.mat .py

Code of Paper: VoI-Guaranteed Task Computing for Massive IoT under Demand and Resource Uncertainties

Abstract—In massive Internet of Things (IoT) deployments,

the efficient allocation of computing resources to IoT devices

while preserving devices’ data poses a significant challenge.

This paper proposes a new online probabilistic model to address

uncertainties in demand and resource allocation for IoT

networks, where the task computing of requesting devices is

addressed by serving devices. The model incorporates uncertainty

and formulates an optimization problem, concerning available

Categories:: Artificial Intelligence
Signal Processing

134 Views

AI-based Online VoI-Aware Healthcare and Medical Monitoring Task Computing

Abstract—In recent years, there has been a significant advancement

in the field of healthcare systems with the introduction

of fifth generation cellular communications and beyond (5GB).

This development has paved the way for the utilization of

telecommunications technologies in healthcare systems with an

level of certainty, reaching up to 99.999 percent. In this paper,

we present a novel task computing framework that can address

the requirements of healthcare systems, such as reliability. In

Categories:: Artificial Intelligence
Signal Processing

287 Views

Code of Paper: Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management

This paper investigates resource management in device-to-device (D2D) networks coexisting with mobile cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network’s energy needs, we formulate an online optimization problem.

Categories:: Artificial Intelligence
Communications

331 Views

CODES OF PAPER: E2E Resilient and Proactive Resource Management with Network Slicing

Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in network slicing (NetS) technology.This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS.

Categories:: Artificial Intelligence
Signal Processing
Communications

253 Views

PEN-FWI network executable files and datasets

The dataset introduces a novel physics-embedded deep learning neural network for accelerating traditional FWI algorithms, thereby reducing the required imaging time while overcoming the challenge of needing a high-quality initial model for traditional FWI inversion. The provided dataset includes training, validation, and testing sets, along with executable files related to PEN-FWI network training and validation.

Artificial Intelligence

Brain

Submitted On:

Thu, 11/09/2023 - 21:01

Last Updated On:

Thu, 11/09/2023 - 22:10