MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

&nbsp;

MIT World Peace University

Engineering and Technology

Wireless network is growing explosively not only in the corporate environment, but also at the consumer space. If the network is left open and unsecured, anyone can not only use the network for downloading illegal content, but also such a network can be used as a hacking medium to bring down other networks. Such open networks needs to be protected against such bad people who might take advantage of such an insecure network. Wireless networks can be vulnerable to various types of attacks such as eavesdropping, hacking, and freeloaders if there are no protections present in such wireless networks. IBM QRadar is security information, and event management (SIEM) solution is used by security professionals to protect their networks and themselves. In this paper, we propose a system that uses war driving for collecting access point information and QRadar to analyze vulnerable networks by correlating real-time traffic with the information present within the network. The work undertaken integrates information collected by war driving with IBM QRadar and then used to refine its correlation using the network information from open networks. After detecting vulnerable attacks and users, alerts are sent out to the security operation center. © 2018, Springer Nature Singapore Pte Ltd.

World University root node

School of Electronics & Communication Engineering

Lecture Notes in Networks and Systems

Vulnerable network analysis using war driving and security intelligence

For privacy protection, many architectures such as trusted third party, cryptography, client side privacy etc. have been proposed. These methods are neither fully support privacy nor efficient. We have proposed limited broadcast based application and one dynamic protocol for cloaking. We have simulated user broadcast model, to achieve location privacy where users are mobile and points of interests (POI) are stationary. Second experiment is a POI broadcast model to achieve location privacy. We have also presented dynamic cloaking protocol that considers speed, direction & location of mobile user for cloaking. We have experimentally recorded the results related to performance, energy and privacy strength. It shows that our system has more social and human value along with commercial attraction. © 2016 The Authors.

School of Computer Engineering & Technology

Fulltext

Physics Procedia

Controlled Broadcast Protocol for Location Privacy in Mobile Applications

Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.

School of Mechanical Engineering

International Journal of Parallel, Emergent and Distributed Systems

Constrained Cohort Intelligence using Static and Dynamic Penalty Function Approach for Mechanical Components Design

At home, on the television, the sheer number of Channels and the vast number of Programs on each Channel has itself made the task of identifying the “appropriate” program to watch difficult for the common user. There is a need of a system to generate suggestions/recommendation to the common user about which Programs to watch and when. In this paper, we propose a method and system which assists the user to choose which Programs on which Channels to watch without any inputs from the Viewer about his “Likes” or “Dislikes”. It learns from the Viewer Implicitly over time and learns all the patterns that the Viewer exhibits over the course of Television watching. © Springer International Publishing Switzerland 2016.

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2

PeTelCoDS—Personalized Television Content Delivery System: A Leap into the Set-Top Box Revolution

Data redundancy is quite common in wireless sensor networks (WSNs) where nodes are deployed densely. The reason behind such deployment is to achieve reliability from communication failure. Communication failure happens when particular node transmitting data fails. In WSN there is no other way than keeping redundant nodes to solve communication failure problem. If redundant nodes are available then at the time of node failure the data of failed node can be recovered from its redundant nodes. Though we can achieve reliability through redundant nodes presence of redundant nodes will generate more number of redundant packets which will consume more energy of network. Because nodes which are densely deployed will sense the same information and send it to sink node and sink will waste its energy in processing redundant data and also redundancy will generate heavy traffic in network. Hence, there is a need to trade-off between energy conservation and reliability. To do this trade-off we need to find optimization point of redundancy in WSN. So that reliability and energy conservation both will be maintained. In this paper we have used clustering-based load shifting policy (LSP) to eliminate redundancy up to an adequate level to achieve optimization point of redundancy. Data aggregation eliminates redundancy from WSN. We are performing data aggregation at two levels and at the same time we are keeping redundant nodes up to 50 % to achieve reliability. In this paper, we have done comparison of traditional cluster-based data aggregation with our LSP-based data aggregation. Simulation result shows that LSPDA has lesser average energy consumption and longer lifetime than traditional cluster-based data aggregation method. © Springer Science+Business Media Singapore 2016.

