Maximizing IoT Performance with Edge Computing

Eseye

IoT Hardware and Connectivity Specialists

While the cloud plays a critical role in centralizing data processing capabilities for IoT, the benefits can only truly be realized if the information generated by IoT sensors gets to the right place at the right time.

Some IoT applications, such as water meters, will not be affected by a data lag of hours or even days as long as the data is collected and processed before the monthly billing cycle.

But payment terminals or smart manufacturing sensors could be adversely affected if the latency runs into milliseconds or seconds, preventing the system being able to take payment from customers in a timely manner, or make snap decisions about the production line.

Other requirements such as data volume, service reliability, privacy and security might also have an impact on the time from data collection to compute, or indeed on where the compute takes place.

Benefits of edge computing for IoT

Although the cloud provides virtually unlimited storage and processing power, IoT devices generate large amounts of data at the edge of the network and sometimes it makes sense to store, process, or analyze that data as close to the source as possible. This would typically be performed by intelligent computation at the network edge.

While centralized cloud compute and storage remain important in IoT deployments, to meet specific use case requirements, data is increasingly being stored, processed, analyzed, and manipulated close to the source.

How to implement edge computing for IoT

Edge cloud or edge compute

Moves hardware to the network edge to be able to perform some computation and storage close to the source, or to act as a gateway that aggregates data from multiple IoT devices or networks.

Fog nodes

Are less ‘dense’ than a cloud and are typically single pieces or clusters of hardware on the network edge. Fog nodes are able to provide the required compute or storage at a physically closer location to the IoT endpoints.

Real-world edge compute use cases

Smart factories and smart manufacturing

Industrial IoT (IIoT) and Industry 4.0 make use of IoT technology to enable Smart Manufacturing, incorporating automation systems for different use cases, from the supervisor level all the way to the control and field levels.

These smart factories run real-time processes leveraging IoT data, and even small changes can have an impact on production efficiency or quality. So smart manufacturing operations run simple but time sensitive processing at the edge, controlling things such as temperature and humidity or getting insight into the real-time operational status of each machine.

In this way, edge computing can help tailor and optimize manufacturing programs by analyzing data at each production facility and machine level.

Smart grid and smart energy

As part of a smart city deployment a smart grid is increasingly essential in ensuring energy efficiency and availability. Edge compute is set to play a key role in smart energy systems, helping to operationalize the energy infrastructure through local decision making.

Edge computing can help monitor power generation, distribution and consumption to avoid energy shortages and help recovery after a disruption.

In developing regions smart grid solutions are already prevalent as mini-grids – decentralized, independent power networks that can function apart from and take the strain off a national grid, but also bridge the gap across to standalone solar home systems which are becoming increasingly popular.

Smart cities

Smart city IoT architecture encompasses a complex network of sensors, devices, and data processing systems. Edge compute already features heavily in these deployments, operating as a number of local management ‘brains’ that work together to enhance urban living and working.

IoT connectivity combined with edge compute means cities can achieve real-time data collection and analysis and improve everything from traffic management and public safety to energy efficiency and environmental monitoring.

In many cases a digital twin model, which itself leverages edge computing, can help municipal operators to run virtual tests on their city’s capabilities and optimize their actions based on specific requirements.

Smart agriculture

IoT is already used extensively in smart farming and agriculture. A multitude of sensors can measure and analyze parameters, such as temperature, humidity, sunlight, carbon dioxide, water levels and soil quality, as well as animal welfare and tracking.

Many smart farming management systems are cloud-based, with data from the sensors aggregated and sent back to a centralized analytical application for insight and actions.

Edge computing can reduce the volume of data sent back and forth and can provide additional capabilities in very remote regions where direct connectivity back to the cloud is unreliable or impossible.

Smart construction

Similar to smart manufacturing, smart construction sites make good use of IoT data in real-time processes for site health and safety and people management. Sensors and cameras can help avoid a wide range of accidents and ensure managers know where workers are at all times.

In adjacent verticals such as mining, IoT sensors can also be used to detect gasses, earthquake vibrations, and equipment location.

Using edge computing, data generated at the construction site can be processed locally, reducing transmission costs and increasing decision speed. It can also provide analytical capabilities in remote locations like mines where cloud connectivity might be impossible.

Smart cars

In some respects smart cars themselves are edge computing devices as they carry more processing and storage capabilities than you typically get in an enterprise or consumer IoT devices. Smart cars also come with a multitude of sensors, cameras, radars, GPS and more and generate a large amount of data that must be processed in real-time – especially in self-driving situations.

In combination with smart city deployments, some local processing can be offloaded to edge computers around the city, where information from all vehicles in the area can be pooled and analyzed to help with traffic flow, congestion, parking and road safety.

Digital twins

Digital twins appear in almost every vertical. A digital twin is a virtual replica of an existing (often physical) system that is used to model different scenarios and predict outcomes based on changing variables or parameters.

Digital twins are used extensively in everything from smart city planning, to logistics, to energy management, to retail, to farming.

Edge computing can facilitate real-time data exchange between physical assets and their digital twins. For example, Enterprises use digital twins to validate strategies and tactics, expose inefficiencies and optimize performance.

Edge computing can increase these efficiencies by working with data closer to the source, doing lots of the heavy lifting in terms of analytics without needing to send data back and forth to a centralized cloud.

Optimizing edge computing with global reach and security

Not every bit of IoT data has to go to a centralized cloud computing resource for analysis to determine what action to take and then be communicated back to the device. Increasingly decisions can be made on the device or closer to it, reducing latency of roundtrip transmissions.

The optimal blend is to utilize intelligence at the edge to deal with straightforward, mission critical, time-specific requirements alongside connection to the cloud for processing of additional, potentially more complex but less urgent data that can be combined with other data sets.

Eseye’s Infinity IoT Platform™ provides that connectivity and the data to support these IoT intelligence operations.

While the data that IoT devices send to the cloud is the prime source for a customer’s analytics operations, this data can be complemented with Infinity’s connectivity data to provide additional context and insights and deliver seamless connectivity at the edge.

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Eseye

IoT Hardware and Connectivity Specialists

Eseye brings decades of end-to-end expertise to integrate and optimise IoT connectivity delivering near 100% uptime. From idea to implementation and beyond, we deliver lasting value from IoT. Nobody does IoT better.

Maximizing IoT Performance with Edge Computing

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