Rapid proliferation in state-of-the art technologies has revolutionized the medical market for providing urgent, effective and economical health facilities to aging society. In this context media (i.e., video) transmission is considered as a quite significant step during first hour of the emergency for presenting a big and better picture of the event. However, the energy hungry media transmission process and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in medical internet of things (MIoT) or internet of medical things (IoMT). So, promoting Green (i.e., energy-efficient) transmission during voluminous and variable bit rate (VBR) video in MIoT is a challenging and crucial problem for researchers and engineers. Therefore, the need arose to conduct research on Green media transmission techniques to cater the need of upcoming wearable healthcare devices. Thus, this research contributes in two distinct ways; first, a novel and sustainable Green Media Transmission Algorithm (GMTA) is proposed, second, a mathematical model and architecture of Green MIoT are designed by considering a 8-min medical media stream named, ‘Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ to minimize transmission energy consumption in media-aware MIoT, and to develop feasible media transmission schedule for sensitive and urgent health information from physian to patients and vice vers through extremely power hungry natured wearable devices. The experimental results demonstrate that proposed GMTA saves energy up to 41%, to serve the community. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Sodhro A.H

Pirphulal S

Sekhari A

Ouzrout Y.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Multimedia Tools and Applications

Energy optimization during node-to-node communication in most of the sensor-enabled networks plays the major role to understand the entire mechanism of any system. With their rapid and emerging role has emphasized the researchers and academicians to focus at the wireless channel and its imperative role in the short-range transmission networks. As wireless channel plays the vital role in achieving the efficient and timely communication between transmitter and receiver in internet of medical things (IoMT) environment. Based on the quality of the channel IoMT system is classified as an energy-efficient or not. To remedy this issue, this paper contributes in two ways. First, the novel energy-efficient framework and channel-aware energy-efficient algorithm (CEA) for the IoMT system in medical healthcare domain are proposed. Second, channel quality analysis indicators such as, received signal strength indicator and transmission power levels are adopted. Besides, main open systems interconnections layers, for example, network, MAC and Physical with crucial energy optimization attributes, i.e., route discovery, duty-cycle, and modulation level or data rate during node-to-node communication in IoMT are adopted to see the effect with and without using CEA in the IoMT system. Experimental results reveal that proposed CEA outperforms by saving more energy in comparison to the Baseline technique. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

International Journal of Parallel Programming

A Channel-Aware Duty Cycle Optimization for Node-to-Node Communications in the Internet of Medical Things

Internet of Medical Things (IoMTs) is a building block for modern healthcare having enormously stringent resource constraints thus lightweight health data security and privacy are crucial requirements. A critical issue in implementing security for the streaming health information is to offer data privacy and validation of a patient's information over networking environment in a resource efficient manner. Therefore, we developed a biometric-based security framework for resource-constrained wearable health monitoring systems by extracting heartbeats from ECG signals. It is analyzed that time-domain based biometric features play a significant role in optimizing security in IoMT based medical applications. Moreover, resource optimization model based on utility function is proposed for clinical information transmission in IoMT. In this study, ECG signals from 40 healthy subjects were employed comprising lab environment and publicly available database i-e-physionet. The experimental results validate that proposed framework requires less processing time and energy consumption (0.0068ms and 0.196 microJoule/Byte) then Alarm-net (0.0128ms and 0.351 microJoule/Byte) and BSN-care (0.0175ms and 0.53 microJoule/Byte). Moreover, from the results, it is also observed that biometric key generation mechanism not only provide random and unique keys but it also offer a trade-off between security and resource optimization. Thus, it can be concluded that the proposed framework has got both social and economic significance for real-time healthcare applications. © 2019 Elsevier B.V.

Future Generation Computer Systems

A joint resource-aware and medical data security framework for wearable healthcare systems

Fog computing has become the revolutionary paradigm and one of the intelligent services of the 5th Generation (5G) emerging network, while Internet of Things (IoT) lies under its main umbrella. Enhancing and optimizing the quality of service (QoS) in Fog computing networks is one of the critical challenges of the present. In the meantime, strong links between the Fog, IoT devices and the supporting back-end servers is done through large scale cloud data centers and with the linear exponential trend of IoT devices and voluminous generated data. Fog computing is one of the vital and potential solutions for IoT in close connection with things and end users with less latency but due to high computational complexity, less storage capacity and more power drain in the cloud it is inappropriate choice. So, to remedy this issue, we propose transmission power control (TPC) based QoS optimization algorithm named (QoS-TPC) in the Fog computing. Besides, we propose the Fog-IoT-TPC-QoS architecture and establish the connection between TPC and Fog computing by considering static and dynamic conditions of wireless channel. Experimental results examine that proposed QoS-TPC optimizes the QoS in terms of maximum throughput, less delay, less jitter and minimum energy drain as compared to the conventional that is, ATPC, SKims and constant TPC methods. © 2018 by the authors.

