Biological Pacemakers – A Review

Slow heart rates, due to sinus node disease or atrioventricular conduction block, are a significant problem for many patients. Currently, these patients are treated with electronic pacemakers, which provide effective therapy, but are also associated with many problems. Use of biological pacemakers is an attractive solution to these problems. Approaches for the creation of such pacemakers include either the injection of cells that have pacemaker activity (cell-based approach) or modification of cells in the heart to induce pacemaker activity by delivering genes (gene-based approach). This article reviews the progress in the development of biological pacemakers

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

The goal of multimodal biometric recognition system is to make a decision by identifying their physiological behavioural traits. Nevertheless, the decision-making process by biometric recognition system can be extremely complex due to high dimension unimodal features in temporal domain. This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris. With the objective of reducing the feature vector dimension in the temporal domain, first pre-processing is performed using Contourlet Transform Model. Next, Local Derivative Ternary Pattern model is applied to the pre-processed features where the feature discrimination power is improved by obtaining the coefficients that has maximum variation across pre-processed multimodality features, therefore improving recognition accuracy. Weighted Rank Level Fusion is applied to the extracted multimodal features, that efficiently combine the biometric matching scores from several modalities (i.e. face, fingerprint and iris). Finally, a deep learning framework is presented for improving the recognition rate of the multimodal biometric system in temporal domain. The results of the proposed multimodal biometric recognition framework were compared with other multimodal methods. Out of these comparisons, the multimodal face, fingerprint and iris fusion offers significant improvements in the recognition rate of the suggested multimodal biometric system

Intelligent Reward based Data Offloading in Next Generation Vehicular Networks

A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module

Control of Clinical Pathogens by the Haemolymph of Paratelphusa hydrodromous, a Freshwater Crab

In the present study, effort has been made to find the antimicrobial activity of haemolymph collected from freshwater crab, Paratelphusa hydrodromous. The haemolymph collected was tested for antimicrobial assay by disc diffusion method against clinical pathogens. Five bacterial species, namely, Escherichia coli, Klebsiella pneumonia, Proteus mirabilis, Pseudomonas aeruginosa, Staphylococcus aureus, and five fungal strains, namely and Aspergillus flavus, Aspergillus fumigatus, Aspergillus niger, Rhizopus sp., and Mucor sp., were selected for the study. The result shows a strong response of haemolymph against the clinical pathogens which confirms the immune mechanism of the freshwater crab

A Console GRID Leveraged Authentication and Key Agreement Mechanism for LTE/SAE

Growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among bulk of the human race that happens to be cellular technology users, has fueled the adaptation to long-term evolution (LTE)/system architecture evolution. The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in authentication mechanism used in LTE that without countermeasures might give super user rights to unauthorized users. The LTE uses static LTE key to derive the entire key hierarchy, i.e., LTE follows Evolved Packet System–Authentication and Key Agreement based authentication, which discloses user identity, location, and other personally identifiable information. To counter this, we propose a public key cryptosystem named “International mobile subscriber identity Protected Console Grid based Authentication and Key Agreement (IPG-AKA) protocol” to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves security of authentication procedures, but also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper prove that IPG-AKA improves security in authentication procedure and performance in the LTE

QoS-Aware Frequency-Based 4G+Relative Authentication Model for Next Generation LTE and Its Dependent Public Safety Networks

Increasing demands for high-speed broadband wireless communications with voice over long term evolution (LTE), video on demand, multimedia, and mission-critical applications for public safety motivate 4th-generation (4G) and 5G communication development. The flat IP-based LTE and LTE-Advanced technologies are the expected key drivers for 5G. However, LTE, with its elapsed security mechanism and open nature, leaves a huge loophole for intruders to jeopardize the entire communication network. The timeand bandwidth-consuming authentication procedure in LTE leads to service disruptions and makes it unfit for public safety applications. To cater the prevailing LTE security and service requirements, we propose the 4G plus relative authentication model (4G+RAM), which is composed of two dependent protocols: 1) Privacy-protected evolved packet system authentication and key agreement protocol for the initial authentication (PEPS-AKA) and 2) 4G plus frequency-based re-authentication protocol for the re-authentication of known and frequent users (4G+FRP). The 4G+RAM supports seamless communication with a minimum signaling load on core elements and conceals users' permanent identifiers to ensure user privacy. We simulate the proposed protocols for formal security verification with the widely accepted automated validation of Internet security protocols and applications tool. A comparative analysis of bandwidth consumption is also performed and proved that the proposed 4G+RAM outperforms the existing solutions

SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

Currently, the increasing demands of user terminals has surged drastically and pulling up the global data traffic along. According to 3GPP, offloading is one of the most beneficial and advantageous options to handle this critical traffic bottleneck, however, both Long Term Evolution (LTE) and Wireless Local Area Network (WLAN) are loosely coupled. To mitigate the User Equipment (UE) from latency issues during offloading and for tighter integration of LTE and WLAN radio networks, LTE-WLAN Aggregation (LWA) was introduced by 3GPP which is apparently suitable for Internet of Things (IoT) devices. However, LWA is not suitable for high mobility scenarios as UEs’ information need to be updated for every new environment because of the frequent aggregation triggers which are mostly non-optimal and demands for a high-level controller. To resolve the disadvantage of non-optimal aggregation triggers, in this paper, we proposed Software Defined Networking (SDN) based approach for LWA, named as LWA under SDN Assistance (LWA-SA). In this approach, SDN initiates aggregation appropriately between LTE and an optimal WLAN Access Point (AP) which avoids frequent reconnections and deprived services. As multiple parameters are required for selection of an optimal WLAN AP, so we use Genetic Algorithm (GA) that considers each parameter as fitness value for the selection of optimal WLAN AP. This maximizes the throughput of UE and reduces the traffic pressure over licensed spectrum. Further, mathematical model is formulated that uses Karush-Kuhn-Tucker (KKT) to find the maximum attainable throughput of a UE. Using NS-3, we compared our approach with offloading scenarios and LWA. The simulation results clearly depict that LWA-SA outperforms existing schemes and achieves higher throughput

Energy-Efficient End-to-End Security for Software Defined Vehicular Networks

One of the most promising application areas of the Industrial Internet of Things (IIoT) is Vehicular Ad hoc NETworks (VANETs). VANETs are largely used by Intelligent Transportation Systems (ITS) to provide smart and safe road transport. To reduce the network burden, Software Defined Networks (SDNs) acts as a remote controller. Motivated by the need for greener IIoT solutions, this paper proposes an energy-efficient end-to-end security solution for Software Defined Vehicular Networks (SDVN). Besides SDN’s flexible network management, network performance, and energy-efficient end-toend security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: i) In RSU-based Group Authentication (RGA) scheme, each vehicle in the RSU range receives a group id-key pair for secure communication and ii) In private-Collaborative Intrusion Detection System (p-CIDS), SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 & MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDV

An optimal multitier resource allocation of cloud RAN in 5G using machine learning

The networks are evolving drastically since last few years in order to meetuser requirements. For example, the 5G is offering most of the available spec-trum under one umbrella. In this work, we will address the resource allocationproblem in fifth-generation (5G) networks, to be exact in the Cloud Radio AccessNetworks (C-RANs). The radio access network mechanisms involve multiplenetwork topologies that are isolated based on the spectrum bands and it shouldbe enhanced with numerous access technology in the deployment of 5G net-work. The C-RAN is one of the optimal technique to combine all the availablespectral bands. However, existing C-RAN mechanisms lacks the intelligence per-spective on choosing the spectral bands. Thus, C-RAN mechanism requires anadvanced tool to identify network topology to allocate the network resources forsubstantial traffic volumes. Therefore, there is a need to propose a frameworkthat handles spectral resources based on user requirements and network behav-ior. In this work, we introduced a new C-RAN architecture modified as multitierHeterogeneous Cloud Radio Access Networks in a 5G environment. This archi-tecture handles spectral resources efficiently. Based on the simulation analysis,the proposed multitier H-CRAN architecture with improved control unit innetwork management perspective enables augmented granularity, end-to-endoptimization, and guaranteed quality of service by 15 percentages over theexisting system

A Quantum Safe Key Hierarchy and Dynamic Security Association for LTE/SAE in 5G Scenario

Millions of devices are going to participate in 5G producing a huge space for security threats. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against Quantum computers. Therefore, in this paper, we propose a security protocol called Quantum Key GRID for Authentication and Key Agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in Long Term Evolution (LTE) architecture without any significant modifications in the underlying base system. The proposed QKGAKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes