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Edge Computing: An Overview and Its Future Implication

by | Jul 21, 2022 | Technology




Why do we need edge computing?

We are living in a smart world. Everything keeps getting smarter by the day with the ever-increasing number of smart connected devices. Globally, there’s an estimated 11.57 billion Internet of Things (IoT) devices in 2022.

The number of IoT devices is expected to reach 15.14 billion in 2023 and 29.42 billion by 2030. There are also 6.6 billion smartphone users around the world and this number is expected to reach 7.7 billion by 2027.

It is estimated that by 2025, there will be 41.6 billion connected devices around the world capable of generating 79.4 zettabytes of data. The Internet of Things has entered many different industries and is bringing with it countless new data points. These data point is opening the door to greater operational and economic efficiency.

The increased number of devices is an amazing thing but the amount of data generated is growing exponentially. Unfortunately, we’re collecting information faster than we can analyze, thus ushering the term big data.

It is estimated that the new generation of airplanes will generate up to 20 terabytes of data per engine per hour of flight and hospitals produce 50 petabytes per year. An average factory that has connected devices inside it generates one terabyte per day.

And with all the high-tech sensors that are in a self-driving car, it is estimated that the latter will generate 40 terabytes of data per hour per car. These numbers represent the explosion of data that some of our technology is going to be generated in a near future.

The data is used to improve processes. With that amount of information being generated, you can’t just send everything to the cloud. Cloud computing up until now has mainly been focused around storing and processing inputs to extract value and seek patterns.

For instance, a self-driving car has cameras, radar, lidar, and other sensors that are gathering real-time information. You can’t send these inputs to the cloud and wait for an answerback. These data have to be processed in real-time time to allow fast reactions.

The latency requirement is way too tight to send data to the cloud for real-time decision-making. Latency is the time data take to travel from one point to another in a network. So to make good use of the information, a mini cloud operating on the perimeter is needed.

The mini cloud has the capability of storing and doing computation and analytics on the spot as well as being connected to the cloud for additional computation. It is estimated that by 2025, 75% of data will be created and processed outside conventional data centers.

As every operation in the world is turning digital, you’ve got to need speed and near-zero latency to compute the data generated. So instead of collecting data and sending it to a server far away for processing, the analysis can be done locally through edge computing.

With edge computing, processing can be done on the spot because the computer chips within our devices are becoming more powerful. And this enables internet-connected devices to make more decisions and process more data.

With the increase of Internet of Things (IoT) devices and 5G networks becoming mainstream, many organizations are incorporating edge computing into their operation.

IoT devices are gathering so much input that the sheer volume requires larger and more expensive connections to data centers and the cloud. The nature of the work performed by IoT devices today is also creating a need for much faster connections between the cloud and the devices.

The 5g deployments are expected to improve the speed of data processing and reduce latency. But it is not enough because usually cloud services are afar and sometimes data needed to be processed on the spot, this is why we need edge computing.

What is edge computing?

Edge computing is exactly what it sounds like which is computational that takes place at the edge of a network. It is the process of placing networked computing resources as close as possible to where data is generated.

During the past couple of decades, cloud computing has been one of the biggest digital trends in delivering computational power over the Internet. But when the cloud was introduced, most devices that were accessing cloud services were PCs and other end-user hardware that generated little data.

But with time the amount of devices accessing cloud services has increased. Also, devices became more powerful and are starting to make real-time decisions.

For instance, if sensors in valves at a refinery detect high pressure in the pipes that could be dangerous, shutoffs need to be triggered as soon as possible. With the analysis of input taking place in a data center afar, the automatic shutoff instructions may come too late.

But with some processing taking place locally, latency is less, and reaction time can be significantly reduced, potentially preventing property from being damaged, saving downtime, and even saving lives.

But even with the introduction of edge devices to provide local processing and storage, there will still be a need to connect them to data centers or in the cloud. Cloud computing will still be relevant.

For example, temperature and humidity sensors in agricultural fields gather valuable information, but that data doesn’t have to be analyzed or stored in real-time. Edge devices can collect, sort, and perform a preliminary analysis of the input and then store them in the cloud for future use.

