Design of Multi-Layer Protocol Architecture using Hybrid Optimal Link State Routing (HOLSR) Protocol for CR Networks

There is a lack of spectrum due to the rising demand for sensing device communication and the inefficient use of the existing available spectrum. Through opportunistic access to licenced bands, which does not obstruct the primary sensory users (PU), it is feasible to enhance the inefficient use of the current sensor device frequency spectrum. Cognitive settings are a demanding environment in which to carry out tasks like sensor network routing and spectrum access since it is difficult to access channels due to the presence of PUs. The basic goal of the routing problem in sensor networks is to establish and maintain wireless sensor multihop paths between cognitive sensor nodes. The frequency to be used as well as the number of hops at each sensor node along the path must be determined for this assignment. In order to improve performance while using less energy, scientists suggested a unique adaptive cross-layer optimisation subcarrier distribution technique with the HOLSR protocol for wireless sensor nodes. Throughput and energy consumption parameters are used to analyse the sensor network architecture protocol that has been developed. The energy usage of the sensor nodes in the network has increased by 50%. The performance of the proposed HOLSR algorithm is assessed using the simulation results, and the results are contrasted with those of a conventional multicarrier (MC) system in terms of bit error rate and throughput

5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network

Bypassing Isophthalate Inhibition by Modulating Glutamate Dehydrogenase (GDH): Purification and Kinetic Characterization of NADP-GDHs from Isophthalate-Degrading Pseudomonas aeruginosa Strain PP4 and Acinetobacter lwoffii Strain ISP4▿ †

Pseudomonas aeruginosa strain PP4 and Acinetobacter lwoffii strain ISP4 metabolize isophthalate as a sole source of carbon and energy. Isophthalate is known to be a competitive inhibitor of glutamate dehydrogenase (GDH), which is involved in C and N metabolism. Strain PP4 showed carbon source-dependent modulation of NADP-GDH; GDHI was produced when cells were grown on isophthalate, while GDHII was produced when cells were grown on glucose. Strain ISP4 produced a single form of NADP-GDH, GDHP, when it was grown on either isophthalate or rich medium (2YT). All of the forms of GDH were purified to homogeneity and characterized. GDHI and GDHII were found to be homotetramers, while GDHP was found to be a homohexamer. GDHII was more sensitive to inhibition by isophthalate (2.5- and 5.5-fold more sensitive for amination and deamination reactions, respectively) than GDHI. Differences in the N-terminal sequences and electrophoretic mobilities in an activity-staining gel confirmed the presence of two forms of GDH, GDHI and GDHII, in strain PP4. In strain ISP4, irrespective of the carbon source, the GDHP produced showed similar levels of inhibition with isophthalate. However, the specific activity of GDHP from isophthalate-grown cells was 2.5- to 3-fold higher than that of GDHP from 2YT-grown cells. Identical N-terminal sequences and electrophoretic mobilities in the activity-staining gel suggested the presence of a single form of GDHP in strain ISP4. These results demonstrate the ability of organisms to modulate GDH either by producing an entirely different form or by increasing the level of the enzyme, thus enabling strains to utilize isophthalate more efficiently as a sole source of carbon and energy

Decision support system for regional electricity planning

Electricity appears to be the energy carrier of choice for modern economics since growth in electricity has outpaced growth in the demand for fuels. A decision maker (DM) for accurate and efficient decisions in electricity distribution requires the sector wise and location wise electricity consumption information to predict the requirement of electricity. In this regard, an interactive computer-based Decision Support System (DSS) has been developed to compile, analyse and present the data at disaggregated levels for regional energy planning. This helps in providing the precise information needed to make timely decisions related to transmission and distribution planning leading to increased efficiency and productivity. This paper discusses the design and implementation of a DSS, which facilitates to analyse the consumption of electricity at various hierarchical levels (division, taluk, sub division, feeder) for selected periods. This DSS is validated with the data of transmission and distribution systems of Kolar district in Karnataka State, India

