Quality of Service Provisioning for Heterogeneous Services in Cognitive Radio-enabled Internet of Things

Journal article


Ali, A, Feng, L, Bashir, AK, Shaker, SH, Ahmed, SH, Iqbal, M and Raja, G (2018). Quality of Service Provisioning for Heterogeneous Services in Cognitive Radio-enabled Internet of Things. IEEE Transactions on Network Science and Engineering.
AuthorsAli, A, Feng, L, Bashir, AK, Shaker, SH, Ahmed, SH, Iqbal, M and Raja, G
Abstract

IEEE The Internet of Things (IoT) is a network of interconnected objects, in which every object in the world seeks to communicate and exchange information actively. This exponential growth of interconnected objects increases the demand for wireless spectrum. However, providing wireless channel access to every communicating object while ensuring its guaranteed quality of service (QoS) requirements is challenging and has not yet been explored, especially for IoT-enabled mission-critical applications and services. Meanwhile, Cognitive Radio-enabled Internet of Things (CR-IoT) is an emerging field that is considered the future of IoT. The combination of CR technology and IoT can better handle the increasing demands of various applications such as manufacturing, logistics, retail, environment, public safety, healthcare, food, and drugs. However, due to the limited and dynamic resource availability, CR-IoT cannot accommodate all types of users. In this paper, we first examine the availability of a licensed channel on the basis of its primary users' activities (e.g., traffic patterns). Second, we propose a priority-based secondary user (SU) call admission and channel allocation scheme, which is further based on a priority-based dynamic channel reservation scheme. The objective of our study is to reduce the blocking probability of higher-priority SU calls while maintaining a sufficient level of channel utilization. The arrival rates of SU calls of all priority classes are estimated using a Markov chain model, and further channels for each priority class are reserved based on this analysis. We compare the performance of the proposed scheme with the greedy non-priority and fair proportion schemes in terms of the SU call-blocking probability, SU call-dropping probability, channel utilization, and throughput. Numerical results show that the proposed priority scheme outperforms the greedy non-priority and fair proportion schemes.

Year2018
JournalIEEE Transactions on Network Science and Engineering
ISSN2327-4697
Digital Object Identifier (DOI)doi:10.1109/TNSE.2018.2877646
Publication dates
Print23 Oct 2018
Publication process dates
Deposited04 Dec 2018
Accepted23 Oct 2018
Accepted author manuscript
License
CC BY 4.0
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https://openresearch.lsbu.ac.uk/item/868z3

Accepted author manuscript

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