Purging of untrustworthy recommendations from a grid

In grid computing, trust has massive significance. There is lot of research to propose various models in providing trusted resource sharing mechanisms. The trust is a belief or perception that various researchers have tried to correlate with some computational model. Trust on any entity can be direct or indirect. Direct trust is the impact of either first impression over the entity or acquired during some direct interaction. Indirect trust is the trust may be due to either reputation gained or recommendations received from various recommenders of a particular domain in a grid or any other domain outside that grid or outside that grid itself. Unfortunately, malicious indirect trust leads to the misuse of valuable resources of the grid. This paper proposes the mechanism of identifying and purging the untrustworthy recommendations in the grid environment. Through the obtained results, we show the way of purging of untrustworthy entities.Comment: 8 pages, 4 figures, 1 table published by IJNGN journal; International Journal of Next-Generation Networks (IJNGN) Vol.3, No.4, December 201

Cat and Mouse Based Task Optimization Model for Optimized Data Collection in Smart Agriculture

Data collection from agricultural fields is tiring and requires novel methodologies to produce reliable outcomes. The combination of edge and wireless sensor networks (WSN) for smart farming enabled the efficient collection of data from remote fields to a vast extent. Adopting an optimization algorithm to achieve the data collection task is prioritized in the proposed work, and a new and effective data collection framework is proposed. The proposed framework initially collects the data from the agricultural fields via sensors and then transmits it to the edge server. The path between the sensors and the edge server is optimally obtained using the cat and mouse based task optimization (CMTO) model. The sensed data are transmitted through the optimal route, and then the edge server obtains and evaluates the data based on the data quality metrics such as precision, correctness, completeness and reliability. The valid data are then identified and transferred to the cloud servers for storage. The simulation of the work is done in Python platform and evaluated using the crop recommender dataset. The evaluations proved the method's efficacy compared to the existing state-of-the-art algorithms. The proposed work also provided upto 12.5% of improvement in terms of energy consumption, 7.14% of improvement in terms of communication latency, 4% of improvement in terms of execution cost, 2.27% of improvement in terms of completeness, 1.12% of improvement in terms of precision, 9.52% of improvement in terms of correctness, and 3.37% of improvement in terms of reliability

Fault Tolerant Scheduling of Partitioned and Grouped Jobs in Grid Computing (FTPG)

Computational grids have the potential for solving scientific and large - scale problems using heterogeneous and geographically distributed resources In addition to the challenges of managing and scheduling resources reliable challenges arise because the grid infrastructure is unreliable There are two major problems in Scheduling the Grid 1 Efficient Scheduling of jobs 2 Providing fault tolerance in a reliable manner Most of the existing strategies do not provide fault tolerance There are some algorithms which provide fault tolerance but they do a large amount of redundant computation to provide fault tolerance This paper addresses this issue and minimizes redundant work by using a group level table of data This technique is suitable for partitioning and group scheduling of job

A Case for using Grid Framework for Indian Rural Healthcare to Meet the Millennium Development Goals (MDGs)

As per the September 2010, Annual Report of Department of Health and Family Welfare, Ministry of Health and Family Welfare, GOI, 75% of human resources and advanced medical technology,70% of hospitals and 40% of beds are in the private sector and mostly in the urban areas. Due to poor Infrastructure, insufficient supply of skilled doctors and dispersed populations the people living in the rural areas do not get any specialist care ,advice and treatment plan resulting in high MMR (Maternal Mortality Rate per 100,000 live births) and IMR(Infant Mortality Rate).We have proposed a HealthGrid Framework using the SWAN as an IT backbone and also formation of a Data Grid EHR to be shared by specialist doctors to provide better medical services to the rural poor which in turn helps us to meet the MDGs by 2015

Collaborative Filtering Based Recommendation System: A survey

Abstract—the most common technique used for recommendations is collaborative filtering. Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships from a group of user who share the same preferences and taste. In this paper we have explored various aspects of collaborative filtering recommendation system. We have categorized collaborative filtering recommendation system and shown how the similarity is computed. The desired criteria for selection of data set are also listed. The measures used for evaluating the performance of collaborative filtering recommendation system are discussed along with the challenges faced by the recommendation system. Types of rating that can be collected from the user to rate items are also discussed along with the uses of collaborative filtering recommendation system