GPS Based Distributed Communication Protocol for Static Sensor Network (GDCP)

AbstractOverall energy of network is a major issue in current sensor network. This paper proposes (GDCP) GPS (Global Positioning System) based reactive communication protocol for Static WSN (Wireless Sensor Network) to extend the life time of entire network. In GDCP, energy efficient routing is achieved using local communication among sensor nodes. While routing, packets are routed via reliable shortest path from source node to sink node. The shortest path is determined with the help of a Neighbouring Table (NT) of a node. This table stores information such as location, distance to neighbour node and distance to a sink node. If the neighbouring node has sufficient energy and its distance to a sink node is less than other neighbours then it becomes the receiver and packet forwards to neighbour node. After receiving the packet, receiver becomes sender node and it checks its neighbouring table for minimum sink node's distance and sufficient energy. This process continues till the packet reaches the sink node. Sink node is assumed to move from one location to another as its neighbour node's energy becomes less than threshold energy. Energy consumption is analysed on 100 static sensor nodes and one sink node. A simulated result shows that overall energy of network is improved

Incorporating substation and switching station related outages in composite system reliability evaluation

This thesis presents the development of a new method for incorporating station related outages in composite or bulk system reliability analysis. Station related failures can cause multiple component outages that can propagate to other parts of the network resulting in severe damages. In order to minimize the effects of station related outages on the composite system performance it is necessary for the designer to assess their effects. This task can be achieved by including station related outages in the composite system evaluation. Monte Carlo simulation is used in this research to assess composite system reliability. The new method described in this thesis is used to include station related outages in the reliability evaluation of two composite test systems. This new method is relatively simple and can be used to consider multiple component outages due to station related failures in composite system reliability evaluation. In this approach, the effects of station related outages are combined with the connected terminal failure parameters. Reliability studies conducted on the two composite test systems demonstrates that station failures significantly affect the system performance. The system reliability can be improved by selecting appropriate station configurations. This is illustrated by application to the two composite test systems

Literature Review on Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

This paper presenting a survey on finding itemsets with high utility. For finding itemsets there are many algorithms but those algorithms having a problem of producing a large number of candidate itemsets for high utility itemsets which reduces mining performance in terms of execution. Here we mainly focus on two algorithms utility pattern growth (UP-Growth) and UP-Growth+. Those algorithms are used for mining high utility itemsets, where effective methods are used for pruning candidate itemsets. Mining high utility itemsets Keep in a special data structure called UP-Tree. This, compact tree structure, UP-Tree, is used for make possible the mining performance and avoid scanning original database repeatedly. In this for generation of candidate itemsets only two scans of database. Another proposed algorithms UP Growth+ reduces the number of candidates effectively. It also has better performance than other algorithms in terms of runtime, especially when databases contain huge amount of long transactions. Utility-based data mining is a new research area which is interested in all types of utility factors in data mining processes. In which utility factors are targeted at integrate utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utility based descriptive data mining. Utility based data mining is used for finding itemsets that contribute most to the total utility in that database

Intrusion detection with Parameterized Methods for Wireless Sensor Networks

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two Adaboost based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types

Self-Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

Unknown target search, in an unknown environment, is a complex problem in Wireless Sensor Network (WSN). It does not have a linear solution when target’s location and searching space is unknown. For the past few years, many researchers have invented novel techniques for finding a target using either Static Sensor Node (SSN) or Mobile Sensor Node (MSN) in WSN i.e. Hybrid WSN. But there is a lack of research to find a solution using hybrid WSN. In the current research, the problem has been addressed mostly using non-biological techniques. Due to its complexity and having a non-linear solution, Bio-inspired techniques are most suited to solve the problem. This paper proposes a solution for searching of randomly moving target in unknown area using only Mobile sensor nodes and combination of both Static and Mobile sensor nodes. In proposed technique coverage area is determined and compared. To perform the work, novel algorithms like MSNs Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), Leader’s Movement Prediction Algorithm (LMPA) and follower algorithm are implemented. Simulation results validate the effectiveness of proposed work. Through the result, it is shown that proposed hybrid WSN approach with less number of sensor nodes (combination of Static and Mobile sensor nodes) finds target faster than only MSN approach

