Establishing Security in Manets Using Friend-Based Ad Hoc Routing Algorithms

Establishing security in MANET is challenging issue in any adhoc routing protocol. Neighbor nodes do not updated their routing status and bandwidth consumption during the transmission. AODV used single chain topology so bandwidth offers single chain transmission. To establishing the secure transmission FACES provide new challenges to its neighbor. Proposed system incorporate cache update and aware of routing information this scheme that has been drawn from a network of friends in real life scenarios. This algorithm send request in the form of challenges and sharing nearby neighbor lists to provide trust worthy nodes to the source node through which data transmission finally takes place. Proposed system taking various packet sizes into their account and deals only best effort traffic and AODV used only simple priority algorithm. Due this algorithm network can easily identifies the malicious node and provide secure neighbor node detection in the mobile adhoc network

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Proceedings of the International Conference on Cognition and Recognition A Fuzzy Image Segmentation using Feedforward Neural Networks with Supervised Learning

A neuro-fuzzy image segmentat ion is proposed in this paper. The given image is clustered by using Fuzzy-C-Means algorithm which is an unsupervised approach. Combining this unsupervised clustering to a neural network with unsupervised learning will lead to unreliable segmentation. Therefore, in the proposed work the labels obtained from clustering through the fuzzy-c-means algorithm is used to define the target of the supervised feedforward neural network and a fuzzy entropy method is deployed to set a threshold value for improving the segmented image. The proposed algorithm is tested on various gray-level images and results good segmentation. 1

Energy efficient greedy tree based algorithm for data aggregation in wireless sensor network

Secure data aggregation is essential in wireless sensor networks for lowering the amount of data transmitted and extending the lifetime of the network. The foundation underlying significantly greater industrial internet of things applications is primarily wireless sensor nodes. Sensors that have already been integrated within can be used to sense data in any form of real-time IoT application. In real-time physical surroundings, sensors utilize as little power as feasible to conduct operations including sensing, communicating, and processing data. Many investigations are being carried out to improve sensor node energy efficiency and network lifetime. To save energy, more attention must be paid to the clustering and routing aspects of communication. In this paper, we introduce Energy Efficient Greedy Tree based Data Aggregation (EE–GTDA) algorithm for efficient data aggregation with increased reliability and reduced energy consumption. It is a two-fold homogeneous technique that supervises safe energy-efficient connectivity and data aggregation with the greedy tree based solution that emphasizes multi-objective function. These methodologies are based on minimizing sensor energy consumption to maximize network lifetime simultaneously decreasing communication overhead. A trade-off between energy and safety is accomplished in order to increase efficient energy consumption with a higher packet delivery ratio

Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN

The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%

Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN

The goal of today&rsquo;s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%