For example, handling data from industrial equipment is collected, and the computing applied to calculate appropriate performance metrics, demands a different approach compared to interacting with a fleet of vehicles. Cloud providers may offer a selection of managed services that can be tailored to specific customer requirements. There is also a vibrant market for third-party software specialised in data ingestion, preparation, storage, and analytics.
An internal aggregation layer may be implemented to bring together data from various sources. An ingestion framework may then be used to direct the data into a processing layer. The processing layer can be organised in various ways: a three-stage approach may accept raw data from the ingestion layer, apply techniques such as machine learning to further refine the data, and finally present usable data to analytics applications that generate actionable insights for purposes such as automating industrial processes, driving business decision making, directing new product development.
A final outbound, or storage layer can provide services such as APIs and managed access that make information available to downstream applications.
Security in the Cloud
Security is a critical aspect of the cloud's role in IoT. The cloud provides robust security measures to protect sensitive IoT data from unauthorised access, ensuring data integrity and confidentiality. Additionally, the cloud's centralised security infrastructure allows for efficient monitoring, threat detection, and rapid response to potential security breaches across the IoT ecosystem.
The cloud platform relies on strong authentication mechanisms, such as cryptographic keys, digital certificates, and multifactor authentication, to verify the identity of IoT devices and ensure that only authorised devices can access services. Role-Based Access Control (RBAC) and fine-grained authorisation policies restrict access to specific resources and actions based on device roles and permissions.
Further security techniques include using secure communication protocols, such as Transport Layer Security (TLS) or Datagram Transport Layer Security (DTLS) to encrypt data transmitted between IoT devices and the cloud. Encryption ensures that data remains confidential and cannot be intercepted or tampered with during transmission. Additionally, secure communication protocols provide mechanisms for endpoint authentication and protection against man-in-the-middle attacks.
Data encryption is also employed to protect IoT data at rest in the cloud, such as when stored in databases or file systems. This helps to prevent unauthorised access even if the storage infrastructure is compromised. Proper encryption key management practices, including secure key storage and rotation, are needed to maintain the confidentiality of encrypted data.
In addition, cloud-based security tools and services, such as intrusion detection systems (IDS) and security information and event management (SIEM) solutions, are used to monitor network traffic, detect anomalous behaviour, and identify potential security threats in real-time. These tools employ machine learning algorithms and behavioural analytics to identify patterns or indicators of malicious activities and trigger appropriate responses.
In the cloud, it is also possible to use advanced analytics techniques including artificial intelligence to identify security risks, detect anomalies, and uncover potential data vulnerabilities. AI can detect patterns and predict threats to enhance security posture and response.
Cloud IoT platforms often provide security auditing capabilities to monitor compliance with security policies, regulations, and industry standards. Auditing helps identify security gaps, track security events, and maintain an audit trail for forensic analysis and compliance reporting purposes.
There are also incident response mechanisms including incident management workflows, automated responses, and recovery processes. These are essential for promptly addressing security incidents, while incident response plans can guide mitigation, recovery, and investigation if a security breach occurs.
In addition, physical security measures at the data-centre premises, such as access control systems, video surveillance, and environmental controls are, of course, essential.
Conclusion
The cloud has a central role in any IoT solution, as the convergence point for data from multiple sources, not only sensor data but also any other relevant data from third-party feeds. Broadly, applications in the cloud are required to collect, organise, and analyse the data. In practice, there are many ways to approach this, depending on the types of data and their sources, and the insights required from the analysis. Several layers may be implemented to ingest, prepare, store, and analyse the data, typically accomplished by selecting managed services from cloud providers or using third-party software.
Some of the cloud’s vast processing power must be directed towards robust security, to protect services and data against threats such as unauthorised access, tampering, and data theft, and so preserve trust, reliability, and confidentiality.
