IoT Gateway: Data Processing and Transmission Through Edge Computing

Today, the applications of IoT are rapidly increasing. Almost all the devices from automotive dishwashers to machinery is connected and controlled through online—enabling new opportunities towards individual and business perspective. IoT today runs on the cloud platform to handle data processing and big data, but limitations such as network connectivity are still degrading the performance of cloud platforms. Moreover, the cloud platforms designed are not efficient to handle IoT generated bandwidth overloads, and data generated through IoT devices is useless it is processed within a fraction of second.

Motivated by these challenges, new technology is developed by researchers to shift the functionality of centralized cloud computing to edge network devices. There are several edge-computing techniques from different application background to decrease the latency, enhance spectral efficiency, and assist a massive machine type communication.

Cloudlet, a mobility-enhanced small-scale cloud data center located at the edge of the internet is developed to analyze the critical challenge of end-to-end communication amongst the devices. The main theme behind the deployment of Cloudlet is to assist resource intensive and interactive wireless communication applications with more powerful computing resources and less latency. High-speed wireless LAN and virtual machine hand-off technology can be used as a medium to access computing resources with nearby cloudlet platform. During the cloudlet design process, the cloudlet system should be more agile in their provisioning because the integration with wireless devices is highly dynamic with considerable churn.

Mobile Edge Computing (MEC) is another edge of mobile technology—which is determined as the key enabler for IoT technologies and vertical solutions. The advanced characteristics such as low latency, high bandwidth, and proximity allow users to capture real-time insight about network information and location awareness, along with expanded services for multiple sectors such as enterprise, consumer, and health. Furthermore, augmented reality (AR) applications are highly inherent to collaborative properties such as data collection in the uplink, computing at the edge, and data delivery at the downlink—requires low latency with high data processing speed to obtain correct information. MEC-based platform with energized ecosystem helps to achieve AR in an efficient and effective manner.

Though there are numerous edge-computing models, MEC and Cloudlet are found to be advantageous compared to other existing techniques. There is still a number of outstanding problems that need to be addressed to achieve better communication. Furthermore, with advanced data mining and network slicing tasks, the degrees of freedom vary along with constraints, which further beckon the design and validation of edge computing models.

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