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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
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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
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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.
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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.
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Results for CTPS 2 are listed in CTPS2.docx.
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