Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

Salih, Qusay Medhat; Rahman, Md. Arafatur; Al‐Turjman, Fadi; Azmi, Zafril Rizal M.

doi:10.1109/access.2020.2986369

Cited by 19 publications

(3 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in wireless communication, a cross-layer framework for smart routing protocol in Mobile Cognitive Radio Networks has been proposed by exploiting machine learning mechanisms. 25 Some scholars use machine learning to analyze the raw electroencephalogram (EEG) signals for lie detection. 26 Some others use machine learning to classify video privacy data.…”

Section: Methodsmentioning

confidence: 99%

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Liu

Jia

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

Frequent collision accidents of ships in intersection waters have caused huge casualties and property losses. Unclear encounter intention, poor communication, or inaccurate judgment of the encounter intention are often the major causes of ships falling into dangerous and urgent situations, leading to collision accidents. There are few methods and models for automatically inferring ship encounter intention. In this study, an intelligent model driven by AIS data is proposed to infer the ship encounter intention in intersection waters. The Hidden Markov Model (HMM) is adopted to formulate the encounter process and perform intention inference. The encounter intentions, including crossing, overtaking and head-on, are modeled as unobservable states of the formulated HMM. The observable measures of HMM extracted from AIS data, include the relative distance, relative speed, and course difference between two ships. Subsequently, the Forward-Backward algorithm is employed to obtain the model parameters and the Viterbi algorithm is exploited to estimate the hidden state with the highest probability, resulting in the inferred intention. The main advantage of the proposed model is its ability to capture the spatial-temporal characteristics of the encounter process, that is, the spatial interaction between ships and the dynamic evolution of states of the encounter process. The AIS data collected from the Lantau Strait intersection waters are adopted to verify the effectiveness of the proposed model. The experimental results reveal that the model can achieve an inference accuracy of 95%, 91.33%, and 92.67% for crossing, overtaking, and head-on, respectively. Moreover, it has real-time performance that ensures the encounter intentions can be recognized at an early stage, which is very critical for the safe navigation of any ships encountered. Our results show that our model can infer the encounter intentions in a timely manner and with high accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Liu

Jia

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

show abstract

“…Data-driven proactive management will be essentially supported by ML/AI techniques. These techniques, once deployed in future networked systems, should offer a new range of networking-based services such as smart routing in networks with a cross-layer design [59], task offloading and resource allocation [60], optimized operation of next-generation mobile networks [61], distributed storage and computation at the network edge [62], and accurate localization estimation of mobile robots [63]. In parallel with this expected network evolution, novel challenges such as privacy, e.g.…”

Section: Data-triggered Management Mechanismmentioning

confidence: 99%

Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

Moura

Hutchison

2020

SN Appl. Sci.

View full text Add to dashboard Cite

Cyber-Physical Systems (CPSs) are increasingly important in everyday applications including the latest mobile devices, power grids and intelligent buildings. CPS functionality has intrinsic characteristics including considerable heterogeneity, variable dynamics, and complexity of operation. These systems also typically have insufficient resources to satisfy their full demand for specialized services such as data edge storage, data fusion, and reasoning. These novel CPS characteristics require new management strategies to support the resilient global operation of CPSs. To reach this goal, we propose a Software Defined Networking based solution scaled out by Network Function Virtualization modules implemented as distributed management agents. Considering the obvious need for orchestrating the distributed agents towards the satisfaction of a common set of global CPS functional goals, we analyze distinct incentive strategies to enact a cooperative behavior among the agents. The repeated operation of each agent's local algorithm allows that agent to learn how to adjust its behavior following both its own experience and observed behavior in neighboring agents. Therefore, global CPS management can evolve iteratively to ensure a state of predictable and resilient operation.

show abstract

“…In other words, if the PU of the currently used channel by the SU becomes active again, the SU must vacate this channel immediately and switch to another available channel [5], [6]. CRNs have unique operation requirements, including spectrum sensing, channel assignment protocols, routing mechanisms, and routing protection [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Alnabelsi

2023

IEEE Access

View full text Add to dashboard Cite

Dynamic Spectrum Access (DSA) technology in wireless Cognitive Radio Networks (CRNs) provides opportunistic access for unlicensed users, also known as Secondary Users (SUs), which can offer huge bandwidth to enable future wireless communication. Mainly, this technology aims to improve the endto-end throughput by allowing SUs to exploit the licensed channels only when their licensed users, also known as Primary Users (PUs), are not using them. Most existing communication protocols designed for CRNs are based on the assumption that the channel availability time is considered based on a memoryless distribution for PUs arrivals. Unfortunately, this assumption is impractical because the PU channels' activity and availability are memory-time correlated. Worse yet, designing communication protocols for CRNs under this assumption can result in overestimating the Probability of Success (PoS) for SU packet transmissions, resulting in severe degradation in network performance in realistic scenarios. This paper derives a closed-form formula under memory-time correlation for channel availability that quantifies the PoS for SUs' packet transmission in CRNs. This will empower the network designers to get practical expectations about network efficiency rather than the overestimated PoS. Therefore, this work is also useful for emerging wireless networks with multi-hop routing, such as 5G, 6G, vehicular networks, etc., which incorporate DSA techniques. Our numerical and simulation results demonstrate that the PoS is overestimated in most of the literature due to adopting memoryless-based distribution in modeling channels' availability; such overestimation can impact communication protocol decisions, resulting in severe network performance degradation.

show abstract

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

Cited by 19 publications

References 106 publications

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Contact Info

Product

Resources

About