Characterization of sparse WLAN data traffic in opportunistic indoor environments as a prior for coexistence scenarios of modern wireless technologies

Ehsan, Muhammad Khurram; Shah, Asghar Ali; Amirzada, Muhammad Rizwan; Naz, Neelma; Igorevich, Kostromitin Konstantin; Sajid, Muhammad; Gardezi, Asad Raza

doi:10.1016/j.aej.2020.08.029

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…The algorithm proposed in this research has worst-case time bound of OðV + EÞ, which is linear in time and hence is better than the one presented in [29] for routing in intelligent networks [38][39][40][41][42].…”

Section: Discussionmentioning

confidence: 95%

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Qureshi

Hassan

Ehsan

et al. 2022

Wireless Communications and Mobile Computing

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Implementation of robust routing is very critical in network communication. Connecting devices like routers maintain databases for the whole network topology in the routing table. Each router needs to keep these tables updated with the best possible routes so that an efficient communication can always take place in nondelay tolerant intelligent networks that include military and tactical systems, vehicular communication networks, underwater acoustic networks, and intelligent sensor networks. The fast construction of shortest-path tree (SPT) is important to devise an efficient routing in a nondelay tolerant networks. That is why a simple and efficient algorithm is the need of the time. A robust routing solution SPT with O V + E time complexity is proposed to supersede the existing landmark.

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Section: Discussionmentioning

confidence: 95%

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Qureshi

Hassan

Ehsan

et al. 2022

Wireless Communications and Mobile Computing

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“…Cognitive radio (CR) has been under rigorous research to overcome spectrum scarcity and underutilization [12]. Application areas of cognitive radio can be smart healthcare systems, disaster relief, military and defense, emergency scenarios, industry, transportation and communication, internet of things (IoT), wireless body area networks (WBANs), and many more [13][14][15]. The throughput of the cognitive radio-enabled unmanned aerial vehicle (CR-UAV) is enhanced by jointly designing the UAV trajectory and resource allocation [16].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

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“…Current research is focused on introducing vehicles as completely driverlesswhich means no human intervention is required anymore. Intelligent vehicles can move around independently withtheir decision-making capabilities [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Intelligence of Autonomous Vehicles: A Concise Revisit

et al. 2022

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Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driving in complex environments. Autonomous vehicles use very up-to-date AI algorithms to localize themselves in known and unknown environments. AI algorithms are also exploited for perception, path planning, and motion control. A concise review of the state-of-the-art techniques to improve the performance of autonomous vehicles is presented.

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Characterization of sparse WLAN data traffic in opportunistic indoor environments as a prior for coexistence scenarios of modern wireless technologies

Cited by 9 publications

References 24 publications

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Intelligence of Autonomous Vehicles: A Concise Revisit

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