Distributed Probabilistic Offloading in Edge Computing for 6G-Enabled Massive Internet of Things

Liao, Zhuofan; Peng, Jingsheng; Huang, Jiawei; Wang, Jianxin; Wang, Jin; Sharma, Pradip Kumar; Ghosh, Uttam

doi:10.1109/jiot.2020.3033298

Cited by 82 publications

(41 citation statements)

References 31 publications

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“…An ACO strategy is described in two studies [189,190], prompted by the requirement to secure 6G IoT networks by adopting various objectives and employing transaction deletion to secure secret and sensitive information.…”

Section: Resource Allocationmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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Section: Resource Allocationmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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“…Fog computing is mostly implemented for real-time data computation and storage. In fog computing, security is the main concern for data aggregation and transmission [24]. Rahul et al [25], present a fog-based framework for healthcare.…”

Section: B Fog Computingmentioning

confidence: 99%

FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

et al. 2021

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Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node FN, respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the fog node minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.

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“…To address this challenge, the scheme [38] minimized the communication cost, energy consumption and utilize the maximum throughput. To further improve it, the fog nodes require high computational capacity to process and analyze the data from sensors [70]. It is quite challenging to achieve accurate boundary with minimum transmission overhead in real-time scenarios [48].…”

Section: Transmission Overheadmentioning

confidence: 99%

A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

et al. 2021

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With the new age of data innovation, the Internet of Things (IoT) proliferation has drawn enormous thought and has applied to help applications in different fields i.e. natural assurance, military observing, and industrial applications. WSNs are the essential segment of IoT for monitoring as well as tracking. The most preeminent applications provide confinement and identification of continuous objects i.e. wildfire, toxic gas, bio synthetics concoctions, and so forth. In the case of continuous objects such as fire and toxic gases are detected to identify the boundary of damage and alert teams for rescue efforts. It is also helpful in identifying safe paths for rescue. We have investigated various existing surveys that carried out different concepts associated with continuous object tracking and find out the deficit of boundary detection of object. In order to replete the present cleft of analysis, we have inspected various current state-of-the-art works on boundary detection of a continuous object that has yet not been added to the current writing. This paper presents an extensive overview of different continuous object tracking schemes which involve energy efficiency, boundary detection, communication, data aggregation, and network structural design in literature with the aid of featuring taxonomy. We summarized, compared, and classified these schemes along with their analysis and performance. Moreover, for further evaluation mechanism, strengths and weaknesses of these schemes are presented. Finally, various state-of-the-art open research challenges are identified. Moreover, there is a need to overcome these challenges through novel and reliable arrangements by the researchers.

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Distributed Probabilistic Offloading in Edge Computing for 6G-Enabled Massive Internet of Things

Cited by 82 publications

References 31 publications

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

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