The IoT cloud is a platform that enables the storage, processing and management of data generated by IoT devices in distributed cloud systems. Cloud platforms provide scalability, flexibility, and efficiency in collecting and analysing large amounts of data in real time. The IoT cloud can be used to centralise the management of IoT systems, integrate different types of devices, and implement advanced analytics and machine learning algorithms. The use of the IoT cloud facilitates the development of smart applications, such as smart city management systems, industrial machine monitoring, remote control, and critical infrastructure management.
IoT Cloud
pl.
Type of technology
Description of the technology
Basic elements
- Data processing platform: Modules for collecting, processing, and analysing large amounts of data in real time.
- Data storage systems: Distributed databases and storage systems, such as NoSQL, SQL, and Hadoop.
- Device management: Modules to remotely manage, configure, and update IoT devices.
- Integration with other systems: Interfaces to connect to other platforms, applications, and cloud systems.
- Data security: Mechanisms for encryption, authorisation, and security of data transmission between IoT devices and the cloud.
Industry usage
- Smart cities: Traffic management, environmental monitoring, and public safety systems.
- Health care: Remote monitoring of patient health and management of medical devices.
- Industry: Remote machine management and predictive maintenance.
- Logistics: Monitoring of shipment status and optimisation of supply chains.
- Energy: Monitoring and management of power grids and critical infrastructure.
Importance for the economy
The IoT cloud is a key component of the Internet-of-things ecosystem, enabling efficient data management and analysis at scale. By integrating with the cloud, companies can use advanced analytical tools to optimise processes, reduce costs, and create new data-driven business models. The IoT cloud also supports the development of new services, such as smart city management, transport optimisation, and remote infrastructure monitoring. With its capabilities, the IoT cloud enables companies to quickly scale solutions and adapt to changing market conditions.
Related technologies
Artificial Intelligence
Implementation of AI algorithms for pattern analysis and state prediction of IoT systems.
Cybersecurity
Securing data transmission between IoT devices and the cloud and protecting against unauthorised access.
Automation
Implementing automated processes based on IoT cloud data in industrial systems and smart cities.
Mechanism of action
- The IoT cloud centrally processes and stores data generated by IoT devices. Sensor data is sent to the cloud via various connectivity technologies, such as Wi-Fi, 4G/5G, Zigbee, and LoRaWAN, where it is processed, analysed and made available for further processing or monitoring. Cloud platforms enable users to remotely access IoT systems for real-time monitoring, control, and analysis. Through integration with analytics tools, the IoT cloud enables the deployment of advanced solutions, such as predictive maintenance, predictive analytics, and automated network management.
Advantages
- Scalability: Easy expansion and integration with new IoT devices.
- Central management: It enables remote monitoring and control of devices from any location.
- High availability: Distributed systems ensure uninterrupted service availability.
- Cost optimisation: Reducing local infrastructure costs by leveraging the cloud.
- AI integration: Ability to implement advanced analytics and machine learning algorithms.
Disadvantages
- Data security: Risk of unauthorised access to data transmitted by IoT devices.
- Latency issues: Data transmission delays can affect the efficiency of real-time systems.
- Dependence on cloud providers: Loss of control over data and infrastructure in case of failure or change in terms of cooperation.
- Compatibility issues: The variety of protocols and standards can make integration with existing systems difficult.
- Maintenance costs: The costs of long-term storage of large amounts of data can be significant.
Implementation of the technology
Required resources
- Server infrastructure: Servers that process data in real time and store large amounts of information.
- Analytics platforms: Cloud-based data analysis and processing software.
- Communication networks: High-bandwidth links for transferring data from IoT devices to the cloud.
- Cloud specialists: Experts in managing, implementing, and optimising cloud systems.
- Cybersecurity systems: Tools for monitoring and securing data in the cloud.
Required competences
- Cloud application programming: Developing applications and services in the cloud, such as Amazon AWS and Microsoft Azure.
- Data management: Storage, processing, and analysis of data from IoT devices.
- Cloud security: Implementation of data security and privacy principles in the cloud.
- DevOps: Automation of processes for deploying and managing cloud infrastructure.
- Cloud architecture: Designing scalable cloud systems and services.
Environmental aspects
- Energy consumption: High energy demand in large data centres supporting IoT cloud.
- Waste generated: Problems with disposal of obsolete servers and hardware components.
- Emissions of pollutants: Emissions from the operation of large data centres.
- Recycling: Difficulties in recycling hardware components used in data centres.
- Raw material consumption: High demand for rare metals and advanced semiconductor materials.
Legal conditions
- Data protection: Regulations for the protection of data collected and processed in the cloud (e.g. GDPR and CCPA).
- Certification: Standards for data security and compliance with international standards (ISO 27001).
- Data storage regulations: Regulations specifying the location and storage of data in data centres.
- Occupational safety: Regulations for the safe operation and maintenance of large data centres.
- Export regulations: Restrictions on the export of advanced data processing technologies.