Advances and Emerging Challenges in Cognitive Internet-of-Things

Li, Feng; Lam, Kwok-Yan; Li, Xiuhua; Sheng, Zhengguo; Hua, Jingyu; Wang, Li

doi:10.1109/tii.2019.2953246

Cited by 75 publications

(26 citation statements)

References 33 publications

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“…6G CR networks are projected to attain superior performance compared to 5G and possess high-performance matrices [30][31][32]. For 6G, the peak data rate is expected 1-10 Tbps with optical frequency band and THz assistance, while this data rate in 5G is 20 Gbps.…”

Section: Performance and Applications Of Crn In 6gmentioning

confidence: 99%

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Aslam

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Recently, 5G installation has been started globally. Different capabilities are in the consistent procedure, like ultrareliability, mass connectivity, and specific low latency. Though, 5G is insufficient to meet all the necessities of the future technology in 2030 and so on. Next generation information and communication technology is playing an important role in attraction of researchers, industries, and technical people. With respect to 5G networks, sixth-generation (6G) CR networks are anticipated to familiarize innovative use cases and performance metrics, such as to offer worldwide coverage, cost efficiency, enhanced spectral, energy improved intelligence, and safety. To reach such requirements, upcoming 6G CRNs will trust novel empowering technologies. Innovative network architecture and transmission technologies and air interface are of excessive position, like multiple accesses, waveform design, multiantenna technologies, and channel coding schemes. (1) To content, the condition should be of worldwide coverage, there will be no limit on 6G to global CR communication networks that may require to be completed with broadcast networks, like satellite communication networks, therefore, attaining a sea integrated communication network. (2) The spectrums overall will be entirely travelled to the supplementary rise connection density data rates in optical frequency bands, millimeter wave (mmWave), sub-6 GHz, and terahertz (THz). (3) To see big datasets created because of tremendously varied CR communication networks, antenna rush, diverse communication scenarios, new provision necessities, wide bandwidth, and 6G CRNs will allow an innovative variety of intelligent applications with the assistance of big data and AI technologies. (4) Need to improve network security when deploying 6G technology in CR networks. 6G is decentralized, intended, intelligent innovative, and distributed network. In this article, we studied a survey of current developments and upcoming trends. We studied the predicted applications, possible technologies, and security issues for 6G CR network communication. We also discussed predicted future key challenges in 6G.

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Section: Performance and Applications Of Crn In 6gmentioning

confidence: 99%

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Aslam

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…And two bottlenecks become more and more complex to achieve an optimal strategy. Hence, the advanced edge computing based on deep learning for the recommender system should be investigated to meet the above challenges [98].…”

Section: Advanced Edge Computingmentioning

confidence: 99%

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun

et al. 2020

IEEE Access

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Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

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“…However, the EU SENSEI project 14 has focused on the functionality of LoWPAN in the current and future global Internet. 15 Security is also one of the core parameters in IoT systems and is being evaluated in different prospects. 16 M2M networks are cognitive and capable to interact with each other without human interference.…”

Section: Related Workmentioning

confidence: 99%

A novel superframe structure and optimal time slot allocation algorithm for IEEE 802.15.4–based Internet of things

Khan

Alvi

Khan

et al. 2020

International Journal of Distributed Sensor Networks

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IEEE 802.15.4 standard is specifically designed for a low-rate and low-processing Internet of things (IoT) applications and offers guaranteed time slots. A beacon-enabled IEEE 802.15.4 consists of a superframe structure that comprises of the contention access period and contention-free period. During contention-free period, nodes transfer their data using guaranteed time slots without any collision. The coordinator node receives data transmission requests in one cycle and allocates guaranteed time slots to the nodes in the next cycle. This allocation process may cause large delay that may not be acceptable for few applications. In this work, a novel superframe structure is proposed that significantly reduces guaranteed time slots allocation delay for the nodes with data requests. The proposed superframe structure comprises of two contention access periods and one contention-free period, where contention-free period precedes both contention access periods with reduced slot size. In addition, the knapsack algorithm is modified for better guaranteed time slots allocation by allowing more guaranteed time slots requesting nodes to send their data as compared to the IEEE 802.15.4 standard. The simulation and analytical results show that the proposed superframe structure reduces the network delay by up to 80%, increases contention-free period utilization up to 50%, and allocates guaranteed time slots up to 16 nodes in a single superframe duration.

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Advances and Emerging Challenges in Cognitive Internet-of-Things

Cited by 75 publications

References 33 publications

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

A novel superframe structure and optimal time slot allocation algorithm for IEEE 802.15.4–based Internet of things

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