Much of the work has been done at National & International level in the field of Hybrid Renewable Systems. Most common hybrid models are Wind-Biogas, Wind-Solar PV, Wind Solar Thermal, Wind-Biomass, Solar Thermal-Biogas, Solar PV-Biogas etc. Most of the academic work is limited to the modelling of various hybrid systems and analysis of results based on the variation in different parameters. Range of parameters includes pure technical designing terms to social and economic factors. Some of the most notable references are mentioned below.

Wireless sensor network is not fixed infrastructure which contains either homogenous or heterogeneous sensor node. Each node has own residual energy. So it’s very important factor of network to prevent the energy of nodes, so that nodes live more and network establishment in long time without disturbance. LEACH is one of hierarchical routing protocols which are used for securing the energy. For past few years, many algorithms have been developed to improve the hierarchical routing protocol to improve the overall network quality and minimize energy consumption, which includes new generate algorithm based on clustering based genetic algorithm i.e. Low Energy Adaptive Clustering hierarchical –cluster based genetic algorithm (LEACH-GAC) and also research on security in cluster so unauthorized not access so private data is easily transferred and received into cluster. Aim of paper is Compare with LEACH-B, LEACH-C, LEACH-GA, LEACH-GAC varying with residual energy, Cluster formation, security.

Despite the fact that we in India have taken a giant leap forward in increasing the installed capacity from a mere 1713 MW in 1950 to over 3,46,000 MW as on date, the renewable energy (RE) sources however contribute over20% with total capacity of over 34,000MW. This contribution has a major social and economic impact on rural and remote area population. The growth of clean energy technologies for mega Power generation, such as the Solar Mega Power, both Solar Thermal and PV, Clean Coal Technologies, CCTs, i.e. Supercritical power plants, Integrated Gasification Combined Cycle (IGCC) and fluidized bed combustion (FBC) are key to the success Green Power Mission for India. Carbon Capture & Sequestration (CCS) is considered as the frontier Green Energy technology. CCS technology is still in the demonstration phase and it is important that India is not left behind in this area. While there is a considerable amount of work already underway domestically, there may still be a need for research collaborations and knowledge sharing and transfer. These areas of research in CCS include development of new adsorbents, better process integration of capture equipment, and conversion of CO2 to useful products, among others. The important challenge of Energy penalty in CCS being faced world-wide, can be met through use of Concentrated Solar Power (CSP) for supplementing steam for regeneration in a MEA and other solvent based post-combustion CCS in Thermal Power Plants. Low Carbon Technologies (LCT) and Carbon Capture & Sequestration (CCS) are key factors for Energy Security Environmental Sustainability and the same are forward in this paper leading to an opportunity for course- correction in our line of thinking in Green Power.

Various problem are encountered in water distribution system such Adequate Pressure, Water quality, Method of distribution (Demand Driven Analyses (DDA) and other is Pressure Driven Analyses (PPA), Estimation of Water Demand (Population based /Landused based etc. Authors had used various information system to handle spatial data in water distribution but Geoinformatic system (remote sensing (RS), geographical information systems (GIS) and Differential Global Positioning Systems (DGPS)) plays a vital role in handle the spatial data for water distribution system. water distribution hydraulic software leverage the spatial analysis abilities of GIS software and use source data types such as geocoded, billing meter records, water production data, census tracts, land use zoning, traffic analysis zones, meter routes and demand density information.

In telecommunication, traditionally telephone networks uses basic concept called circuit switching for doing communication between sender and receiver such as Public Switched Telephone Network (PSTN). Nowadays this type of switching is being replaced by packet switching where the transmission of data is done in packets and networks based on this are referred to as connectionless networks. WIMAX Technology is an upcoming wireless technology that provides us high speed mobile data and telecommunication services. This technology is used to replace the traditional telephone networks i.e., Public Switched Telephone Network (PSTN). In this paper performance of such networks which uses VOIP technology in WIMAX Networks is investigated using different service protocols and for which OPNET 14.5 Simulator is chosen as the platform. OPNET Simulator provides simulated real-life environment, in order to study the performance of the next generation networks. The parameters used to evaluate the performance of the networks are jitter and end to end delay. Finally based on the simulations best VOIP codec is also found out.

There is drastic increase in needs of networking and data sharing in today’s world. Such globalization of increased information technology and development there exists need of network security. Firewalls may provide some level of security but they never alert administrator for upcoming attacks. In order to find such abnormal behavior of network packets there is need of reliable detection system for improvement of efficiency and accuracy. As in today’s developing network environment there is threat of new type of attacks daily in the network. So, the network administration system is also needed to be updated regularly for upgradation of security level. One of the network packets monitoring system is Intrusion detection systems (IDS). The proposed model is designed using machine learning approach for detection of malicious activities of the network packets. For that KDD-99 dataset is used. First of all the dataset is normalized for reducing calculation complexity, further features are reduced using co-relation algorithm. The reduced features determine that only efficient features can be used for malicious behavior detection. From result analysis it is seen that while selecting 15 features using co-relation outperforms best. After feature reduction data clustering is performed using k-mean clustering algorithm. By using clustering, small datasets is built that represents the entire original dataset which can expressively reduce the training time of classifiers and improve the efficiency. In final step of proposed algorithm multilevel hybrid classifiers, based on support vector machine, extreme learning machine and random forest, are designed for classification of dataset into five attack categories i.e. DOS, U2R, R2L, Probe and Normal. As compared to some other multilevel classifier work the proposed algorithm proves its efficiency in terms of high accuracy, high detection rate and false alarm rate (FAR).