Modeling an Edge Computing Arithmetic Framework for IoT Environments

Abstract

IoT environments are forecasted to grow exponentially in the coming years thanks to the recent advances in both edge computing and artificial intelligence. In this paper, a model of remote computing scheme is presented, where three layers of computing nodes are put in place in order to optimize the computing and forwarding tasks. In this sense, a generic layout has been designed so as to easily achieve communications among the diverse layers by means of simple arithmetic operations, which may result in saving resources in all nodes involved. Traffic forwarding is undertaken by means of forwarding tables within network devices, which need to be searched upon in order to find the proper destination, and that process may be resource-consuming as the number of entries in such tables grow. However, the arithmetic framework proposed may speed up the traffic forwarding decisions as relaying on integer divisions and modular arithmetic, which may result more straightforward. Furthermore, two diverse approaches have been proposed to formally describe such a design by means of coding with Spin/Promela, or otherwise, by using an algebraic approach with Algebra of Communicating Processes (ACP), resulting in a explosion state for the former and a specified and verified model in the latter.

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

“…DATA USING EDGE/FOG COMPUTING Cloud computing has a lot of advantages when it comes to dealing with devices with constraint capabilities, such as IoT devices furnished with sensors and actuators, as it allows to defer their computing resources to remote facilities whilst keeping its own processing and power necessities to a minimum. However, network connectivity related to the cloud uses WAN technologies due to the long distances between the location of the users and that of their computing resources, which brings about a challenge when it comes to latency and bandwidth [66]. Hence, in order to keep the remote computing frameset for limited-resource devices whilst trying to address the issues related to latency and bandwidth, an interesting solution is to bring the remote computing resources closer to the end users, which is achieved by using the edge/fog computing paradigm [67].…”