Advances in Intelligent Systems and Computing

Energy efficient data aggregation technique using load shifting policy for wireless sensor network

With an ever expanding human population the number of vehicles on road is also increasing. The traditional natured traffic management system and limited road lengths available, it's time that some improvisation and thought is laid upon on this subject as well. The evolution of Information Technology era is leading to the Internet of Things (IoT) and accordingly there are several technologies that have come into existence like Vehicular Ad-hoc Network (VANET). In this paper an innovative idea for traffic management system using radio frequency identification (RFID) tags and RFID Reader is described which has the ability to monitor and manage traffic as well as can be used to detect the collisions. It also introduces a Smart city framework and explains the storage of vehicle history and information related, automated traffic violation monitoring, and automated fine/challan delivery. It also describes how RFID tracker applications for mobile and computers can be modified by software upgrades and create real time monitoring systems. Resulting system would be far cheaper and reliable than the current CCTV camera based system and it could be used as a platform for various other applications. © 2016 ACM.

ACM International Conference Proceeding Series

An efficient and economical solution for future traffic management system using RFID

International Journal of Advance Engineering and Research Development (IJAERD)

Ontology Based Information search platform for comparative analysis of Educational Data using NLP

Mankind has always strived to give life like qualities to its artifacts in an attempt to find substitutes for himself to carry out his orders and also to work in a hostile environment. The popular concept of a pick and place robot with mechanical gripper whose arm looks and works like a human arm is one of those attempts. In this paper we have outlined the design procedure and analysis of prototype of pick and place robot with mechanical gripper. We have described a simple process to design pick and place robot using pulley mechanism to actuate its arm. Depending on requirement of weight to be lifted easy changes can be made to this robot using simple relations.

Industrial Science 2015.

Design analysis of pick and place robot with mechanical gripper.

Data stream classification is a challenging task. For real-time, data concepts of instances keep varying with time, such as weather prediction or intrusion detection etc. Classification is a supervised technique to mine useful patterns from these real data. There is a need to mine knowledge from these large data streams with techniques those provide accurate results and memory efficient. Using a set of classifiers i.e. ensemble classifier than a single one, proves to be more efficient. In this paper, Revised Accuracy Updated Ensemble(RAUE) classifier algorithm is proposed. In this algorithm, the data streams are processed in blocks. The ensemble used in RAUE uses Hoeffding Option Trees(HOT) as basic classifier set. The experimental evaluation and comparison of the proposed RAUE with existing ensemble algorithms is carried out. Out of all the compared algorithms, RAUE shows increase in accuracy and also uses less memory for identifying class labels for instances.

International Journal of Emerging Technologies in Computationaland Applied Sciences (IJETCAS)

DATA STREAM CLASSIFICATION THROUGH ENSEMBLE CLASSIFIER USING HOEFFDING OPTION TREES

Classification is important task of data mining used to extract knowledge from huge volume of data. By nature, classification is multi-objective problem, as it required optimization of multiple objectives simultaneously like accuracy, sensitivity, squared error, precision etc. Traditionally, evolutionary algorithms were used to solve multi-objective classification problem by considering it as single-objective problem, but this approach gives single solution to problem. Therefore, multi-objective evolutionary algorithms are used to solve classification problem. In this paper, we have used Pareto approach to optimize neural network for classification. Non-dominated sorting genetic algorithm is used to simultaneously optimize accuracy and mean squared error objectives of neural network along with local search. As slow convergence to optimal solutions is major disadvantage of evolutionary algorithm. To speed up convergence to optimal solutions hybrid technique is adopted by augmenting evolutionary technique with local search algorithm. This proposed approach gives set of Pareto optimal solutions which represent learned neural network models.