5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices

Sodhro, Ali Hassan; Pirbhulal, Sandeep; Sangaiah, Arun Kumar; Lohano, Sonia; Sodhro, Gul Hassan; Luo, Zongwei

doi:10.3390/su10041258

Cited by 42 publications

(25 citation statements)

References 48 publications

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“…In the field of BSN, there are many studies aimed at dealing with problems in BSN such as power consumption, latency, response time, the necessity of reliable and secure communication maintenance, and so on [ 21 , 22 , 23 , 35 ]. In these studies, a simple signal processing method in a sensor device or framework to reduce the amount of information to be transmitted to a server or mobile devices has been proposed.…”

Section: Discussionmentioning

confidence: 99%

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Mitsukura

Sumali

Nagura

et al. 2020

Sensors

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Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

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Section: Discussionmentioning

confidence: 99%

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Mitsukura

Sumali

Nagura

et al. 2020

Sensors

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“…[16] design a joint TPC and relay strategy for the BANs and their proper correlation. In [18][19] develop combined user allocation and TPC approach for energy saving in femto-cell networks. [20][21], design TPC based technique for energy optimization in WSNs.…”

Section: Related Workmentioning

confidence: 99%

“…Fig.5 reveals the proposed system model with harvest-store-usecontrol (in the presence of TPC and duty-cycle adaptor) mechanism, and the BSN is configured as a star topology linked directly to the BS. In addition, energy and frequency of 1.9 nJ/b, and 2.4GHz in various standards such as, Bluetooth low energy (BLE) and IEEE 802.15.6,etc are adopted accordingly [18], while the modeled sensor and the reader are use 30mW analog signal processor integrated circuit (IC) to manage and monitor biomedical signals [19]. Besides, energy is harvested with (AP) at harvesting rate of () t  by following wireless energy transfer (WET) mechanism and stored in the battery then transferred to the BS in order to save the energy.…”

Section: Wireless Channelmentioning

confidence: 99%

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

et al. 2019

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Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the meantime , Body Sensor Networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio-signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (PTPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus this paper first, proposes a novel joint transmission power control (TPC) and duty-cycle adaptation based framework for pervasive healthcare. Second, adaptive energy-efficient transmission power control (AETPC) algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a Feedback Control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio (PLR) by adjusting both transmission power and duty-cycle unlike the conventional constant TPC and PTPC methods.

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“…Sodhro et al [20] examined the integration of Fog computing and 5G based IoT devices [21,22]. The author highlighted the shortcoming of Fog computing in terms of less storage capacity and more power drain.…”

Section: A Brief Review Of Articles Of This Special Issuementioning

confidence: 99%

5G Mobile Services and Scenarios: Challenges and Solutions

et al. 2018

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The Fifth generation (5G) network is projected to support large amount of data traffic and massive number of wireless connections. Different data traffic has different Quality of Service (QoS) requirements. 5G mobile network aims to address the limitations of previous cellular standards (i.e., 2G/3G/4G) and be a prospective key enabler for future Internet of Things (IoT). 5G networks support a wide range of applications such as smart home, autonomous driving, drone operations, health and mission critical applications, Industrial IoT (IIoT), and entertainment and multimedia. Based on end users’ experience, several 5G services are categorized into immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. In this paper, we present a brief overview of 5G technical scenarios. We then provide a brief overview of accepted papers in our Special Issue on 5G mobile services and scenarios. Finally, we conclude this paper.

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5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices

Cited by 42 publications

References 48 publications

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

5G Mobile Services and Scenarios: Challenges and Solutions

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