Edge computing will process data in 59% of IoT deployments before 2025. This assumption is based on the key benefits of edge computing: faster response times, security, and more efficient use. Edge computing isn’t just a new trend, but it has already started to deliver results. Edge computing can be a great way to gain a competitive advantage, whether you are looking to optimize existing products or build IoT apps for your business.
Digital devices are generating more data every day. The cloud is becoming more expensive and not as fast as it should be due to the increasing number of IoT devices. Edge computing is the answer. Edge computing reduces cloud workloads. Computing workflows can be shifted to local IoT devices, such as the smartphones of users. This reduces long-distance connections between clients and servers.
Edge Computing is the latest solution to the Internet of things. Edge Computing allows for fast analysis and processing. This makes the entire process faster and more efficient.
To know more on Edge computing refer to this article – What is Edge computing know the basics
Why Edge Computing?
Many industries rely on Cloud computing for their workloads. However, there are some drawbacks to this approach when it comes to IoT.
- Data security risks: Cloud computing allows data to move back and forth between devices and the cloud. This increases privacy risks and data security.
- Operational expenses
As the data is processed and shared increases, so does the operation cost. - Performance issues
IoT applications are dependent on real-time activity. Cloud computing increases latency due to the distance between the device and the server. Sometimes, IoT devices may not be able to connect to the central cloud because they have poor connectivity.
Real-time data analysis is essential for industries like healthcare, finance, manufacturing, and telecommunications. In this hyper-connected world, speed and rapid data are crucial in many situations. Edge computing streamlines the flow of data traffic between IoT devices. In many situations, edge computing is useful. The response time to data will depend on the circumstances and the extent of the project. We can make faster decisions if we have more data. Data can be more valuable if it is immediately analyzed and leveraged. It is a time when it is crucial to get the right insights quickly.
Edge computing promises to resolve many problems. Let’s explore its advantages:
- Low latency
In situations where latency is unacceptable, edge computing can be a benefit. Because it doesn’t travel across a network, it reduces latency. Latency refers to the time it takes for data processing and analysis. The response time of connected devices is less than one second.
The response time may take a while or be interrupted in some cases. This can happen if the internet connection is slow or the data center location is far from the device. In these cases, data processing speeds are hampered. To counteract the latency of data transmission over long distances, compute resources must be available locally.
In a car accident, for example, car sensors determine where there is a possibility of an accident. They also determine the time frame in which to deploy the airbags. Airbags won’t deploy if there is a delay or lag in data transmission over long distances. Edge computing is used to ensure that this scenario is not dangerous. - Security threats to data
Cloud computing is a system where every device can be connected to the cloud and raw data is transmitted over the internet. This can have serious implications for security and privacy as well as legal repercussions. This threat can be eliminated by using edge computing to process data near the source. Administrations can therefore keep data within their boundaries and comply with data sovereignty regulations. These regulations give citizens more control over their personal data. - Bandwidth issues
Cloud computing is used by companies to analyze and process large amounts of data. IoT data requires large amounts of bandwidth and power to send to the cloud. IoT devices that generate data or run software should connect to the cloud in order to collect and process that data. Edge computing allows businesses to reduce their internet bandwidth usage. It is possible to manage large amounts of data close to the source. Edge-computing cameras can be deployed by law enforcement agencies with reduced bandwidth. In real-time, it is possible to analyze large volumes of audio and video recordings taken by cameras. - Security issues in IoT
Organizations express concern about the security of sensitive data, even though the cloud offered excellent security for IoT applications. They fear that once data leaves their company and goes to the cloud, it won’t be safe. Edge computing is a solution to this problem as it offers more security at the edge. This will increase the security of data storage and processing against hacks and intrusions.
Edge devices can also be updated with security updates. Any outage that occurs will only affect the device and its applications. - Bandwidth issues
IoT devices produce large volumes of data and send small updates to the cloud. This requires more bandwidth, which means that bandwidth costs increase. This increases the cost of the device needed to access that bandwidth, as well as the storage and analysis costs. This data can be obtained and analyzed locally before being sent to the cloud using edge computing. This will make it much cheaper than sending unfiltered data over expensive WAN links. - Scalability
Edge computing allows data processing to be decentralized, thereby putting less strain on the network. Computing IoT devices that are located at the edge of the network next to data have a lower impact on the system’s resources. - Improved app performance: Data is processed and stored close to the source. This reduces the time it takes to process the data. This improves app performance.
Use Cases for IoT Edge
In many industrial IoT applications, data processing speed and analysis speed are crucial. They are also crucial in industrial transformation. Automation will be the next phase of the industrial revolution. Edge computing makes it easier.
There are very few examples and use cases that take advantage of edge technology.
- Device Management
Many devices can be managed from the edge. Many device management platforms allow users to extend their functionality by connecting to the edge infrastructure. The device management at the edge can support a few attributes:
- Distributed firmware updates
Local distribution of firmware updates can be done by the edge gateway. The edge node manages the distribution in this case. In cloud computing, the firmware update is distributed centrally by a queuing network. - Diagnostics for connected devices
Analytics at the edge can help identify specific problems. A key role in machine learning is pattern recognition. - Device configuration updates
When services change, the devices at the edge must be set up locally. It can also remotely manage it.
