Enabling Processing Power Scalability with Internet of Things (IoT) Clusters

Abstract

Internet of things (IoT) devices play a crucial role in the design of state-of-the-art infrastructures, with an increasing demand to support more complex services and applications. However, IoT devices are known for having limited computational capacities. Traditional approaches used to offload applications to the cloud to ease the burden on end-user devices, at the expense of a greater latency and increased network traffic. Our goal is to optimize the use of IoT devices, particularly those being underutilized. In this paper, we propose a pragmatic solution, built upon the Erlang programming language, that allows a group of IoT devices to collectively execute services, using their spare resources with minimal interference, and achieving a level of performance that otherwise would not be met by individual execution.

Edge Computing for Internet of Things

“…With the increasingly complex demands from IoT applications regarding end nodes and edge computing, there is an increasing need to effectively utilize the available resources. In [1], the authors focus on maximizing the use of available IoT devices by allowing them to collectively execute services using their spare resources. This reduces latency and data transfer to cloud services and improves the overall performance of IoT applications.…”

Internet of Things: Development Intelligent Programmable IoT Controller for Emerging Industry Applications

“…The importance of IoT-related technology development and application is increasing day by day [ 36 , 37 , 38 , 39 ]. The collection of data on industrial implementation processes in the industrial environment is a critical aspect of the IoT [ 40 ].…”

IoT Clusters for Enhancing Multimedia Applications

“…This means that there is a considerable amount of computational power distributed by these devices. It has been our goal to explore the use of these devices, particularly through the creation of tools that enable code distribution and execution to explore the use of the spare computational capacity of IoT devices [ 4 , 5 ].…”