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In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale image and remove noise by applying an erode operation. Finally, the brain tumors in the images are detected using a watershed algorithm. The proposed method was compared with two well-known methods namely the conventional K-mean clustering and Fuzzy C Means (FCM) clustering. We verified the precision and the objectivity of our proposed method. The average precision and recall for our proposed method were excellent with values of 0.914052 and 0.995641, respectively. Our method detected more brain tumors than the conventional K-means clustering and FCM clustering methods and was able to provide for an efficient image data processing with reduced file sizes. [ABSTRACT FROM AUTHOR]