International Journal of Autonomic Computing (5 papers in press)
Internet of Things (IoT) for the Future Modernization of Agriculture
by Prashanta Das
Abstract: According to a report by the Department of Economic and Social Affairs of the UN the current world population is likely to reach 11200 million in 2100. World Bank data for Agricultural land (% of landarea) for 264 countries shows a meager increase in land use in Agriculture of approximate 2% in 53 years of period (1961 2014). These data shows that, land use in agriculture is fixed, whereas the demand for agriculture produced (food) increase by every passing year. In order to match the Global demand for affordably priced food by 2050 the yearly food production must increase by more than 1% annually. This paper discusses about the prospects of application of IoT in e-Agriculture to meet the growing demand for food for the exponentially growing population. Finally the paper discusses an IoT setup to measure the Atmospheric Sensors having the prospect in e-Agriculture as an use case.
Keywords: IoT; Precision Agriculture; e-Agriculture.
Content Extraction from News Web Pages using Tag Tree
by Chandrakala Arya, Sanjay K. Dwivedi
Abstract: As the web endures to develop, there is an enormous amount of information which is typically designed for its users, which makes it difficult to extract relevant data from numerous sources. In this paper, we propose an approach for extracting the main content from news web pages. Our approach is based on the concept of tokenization of HTML page, these tokens construct the tag tree; web pages from different websites are parsed into Tag tree and generated a template from each web pages and discover matching patterns and multiple sequence alignment. It finds and removed shared token sequences from the web pages until the relevant information is extracted from them. We perform experiments on 500 web pages from ten different news websites. Experimental results show that our approach efficiently extracts the relevant information.
Keywords: Content extraction; Tag Tree; News web page; Information extraction; Pattern matching.
An Efficient Sensor Integrated Model for hosting real time data monitoring applications on cloud
by Sudhakar Yadav, Eswar Reddy, K.G. Srinivasa
Abstract: Wireless Sensor Networks (WSNs) have become an integral part of weather monitoring applications in a wide range of domains such as environmental monitoring, health-care, asset monitoring modern warfare scenarios, industrial and production monitoring. A number of solutions have been proposed so far which provides assimilation of data on an hourly basis and the capability of sensors are approximated to almost ideal situation. With the exploration and advancement in the field of Internet of Things (IoT), the focal point has shifted towards the interoperability of WSNs and a cloud based central data repository which collaborates and comprehends a uniquely identifiable internet like structure. This bottom up internet-like structure has paved the way for a Sensor Integrated Cloud based architecture REALSENSE. This paper provides a detailed run down on the REALSENSE architecture which integrates WSN with Internet of Things in a robust efficient approach. Using the REALSENSE architecture, a set of real time applications can be deployed, some of them are illustrated in this paper
Keywords: Wireless sensor networks (WSNs); IoT (Internet of Things); Centralized management system (CMS); Interactive Voice response system (IVRS); unstructured supplementary service data (USSD); Application programming interface (API).
WT_ DMDA New Scheduling strategy for Conjugate Gradient Solver on Heterogeneous Architecture.
by Najlae Kasmi, Mostapha Zbakh, Mahmoudi Sidi Ahmed, Pierre Manneback
Abstract: Heterogeneous systems which are composed of multiple CPUs and GPUs are more rnand more attractive as platforms for high performance computing. With thern evolution of General Purpose computation on GPU (GPGPU) and correspondingrn programming frameworks (OpenCL and CUDA), more applications are using GPUs rnas a co-processor to achieve performance that could not be accomplished usingrn just the traditional processors.However, the main problem is identifying which rntask or job should be allocated to a particular device.rnThe problem is even complicated due to the dissimilar computational power of rnthe CPU and the GPU. In this work we propose a new scheduling strategy WT_ dmdarnwhich aims to optimize the performance of the preconditioned conjugate gradientrnsolver, in CPU-GPU heterogeneous environment.We use StarPU runtime system to rnassess the efficiency of the approach on a computational platform consistingrnof three NVIDIA Fermi GPUs and twelve Intel CPUs. We show important speed up rn(up to 5.13) may be reached (relatively to default scheduler of StarPU)rnwhen processing large matrices and that the performance is advantageous rnwhen changing the granularity of tasks. An analysis and evaluation of rnthese results is discussed.rn
Keywords: Conjugate gradient; Scheduling; GPU/CPU architecture; StarPU runtime system.
Special Issue on: Big Data Analytics with NoSQL Databases
CLUSTERING TRAJECTORY BASED OBJECTS IN SPATIO NETWORKS
by Ranjit Reddy Midde, Srinivasa K. G, Eswara Reddy B
Abstract: Clustering is a proficient approach to breaking down and locate the enormous, concealed, obscure and fascinating information in expansive scale dataset, which encourages the fast improvement of information mining innovation in late decades. With the advancement of area based administration, moving article clustering turns into a blossoming subject in related fields as a key some portion of information mining innovation. Spatio-temporal clustering is a procedure of grouping objects based on their spatial and temporal similarity. It is a moderately new subfield of information mining which increased high notoriety, particularly in geographic data sciences because of the pervasiveness of a wide range of area based or natural gadgets that record position, time or/and ecological properties of an item or set of articles progressively. This paper considers the issue of proficiently keeping up a clustering of a active arrangement of information focuses that move persistently in 2D Euclidean space. This paper recommends an improved k-means (i-kmeans) algorithm which is done in four stages, which uses segmentation cluster as part of improved k-means. To describe the effectiveness of the obtained cluster we use Silhouette Coefficient metric. Experimental results reveal that improved i-kmeans technique gives better results in terms of accuracy and quality than the traditional one.
Keywords: Clustering; K-means; Sspatial networks; Segments; Accuracy.