Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

Hadi, Mohammed S.; Lawey, Ahmed Q.; El-Gorashi, Taisir E. H.; Elmirghani, Jaafar M. H.

doi:10.1109/infcomw.2019.8845225

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…This likelihood is then feedback as a priority factor in a radio resource optimization model for an LTE-A network guaranteeing the assignment of high-quality PRBs to the OPs. Furthermore, we extended the previous work to include multi-tier HetNets in [7] with a spectrum partitioning strategy [22] so that inter-tier interference is mitigated. Moreover, the system response was investigated over seven different current states resulting in different priority levels granted to the OPs.…”

Section: Bridging the Gap Between The Two Topicsmentioning

confidence: 99%

“…The main contributions of this paper are: (i) extending our previous work in [6] to include a larger dataset, and the incorporation of new ML algorithms including decision tree (DT), logistic regression (LR), and the ML algorithm we deployed in [6] (naïve Bayesian (NB) classifier) in an ensemble system where a soft voting (SV) classifier resides; (ii) rigorously scrutinizing the classifiers' performance by conducting various tests of accuracy, recall, specificity, false-positive rate, false-negative rate, negative prediction rate, precision, and F1 score. Furthermore, reporting the cross-validation test scores for all datasets; (iii) extending the work in [7] to study the effects of inter-cell interference in HetNets, where we also added a reliability-aware aspect to the PF approach; (iv) testing the fairness among users, and conducting the required sensitivity analysis over 300 instances. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

et al. 2020

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Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naïve Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

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Section: Bridging the Gap Between The Two Topicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

et al. 2020

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“…Using our track record in MILP optimization and heuristics formulation in [59]- [67], and physical layer modeling track record in [68]- [73], we developed the following MILP models to optimize the cellular system resource allocation for OPs and normal users. We consider the We formalize this problem as a MILP model.…”

Section: Problem Formulationmentioning

confidence: 99%

Patient-Centric Cellular Networks Optimization Using Big Data Analytics

et al. 2019

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Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for OutPatient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body-connected medical IoT sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.

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“…In this paper, we focus on an energy-efficient resource provisioning approach by virtualizing the resources in a distributed processing network that we refer to as the CFN architecture. This work builds on our earlier proposals in various areas such as distributed processing in the IoT/Fog [2,9,31,46], green core and DC networks [4, 5, 10, 12, 15-20, 28, 29, 32, 36, 39, 44], network virtualization and service embedding in core and IoT networks [3,6,7,37] and machine learning and network optimization for healthcare systems [23][24][25][26], and network coding in the core network [34,35]. Our previous work in [8] dealt with the idea of generic service embedding in an IoT setting, we take the work further in this paper by refining the optimization model and abstracting the virtual service requests (VSRs) that comprise of multiple Virtual Machines (VMs) inter-connected in a virtual topology.…”

Section: Introductionmentioning

confidence: 97%

Energy-Efficient AI over a Virtualized Cloud Fog Network

Yosuf

Mohamed

Alenazi

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Deep Neural Networks (DNNs) have served as a catalyst in introducing a plethora of next-generation services in the era of Internet of Things (IoT), thanks to the availability of massive amounts of data collected by the objects on the edge. Currently, DNN models are used to deliver many Artificial Intelligence (AI) services that include image and natural language processing, speech recognition, and robotics. Accordingly, such services utilize various DNN models that make it computationally intensive for deployment on the edge devices alone. Thus, most AI models are offloaded to distant cloud data centers (CDCs), which tend to consolidate large amounts of computing and storage resources into one or more CDCs. Deploying services in the CDC will inevitably lead to excessive latencies and overall increase in power consumption. Instead, fog computing allows for cloud services to be extended to the edge of the network, which allows for data processing to be performed closer to the end-user device. However, different from cloud data centers, fog nodes have limited computational power and are highly distributed in the network. In this paper, using Mixed Integer Linear Programming (MILP), we formulate the placement of DNN inference models, which is abstracted as a network embedding problem in a Cloud Fog Network (CFN) architecture, where power savings are introduced through trade-offs between processing and networking. We study the performance of the CFN architecture by comparing the energy savings when compared to the baseline approach which is the CDC. CCS CONCEPTS• Computing methodologies → Machine learning; • Networks → Network architectures.

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Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

Cited by 10 publications

References 29 publications

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Patient-Centric Cellular Networks Optimization Using Big Data Analytics

Energy-Efficient AI over a Virtualized Cloud Fog Network

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