Shiv Prakash

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

Maximizing Availability and Minimizing Markesan for Task Scheduling in Grid Computing Using NSGA II

Large distributed platform for computationally exhaustive applications is provided by the Computational Grid (CG). Required jobs are allotted to the computational grid nodes in grid scheduling in order to optimize few characteristic qualities of service parameters. Availability is the most important parameter of the computational nodes which is the likelihood of computational nodes accessible for service in specified period of time. In this paper, emphasis has given on optimization of two quality of service (QoS) parameter makespan (MS) and availability grid system for the task execution. Since, the scheduling problem is NP-Hard, so a meta-heuristics-based evolutionary techniques are often applied to solve this. We have proposed NSGA II for this purpose. The performance estimation of the proposed Availability Aware NSGA II (AANSGA II) has been done by writing program in Java and integrated with gridsim. The simulation results evaluate the performance of the proposed algorithm.

An elitist non-dominated sorting bat algorithm NSBAT-II for multi-objective optimization of phthalic anhydride reactor

Abstract Nature inspired meta-heuristic algorithms are an integral part of modern optimization techniques. One such algorithm is bat algorithm which is inspired from echolocation behavior of bats and has been successfully applied to non-linear single-objective optimization problems. In this paper, a multi-objective extension of bat algorithm is proposed using the concepts of Pareto non-dominance and elitism. The novel algorithm is tested using thirty multi-objective test problems. The performance is measured using metrics namely, hyper-volume ratio, generational distance and spacing. The newly developed algorithm is then applied to a real-world multi-objective optimization problem of a phthalic anhydride reactor. It shows faster convergence for test problems as well as the industrial optimization problem than two popular nature inspired meta-heuristic algorithms, i.e. multi-objective non-dominated sorting particle swarm optimization and real-coded elitist non-dominated sorting genetic algorithm.

Geometry based Inter Vehicle Distance Estimation for Instantaneous GPS Failure in VANETs

Localization in Vehicular Adhoc Networks (VANETs) has witnessed significant attention due the instantaneous failure of Global Positioning System (GPS). Performance of Intelligent Transport Systems (ITS) applications is based on efficient and reliable information dissemination techniques. Efficiency and reliability of information dissemination is dependent on the availability of accurate location information about vehicles. Most of the localization techniques suggested in the past are based on either real time radio signal measurement or Road Side Unit (RSU). Signal measurement is performed in terms of Time of Arrival (ToA) or Received Signal Strength Indicator (RSSI) which is susceptible to measurement error due to the high mobility of vehicles in vehicular traffic environment. Availability of RSU can not be guaranteed. This paper proposes a Geometry based Inter Vehicle Distance (Geo-IVD) calculation techniques which can be utilized for information dissemination in VANETs. Geo-IVD is a cooperative technique which utilizes past and present knowledge about the vehicle. It utilizes mathematical geometry to calculate present unknown location of a vehicle. More specifically, Geo-IVD find the current unknown location of a vehicle considering two cases. In the first case, vehicles current direction is considered to be the same or opposite from its past direction. In the second case, vehicles current direction is considered different (other than opposite) from its past direction. Mathematical geometry is utilized to solve the problem of finding current location in both these cases. Simulations are carried out in ns2 and results are comparatively analyzed with that of the state-of-the-art technique: ToA-IVD. Analysis of results attests the superiority of Geo-IVD to the state-of-the-art technique.

Minimizing energy consumption in scheduling of dependent tasks using genetic algorithm in computational grid

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

Computational Grid (CG) is a wide network of computational resources that provides a distributed platform for high end compute intensive applications. The resources in the computational grid are usually heterogeneous and being a highly heterogeneous system, Computational Grid poses a number of constraints. It is difficult to allocate and schedule the applications properly to achieve the benefit of the grid resources from the applications point of view, as the resources are heterogeneous and dynamic in nature. There are no common scheduling strategies that fulfill all the needs with respect to both, user and the system. The available scheduling implementations consider specific characteristics of the available resources and the application. The complexity of application, user requirements and system heterogeneity prevents any scheduling procedure in achieving its best performance. The aim of a grid scheduling algorithm is to find an appropriate set of resources and maintain its userdemanded Quality of Service (QoS) requirements. Scheduling in CG is an NP-hard problem which requires an efficient solution. The problem, considered in this work, is task scheduling in Computational Grid (CG). Task scheduling in CG is a complex problem as many QoS parameters and system constraints are involved. This paper deliberates over the problem and various tools used in order to solve this problem.

