IoT: Cloud or Edge (Security Vs Simplicity)


The Internet of Things (IoT) involves the creation of smart atmosphere around us, by using modern networks and the ability of sensors to share information with each other to automate various tasks. The latest technology in IoT solutions is edge computing, although most solutions available today use the conventional cloud computing with processing and data storage both occurring on datacenters.

Here, we compare them as technologies that place the concepts of security and simplicity against one another in IoT applications.

Cloud Computing (Security Vs Simplicity)

Cloud computing refers to using the large processing power of datacenters. Companies can choose to get services that do not have to be placed on local computers. Services, software elements and database storage can all be present on a remote resource and available to the clients over a dedicated network connection.

The problem with cloud computing is that currently, sensor devices do not enjoy a dedicated networking solution. They have to rely on crowded network connections to send information of IoT sensors to the cloud computing backbone. With standards like 5G and other low-power networks still under development, cloud computing solutions can often become difficult to create in environments, where the number of smart devices can get scaled.

The security of cloud computing is certainly better, when it is compared to onsite solutions. Since human intervention is not possible, IoT solutions that employ cloud computing can often use multiple security levels to ensure that information theft or damage is not a possibility anymore. Cloud computing is certainly an excellent choice with most solutions that are currently available. However, edge computing may offer improved security benefits.

Edge Computing (Security Vs Simplicity)

Edge computing refers to the processing of data in IoT solutions near the sensors. It is designed to reduce the workload of the datacenters and can significantly alter the required network resources to host a large set of smart devices. A key advantage of edge computing is the physical addition of security layers. It certainly offers the use of better authentication and eliminate the need for sending simplistic data over the public networks.

Each node in edge computing can also serve as authentication points. This eliminates the errors that are present in the transmitted data, as well as ensuring the security of the information, which is finally used to create the all-important data analytics. The hardware layer, the communication layer and then the cloud layer is involved with cloud computing. A good IoT solution now must have the security of edge computing.

Edge computing also brings simplicity, since it allows the automation of mundane tasks. There are no technical experts who are required to manage the system. This results in the creation of a simple system, which is easy to manage and can produce dedicated results. However, the simplicity is further enhanced, if a dedicated IoT solution like IoTSense provides an efficient data transfer mechanism.

Proactive M2M Communication with Edge Computing


Proactive M2M Communication with Edge Computing

The Internet of Things is based on the principles of either a centralized, or distributed network, and works to enable all the possible types of communication with man and machine as the involved parties.

In the practical sense, particularly in the M2M department, IoT has aided communication a lot, through the application of edge computing.

How Edge Computing is Applied to M2M Communication

Hollywood and military fiction shows drones in the field receiving data and detailed, real-time analytics from other drones currently operational within their vicinity. This is actually based in fact, and is a technology that is in widespread use, outside of the military and Hollywood!

Military Drones: A Practical Example of M2M Communication

Since devices such as drones (essentially devices, albeit very large and complex ones) operate on a highly sophisticated network, especially when numbers are deployed, they require a central server to facilitate communication. This is not limited to centralized networks either, since advanced fly-by-wire systems can enable the machines to receive intelligence and guidance from other machines, without the need for data processing through the base.

Civilian Examples

Take, for instance, a number of diagnostic systems within a company that produces vehicles. Each aspect of the production and assembly is backed by a diagnostics system which identifies potential or currently occurring issues.

Now, if one system detects an error of glitch, it can warn the subsequent systems of the error in real-time, without the human supervisor having to step in. the issue can then be resolved by the involved machinery.

Such applications are already in play, and continue to increase in number on a daily basis, leading to the development of proactive communication between the involved machines, leading to seamless functioning.

Energy-Efficiency, Versatility and Efficient Resource Allocation

With a shared center network, resource allocation is not a problem, since each sensor is the relay between a network which can vary in size from a few to a few million! A server, which requires a considerable amount of computing power to function at its best, especially when considering a larger amount of data transaction, also consumes a large amount of energy.

When you have a network that allocates all the data and the computing power accumulated by each member, to said member, you cut down on a lot of the spending and maintenance that big, extensive and inefficient servers require. Doing so also increases the versatility of the network, meaning that it can run on just about any network type, irrespective of potential for speed and accuracy, with minimal effect on the transfer of data, since the entire network will be operating on the same connection type.


M2M communication has improved over the years, with machines running on software which feature deep learning algorithms, allowing the accumulated data to be leveraged even more effectively, and processes to be automated. This means that once a machine learns of a process, it can relay said learning to the entire network, thereby creating an efficient physical framework, and creating highly advanced infrastructure.

IoT: power of edge computing


IoT: power of edge computing

The internet of things (IoT) is a concept where embedded sensors present in everyday objects communicate with each other to share data, which when processed in real-time can be used to create solutions that rely on existing networks, like the internet. One of the amazing avenues that may empower the current capacity of IoT applications is the use of edge computing. Here, we describe the power and effectiveness of this method in improving the current usefulness of internet of things:

What is Edge Computing?

It is a computing concept where we may employ the available resources at the edge of a network, near the actual sources that are producing the data, especially in a cloud environment. These resources include laptops, smartphones and other networked devices. The use of the freely available resources may not be necessary in the present IoT application, but with the constantly increasing volume of IoT data, it will be important to find innovative methods for ensuring efficient performance.

The Application

The current cloud computing solutions put a direct strain on the internet resources that you have. Data and other information parameters must get transferred to the cloud and then be processed in real-time, which requires tremendous processing power. While this method is sufficient if we have limited data sources, it becomes exponentially difficult to employ when millions of sensors are integrated into a single system, and require thousands of processing threads at the same time.

Edge computing ensures that most of the data processing is already carried out near the data source and the internet is simply used as a vehicle for communicating that data. The information which is finally transferred to a cloud in an IoT solution will already be in its final form and can be directly used to find out the results that are required for an efficient IoT platform.

The Benefits

There are various benefits of using edge computing in IoT applications. The first benefit is that it certainly reduces the costs of operating a smart solution in a commercial setting. This happens because lower server resources are required to get the same task done in a reliable manner. The data management needs are reduced, producing lower operating costs with consistent use.

Another benefit is that you can analyze data in real-time at a local location. This ensures that your organizational performance speeds up with the use of an IoT application that employs edge computing. The network traffic in an IoT solution is significantly reduces, as most of the data is processed and turned into easier to transmit information.

Edge computing also ensures that you can process information locally and then implement a suitable compression format for sending information across the internet. This way, your internet connection is fully optimized and you can produce the ideal results with as little effort as possible.

Improved IoT application performance is a key benefit that should help you decide the ideal IoT solution for your business. Edge computing certainly allows you to produce IoT platforms that are customized and fully dedicated for your specific use.

With edge computing, the applications for IoT technology are countless!