Edge processing in IoT

What is Edge computing?

Edge computing is a deployment model which aims to push the relevant data processing and storage attributes closer to where the device is located. This means that data can be processed more efficiently, and many attributes do not need to be centralized.

Benefits

Low latency.

By its nature, the edge is closer to the IoT device than the core or cloud. This means a shorter round-trip for communications to reach local processing power, significantly speeding up data communications and processing.

Longer battery life for IoT devices.

Being able to open communication channels for shorter periods of time due to improved latency, means that battery life of battery powered IoT devices could be extended. distributed ledger, or a hybrid open source ledger implementation such as Big chain DB could be used to obtain the advantage of a distributed ledger which provides features from the No-SQL database Mongo DB on which it is based.

More efficient data management.

Processing data at the edge makes simple data quality management such as filtering and prioritization more efficient. Completing this data administration at the edge, means cleaner data sets can be presented to cloud based processing for further analytics.

Access to data analytics and AI.

Edge processing power and data storage could all be combined to enable analytics and AI, which require very fast response times or involve the processing of large ‘real-time’ data sets that are impractical to send to centralized systems.

Resilience.

The edge offers more possible communication paths than a centralized model. This distribution means that resilience of data communications is more assured. If there is a failure at the edge, other resources are available to provide continuous operation.

Scalability.

As processing is decentralized with the edge model, less load should ultimately be placed on the network. This means that scaling IoT devices should have less resource impact on the network, especially if application and control planes are located at the edge alongside the data.

Use cases for IoT edge

Device Management

device management at the edge will need to support:

• Distributed firmware updates — the use of the edge gateway to distribute firmware updates locally, with distribution being managed by the edge node as opposed to the queuing system typically used when a firmware update is distributed centrally.
• Device configuration updates — devices at the edge will need to be configured locally as services change. The edge could be able to manage this remotely.
• Diagnostics of connected devices — the use of analytics at the edge can be used to identify specific problems with devices in the field through machine learning or pattern recognition
• Edge node or gateway management — device management platforms can be used to manage the operator’s edge infrastructure as well as the IoT device.

Security

Having security services also distributed at the edge offers the opportunity to improve security capabilities, as well as offering native security for new low latency applications. A number of security issues can be addressed at the edge in an IoT environment:

• Firmware and other updates: Secure update of firmware and other device updates from the edge using public key certification or secure transmissions such as SSL ensure that firmware upgrades are carried out securely.
• Data Authentication: Authentication of data and updates at the edge is important to retain secure environments. Authentication is likely to be via a certificate-based system. Implementation of this will need careful consideration to prevent poor performance of edge processing and latency.
• Access Control: Identity and permissions management at the edge is important to ensure that access to data at the edge is managed securely. Granting data access to third parties means that full access control policies must be in place.
• Prevention of Denial of Service attacks: Analysis of the data flow from IoT devices to spot and prevent characteristics of DDoS attacks.

Priority messaging

Much of the data generated by the IoT will be of low value — unexceptional status updates and low priority data. However, some data will be of great importance and needs to be prioritized to ensure it is acted upon rapidly. This ‘critical data’ is likely to be a very small percentage of the total volume generated, yet is the most important. The scope of priority messaging goes beyond just single applications, as these message types could be used to initiate a cascade of actions across different applications and devices.

Examples of priority messaging include:

• Transportation — accident alert that needs to be sent to following vehicles to enable them to avoid collision.
• Health & Safety — fire alarm linked to building evacuation.
• Environmental — rainfall or pollution above maximum safe levels linked to remedial activities.
• Security — unauthorized activity leading to automated security actions e.g. doors closing; terrorism response in immediate vicinity; drones flying into no-fly areas.
• Industrial — failure of critical component required immediate shutdown of other systems; construction worker in unsafe location.

The edge enables high priority data needs to be generated, sent, processed, and actioned more quickly than sending the data to the cloud.

Data Aggregation

As more IoT devices are connected, and more data generated, so it is likely there will be more replication of data from those devices. This could be multiple temperature readings from sensors in the same vicinity, or multiple vehicles reporting that they are stuck in the same traffic jam. Not all of this data needs to be sent back to centralized services, and the edge therefore has a role in either selecting which data to send or aggregating common data from multiple sensors together.

Examples of data aggregation:

• Data from multiple temperature sensors in the same location can be aggregated to produce statistical measures (min, max, mean etc).
• Traffic data derived from multiple vehicles in the same queue.
• Power outage reports from meters sending last gasp communications.
• Positive status reports from widespread connected equipment such as streetlights.

Benefits of Edge for this Use Case

• Network efficiencies

Data aggregation can create significant efficiencies in the IoT network. Aggregation can remove the need for replicating data across multiple systems, and performing the same processing multiple times on different systems. This means that there is no need to backhaul masses of replicated data, and therefore resources for data analytics can be used more effectively and data storage needs can be lowered. All of this means that the load on the core infrastructure is significantly reduced.

• Latency improvements

By having less data to sift through, quicker decisions could be made, so appropriate actions

can be taken faster. By reducing the amount of data to be communicated and processed, latency should be improved.

• Richer data sets

There are several benefits to aggregating IoT data at the edge, before sending it onto the core.

Aggregated data provides valuable data sets where much of the data pre-processing has already been completed. This could aid machine learning in making more reliable predictions and allows patterns and trends to be more readily identified.

For more info check here