Importance of edge computing

Edge computing is a distributed architecture that reduces latency by providing in-house applications and processing resources closer to where users are generating the data.

This distributed processing paradigm delivers a faster response time for applications that require on-spot data processing. Reducing the dependence on expensive long-haul connections to processing and storage centers.

Edge computing allows devices that were otherwise heavily dependent on the cloud to process some of their inputs. This feature improves latency and time. It also reduces the cost and requirement for mass data transmission.

We are generating a lot of data. Transmitting them to remote, centralized processing services is becoming problematic. Edge hardware is very useful for the roll-out of local computational power at the extremities of a network and can reduce reliance on the cloud.

A good example can be a CCTV camera that records home security footage. Now the vast majority of the time nothing happens and the camera is not intelligent enough, it just records everything and the processing will take place somewhere in the cloud.

Modern-day security cameras have more powerful processors built in where it knows that nothing is happening and don’t have to send this data anywhere. It will only record the bits when something is happening or when there is movement.

With distributed processing architecture, there is no need to send terabytes or petabytes of mostly useless data back to a cloud server. Users can only send the bits of information that matter.

This is the power of edge computing, which is delivering more processing power in an intelligent manner. Processing takes place on the extremity which also reduces the bandwidth that is needed.

Another great example can be today’s smartphones with fingerprint and facial recognition. So if a user wants to use face ID or fingerprint ID, they simply have to insert their biometric in the identifier and the phone will use the artificial intelligence power by machine learning to recognize it.

If the biometric identification needed to run through the cloud it would take forever for your phone to unlock, that’s why the edge computing application are implemented in devices.

So now we can perform more and more calculations on devices. The processor within our devices are becoming more powerful and this is enabling a whole new world of internet-connected devices that can make more decisions and process more data.

How does edge computing increase efficiency?

Edge computing is a promising technology that solves several problems associated with data processing speed. It also allows for reliable offline operation, which makes it useful in remote areas.

This capability is essential in times of crisis and when businesses are under enormous pressure to make quick decisions. With edge processing, businesses can process more information at a lower cost and a faster rate.

For example, real-time decision-making is critical in the financial industry, where lags in processing data could lead to huge losses. Mastercard recently patented an edge computing kiosk that would reduce processing time and protect the user’s identity.

Further, distributed processing architecture eliminates the high costs associated with centralized computing and cloud servers. It can help carriers transform their backhaul business model. Today, sending data constantly to the cloud costs money and deteriorates the customer experience.

Another example, the field of energy management could benefit from edge computing, which would make it possible to use data on-site without sending it to a central database.

By storing data locally, information can be filtered and rationalized, without having to cross the network. It can also help operators decouple access and backhaul connectivity pricing, as well as incentivize application developers to use edge computing sites.

By minimizing network loads and latency, edge computing helps reduce costs. It also reduces network loads and frees up capacity for the most critical workloads. The broader impact of edge processing isn’t just for businesses. It can benefit consumers, too.

Besides advancing mobile processing, distributed computing devices are also being used in many service-based industries. For instance, smartphones, tablets, and other mobile devices are delivering personalized services to the population.

As mentioned, edge processing can improve service latency and improve the quality of service. It also allows for more dynamic data distribution, resulting in a superior user experience. It also supports emerging applications like Metaverse, digital twin, and densely distributed information collection points.

The future of edge computing

The future of edge computing lies in the distribution of data planes and pushing advanced services to the edges. By doing so, administrators will be able to deliver rich content and data faster, more efficiently, and more economically.

In addition, edge computing will help improve corporate analytics capabilities and enhance end-user experiences. Whether you’re looking for a better parking space or a more convenient route, edge processing will help you out.

The healthcare industry is a prime example of where distributed information technology architecture can help. In real-time, the technology can monitor patients’ conditions and create personalized health packages.

Edge computation can be applied to any device, and will eventually extend to cities. There are a lot of smart city initiatives around the world. On top of that, the internet of things is turning into the internet of everything.

The global market size of edge computing was valued at USD 40.49 billion in 2021.  A CAGR of 12.46% is expected between the period of 2022- 2030. Thus estimates reveal that the value will be USD 116.5 billion by 2030.

