Edge Computing

Edge computing is a distributed computing model that processes data closer to its source (where the data is created), such as sensors and IoT devices, instead of relying on centralized cloud servers or data centers.

The purpose behind processing data closer to where it’s created is to reduce latency, improve response times, and optimize bandwidth usage, which is crucial in applications like autonomous vehicles, smart cities, remote healthcare, and more.

Breaking Down Edge Computing

Let’s understand the key components of edge or distributed computing:

Edge Devices

Edge devices, such as sensors, IoT devices, smartphones, and autonomous machines, are physical devices that create and process data at the edge. These devices handle the computations locally and can connect directly to the cloud or other devices.

Network Edge

Network edge is the infrastructure that connects the edge devices and the cloud, including modems, personal computers, etc. The point to note here is that the network edge is near the devices they’re communicating with, unlike remotely installed cloud servers.

Edge Infrastructure

Edge infrastructure is the physical on-premise infrastructure that enables local computing and processing. Examples may include edge servers, storage devices, routers, gateways, etc.

Cloud Integration

While edge computing processes data locally, it must be integrated with the cloud for long-term data storage, heavy computational tasks, and analytical purposes—edge and cloud computing work harmoniously to optimize the system's overall architecture and efficiency.

Why Is Edge Computing Important?

Edge computing changes the way how data is processed and utilized, bringing several benefits, including:

Reduced Latency

Autonomous vehicles produce and process huge data from sensors to make real-time navigation and safety decisions. If the data is sent to a cloud server far from the vehicle’s location, the response can be delayed, leading to devastating consequences.

However, edge computing processes data closer to its source (at the network’s edge), substantially reducing the travel time for data and enabling faster responses.

Reduced Bandwidth Costs

Considering the rate at which IoT devices produce data, uploading everything to the cloud for processing would require massive bandwidth, leading to high network costs.

Take the example of CCTV cameras installed around a building that send multiple videos to the cloud for analysis. The cloud server then processes the videos and stores the bits where motion is recorded. This process requires substantial network bandwidth and storage issues at the user and cloud service provider’s end.

However, with edge computing, motion analysis can be done with the CCTV camera. This will send only selective videos to the cloud, reducing the bandwidth requirements and saving costs.

Examples of Edge Computing in Practice

Here are the potential use cases of edge computing:

Healthcare

Edge computing enables smartwatches to monitor blood pressure, oxygen, and heart rate. By processing the data locally, the watch can detect any abnormality and instantly alert the authorities without needing to send data to the cloud and wait for a response.

Retail

Retail stores use IoT devices (RFID tags, cameras, and sensors) to monitor customer movements and inventory levels. These devices leverage edge computing to analyze customer behavior to dynamically change layouts, optimize inventory management, and deliver personalized shopping experiences.

Self Driving Cars

Self-driving cars or autonomous vehicles are a great use case of edge computing. These vehicles use edge computing to process data locally and make instant decisions to ensure the safety of the driver, passengers, and the people on the road.

Smart Cities

Edge computing can help manage traffic by processing data captured from sensors and cameras installed at intersections. Also, authorities can use real-time traffic data to adjust traffic patterns and optimize traffic flow.

Challenges in Using Edge Computing

Here are some challenges that you might face with edge computing:

Security Risks Due to Decentralized Data Processing

In edge computing, the data is processed locally on distributed devices instead of a centralized server. This decentralization increases the number of entry points the attackers can target. 

Also, edge devices are prone to hacking or physical tempering as they are usually installed in unsafe locations.

Higher Initial Deployment and Hardware Costs

To enjoy the benefits of edge computing, one needs to set up an edge computing infrastructure, which requires massive investment in hardware, including sensors, local servers, network components, edge devices, etc. These expenses can be way more than one would invest in traditional cloud-based solutions.

Complexity in Managing Distributed Systems

Because data is processed on multiple devices located at different locations, monitoring performance, maintaining control, and ensuring consistency can become challenging. It would require multiple resources, making it an overwhelming process for smaller companies.

Alternatives and Extensions to Edge Computing

Here are some potential alternatives or extensions to edge computing:

Fog Computing

Fog computing extends the concept of edge computing by adding layers between the cloud and edge devices. It provides additional processing power, intelligence, and storage closer to the edge, enabling faster response times.

Cloud Computing

Cloud computing is a centralized computing model that complements edge computing by performing complex processing tasks that demand higher processing power lacking in edge devices. 

Edge computing handles real-time and localized data processing; cloud computing manages large-scale data.

Hybrid Models

Hybrid computing models combine the goodness of both cloud and edge computing. In a hybrid computing model, time-sensitive data is processed locally while complex computations are sent to the cloud.

FAQs

How does edge computing differ from cloud computing?

Edge and cloud computing are two related yet distinct technologies.

In edge computing, the data is processed close to its source to reduce latency and improve response times, and only selective data is uploaded to the cloud. This computing model is crucial for operations that require low latency and quick decision-making (ex., autonomous vehicles).

On the other hand, cloud computing involves relying entirely on cloud servers for tasks such as data processing and storage. This computing model is suited for operations that require substantial computational power, storage, and scalability but don’t need real-time processing.

What are the benefits of edge computing for businesses?

The primary benefits of edge computing involve real-time data processing, low latency & quick responses, and optimized network bandwidth usage. Because of these benefits/features, edge computing makes operations like autonomous vehicles, IoT devices, and smart cities possible.