1,237 research outputs found
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
AI-Based Sustainable and Intelligent Offloading Framework for IIoT in Collaborative Cloud-Fog Environments
The cloud paradigm is one of the most trending areas in today’s era due to its rich profusion of services. However, it fails to serve the latency-sensitive Industrial Internet of Things (IIoT) applications associated with automotives, robotics, oil and gas, smart communications, Industry 5.0, etc. Hence, to strengthen the capabilities of IIoT, fog computing has emerged as a promising solution for latency-aware IIoT tasks. However, the resource-constrained nature of fog nodes puts forth another substantial issue of offloading decisions in resource management. Therefore, we propose an Artificial Intelligence (AI)-enabled intelligent and sustainable framework for an optimized multi-layered integrated cloud fog-based environment where real-time offloading decisions are accomplished as per the demand of IIoT applications and analyzed by a fuzzy based offloading controller. Moreover, an AI based Whale Optimization Algorithm (WOA) has been incorporated into a framework that promises to search for the best possible resources and make accurate decisions to ameliorate various Quality-of-Service (QoS) parameters. The experimental results show an escalation in makespan time up to 37.17%, energy consumption up to 27.32%, and execution cost up to 13.36% in comparison to benchmark offloading and allocation schemes
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