Aditi Shrivastava

Bio: Aditi Shrivastava is an academic researcher. The author has contributed to research in topics: Brassica & Biology. The author has an hindex of 1, co-authored 2 publications receiving 65 citations.

Localization Techniques for Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have recently emerges as promising technology in wireless communication field and gained special attention by research groups. It uses small and cheap gadgets with low energy requirements and limited on board computing resourceswhich communicates with each other’s or base stations without any pre-defined infrastructure. The property of being infrastructure less makes it suitable in distinctive application situations including remotemonitoring, disaster management, military applications and biomedical health observing devices. In many of these applications, node localization is unavoidably one of the important system parameters for example in target tracking if the nodes are not able to obtain the accurate location information, the related task cannot be performed.It is also helpful in routing, network coverage and quarry management of sensors. In general the localization techniques are ordered into two general classifications: range based and range free. In this paper, we discussed the various localization algorithms with their applicable areas, requirements and limitations. Moreover, on conclusion we compare these localization algorithms and analyze the future research directions for the localization algorithms in WSNs.

Genetic Correlation and Path Coefficient Analysis of Yield Attributing Parameters in Indian Mustard

Abstract: Indian mustard (Brassica juncea L. Czern. & Coss) is a natural amphidiploid which is the greatest pre-dominating crop of oilseed Brassica group. A study was undertaken to estimate the genetic variability, correlation and path coefficient analysis of yield and its contributing traits in 75 mustard genotypes grown in Randomized Block Design with two replications. The analysis of variance was highly significant for all the characters investigated. All thirteen characters were showed higher values of phenotypic coefficients of variation than genotypic coefficients of variation. The higher heritability in broad sense was estimated for all the characters. High value of heritability indicates that it may be due to higher contribution of genotypic components. High heritability coupled with high genetic advance as percent of means were recorded for days to 50% flowering, plant height (cm), number(s) of secondary branches per plant, length of main raceme (cm), siliquae length (cm), seed yield per plant (g), yield per plot (g), harvest index and biological yield that indicated predominance of additive gene action in the inheritance of these traits. The higher direct positive genotypic and phenotypic correlations for the biological yield, numbers of primary branches, numbers of siliquae on main raceme and numbers of secondary branches were documented. Whereas, days to maturity and siliquae length showed direct negative correlations with grain yield. Seventy-five genotypes, included in study were grouped into 6 clusters. The maximum inter cluster D2 value indicated that genotypes of cluster III and IV are not so closely related while the genotypes of cluster I and III are closely related. It is apparent therefore; the genotypes of various clusters differ so significantly with regards to their relative genetic distance as indicated from the high variation of D2 values. This makes it clear that the genotypes included in these clusters have a wide range of genetic diversity and may be used in a mustard hybridization programme to develop higher yielding cultivars.

Selection of Powdery Mildew Resistant Brassica Genotypes Based on Disease Indexing and Microsatellite Markers

Abstract: Powdery mildew disease of oilseed mustard caused by Erysiphe cruciferarum is a primary reason of yield reduction not only in India but also throughout the world. Identification and cultivation of resistant mustard genotypes against powdery mildew is the only way to overcome this challenge. In the present investigation, we targeted to screen 75 Brassica genotypes against powdery mildew based on disease indexing under field conditions and gene-specific molecular markers. Disease reaction on both the cotyledonary and true leaves was screened with using a modified 0-9 scale score as well as with nineteen disease linked microsatellite markers. In disease indexing under field conditions, genotypes viz., L-4 and PC-5 were identified as immune, China and RP-9 were considered as highly resistant and GSC-7 and PC-6 were recognized as resistant whilst genotypes i.e., RB-50, Pusa Bold, WRR-10 and GSL-1 were accredited as moderately resistant. Molecular markers based UPGMA dendrogram classified Rohini, WRR-22, PC-6, PusaBold, China, WRR-8, GSL-1, WRR-7, RH-749, L-4 and RB-50 as highly resistant mustard genotypes. In addition, disease linked marker cnu_m616 had the highest polymorphic information content (0.75) with greatest ability to differentiate resistant genotypes from susceptible genotypes, may be employed directly in mustard breeding programmes in future.

Review of Color Image Compression using Discrete Wavelet Transform and Block based Image Coding

Abstract: In this modern era of multimedia, the need of image/video storage and transmission for video conferencing, image and video retrieval, video playback, etc. are increasing at very high rate. As a result, the need for more satisfactory compression technology is always in demand. Modern applications, notwithstanding high pressure proportion, additionally interest for proficient encoding and translating forms, so that to fulfill computational requirement of some continuous applications. Two generally utilized spatial space pressure methods are discrete wavelet change and staggered block truncation coding (BTC).DWT method is used to stationary and non-stationary images and applied to all average pixel value of image. Muli-level BTC is a type of lossy picture pressure system for grayscale pictures. In this, it separates the first pictures into squares and after that a quantizer is utilized to lessen the quantity of dark dimensions in each square yet keeping up a similar mean and standard deviation. In this paper is studied of Multi-level BTCand DWT technique for for gray and color image.

Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Mobility-Assisted Localization in Wireless Sensor Networks

Abstract: In many applications of wireless sensor networks (WSNs), node location is required to locate the monitored event once occurs. Mobility-assisted localization has emerged as an efficient technique for node localization. It works on optimizing a path planning of a location-aware mobile node, called mobile anchor (MA). The task of the MA is to traverse the area of interest (network) in a way that minimizes the localization error while maximizing the number of successful localized nodes. For simplicity, many path planning models assume that the MA has a sufficient source of energy and time, and the network area is obstacle-free. However, in many real-life applications such assumptions are rare. When the network area includes many obstacles, which need to be avoided, and the MA itself has a limited movement distance that cannot be exceeded, a dynamic movement approach is needed. In this paper, we propose two novel dynamic movement techniques that offer obstacle-avoidance path planning for mobility-assisted localization in WSNs. The movement planning is designed in a real-time using two swarm intelligence based algorithms, namely grey wolf optimizer and whale optimization algorithm. Both of our proposed models, grey wolf optimizer-based path planning and whale optimization algorithm-based path planning, provide superior outcomes in comparison to other existing works in several metrics including both localization ratio and localization error rate.

A Self-Powered, Real-Time, LoRaWAN IoT-Based Soil Health Monitoring System

Abstract: Typical soil health assessment requires intensive field sampling and laboratory analysis. Although this approach yields accurate results, it can be costly and labor intensive and not suitable for continuous tracking of soil properties. Advances in soil sensor and wireless technologies are poised to replace physical sampling and offline measurement with in-field monitoring. This article reports the development, deployment, and validation of an Internet-of-Things (IoT) system for continuous monitoring of soil health. The end nodes of the proposed system, called soil health monitoring units (SHMUs), are solar powered and can be installed on a field for extended periods of time. Each SHMU transmits soil temperature, moisture, electrical conductivity, carbon dioxide (CO2), and geolocation data wirelessly using long-range wide-area network (LoRaWAN) radio technology. Data are received by a LoRaWAN gateway, which uploads it to a server for long-term storage and analysis. Users can view acquired data through a Web-based dashboard. The following significant experiments were carried out to validate the developed system: 1) a network consisting of eight SHMUs was deployed at an agricultural field site for several weeks and soil health metrics were analyzed using the soil health dashboard; 2) the flexibility of the system was demonstrated by the addition of an extra CO2 sensor allowing an additional variable directly linked to soil health to be recorded; 3) a wireless communication range of 3422 m was estimated at a transmission power of 10 dBm by deploying the developed system on a large field; 4) the average current consumption of a SHMU (including its associated sensors) was estimated to be 13 mA, at this rate, the onboard Li-ion battery is able to sustain a SHMU for several days; and 5) a 7 cm $\times6.5$ cm solar panel was able to fully charge the onboard battery in 14 days while supplying power to the SHMU.

D-LPCN: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network

Abstract: A boundary of wireless sensor networks (WSNs) can be used in many fields, for example, to monitor a frontier or a secure place of strategic sensitive sites like oil fields or frontiers of a country. This situation is modeled as the problem of finding a polygon hull in a connected Euclidean graph, which represents a minimal set of connected boundary nodes. In this paper we propose a new algorithm called D-LPCN (Distributed Least Polar-angle Connected Node) which represents the distributed version of the LPCN algorithm introduced in [1]. In each iteration, any boundary node, except the first one, chooses its nearest polar angle node among its neighbors with respect to the node found in the previous iteration. The first starting node can be automatically determined using the Minimum Finding algorithm, which has two main advantages. The first one is that the algorithm works with any type of a connected network, given as planar or not. Furthermore, it takes into account any blocking situation and contains the necessary elements to avoid them. The second advantage is that the algorithm can determine all the boundaries of the different connected parts of the network. The proposed algorithm is validated using the CupCarbon, Tossim and Contiki simulators. It has also been implemented using real sensor nodes based on the TelosB and Arduino/XBee platforms. We have estimated the energy consumption of each node and we have found that the consumption of the network depends on the number of the boundary nodes and their neighbors. The simulation results show that the proposed algorithm is less energy consuming than the existing algorithms and its distributed version is less energy consuming than the centralized version.