Edge and cloud Computing in IoT solutions

Edge computing is a distributed information technology paradigm wherein the client data is stored and processed as close to the originating source as possible. Whereas in Cloud computing, data is processed at the central server. Edge computing and cloud Computing in IoT plays an equally important role. 

In other words, edge computing brings data processing and storage resources away from the central data centre and closer to the data source itself. Let’s figure out When and where Edge computing and cloud Computing in IoT are useful.

Edge computing in IoT: Processing, analytics and more

Edge computing Improves network latency

Most IoT applications are basically high-end monitoring systems that collect and analyze data and then take actions based on the insights obtained from the analysis of this data. It is sometimes done hourly or daily basis, or only when a specific event causes the trigger. Edge computing can prove to be useful to IoT when these insights are needed in real-time. By providing processing closer to the IoT device, data collection and analytics take place at a physically closer location (i.e. often within the same country or region, perhaps even on the premises, rather than in a large centralized data centre). Through this, network latency is minimized as the time for data to go back and forth between the data centre and back is shorter.

In this way, edge computing can prove to be beneficial for IoT applications that require real-time actions.

Optimization of bandwidth requirement

Most IoT-connected devices transmit very small packets of data back to a data management platform that processes this data to yield insights. This is fine with data being streamed to a platform running in a centralized cloud. But, in the future, with a predicted explosive increase in the number of connected devices, this may strain operators’ existing networks. Even if individual data packets are only a few bytes, when this is being streamed in real-time from very many devices in a relatively small geographic area, e.g. a manufacturing plant or a metro centre, the cumulative effect could be very large.

With Edge Computing, processing and filtering of IoT-generated data can be done closer to the devices, thereby optimizing bandwidth requirements. 

Edge Computing provides Better Security

One of the most glaring challenges in the IoT domain is security, especially as more and more devices are connected through IoT. Although edge computing is not more secure than a private cloud, it does have the benefits of being more local. When considering locations which have different data protection laws than where the data is being generated, edge computing can have good security advantages. Especially when the edge servers are located on the periphery of data sources, companies can control all access to the servers that store the information.

Edge Computing and Cloud computing in IoT

IoT Edge platforms: IoT requirements for Edge computing

The efficient functioning of Edge platforms for IoT applications requires certain IoT-specific requirements to be met.

As an example, some IoT applications which need AI, ML or data analytics at the edge may have certain hardware requirements, such as servers with the necessary GPU capacity. Edge gateways for IoT devices will also have to support connectivity for a wide range of IoT devices communication such as LoRaWAN, Sub-1 GHz, 6lowpan and Bluetooth, and also connectivity via cellulars such as NBIoT snd LTE M and Wi-Fi technology.

Besides, operators have to keep in mind the type of IoT platform they are using and how compatible it is with their edge infrastructure. Many operators with IoT businesses rely on third-party vendor platforms to manage their IoT networks. With the demand for edge applications growing, platform vendors are investing more resources in introducing capabilities to make their edge platforms better.

AWS IoT cloud

AWS IoT Core is a managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices. AWS IoT Core can support billions of devices and trillions of messages and can process and route those messages to AWS endpoints and to other devices reliably and securely. With AWS IoT Core, your applications can keep track of and communicate with all your devices, all the time, even when they aren’t connected.

Google IoT

It is a fully managed service to easily and securely connect, manage, and process data from globally dispersed devices.

Google IoT Core, in combination with other services on Google Cloud, provides a complete solution for collecting, processing, analyzing, and visualizing IoT data in real-time to support improved operational efficiency. IoT Core supports the standard MQTT and HTTP protocols, so you can use your existing devices with minimal requirement of firmware updates.


What is cloud computing?

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. 

Key advantages of IoT cloud computing

Multiple connectivity options

There are many connectivity options available in IoT cloud computing, thereby ensuring large network access. People make use of different types of devices to access cloud computing resources: mobile devices, tablets, laptops etc. 

Internet-based service

Since Cloud computing is a web-accessed service, there is complete ease and convenience for developers for IoT development.

Scalability advantages

One of the most valuable aspects of cloud computing is the scalability it offers. Users of cloud computing platforms can scale the service according to their needs. With speed and flexibility, you have the option of expanding storage space and altering software settings. This characteristic of cloud computing has enabled deep computing power and storage.

Better collaboration and coordination in work

With Cloud, Computing clients can benefit from the pooling of resources. It aids in collaborative projects and helps in building closer connections between users.

Higher security

As the numbers and scale of IoT-enabled devices and applications increase, and automation grows, cyber security concerns become quite apparent. With their robust and reliable authentication and encryption protocols, cloud solutions provide companies and clients with the highest security and privacy.  


IoT cloud computing is cost-effective because you only pay for the resources you use. 

A key IoT challenge is deploying software updates to edge devices without interrupting the already running services. Kubernetes provides an ecosystem to IoT which can run microservices that progressively roll out changes achieving Zero service downtime.

Comparison between cloud computing and Edge computing

Comparison between cloud computing and edge computing

Let us understand the comparison between the two through the key features of these two technologies.

Cloud computing

  • It is highly centralized.
  • It possesses high processing and computing power.
  • It has high latency.
  • Its processing capabilities are powered by Artificial Intelligence (AI).
  • It is cyber secure 
  • It has a very high storage capacity

Edge computing

  • It is decentralized.
  • It has the lowest latency and is thus useful in applications where latency is a  concern.
  • It saves bandwidth as data processing and storage are done close to the data source.
  • It has limitations in its processing and networking capabilities.
  • It has data privacy and cybersecurity concerns.

An example of a case where an IoT Edge cloud system, that takes advantage of both Edge and cloud computing, is healthcare facilities and other emergency systems that require immediacy in IoT service delivery.

Edge Gateway devices sit at the intersection of the cloud and IoT device nodes or sensor devices connecting over a network. The data collected from wireless sensor networks or the other IoT devices will be transmitted through gateways(Data is computed at gateways) to the cloud(for further processing). The analyzed data received from IoT Devices is then stored in the cloud and from there it is provided as a service to the users.

In these situations, Edge computing helps in achieving decreased latency, whereas cloud computing helps achieve high data processing capabilities.

Also, Read- How do Sensor devices communicate data to the edge gateway? and COMPUTING AT THE CLOUD AND EDGE IN THE IOT.


In conclusion, it can be said that although edge and cloud computing have their own strengths and weaknesses, when the two combine, they can really bring greater efficiency to the IoT ecosystem.

PsiBorg is a full-stack IoT Development service provider, having immense experience in developing IoT solutions for global deployment.

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