A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

Oyewobi, Stephen S.; Hancke, Gerhard P.; Abu‐Mahfouz, Adnan M.; Onumanyi, A. J.

doi:10.1016/j.comcom.2019.05.003

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…Based on a PU traffic model prediction a CR user is able to select a target channel for spectrum handoff before it commences spectrum mobility by correctly predicting when a PU will be available on or vacate a channel. 111 As a result, a CR user can reconfiguration its RF front-end and execute spectrum switching before a PU appears on the channel. 112 The major disadvantage is that the performance of this technique depends on accurate prediction of PU traffic model.…”

Section: Pure Proactive Spectrum Handoff Strategymentioning

confidence: 99%

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A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

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Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial‐WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial‐WSN. Accordingly, we have suggested and proffered appropriate CR‐based solutions in mitigating some of the challenges where necessary.

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Section: Pure Proactive Spectrum Handoff Strategymentioning

confidence: 99%

“…TA B L E 3 Summary of different spectrum handoff strategies 85,105,[107][108][109][111][112][113][114]…”

Section: Comparison Of Different Spectrum Handoff Strategiesmentioning

confidence: 99%

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

Self Cite

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“…The steady‐state probability π g that a PPN g packet will arrive at the server which is in a state j where j ∈ {0, α , g , β } (using the earlier described delay cycles and the assumption that packet arrival follows a Poisson) can be denoted as 17,18

π_{0} = 1 - ρ, π_{α} = ρ_{α} ((), 1 - ρ) / ((), 1 - ρ_{g} - ρ_{α}), π_{g} = ρ_{g} ((), 1 - ρ) / 1 - ρ_{g} - ρ_{α} false), π_{β =} ρ_{β} / ((), 1 - ρ_{g} - ρ_{α}) .

The utilization factor of the PPNs ρ = λ g E [ S eg ] where S eg is the effective service time of PPN g packets. Also, note that we assume that the server is in state 0 when the server is empty.…”

Section: Queuing Delay Analysismentioning

confidence: 99%

“…The steady-state probability π g that a PPN g packet will arrive at the server which is in a state j where j {0, α, g, β} (using the earlier described delay cycles and the assumption that packet arrival follows a Poisson) can be denoted as 17,18…”

Section: Analysis Of the Queuing Timementioning

confidence: 99%

“…Nonetheless, each breakdown time happens with probability of λ g − 1 /Λ g − 1 and Λ g − 1 − λ g − 1 /Λ g − 1 respectively. Therefore, the Laplace transform of D g in a recursive form can be represented as 17,18

D_{g}^{*} ((), s) = \frac{λ_{g - 1}}{{normalΛ}_{g - 1}} B_{g - 1}^{*} ((), s) + \frac{Λ_{g - 1} - λ_{g - 1}}{{normalΛ}_{g - 1}} D_{g - 1}^{*} ((), s + λ_{g - 1} - λ_{g - 1} B_{g - 1}^{*} ((), s)),

where g ≥ 2,

D_{1}^{*}

(s) = 1, and

Λ_{g} = \sum_{1}^{g} λ_{g}

. Following this, the number of interruptions that a PPN g packet encounters before it completely departs the server is equal to the number of high priority packets arriving during

S_{A_{g}}

.…”

Section: Queuing Delay Analysismentioning

confidence: 99%

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Using priority queuing for congestion control in IoT‐based technologies for IoT applications

Oyewobi

Djouani

Kurien

2020

Int J Communication

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The Internet of Things (IoT) connect millions of devices in diverse areas such as smart cities, e-health, transportation and defense to meet a wide range of human needs. To provide these services, a large amount of data needs to be transmitted to the IoT network servers. However, the IoT networks suffer from limited resources such as buffer size, node processing capabilities, and server capacities adversely affecting throughputs, latency, and energy consumption.Additionally, the ensuing heavy network traffic due to large amount of data transmitted results in congestion which degrades IoT network performance.Therefore, innovative congestion control techniques, e.g., queue management approach needs to be developed to overcome congestion problems in IoT networks. In this paper, a novel priority queuing technique (Npqt++) is developed to control congestion in IoT networks. The Npqt++ implements a preemptive/nonpreemptive discipline with a discretion rule to classify network traffic based on their real-time requirement into priority groups. If the discretion rule for low priority packets is satisfied, high priority packets are pushed to the front of the queue; otherwise, they wait in the queue. Our approach significantly outperforms existing techniques in terms of throughput, delay, and energy consumption.

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Smart Vehicle-Zone: Device to Device Handoff Techniques in Vehicle Ad Hoc Networks

Kulanthaiyappan

Chellaih

2021

Communications in Computer and Information Science

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A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

Cited by 5 publications

References 43 publications

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Using priority queuing for congestion control in IoT‐based technologies for IoT applications

Smart Vehicle-Zone: Device to Device Handoff Techniques in Vehicle Ad Hoc Networks

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