Our infrastructure is becoming smart. We are starting to see smart homes, smart offices, smart buildings, and smart spaces. People are relying more and more on IoT as digital trust is increasing by the day.

As more services are developed, the concept of edge computing will become more popular. Many organizations are already adopting the concept. It is estimated that by 2025, 50% of enterprise data will be processed a the edge. However, while edge computing brings a lot of ease, there are several concerns.

Cyberattacks have more than quadrupled during the past couple of years, and there is a lot of trend in cybersecurity right now. IoT devices create a lot of vulnerable points that hackers can exploit.

Cybersecurity is a key concern for enterprises and individuals. Edge computing provides a way to improve data security and harden against malicious activities. By preventing data from leaving the premises, it is protected from malicious hackers.

In a factory, edge computing can help improve the quality of baked goods while keeping data secure and out of reach of hackers. It can also eliminate costly procedural bottlenecks, such as long data transfer across international boundaries.

In the near future, autonomous vehicles will generate enormous volumes of data. In order to analyze these data in real-time, they will need efficient onboard ciphering power.

The problem with feeding all the sensors into the cloud is latency. The cellular network is too inconsistent to support self-driving cars. Edge computing technology addresses these limitations.

Instead, edge processing allows for near-zero latency and reliable connectivity. Ultimately, this technology makes our lives more convenient. And it will be necessary for the future of transportation.

The implementation 5G network is opening up more opportunities for computing capacity in different environments. Edge use-case can also be done in 4G, but 5G will provide a whole new set of opportunities that never existed in a 4G environment.

A unique feature of 5G is that it can provide new capabilities for massive machine-to-machine communication with ultra-reliable low-latency communication. So imagine a bunch of autonomous vehicles driving while also coordinating their actions.

The 5G networking is going to be an essential part of autonomous vehicles and how they communicate and transfer data to surrounding vehicles. Self-driving cars can even be the solution to traffic congestion.

Similarly, farmlands are adopting precision agriculture that collects data from various sensors, such as soil moisture and temperature. Edge computing is a huge benefit to the industry and more importantly to vertical farming.

The future benefits of edge computing in the retail sector are immense. It is going to create personalized experiences for consumers. With the ever-increasing consumerism pattern and point of sales available on the internet, personalization is going to be extremely important.

Retailers will need to sell to everybody, but with different and specific customer experiences. Nowadays retailers are gathering a lot of data about their consumers, both online and offline as well as internally and externally. They can even buy customer data from data brokers.

Retailers are recording every movement of the customer from what they buy, at what price, when they buy, and how frequently they buy a product. They are also measuring the different products customers look at and touch in a supermarket. We are entering the internet of behavior.

Smart devices in different areas of a store are collecting those touch points to understand more deeply customer behavior. For example, Amazon Go opened its first cashierless grocery store in January 2018. Customers scan a QR code on their smartphones to enter the store.

Cameras and sensors are used to identify customers, enabling them to make purchases. Edge computing makes monitoring easier. Meanwhile, Ahold Delhaize is testing a micro-fulfillment store. The future of shopping looks promising.

The ability to leverage data that is not generated in the store is very exciting. Retailers can use edge computing to provide in-store virtual reality shopping assistants and augmented reality shopping experiences to customers from the comfort of their homes.

The Internet of Things is expanding rapidly, and we’ll need more computational power to reach all these devices. Edge computation is a vital part of public infrastructure and law enforcement.

Police officers can install surveillance cameras that scan people for loitering and send an alarm if trespassers do so. They can also upload dashcam footage to a central repository using dual-LTE links.

As a distributed computing paradigm, edge computing brings storage, processing, and applications closer to the source of data. It can be used on a retail store, factory floor, utility site, smart city, or even a sprawling utility.

This approach enables the processing of data closer to the point of use, reducing latency and allowing for faster action. This can help businesses make more informed decisions that can benefit their customers. In the industrial world, reliability and low latency are the keys nowadays.

While the edge computing market is still in its infancy, it’s expected to gain more attention as everything gets more connected. As the Internet continues to become ubiquitous, edge processing becomes a necessity.