2. Priority Messaging
The majority of data generated by IoT are low-priority and have more economic value. There are critical data that must be prioritized and acted on immediately. There are many priority messaging options available. Not only for single applications.
It can trigger a series of actions across multiple devices and applications. Priority messaging can be used in the following examples:
- Transportation:
It is necessary to send a priority message of collision alert to all vehicles. It allows automobiles to avoid collisions. - Environmental:
Priority messages regarding pollution and rainfall that exceeds the safe index. - Safety & Health:
Priority messages to a building evacuation fire alarm - Security:
Priority messages to be used for security measures in the event of unauthorized activity. Drones flying in no-fly zones
3. Data Aggregation
As many IoT devices that are connected generate more data, so does the number of data generated. Data generated by more devices leads to greater replication. Reports of multiple vehicles stuck in a traffic jams are one example. All of this data can be replicated and not all must be sent back to central servers. Edge can combine data from different sensors and choose which data to send. Edge can also combine data from multiple temperature sensors at the same place and create statistical measures. IoT data aggregate is a great feature before it gets sent to the core.
- Improved network efficiency:
Data aggregation is a way to eliminate the need for multiple data processing and replication. This means that the core infrastructure is less stressed. - Latency improvements:
Processing lesser data means quicker decisions and faster appropriate actions. Reduced data processing and communication will reduce latency. - Richer data sets:
Aggregated data provides valuable data sets. This data could be used to aid machine learning in making better predictions. This will allow you to identify patterns and trends easily.
4. Cloud Enablement:
Cloud vendors have attractive options for the edge because it offers better data storage and processing speed. Edge can increase their system availability and reduce their data center load. At the edge, a mutually beneficial relationship should exist between mobile operators and cloud providers.
5. IoT Image Processing and Audio Processing
IoT edge allows for new ways to analyze data from devices such as cameras, microphones, and CCTV without having to backhaul the entire audio stream. This is useful in many IoT applications such as monitoring noise pollution and reading license plates.
An edge cloudlet is able to process image, audio, and video data in order to determine critical information such as license plate numbers. A small amount of data is stored and forwarded to cloud storage. The camera can be used as a sensor to monitor many elements of IoT within an industrial domain, such as monitoring power lines using a drone. This use case has several benefits:
- Low cost
Cameras and microphones are a close relative that are less costly to purchase, install, and maintain for the insights. Edge computing technology allows for easy management of network costs. - Massive reduction in network backhaul
It is possible to reduce the amount of data that is transmitted back to the cloud core. - Quick decision making
Rapid processing allows for faster decisions and support for a wider range of real-time applications.
6. Healthcare Devices:
These devices and health monitors keep an eye on the patient’s condition. They can send instant alerts to save lives in an emergency. Robots are able to quickly analyze data and assist with accurate, safe surgery. These devices could cause serious harm if they rely on sending data to the cloud before making any decisions.
7. Security Solutions
Emergencies and threats require immediate and appropriate response actions. The IoT Edge can be a valuable tool in security surveillance systems. Security systems can detect potential dangers and alert you if there is unusual activity.
8. Retail Advertising
Edge computing technology is a useful tool in Retail advertising to protect user privacy. Edge computing technology can encrypt data and keep it close to the source, rather than sending unprotected data to the cloud. For example, targeted ads for retail establishments that include demographic information.
9. Smart Speakers
Edge IoT-enabled smart speakers will be able to understand basic commands and can also interpret voice commands locally. It will be possible to turn the lights on and off, even without internet connectivity.
10. Video Conferencing:
Slow links to the cloud may cause video conferencing problems such as frozen screens, poor quality, and voice delays. By bringing the server-side closer to the users, quality problems can be minimized.
11. Automotive Industry:
Autonomous cars are the future of technology and the automotive industry. Industry giants have made big bets on self-drive vehicles. Gartner estimates that autonomous vehicles will upload more than 1TB per month to the cloud by 2025.
Edge computing, AI (Artificial Intelligence), and 5G will allow autonomous vehicles to collect data and analyze it. These autonomous vehicles will be able to make intelligent and logical decisions in real-time. 5G technology will enable high-speed data transmissions between automobiles and communication towers. Edge computing and AI will enable vehicles to share and process critical data among cars and wider networks. The outcome will be real-time, almost zero latency actions and decisions.
Summarising
Edge computing allows devices to access the processing power, intelligence, communication capabilities, and processing power directly from the internet. Edge computing is vital for IoT. Data, speed, and analytics are all essential. IoT edge has significant potential benefits for mobile operators, customers, and partners.
It is focused on managing multiple devices and the large amount of data they produce. IoT promises faster data processing and lowers operational complexity in device management. This technology will reduce dependence on the cloud, and allow for better control over the huge amount of data.
Edge computing provides quantifiable value for many IoT applications, both industrial and consumer.
Critical applications will also benefit from almost zero latency. IoT Edge can be profitable for many verticals.
Data processing and storage can be done at the edge in smart cities, factories, smart transport, energy companies, and other intelligence organizations. IoT Edge can be a benefit to mobile networks as it lowers connectivity costs. It transmits only relevant information and not raw data.
Integration of various verticals is possible. Intelligent buses, for example, are routed to large numbers of people waiting. This will be possible thanks to the high-speed and low latency data processing capabilities at the edge.
Edge computing should be used by businesses to support the development of new products. New opportunities will be created by the launch of IoT-specific services and tools.