Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System

Sodhro, Ali Hassan; Pirbhulal, Sandeep; Sodhro, Gul Hassan; Muzammal, Muhammad; Luo, Zongwei; Gurtov, Andrei; Macedo, Antonio Roberto L. de; Wang, Lei; García, Nuno M.; Albuquerque, Victor Hugo C. de

doi:10.1109/tits.2020.3019227

Cited by 57 publications

(15 citation statements)

References 25 publications

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“…We did not discuss the case of exceptional conditions, or erroneous data in the recorded data, or actual situation. Such cases might threaten the datadriven foundations of the concept and require a data collection and transmission method of high quality (as applied in, e.g., [8], [58]). The impact of higher predictability towards demand can also be identified, similar to [59].…”

Section: Discussionmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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Predictions of transit delays are crucial to passengers and operators. Passengers utilize the predictions to decide on departure time, route choice, and mode choice, whereas operators decide on schedules, timetables, rolling stock allocation, and control actions. We introduce the concept of predictability of transit travel times as the study of the reduction of the predicted variability with the temporal approaching of a predicted event.We evaluate predictability on a real-life test case in Zurich, Switzerland, spanning multiple transit lines over one year of operations. The concept is shown based on predictions obtained by a state-of-the-art Bayesian network approach, where we show how predictability (in general) can be modeled as an exponential decay phenomenon. The study of predictability of transit operations leads to additional insights for control actions and system analysis compared to other complementary concepts such as punctuality or regularity, for instance, concerning bunching, identification of bottlenecks, and passenger routing.

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Section: Discussionmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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“…Most of the existing research works on 6G focused on the detailed survey and reviews to highlight the need and important limitations and recommendations, but they overlooked developing, testing, and validating the 6G- and AI-based methods and framework [ 25 , 26 , 27 , 28 , 29 ]. Thus, this gap is filled by our conducted research by proposing a 6G-based intelligent resource allocation approach at the fog nodes for healthcare applications.…”

Section: Existing Workmentioning

confidence: 99%

“…Their work is a potential guideline for future research in healthcare. In [ 26 ], green and reliable techniques were developed for vehicular communication over 5G networks. In addition, they validated the results by setting the 5G testbed.…”

Section: Existing Workmentioning

confidence: 99%

“…In addition, Virtual/Augmented Reality (VR and AR) are the key indicators for diagnosis in healthcare with high bandwidth and less delay. The main purpose of VR/AR in healthcare is to empower high-resolution multimedia (video, audio, voice) transmission [ 25 , 26 , 27 ]. One of the limitations of 5G driven VR/AR in healthcare for multimedia transmission is that it does not support the real-time platform to end-users [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…The main purpose of VR/AR in healthcare is to empower high-resolution multimedia (video, audio, voice) transmission [ 25 , 26 , 27 ]. One of the limitations of 5G driven VR/AR in healthcare for multimedia transmission is that it does not support the real-time platform to end-users [ 26 ]. Thus, it is vital to validate the fuzzy-based sustainable, interoperable, and reliable algorithm (FISRA) with appropriate parameters in e-healthcare applications.…”

Section: Introductionmentioning

confidence: 99%

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AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

Sodhro

Zahid

2021

Sensors

Self Cite

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Artificial Intelligence (AI) is the revolutionary paradigm to empower sixth generation (6G) edge computing based e-healthcare for everyone. Thus, this research aims to promote an AI-based cost-effective and efficient healthcare application. The cyber physical system (CPS) is a key player in the internet world where humans and their personal devices such as cell phones, laptops, wearables, etc., facilitate the healthcare environment. The data extracting, examining and monitoring strategies from sensors and actuators in the entire medical landscape are facilitated by cloud-enabled technologies for absorbing and accepting the entire emerging wave of revolution. The efficient and accurate examination of voluminous data from the sensor devices poses restrictions in terms of bandwidth, delay and energy. Due to the heterogeneous nature of the Internet of Medical Things (IoMT), the driven healthcare system must be smart, interoperable, convergent, and reliable to provide pervasive and cost-effective healthcare platforms. Unfortunately, because of higher power consumption and lesser packet delivery rate, achieving interoperable, convergent, and reliable transmission is challenging in connected healthcare. In such a scenario, this paper has fourfold major contributions. The first contribution is the development of a single chip wearable electrocardiogram (ECG) with the support of an analog front end (AFE) chip model (i.e., ADS1292R) for gathering the ECG data to examine the health status of elderly or chronic patients with the IoT-based cyber physical system (CPS). The second proposes a fuzzy-based sustainable, interoperable, and reliable algorithm (FSIRA), which is an intelligent and self-adaptive decision-making approach to prioritize emergency and critical patients in association with the selected parameters for improving healthcare quality at reasonable costs. The third is the proposal of a specific cloud-based architecture for mobile and connected healthcare. The fourth is the identification of the right balance between reliability, packet loss ratio, convergence, latency, interoperability, and throughput to support an adaptive IoMT driven connected healthcare. It is examined and observed that our proposed approaches outperform the conventional techniques by providing high reliability, high convergence, interoperability, and a better foundation to analyze and interpret the accuracy in systems from a medical health aspect. As for the IoMT, an enabled healthcare cloud is the key ingredient on which to focus, as it also faces the big hurdle of less bandwidth, more delay and energy drain. Thus, we propose the mathematical trade-offs between bandwidth, interoperability, reliability, delay, and energy dissipation for IoMT-oriented smart healthcare over a 6G platform.

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