Abstract-Mobile ad-hoc network is a collection of mobile nodes that are connected wirelessly forming random topology through decentralized administration. In Mobile ad-hoc networks, multicasting is one of the important mechanisms which can increase network efficiency and reliability by sending multiple copies in a single transmission without using several unicast transmissions. Receiver initiated mesh based multicasting approach provides reliability to Mobile ad-hoc network by reducing overhead.Receiver initiated mesh based multicast routing strongly relies on proper selection of a core node. The existing schemes suffer from two main problems. First, the core selection process is not efficient, that usually selects core in a manner that may decrease core lifetime and deteriorate network performance in the form of frequent core failures. Second, the existing schemes cause too much delay for core re-selection(s) process. The performance becomes worse in situations where frequent core failures occur due to high mobility which causes excessive flooding for reconfigurations of another core and hence delays the on-going communication and compromising the network reliability.The objectives of the paper are as follows. First, we propose an efficient method in which the core is selected within the receiver group on the basis of multiple parameters like battery capacity and location, as a result, a more stable core is selected with minimum core failure. Second, to increase the reliability and decrease the delay, we introduce the idea of the mirror core. The mirror core takes the responsibility as a main core after the failure of the primary core and has certain advantages such as maximum reliability, minimum delay and minimizing the data collection process. We implement and evaluate the proposed solution in Network Simulator 2. The result shows that this scheme performs better than the existing benchmark schemes in terms of the packet delivery ratio, overhead and throughput.

Lakes are an inherent component of the global carbon cycle. They receive dissolved organic matter (DOM) from the catchment, which is stored, transformed and respired, or delivered downstream. In this study, we show that a subalpine lake shifts its role from DOM “transporter” to “transformer” depending on season and climate. We monitored dissolved organic carbon (DOC) concentration and DOM optical properties at the inlet and outlet of subalpine Lake Lunz (Austria) at high frequency during two contrasting years: an extreme drought in 2015, and regular precipitation regime in 2016. During both years, the DOC mass balance revealed that inflowing and outflowing DOC loads were nearly balanced (+6.57% and +1.70% DOC production in 2015 and 2016, respectively). However, DOM optical properties revealed an in‐lake turnover of DOM compounds, so that the terrestrial and aromatic signature of inflowing DOM was modified into autochthonous, protein‐like DOM. The magnitude of this transformation varied seasonally, being maximal in summer and minimal in winter, presumably following periods of high and low primary production and photo‐degradation. Inter‐annually, we found that drought further increased DOM transformation during summer by extending the lake water residence time. Finally, our results demonstrate a rapid response of DOM dynamics to hydrological and meteorological changes at both seasonal and inter‐annual scales, suggesting that carbon cycling in clear‐water mountain lakes may be highly sensitive to hydrological variation.

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework’s development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

Wireless sensor network is a collection of small devices called sensors nodes, which are deployed in the sensing field to monitor physical and environmental information. Location information of sensor node is a critical issue for many applications in wireless sensor network. The main problem is to design a path for a mobile landmark to maximize the location accuracy as well as to reduce energy consumption. Different path planning schemes have been proposed for localization.Here, this study focused only on static path planning scheme. In this article, the performance of five static path planning schemes is evaluated, namely, random way point, Scan, D-Scan, Hilbert, and Circles based on three parameters such as location error ratio, energy consumption, and number of references. Network simulator-2 is used as a simulation tool. Simulation scenarios with three node densities are used in this research study such as sparse node density, medium node density, and dense node density. The analysis of simulation results concludes that random way point has higher performance efficiency compared to rest of the static path planning algorithms concerning location error ratio (accuracy), energy consumption, and number of references in medium and dense node density scenarios. Hilbert performance was found good only in sparse node density scenario.

Abstract-Wireless Sensor Networks assume an imperative part to monitor and gather information from complex geological ranges. Energy conservation plays a fundamental role in WSNs since such sensor networks are designed to be located in dangerous and non-accessible areas and has gained popularity since the last decade. The main issue of Wireless Sensor Network is energy consumption. Therefore, management of energy consumption of the sensor node is the main area of our research. Sensor nodes use non-changeable batteries for power supply and the lifetime of Sensor node greatly depends on these batteries. The replacement of these batteries is very difficult in many applications, such as an alternative solution to this problem is to use Energy Harvesting system in Wireless Sensor Network to provide a permanent power supply to sensor nodes. This process of extracting energies from nature and converting it into electrical energy is called energy harvesting. Energy can be harvested from the environment for sensor nodes. There are many sources of energies in nature like solar, wind and thermal which can be harvested and used for WSNs. In this research, we suggest to use energy harvesting system for Cluster Heads in a clustering based Wireless Sensor Networks. We will compare our proposed technique to a well-known clustering algorithm Low Energy Adaptive Cluster Hierarchy (LEACH).