Advance Cloud Data Analytics for 5G Enabled IoT

Prasad, Vivek Kumar; Tanwar, Sudeep; Bhavsar, Madhuri

doi:10.1007/978-3-030-67490-8_7

Cited by 10 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5G allows CPHS systems, components, and processes to communicate in real-time, with a high density of connection requests. Services like massive machine type communication (mMTC) in 5G allow large numbers of IoT devices and sensors to collect real-time data on the production process [37], equipment performance, and worker safety. It is particularly useful in applications that require high data throughput and network scalability, such as smart factories [38].…”

Section: Fwf 2020-2023 Medicalmentioning

confidence: 99%

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Trivedi,

Bhattacharya,

Prasad

et al. 2024

IEEE Open J. Ind. Applicat.

Self Cite

View full text Add to dashboard Cite

The Industrial Revolution has shifted towards Industry 5.0, reinventing the Industry 4.0 operational process by introducing human elements into critical decision processes. Industry 5.0 would present massive customization via transformative technologies like cyber-physical systems, artificial intelligence (AI), and big-data analytics. In Industry 5.0, the AI models must be transparent, valid, and interpretable. AI models employ machine learning (ML) and deep learning (DL) mechanisms to make the industrial process autonomous, reduce downtime, and improve operational and maintenance costs. However, the models require explainability in the learning process. Thus explainable AI (EXAI) adds interpretability and improves the diagnosis of critical industrial processes, which augments the machine-to-human (M2H) explanations and vice-versa. Recent surveys of EXAI in industrial applications are mostly oriented towards EXAI models, the underlying assumptions. Still, fewer studies are conducted towards a holistic integration of EXAI with human-centric processes that drives the Industry 5.0 applicative verticals. Thus, to address the gap, we propose a first-of-its-kind survey that systematically untangles EXAI integration and its potential in Industry 5.0 applications. First, we present the background of EXAI in Industry 5.0 and cyber-physical systems (CPS) and a reference EXAI-based Industry 5.0 architecture with insights into Large Language Models (LLMs). Then, based on the research questions, a solution taxonomy of EXAI in Industry 5.0 is presented, which is ably supported by applicative use cases (cloud, digital twins, smart grids, augmented reality, and unmanned aerial vehicles). Finally, a case study of EXAI in manufacturing cost assessment is discussed, followed by open issues and future directions. The survey is designed to extend novel prototypes and designs to realize EXAI-based in real-time Industry 5.0 applications.

show abstract

Section: Fwf 2020-2023 Medicalmentioning

confidence: 99%

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Trivedi,

Bhattacharya,

Prasad

et al. 2024

IEEE Open J. Ind. Applicat.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Performance Efficiency [34,34,36,37,[37][38][39][40][41][42][43][44][45][46] Reliability [47][48][49][50][50][51][52][53][54][55][56][57] Security [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] Operational Policy-Based Functions [74][75][76][77][78][79][80][81][82][83]…”

Section: Quality Attributes Referencesmentioning

confidence: 99%

Efficient Resource Utilization in IoT and Cloud Computing

Prasad,

Dansana,

Bhavsar

et al. 2023

Information

View full text Add to dashboard Cite

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability.

show abstract

“…It is envisioned that the number of concurrent connections increase from one million per square kilometre in 5G to ten million per square kilometre in 6G [3], [4]. Together with artificial intelligence (AI), the next generation of wireless technologies intends to enable the communication infrastructure required for the massive IoT (mIoT) use cases [5], [6]. In mIoT, a large number of low-cost low-power endpoints communicate with the central node (CN) [7].…”

Section: Introductionmentioning

confidence: 99%

Rate-Convergence Tradeoff of Federated Learning over Wireless Channel

Salari¹,

Shirvanimoghaddam²,

Vucetic³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, we consider a federated learning problem over wireless channel that takes into account the coding rate and packet transmission errors. Communication channels are modelled as packet erasure channels (PEC), where the erasure probability is determined by the block length, code rate, and signal-to-noise ratio (SNR). To lessen the effect of packet erasure on the FL performance, we propose two schemes in which the central node (CN) reuses either the past local updates or the previous global parameters in case of packet erasure. We investigate the impact of coding rate on the convergence of federated learning (FL) for both short packet and long packet communications considering erroneous transmissions. Our simulation results shows that even one unit of memory has considerable impact on the performance of FL in erroneous communication.

show abstract

Advance Cloud Data Analytics for 5G Enabled IoT

Cited by 10 publications

References 52 publications

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Efficient Resource Utilization in IoT and Cloud Computing

Rate-Convergence Tradeoff of Federated Learning over Wireless Channel

Contact Info

Product

Resources

About