-time monitoring |
| 2. | Communication protocol | Wi-Fi, Bluetooth, and Zig bee | 5G, NB-IoT, and LoRa WAN for low-power, wide-area networks |
| 3. | Data analytics | Cloud-based platforms (e.g., AWS, Google Cloud), ML algorithms (e.g., regression, decision trees) | Edge computing, AI, and DL for real-time decision making |
| 4. | Power management | Battery-powered devices, solar-powered devices | Energy harvesting technologies (e.g., piezoelectric and thermoelectric) for self-sustaining systems |
| 5. | Security | Encryption algorithms (e.g., AES and RSA) and secure communication protocols (e.g., TLS and SSL) | Block chain-based security and homomorphic encryption for secure data processing |
| 6. | User interface | Web-based dashboards and mobile apps | Voice assistants (e.g., Alexa and Google Assistant) and AR interfaces for immersive experiences |
| 7. | Storage | Cloud storage (e.g., AWS S3 and Google Cloud Storage) and local storage (e.g., SD cards and hard drives) | Distributed storage solutions (e.g., block chain-based storage) and Edge storage for reduced latency |
Onion production, consumption, and export analysis of top three countries for the year 2020
| Country | Production (m/tons) | Export (m/tons) | Worldwide % share of export |
|---|---|---|---|
| India | 26.73 | 1.448 | 17.14% |
| China | 23.660 | 881.3 | 10.43% |
| USA | 3.821 | 365.4 | 4.32% |
| Egypt | 3.156 | 369.2 | 4.37% |
| Turkey | 2.280 | 220.7 | 2.61% |
Evolution of IoT (from 1999 to 2024)
| Phase | Researcher/lab | Year | Description |
|---|---|---|---|
| First phase | Kevin Ashton | 1999 | Kevin Ashton, a British entrepreneur, coins the term “Internet of Things” and discusses the concept. |
| Second phase | Auto-ID labs | 2000 onward | Auto-ID labs, a network of research labs, conducts pioneering work in RFID technology and IoT connectivity. |
| Third phase | Various researchers/labs | 2010 onward | Multiple researchers and labs contribute to IoT advancements, expanding its application into various domains. |
| Fourth phase | Cloud providers (e.g., AWS) | 2015 onward | Cloud providers, such as AWS, offer scalable infrastructure for storing and processing IoT data. |
| Current phase | Various researchers/labs | Ongoing | Researchers and labs worldwide continue to innovate in IoT technologies, exploring areas, such as edge computing |
| Future phase | Various researchers/labs | Ongoing | Ongoing researchers focus on integrating IoT with emerging technologies such as AI, blockchain, and 5G for future applications. |