Text Mining Method to Develop D-Matrix for Fault Diagnosis

The D-matrix is one amongst the quality diagnostic models specified by IEEE Standard. This framework catches underlying connections between symptoms and failure modes in structured fashion. This framework is called as Dependency or Diagnosis framework (D-matrix).Proposed system describes text mining method based on an ontology to develop D-matrix by mining repair verbatim written in unstructured text. Here repair verbatim are collected during fault diagnosis. Then mining algorithms are applied to find dependencies. D-Matrix is constructed for different dataset, then we generate a combined D-matrix by taking common parameters from each D-matrix and then a graph is formed for that D-matrix

A Review of Wireless Sensor Network For Agriculture

Agriculture has key role in supplying the food but climate change, more demand for food and scarcity of water demands modified agriculture methods for agriculture practices. Advancement of wireless sensor network, size reduction has increased chances of application of WSN in agriculture field. The proper selection of WSN specific to particular application is complex task for novice user. The objective of this paper is providing an overview of different WSN technologies used in agriculture domain. In this work, we provide information about WSN, their standard and technologies such as types of WSN architecture, Wireless communication, different wireless sensors along with specific application and case study of WSN. At the end we discussed the challenges in WSN and future direction for the work in this field

Optimization of WSN using Biological Inspired Self-Organized Secure Autonomous Routing Protocol

Since last three decade, Wireless Sensor Network is one of the biggest innovative technologies; it provides facility of heavy data traffic and management telecommunication by sensing, computation and communication into a small device. Main threat for this type of data transfer is data security in terms of maintains data integrity, high consumption of energy, end-to-end delay and high cost of nodes i.e. sensor. Handling all h issue at same time is the difficult task. SRTLD and BIOSARP are two routing protocol which helps in improving performance of the WSN. This paper is a detail description of secure architecture which is based on SRTLD and BIOSARP protocol. The main objective of this architecture is to provide high security by taking into account low energy consumption, low end-to-end delay and low node level cost. This mechanism uses concept of ACO (Ant Colony Optimization) which helps in achieving objective of the architectur

Survival According to Primary Tumor Location, Stage, and Treatment Patterns in Locoregional Gastroenteropancreatic High-grade Neuroendocrine Carcinomas

Background: Although the gastrointestinal tract (including the pancreas, gastroenteropancreatic (GEP) is the most common site for extrapulmonary neuroendocrine carcinoma (NEC), the current treatment patterns of locoregional GEP NEC and in particular, the role of surgical resection is unclear. Methods: Data from the National Cancer Database between 2004 and 2016 were used for this study. Results: Of 2314 GEP NEC cases (stages I–III), 52.5% were stage III. Colon was the most common site (30%); 30.9% of all cases were small cell morphology. Age, morphology, stage, and primary site were associated with significant differences in treatment patterns. Management of NEC mimicked that of adenocarcinomas arising at the respective sites: colon NEC most likely to be treated with surgery and chemotherapy; anal and esophageal NEC was primarily likely to receive chemotherapy and radiation, and rectal NEC mostly likely to receive trimodality therapy. However, 25%-40% of patients did not undergo surgical resection even at sites typically managed with curative resection, and there was a trend toward lesser resection over time. The prognostic impact of surgical resection was significant across all stages and correlated with variations in survival across primary sites. Even in patients undergoing chemoradiation, surgery was the only prognostic variable that significantly affected survival in stages I–II patients (HR 0.63) and showed a strong trend in stage III (HR 0.77) patients. Conclusions: Treatment patterns in GEP NEC vary considerably according to stage and primary tumor site. Surgery significantly improved survival in stages I–II patients and showed a strong trend in stage III patients regardless of primary tumor location and other perioperative therapies.publishedVersio

Chloride Channel ClC-2 is a Key Factor in the Development of DSS-induced Murine Colitis:

Previously, we have shown that the chloride channel ClC-2 modulates intestinal tight junction (TJ) barrier function. The aim of the present study was to investigate the role of ClC-2 in epithelial barrier function and recovery in the event of epithelial injury