Birla Institute of Technology, Mesra

About: Birla Institute of Technology, Mesra is a education organization based out in Ranchi, India. It is known for research contribution in the topics: Computer science & Dielectric. The organization has 2801 authors who have published 4789 publications receiving 52426 citations. The organization is also known as: BIT.

Two time-delay dynamic model on the transmission of malicious signals in wireless sensor network

Abstract: Deployed in a hostile environment, motes of a Wireless sensor network (WSN) could be easily compromised by the attackers because of several constraints such as limited processing capabilities, memory space, and limited battery life time etc. While transmitting the data to their neighbour motes within the network, motes are easily compromised due to resource constraints. Here time delay can play an efficient role to reduce the adversary effect on motes. In this paper, we propose an epidemic model SEIR (Susceptible–Exposed–Infectious–Recovered) with two time delays to describe the transmission dynamics of malicious signals in wireless sensor network. The first delay accounts for an exposed (latent) period while the second delay is for the temporary immunity period due to multiple worm outbreaks. The dynamical behaviour of worm-free equilibrium and endemic equilibrium is shown from the point of stability which switches under some threshold condition specified by the basic reproduction number. Our results show that the global properties of equilibria also depends on the threshold condition and that latent and temporary immunity period in a mote does not affect the stability, but they play a positive role to control malicious attack. Moreover, numerical simulations are given to support the theoretical analysis.

Facile tuning of plasmon bands in hollow silver nanoshells using mild reductant and mild stabilizer.

Abstract: Hollow silver nanoshells with tunable plasmon bands have been synthesized using Ag2O nanoparticles at an optimized temperature of 20 °C. The plasmon peak has been tuned in a wide range from 460 nm to 605 nm employing a combination of mild reductant and a mild stabilizer, hydrazine hydrate and sodium citrate, respectively. In contrast, the combination of strong reductant and strong stabilizer, NaBH4 and hydrophilic thiols, resulted in limited plasmon tunability (455–510 nm). The differential behaviour is attributed to the change in dynamics of the diffusion–reaction process. For thiols, the effect of free end-groups was quite evident as plasmon peak shifted from 449 nm to 470 nm on replacing thioglycolic acid (HS–CH2–COOH) with mercaptoethanol (HS–CH2–CH2–OH). Transmission electron microscopy (TEM) revealed that the aspect ratio [outer diameter (d)/shell thickness (t)] was 2.8 (d: 40.0 ± 1.6 nm, t: 14.0 ± 1.3 nm) and 5 (d: 84 ± 2.3 nm, t: 16.8 ± 1.9 nm) for the nanoshells exhibiting a plasmon peak at 460 nm and 605 nm, respectively. The crystal phase of nanoshells was found to be face centered cubic (fcc) as deduced from HR-TEM and electron diffraction data. Using the same Ag2O template, morphological transformation from non-porous to mesoporous has also been achieved by simply reversing the order of addition.

SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Abstract: Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.