Cyber Forensic Investigation in IoT Using Deep Learning Based Feature Fusion in Big Data

International Journal of Wireless Information Networks - The Internet of Things (IoT) device is becoming universal domain and its success cannot be ignored, but its threats on IoT devices increases...     

A novel chain-based clustering for green communication in wireless sensor network

The rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource-constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain-based Energy-Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state-of-the-art routing protocols.     

Investigating the Effect of Multi-pass Friction Stir Processing of SiC Particles on Temperature Distribution,Microstructure and Mechanical Properties of AA6061-T6 Plate

Silicon - In this paper, silicon carbide (SiC) particles were successfully inserted into AA6061-T6 aluminium matrix using multi-pass friction stir processing (MPFSP). The effects of MPFSP and SiC...     

Thermal modeling of a dual-purposed snap biopsy acquisition procedure in a suspected cancerous lesion

The needle-based biopsy procedure is common in cancer detection and patient-specific targeted therapy, wherein a tissue sample from the potential diseased site is acquired and frozen instantly with the help of a coolant medium. While liquid nitrogen (LN₂) is the most widely used coolant for preserving the acquired sample and performing biopsy tests on the same at a later time, cold ischemia leads to inevitable cell degradation beyond a threshold time. In an effort to circumvent this challenge, here we aim to put forward the concept of an integrated biopsy sample acquisition and cryotherapy procedure, by incorporating an exclusively designed cooling circuit in a conventional biopsy-needle for freezing the sample in vivo as soon as it is acquired, while causing cryoablation in the surrounding tissues simultaneously. An enthalpy-based approach is employed to develop a bioheat transfer model for the cryotherapy design, with illustrative simulation data presented for breast cancer. Our model is demonstrated by considering a constant LN₂ cooling temperature of 77.15 K, and cooling powers ranging from 2 to 10 W. The model results elucidate procedure-specific insights such as the thermal penetration depth and the cooling time on being subjected to the cryoablation. The cooling rates thus obtained are further assessed from the simultaneous considerations of cryopreservation and cryosurgery, deriving critical insights on tissue survival and damage for acting as a precursor to patient-specific treatment planning.     

Impact & Analysis of Inverted-T shaped Fin on the Performance parameters of 14-nm heterojunction FinFET

Silicon - A new high-performance inverted-T shaped 14 nm heterojunction FinFETs has been proposed that originate from the rectangular fin structures. The rationale for proposing this...     

A Chimeric Peptide Inhibits Red Blood Cell Invasion by Plasmodium falciparum with Hundredfold Increased Efficacy**

Inhibiting the formation of a tight junction between two malaria parasite proteins, apical membrane antigen 1 and rhoptry neck protein 2, crucial for red blood cell invasion, prevents progression of the disease. In this work, we have used a unique approach to design a chimeric peptide, prepared by fusion of the best features of two peptide inhibitors, that has displayed parasite growth inhibition ex vivo with nanomolar IC₅₀, which is 100 times better than any of its parent peptides. Furthermore, to gain structural insights, we computationally modelled the hybrid peptide on its receptor.     

RNN-EdgeQL: An auto-scaling and placement approach for SFC

This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement.     

Architecture- and Gate-Oxide-Level Optimization of a Si-Based Asymmetric U-TFET for Low Power Operation: a Real-Time Gate/Drain Electrostatic Based Leakage Perspective

Silicon - In this paper, for the first time, an optimized asymmetric U-shaped TFET, suitable for low power application, has been proposed after a hardcore performance analysis, considering the...