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.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Suchintan Mishra

Manmath Narayan Sahoo

Sambit Bakshi

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

Nature-inspired cost optimisation for enterprise cloud systems using joint allocation of resources

Enterprise Information Systems

Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs. © 2005-2012 IEEE.

IEEE Transactions on Industrial Informatics

Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics

This study proposed an efficient fuzzy vault-based security method that adopts time-domain parameters extracted from physiological signals for tele-healthcare applications, including rehabilitation and stress management systems among others. One of the main challenges of the existing fuzzy vault-based approaches is that they depend on frequency-domain parameters of bio-signals to generate entities identifiers which require more processing time and energy consumption. Hence, we firstly designed a wearable platform for bio-signals collection, and later extracted time-domain features from the signals to generate efficient and distinctive identifiers for securing medical data in tele-healthcare applications. The statistical tests and hamming distances were applied to verify the performance of the generated identifiers regarding their uniqueness and randomness, respectively. This research work considered a total of 30 subjects data and the experimental results reveal that the proposed approach has better performance in WBSNs regarding processing time (0.168 ms) and energy consumption (1.423 mJ) than the traditional techniques. © 2018 Elsevier Ltd

Computers & Electrical Engineering

Fuzzy vault-based biometric security method for tele-health monitoring systems

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.

Future Generation Computer Systems

Convergence of IoT and product lifecycle management in medical health care

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.

Journal	Enterprise Information Systems
Publisher	Informa UK Limited
ISSN	1751-7575
Open Access	0