Emerging trends in Internet of Medical Things (IoMT) or Medical Internet of Things (MIoT), and miniaturized devices with have entirely changed the landscape of the every corner. Main challenges that heterogeneous sensor-enabled devices are facing during the connectivity and convergence with other domains are, first, the information/knowledge sharing and collaboration between several communicating parties such as, from manufacturing engineer to medical expert, then from hospitals/healthcare centers to patients during disease diagnosis and treatment. Second, battery lifecycle and energy management of wearable/portable devices. This paper solves first problem by integrating IoMT with Product Lifecycle Management (PLM), to regulate the information transfer from one entity to another and between devices in an efficient and accurate way. While, second issue is resolved by proposing two, battery recovery-based algorithm (BRA), and joint energy harvesting and duty-cycle optimization-based (JEHDO) algorithm for managing the battery lifecycle and energy of the resource-constrained tiny wearable devices, respectively. Besides, a novel joint IoMT and PLM based framework is proposed for medical healthcare applications. Experimental results reveal that BRA and JEHDO are battery-efficient and energy-efficient respectively. © 2018 Elsevier B.V.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Ali HassanSodhro

Sandeep Pirbhulal

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

Future Generation Computer Systems

The rapid growth of the internet and the internet of things (IoT) refers to the next phase of information revolution whose context involves billions of smart devices and sensors interconnected to facilitate speedy information and data exchange under soft real-time constraints. Digital information revolution has caused significant changes in the data communication. This data communication may require private, secure, and sometimes malicious communication. Competent secrecy can be accomplished by applying novel and inventive audio steganography. This article focuses on the secret message followed by shuffled embedded bit substitution in original audio stream by adopting optimized audio embedding technique (OAET) from the technological observation. To hide the information in the deeper layer of the audio stream, this method uses a new elevated bit range least significant bit (LSB) audio steganography technique that decreases distortion and improves the robustness of the embedded audio stream. The proposed technique proves that the perceptual quality of audio steganography is better than the previous standard LSB technique. Experiment results proved that the cladding of the OAET provides high-level security to the universal cyber data. The interpretation of results shows that embedding data in audio enhances the level of security when used as IoT smart speakers, where the attackers could not distinguish between the original audio and the embedded audio streams. © 2019 Wiley Periodicals, Inc.

Computational Intelligence

A steganographic method based on optimized audio embedding technique for secure data communication in the internet of things

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

Optimal resource allocation in cloud systems is NP-hard due to the involvement of several conflicting objectives and unpredictable cloud traffic. To improve user satisfaction and resource utilization while minimizing end-user cost, the joint allocation of cloud resources is inevitable. In this work, we model end-user cost in cloud as the optimization objective using bandwidth and compute allocation as the decision variables. To solve the joint Virtual Machine Placement (VMP) problem we propose a single point, greedy, software-defined network (SDN)-based solution that minimizes end-user cost by making certain changes to the fat-tree datacenter architecture. Mathematically, we show that the overall objective function is convex hence solving it using a weighted-sum greedy approach will induce solutions that are Pareto-optimal. Experimental evaluations confirm up to 15% reduction in the response time and up to 14% increase in the efficiency of resources. To analyse the risks involved in deploying delay-sensitive applications over cloud and to show the effects of resource allocation approaches, we perform a risk analysis of delay-sensitivity in cloud using real-time CVD detection. The results confirm the reduced response time due to the proposed approach while maintaining the efficiency of detection. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

Pareto-optimal cost optimization for large scale cloud systems using joint allocation of resources

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.

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

Resource allocation in cloud being NP-complete lacks a generalised optimal solution. In this work, we propose a nature-inspired ACO-Fuzzy framework based joint resource allocation algorithm for cloud datacenters. The algorithm assigns compute and network resources efficiently to minimize end-user cost. An ACO-based local search heuristic is used to discover the current status of resources and then a Fuzzy-based decision maker finds the most optimal set of compute and network resources minimising the end-user cost. Analytical and experimental evaluations validate the efficiency of joint allocation. Abbreviations: CSP: cloud service provider; VM: virtual machine; VMP: virtual machine placement; NIJA: nature-inspired joint allocation; DM: decision maker; WAN: wireless area network; ACO: ant colony optimisation; SDFF: server defined first-fit; GA: genetic algorithm; PSO: particle swarm optimisation; SA: simulated annealing. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Journal	Data powered by TypesetFuture Generation Computer Systems
Publisher	Data powered by TypesetElsevier BV
ISSN	0167-739X
Open Access	0