Resource Allocation and Provisioning in Computational Mobile Grid

Recent years have seen drastic increase in number of mobile devices which are becoming popular not only by their communication flexibility but also for their computational capability. A collection of mobile devices together form a grid. In the proposed model, it is assumed that the set of jobs are accumulated to the primary machine, though they might have been submitted anywhere in the grid. It is also assumed that each job consists of one or more number of sub jobs. Mobile Grid comprises with number of machines and speed of execution of individual processor may be different. Each machine can handle fixed number of sub jobs. A set of jobs accumulated at the primary machines are distributed to different secondary machines. A rigorous set of experiment has been carried out by simulating the model using java language on Eclipse IDE integrated with Gridsim. The model has been tested with various numbers of inputs in different cases and result has been observed. The authors found some of the key findings of the experiments. In most of the cases, resource allocation is better when mobile agent is employed for the work.

An investigation on biometric internet security

Due to the Internet revolution in the last decade, each and every work area of society are directly or indirectly depending on computers, highly integrated computer net- works and communication systems, electronic data storage and high transfer based devices, e-commerce, e-security, e-governance, and e-business. The Internet revolution is also emerged as significant challenge due to the threats of hacking systems and individual accounts, mal- ware, fraud and vulnerabilities of system and networks, etc. In this context, this paper explores E-Security in terms of challenges and measurements. Biometric recognition is also investigated as a key e-security solution. E-Security is precisely described to understand the concept and requirements. The major challenges of e-security; namely, threats, attacks, vulnerabilities are presented in detail. Some measurement are identified and discussed for the challenges. Biometric recognition is discussed in detail wit pros and cons of the approach as a key e-security solution. This investigation helps in clear understating of e-security challenges and possible implementation of the identified measurements for the challenges in wide area of network communications.

Patient Data Dissemination in Wireless Body Area Network: A Qualitative Analysis

With the availability of various sensors for vital signs monitoring of a human body, Wireless Sensor Networks (WSNs) have envisioned several applications in the health-care domain. Different wearable and implanted sensor nodes report the vital signs information to a remote medical facility via the local body coordinator. Together the set of sensor nodes and body coordinator form a network known as Wireless Body Area Network (WBAN). In addition to the common routing constraints of WSNs, WBAN poses unique challenges constrained by heterogeneous nature of data, postural movements, and temperature-rise issues. Numerous contributions have been made to address the aforementioned issues of WBAN and are classified as temperature-aware routing protocols, cluster-based routing protocols, QoS-aware routing protocols and postural movement based routing protocols. This paper qualitatively analyzes the performance of the variously proposed protocols in terms of energy efficiency, provision of QoS, mobility, end-to-end delay, and temperature-rise. A relative performance of all the protocols is also given in order to develop a general understanding of the overall efficacy in-line with the unique constraints of WBANs.

Maximizing Fault Tolerance and Minimizing Delay in Virtual Network Embedding using NSGA-II

Due to growing interest in network cost optimization through resource sharing, virtual network embedding has significantly attracted the attention of researchers. Recently, various virtual network embedding algorithms have been suggested. The mapping of these algorithms is not reliable due to the absence of fault tolerance capability. Therefore, this paper proposes a technique for maximizing fault tolerance and minimizing delay in Virtual Network Embedding (VNE) using Non-dominated Sorting Genetic Algorithm (NSGA-II). The multi-objective optimization problem is mathematically formulated. An adapted NSGA-II is proposed for solving the optimization problem. The major components of adapted NSGA-II are representation of chromosome, computation of fault tolerance and delay, sorting using non-domination, and crossover and mutation operation. Two novel mathematical functions for computing fault tolerance and delay are developed. The analysis of simulation results clearly indicates that the proposed technique effectively optimizes both the considered objectives in VNE.