Fulltext

Sustainability

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

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods). © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Sensors

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

Heart attack, a complex health problem in which the electrical activity of the heart becomes chaotic due to extreme heart failure conditions, has been ranked the deadliest human diseases. Recent studies have reported that remote monitoring of patients with heart failure disease could help quantify their level of risks and provide useful information for efficient therapy. Additionally, such platforms could help increase accessibility to health care delivery at a relatively lower cost. Therefore this study proposed a context aware clinical decision support model using support vector machine (SVM) for heart failure risk prediction. The proposed model's performance was evaluated using dataset of potential heart failure patients with metrics including prediction accuracy, sensitive, specificity, and receiving operating characteristic (ROC). An average prediction accuracy of 87.9% and 82.0%, were respectively achieved for the training and testing sessions of the built Radial basis function (RBF) based SVM classifier with a sensitivity value of 76.9%. The results obtained from this study might aid the development of an efficient context aware clinical decision support systems in smart cities and the society at large. © 2017 Elsevier Ltd

Sustainable Cities and Society

Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis

Healthcare industry is one of the areas where wireless sensor network provides a lot of opportunities. Online data sharing in healthcare industry not only increases the efficiency but also reduces the time constraints. In the healthcare wireless sensor network, patient's report is available online for health professionals without any delay after patient's checkup. Data privacy becomes an important issue in healthcare industry due to direct involvement of personal health related data of patients. Modified data may become a serious cause of casualty for patient. Digital signature scheme is a technique of public key cryptography that is widely accepted in digital world to retain privacy and integrity. Certificateless public key cryptography was proposed to remove the complication of certificate management in public key cryptography as well as the key escrow problem inherited in identity based cryptography. An aggregate signature scheme is a many to one map which maps different signatures on different messages to a single signature. This feature is very beneficial in an environment which is constrained by limited bandwidth and low computational time/effort, such as wireless sensor network, vehicular ad-hoc network and Internet of things. Our proposed certificateless aggregate signature enjoys the goodness of both the concepts, certificateless and aggregate. We construct a certificateless aggregate signature scheme and prove the security of constructed scheme by using widely-accepted Random Oracle Model under the computationally hard Diffie-Hellman assumption. Random Oracle Model based security analysis proves that our proposed scheme is provably secure against existential forgery on adaptive chosen message and identity attacks under the hardness of computational Diffie-Hellman problem and achieves the required goals such as confidentiality, non-repudiation and integrity. We use batch verification technique for speedy verification of signatures. Since energy consumption for aggregate signature verification in our scheme is lesser than that in many of the related schemes, the proposed CL-AS scheme is compatible with the resource scanty nature of wireless sensor networks. Thus the proposed CL-AS scheme is efficient and suitable for green healthcare wireless sensor networks (HWSNs). © 2017 Elsevier Inc.

Sustainable Computing: Informatics and Systems

A certificateless aggregate signature scheme for healthcare wireless sensor network

A backpressure-based data collecting scheme has been applied in the Internet of Things, which can control the network congestion effectively and increase the network throughput. However, there is an obvious shortcoming in the traditional backpressure data collecting scheme for the network service chain. It attempts to search all possible paths between source node and destination node in the networks, causing an unnecessary long path for data collection, which results in large end-toend delay and redundant energy consumption. To address this shortcoming of backpressure data collecting scheme in the Internet of Things, this article proposes an energy-aware and distance-aware data collecting scheme to enhance the lifetime of backpressure-based data collecting schemes. We propose an energy-and distance-based model that combines the factors of queue backlog, hop counts, and residual energy for making routing decisions. It not only reduces the unnecessary energy consumption, but also balances the residual energy. The experiment results show that the proposed scheme can reduce unnecessary energy consumption and end-to-end delay compared to the traditional and LIFO-based schemes. Meanwhile, it balances the energy of nodes and extends the lifetime of an Internet of Things. © 1979-2012 IEEE.

Journal	Data powered by TypesetMultimedia Tools and Applications
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1380-7501
Open Access	0