This paper aims to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, this knowledge is used to compute locations of deployment such that the network lifetime is maximum paper. In this paper ultimate goal is to realize an automated monitoring network so that detection applications of various emergency events can be practically implemented. Further, the nodes are scheduled to achieve this upper bound. This proposed system uses artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling. In addition, ANT colony optimization technique is used to provide maximum network lifetime utilization. The comparative study shows that artificial ACO performs better than bee colony algorithm for sensor deployment problem. The proposed heuristic was able to achieve the theoretical upper bound in all the experimented cases.

Wireless sensor networks (WSN) are spatially distributed autonomous sensors to monitor the physical or environmental conditions such as temperature, sound, pressure, etc. It is the collection of large number of sensor nodes in sensor fields. The major application in WSN is like remote environmental monitoring, detections of forest fire and target tracking. This environment is particularly in sensors for recent years that are smaller, cheaper and intelligent. The sensors are associated with wireless interfaces with which the communication takes place with one another to form a network. This paper discuss about wireless sensor networks in addition to that it includes some of the fields in radio networks and also provides new applications for sensing and transferring of information from various environments.

To investigate the antibacterial activity of hydroalcoholic extract of leaves of Gymnema sylvestre . The antibacterial activity was evaluated by agar well diffusion method against gram negative (Escherichia coli, Pseudomonas aeruginosa) and gram positive bacteria (Staphylococcus aureus) at various concentrations. The results of the present study suggest that leaf extract of Gymnema sylvestre can be used for treating infectious diseases caused by Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus.

Brain-based learning refers to teaching methods, lesson designs, and school programs that are based on the latest scientific research about how the brain learns, including such factors as cognitive development—how students learn differently as they age, grow and mature socially, emotionally, and cognitively.Brain based learning draws upon the functioning of the brain and takes into consideration the rules of the brain for meaningful learning. Brain-based learning is supported by the general belief that learning can be accelerated and improved if educators base how and what they teach, rather than on past educational practices, established conventions, or assumptions about the learning process.