A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Abstract

Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource-constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end-devices. Mist computing is a new paradigm that attempts to make use of the more resource-rich nodes that are closer than Edge nodes to end-users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive-scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning-to-rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource-rich nodes, while considering the selection of other nodes. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Analysis of architectures implemented for IIoT

“…Despite this information, studies that were simulated and/or emulated for CF are presented below, from which there are several proposals on the improvement of infrastructures in dynamic resource provisioning [ 52 ] developing different methodologies that operate close to a shopfloor (Fog or Edge architecture). In this sense [ 53 ], proposes an FC framework called FITOR, through orchestration of resources for devices of different layers, services and links, adding value to Service Deployer for the optimization of location, computation and network requirements in Fog nodes (FN) and Mist nodes (MN) [ 54 , 55 ]. The author in Ref.…”

In-depth analysis and open challenges of Mist Computing

“…Theoretical models to rank node suitability for a kind of application and identify the best ones in a Cloud-Fog-Mist architecture (according to parameters like processing power, storage, energy consumption, neighborhood topology, etc.) have been developed in [64] and [65]. Another outstanding work targeting persistent storage is the Fair Storage Distribution (FSD) [66] algorithm, which composes a distributed file system for replication of sensor data using physical storage of extreme edge devices instead of the Cloud.…”

Using Dynamic Perceptually Important Points for Data Reduction in IoT

“…Most state-of-the-art data reduction techniques measure performance only in terms of amount of data reduction and accuracy of the basic quires such as reconstituting the average of the original data [22]. Moreover, over the last few years, the computational power of IoT devices has increased [21]. Thus, IoT devices now have the ability to run sophisticated algorithms.…”