Is IIoT edge computing ready?

Edge computing, a powerful technology that has been developed in recent years, is now a good time for companies with IoT devices to consider enhancing their device security and analytics capabilities.

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Edge computing is a hot topic in the IIoT field. More and more applications require computer processing to be closer to sensors to reduce latency and improve efficiency. Under the catalysis of this high demand, the development of edge computing has gradually matured.

Edge computing is a mesh network of micro data IoT centers that process and store critical data locally before transferring it to a central data center or cloud repository. In this way, edge computing can help optimize cloud computing systems, reduce the load on central data centers, and protect them from data transmission interruptions.

Through a containerized microservices architecture, cloud servers become the control nodes for intelligent edge devices, performing aggregate analysis while leaving real-time decision making to edge servers.

IoT devices at the edge must take on the responsibilities of computing, storage, and network connectivity. Depending on demand, IoT devices will send some or all of the data generated by sensors to the cloud.

1. Which scenarios require edge computing?

Edge computing can be deployed in the following situations:

• Poor connectivity of IoT devices;
• The application relies on machine learning, which requires large amounts of data to provide fast feedback;
• For security and privacy reasons, it is necessary to keep the data within the factory;
• The raw data at the edge needs to be preprocessed to reduce the amount of calculation.

Typical use cases for edge computing include face recognition, smart navigation, etc. It is worth noting that edge computing is ineffective if IoT devices must be constantly connected to the central cloud.

2. How is edge computing different from fog computing?

Fog computing refers to operations based on the interaction between edge devices and the cloud. Edge computing refers to IoT devices with computing power; they act as a gateway between sensors and people inside the factory. In a sense, edge computing is a subset of fog computing.

Edge computing brings technology closer to end-user applications, so that rather than having to constantly connect to a centralized cloud infrastructure for instruction or analysis, devices are empowered to complete these tasks independently.

3. Security of edge computing

The level of security associated with edge computing is generally higher because the data is not sent over the network to the cloud. In edge computing, the data is decentralized.

Because edge computing is a relatively new technology, traditional issues still exist, including login credentials, security vulnerabilities, lack of updates, and less-than-ideal network architectures.

On the other hand, edge devices are inherently vulnerable to hackers, and this should be kept in mind when designing a security architecture.

The system composed of cloud computing and edge computing can store and process data more efficiently.

The following safeguards can be taken to protect sensor data:

• Insert Gaussian noise of a certain variance into the data to reduce the chance of sniffing;
• Split the data into chunks and scramble them to avoid MITM (Man-in-the-Middle);
• Public key infrastructure that encrypts each block of data.

(1) Identity verification

IoT devices, especially those in smart grids, are vulnerable to data tampering and spoofing, which can be prevented through public key infrastructure (PKI), Diffie-Hellman key exchange, detection techniques, and monitoring of modified input values.

(2) Data encryption

For static data, the AES algorithm with a key size of 256 bits can be used to ensure security, while the Secure Sockets Layer (SSL) protocol can be used to establish secure communications between the server and the client.

(3) Network monitoring

Since a large number of heterogeneous IoT devices transmit and process heterogeneous data at multiple levels (hypervisor, operating system, and application), artificial neural networks (ANS) and rule matching can be used for threat detection.

(4) Security vulnerabilities

Machine learning techniques can be used to accurately identify security threats. These techniques train algorithms such as support vector machines with models of benign software, after which any abnormal behavior can trigger a detection event. In addition to stealing data or modifying core system functions, the presence of malware can also degrade system performance.

In healthcare, it is critical that if a Fog system is compromised, critical data and functionality remain protected by tight and intact security systems, and that the system is isolated in the event of malicious activity in the host operating system.

4. Edge computing: a catalyst for IT and OT convergence

IT consists of computing/processing systems and data storage. OT includes the hardware and software required to run and monitor production systems, such as SCADA (Supervisory Control and Data Acquisition), DCS (Distributed Control Systems), and ICS (Industrial Control Systems). Technology aims to converge IT and OT into a common domain to facilitate communication and action. Edge computing is accelerating this convergence.

Companies at the forefront of the Industrial Internet of Things (IIoT) have established a common foundation for IT and OT to function as a unified system. For example, health monitors are converged systems. Edge computing, where computing is performed close to the sensor (hardware), brings IT and OT closer together.

With IT (particularly data science and ML models), users can build algorithms that continuously learn and adapt to provide better services. OT can automate their workflows while providing better monitoring and analysis of abnormal conditions. Factories with integrated OT/IT teams have achieved successful results such as reduced energy consumption, higher product quality and asset health, and less downtime.