As the IoT network continues to grow, the data has to travel long distances in order to be accessible to every device connected to the IoT network. As the data was formerly stored at a central location, it requires high bandwidth for pushing the data to and fro the nodal devices where it was actually needed. This also resulted in high latency rates. The need to reduce bandwidth requirements and latency rates gave rise to ‘Edge Computing’.
Definition: Edge computing is defined as a part of a distributed computing topology in which the information processing is located close to the edge – where devices or people connected to the network produce and consume the information.
Thus, it is a Microdata center network that processes or stores the vital data locally and pushes all data inward to a central location or cloud storage. Broadly speaking, edge computing is all computing that happens outside the cloud, at the edge of the network, where real-time processing is required. The basic difference between cloud computing and edge computing is that cloud computing feeds on big data while edge computing feeds on real-time data generated by sensors or users.
How Edge Computing Works?
In order to understand how edge computing works, let us consider a corporate scenario. Think about monitoring devices in a manufacturing company. While it is easier for a single device to capture data and send it to cloud storage, the problem arises in the case of multiple monitoring devices as they would produce a large amount of data.
Thus, the edge gateway collects data from the devices and processes it locally to separate the relevant information from junk data. Once the processing is complete, only the relevant information is sent to the cloud storage. Additionally, in case an application needs this information, the edge gateway sends it back in real-time reducing the latency period which would have occurred if the information request was to be processed at cloud location.
Privacy & Security Risks:
As the data is handled by different devices, it gives rise to security and privacy risks.
- Bots: A great degree of edge computing is done via Application Programming Interfaces (APIs). Failing to encrypt the data and authenticate third-party APIs result in a lack of control. This gives rise to a loophole that can be exploited by hackers to steal data or infect the connected devices with malicious code or bots.
- Distributed Denial of Service (DDoS): The hackers may lay silent for an extended period after infecting your system. This gives them time to spread the infection through a larger number of devices while staying unnoticed. Once their code is deep-rooted, they may initiate a DDoS attack which will spread at a greater speed owing to the low latency of edge computing paired with the upcoming 5G network.
It is imperative for organizations to pay attention to data security before implementing an edge computing model in their network. For more information about edge computing and ways to manage privacy issues related to it, call Centex Technologies at (972) 375 - 9654.