Publication

Wireless Sensor Networks (WSNs) refers to a collection of many sensor nodes which are of low cost. WSNs are deployed in many applications like inhospitable terrain, health applications, military etc. Nowadays WSN related protocols and algorithms are designed to achieve higher life time and energy efficiency in the deployed networks. In this regard, clustering is the appropriate technique to with stand the load among the clusters. Various energy efficient cluster based algorithms are studied and Energy Aware Sleep Scheduling Routing (EASSR) being one of the best approaches is implemented because of its advantages over their counterpart and simulated in MATLAB. � 2017 IEEE.

Server virtualization is a fundamental technological innovation that is used extensively in IT enterprises. Server virtualization enables creation of multiple virtual machines on single underlying physical machine. It is realized either in form of hypervisors or containers. Hypervisor is an extra layer of abstraction between the hardware and virtual machines that emulates underlying hardware. In contrast, the more recent container-based virtualization technology runs on host kernel without additional layer of abstraction. Thus container technology is expected to provide near native performance compared to hypervisor based technology. We have conducted a series of experiments to measure and compare the performance of workloads over hypervisor based virtual machines, Docker containers and native bare metal machine. We use a standard benchmark workload suite that stresses CPU, memory, disk IO and system. The results obtained show that Docker containers provide better or similar performance compared to traditional hypervisor based virtual machines in almost all the tests. However as expected the native system still provides the best performance as compared to either containers or hypervisors. � 2017 Institute of Advanced Engineering and Science. All rights reserved.

Mobile Ad-hoc networks (MANETs) have a huge importance in the areas of military and emergency situations where there is a possibility of an infrastructure less network. The main challenge for the MANET is to have a mechanism in such a way that the node can continuously maintain the information required to properly route the traffic. A routing protocol specifies a path for forwarding data and control packets to a specified destination. It should be such a way that routing can minimize the delay and maximize the throughput. The proposed work deals with the simulation of an enhanced zone based route discovery for MANETs to improve the efficiency in controlling the flooding so that it can be used in tactical networks like military and emergency situations. And to prove the efficiency routing mechanism is evaluated with respect to throughput, routing overhead, delay, energy consumption and compared with the existing methodologies. Finally, the enhanced algorithm is implemented in software defined networking framework to evaluate the mechanism in presence of a centralized controller. � 2021 IEEE.

Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications. � 2021 IEEE.

The spectrum efficiency in wireless networks is becoming increasingly important to satisfy the expanding need for remote services, particularly reasonable remote Internet services. Various wireless users will encounter distinct circumstances on the channel, which are time-varying and location dependent at a given time. To exploit the wireless time-varying nature, a cross-layer design method called Opportunistic scheduling is used. Opportunistic scheduling increases the overall system performance and user fairness requirements under certain Quality of Service (QoS). The main idea behind this opportunistic scheduling algorithm is to make use of the time-varying channel and a user with the highest channel condition should be scheduled at a specified moment. The progressions in Wireless innovation made opportunistic scheduling a famous research point as of late. The demand for QoS provisioning is increasing and using a scheme which allows only users with best channel conditions to transmit at high transmission power cannot be satisfied. The objective of this paper is to implement opportunistic scheduling while adhering to fairness and QoS constraints, using Max-Min fair algorithm. In brief, a wireless network has been simulated using Qualnet simulator. An opportunistic scheduler that uses Max-Min fairness scheduling has been implemented, at the Media Access Control (MAC) layer. Here, the base station gathers the Signal to Noise Ratio (SNR) of all the nodes and then schedules the users using Max-Min algorithm, based on these SNR values. The same scenario is then implemented using Strict Priority, which is a Non-opportunistic scheduling algorithm. The resulting throughput, fairness, delay and jitter of both the algorithms are then compared. � BEIESP.