On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain

Time-optimized management of mobile IoT nodes for pervasive applications

The Internet of Things (IoT) incorporates numerous nodes adopted to support novel pervasive computing applications. Nodes are capable of interacting each other and/or collect/process huge volumes of ambient data. Any service or application executed on top of the collected data is hosted by the operating software/firmware of nodes, thus, such software should be up-to-date. Legacy techniques dealing with the update task cannot efficiently support it due to the adopted centralized approach that suffers from a number of disadvantages. In this paper, we go a step forward and propose a time-optimized and network performance-aware model for initiating and concluding the update process. Our aim is to have the nodes independently deciding the initiation of the update process by finding the appropriate time to execute it. Every node acts autonomously and monitors the network's performance to find a slot where performance parameters advocate for an efficient and uninterrupted conclusion of the update task. Hence, the proposed model can be adapted to the environment and the status of each node. The final decision is made taking into consideration multiple parameters and it is based on the solution of the widely known Secretary Problem (SP) originated in the Optimal Stopping Theory (OST). We provide the description of the problem, specific formulations and the analysis of our solution while extensive experiments reveal the advantages of the proposed scheme. © 2018 Elsevier Lt