Decision Support System to Assess Regional Biomass Energy Potential

Biomass is a renewable source that accounts for nearly 33% of a developing country's energy needs. In India, it meets about 75% of the rural energy needs and the rural population constitutes 70% of the total population. Sustainable management of these resources requires better and timely decisions, which can lead to increased cost-efficiency and productivity. This would help in regional energy planning and conservation through appropriate decision interventions. To assist in strategic decision-making activities, considering spatial and temporal variables, Spatial Decision Support Systems (SDSS) are required. Spatial decision support system is an interactive computerized system that gathers data from a wide range of data sources, analyze the collected data, and then present it in a way that can be interpreted by the decision maker to deliver the precise information needed to make timely decisions. Decision support system (DSS) framework is designed and implemented to ease and speed up the use of environmental systems. In this regard, to assist planners to plan and manage bioresources in a sustainable way, Biomass Energy Potential Assessment (BEPA) decision support system is designed and is being implemented at regional levels through proper training. Overall objective of this DSS is the development of a set of tools aimed at transforming data into information and aid decisions for bioresources. This article outlines the design and implementation of DSS for assessment of biomass energy potential of a region considering the resources available and the demand. It is designed with user friendly GUI's (Graphic User Interface) using VB (Visual Basic) as frontend with Microsoft Access database as the backend. This helps to build executive information systems and reporting tools that tap vast data resources and deliver information in the context of daily processes. This tool can be used to form a core of practical methodology that will result in more resilience in less time and can be used by decision-making bodies to assess the impacts of various scenarios and to review cost and benefits of decisions to be made. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision making

Decision support system for regional domestic energy planning

163-174 Rural energy planning depends solely on the existing levels of energy consumption in domestic sector. In India, energy requirements for cooking and water heating depend predominantly on biomass fuels, which are often burnt in traditional stoves (efficiency 3/capita/d. </smarttagtype

WEPA: Wind energy potential assessment-spatial decision support system

Spatial Decision Support System (SDSS) assist in strategic decision-making activities considering spatial and temporal variables, which help in Regional planning. WEPA is a SDSS designed for assessment of wind potential spatially. A wind energy system transforms the kinetic energy of the wind into mechanical or electrical energy that can be harnessed for practical use. Wind energy can diversify the economies of rural communities, adding to the tax base and providing new types of income. Wind turbines can add a new source of property value in rural areas that have a hard time attracting new industry. Wind speed is extremely important parameter for assessing the amount of energy a wind turbine can convert to electricity: The energy content of the wind varies with the cube (the third power) of the average wind speed. Estimation of the wind power potential for a site is the most important requirement for selecting a site for the installation of a wind electric generator and evaluating projects in economic terms. It is based on data of the wind frequency distribution at the site, which are collected from a meteorological mast consisting of wind anemometer and a wind vane and spatial parameters (like area available for setting up wind farm, landscape, etc.). The wind resource is governed by the climatology of the region concerned and has large variability with reference to space (spatial expanse) and time (season) at any fixed location. Hence the need to conduct wind resource surveys and spatial analysis constitute vital components in programs for exploiting wind energy. SDSS for assessing wind potential of a region / location is designed with user friendly GUI’s (Graphic User Interface) using VB as front end with MS Access database (backend). Validation and pilot testing of WEPA SDSS has been done with the data collected for 45 locations in Karnataka based on primary data at selected locations and data collected from the meteorological observatories of the India Meteorological Department (IMD). Wind energy and its characteristics have been analysed for these locations to generate user-friendly reports and spatial maps. Energy Pattern Factor (EPF) and Power Densities are computed for sites with hourly wind data. With the knowledge of EPF and mean wind speed, mean power density is computed for the locations with only monthly data. Wind energy conversion systems would be most effective in these locations during May to August. The analyses show that coastal and dry arid zones in Karnataka have good wind potential, which if exploited would help local industries, coconut and areca plantations, and agriculture. Pre-monsoon availability of wind energy would help in irrigating these orchards, making wind energy a desirable alternative

Solar energy decision support system

Energy plays a prominent role in human society. As a result of technological and industrial development,the demand for energy is rapidly increasing. Existing power sources that are mainly fossil fuel based are leaving an unacceptable legacy of waste and pollution apart from diminishing stock of fuels.Hence, the focus is now shifted to large-scale propagation of renewable energy. Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Solar energy is one such renewable energy. Most renewable energy comes either directly or indirectly from the sun. Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. In this regard, a decision support system (DSS)would help in estimating solar energy potential considering the region’s energy requirement.This article explains the design and implementation of DSS for assessment of solar energy. The DSS with executive information systems and reporting tools helps to tap vast data resources and deliver information. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in time and can be used by decision-making bodies to assess various scenarios. It also offers means of entering, accessing, and interpreting the information for the purpose of sound decision making

Spatial Decision Support System for Assessing Micro, Mini and Small Hydel Potential

This study describes the design and implementation of DSS for assessment of Mini, Micro and Small Schemes. The design links a set of modelling, manipulation, spatial analyses and display tools to a structured database that has the facility to store both observed and simulated data. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in less time and can be used by decision-making bodies to assess the impacts of various scenarios (e.g.: changes in land use pattern) and to review, cost and benefits of decisions to be made. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision making. Thus, the overall objective of this DSS is the development of set of tools aimed at transforming data into information and aid decisions at different scales