Sugam Sharma

A Brief Review on Leading Big Data Models

Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003) into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

Energy Efficient Data Dissemination with ATIM Window and Dynamic Sink in Wireless Sensor Networks

We illustrate the effective use of ATIM (Ad hoc traffic Indication Message) window in static sensors to obtain potential energy savings for sensors during data dissemination in wireless sensor networks. The network consists of resource-rich mobile sink node and resource-constrained sensor nodes forming a square grid. Sink may be multi-hop away from the sensor nodes and in order to collect the data, it moves to the vicinity of the sensor nodes in the grid. Each sensor node in the grid uses an ATIM window and sleeps during that time to conserve energy. The sleeping sensor node wakes up at regular interval of time to read the beacon messages. If the message comes from sink, sensor node immediately reads the header part of query packet to check whether it is the targeted sensor. The targeted sensor further reads the remaining part of query packet and responds back to the sink with requested data. The rest of the sensor nodes just ignore the remaining part of the query packet and go to sleep mode. If the message comes from the peer sensor node, the neighbor nodes just ignore it. We consider an affordable wireless network system with four sensor nodes and one mobile sink. Mobile sink transfers the collected data directly to the control station.

Bidirectional migration between variability and commonality in product line engineering of smart homes

The product line engineering (PLE) has proven to be the paradigm for developing diversified products and systems in shorter time, at lower cost, and higher quality. Ultimately the goal of PLE is to evolve a set of products that have both commonality and variations built into them, which allows a high degree of variability between the different products. The current trend in smart home deployment—based on commissions—guarantees a complete satisfaction for each inhabitant, as designed per resident basis. To reduce high deployment cost and to resolve the non-reusability of the resources, the product line engineering methodology can be employed in selecting or configuring the appropriate smart home technology. Within the smart homes and ambient assisted living, not only do the reusable features reduce cost, they also do not compromise with the guarantee of complete satisfaction of each resident as few senior inhabitants may impose severe constraints on the binding of few common features. In our previous work (Sharma et al., 9th International Conference on Smart Homes and Health Telematics (ICOST), 2011), we propose an approach called, wrenching that claims the best resident satisfaction at the reduced deployment cost. Today seniors are fast growing population globally (http://www.aoa.gov/agingstatsdotnet/Main_Site/Data/2008) and the increased demand for smart homes in the near future is undeniable. Thus to provide assistance and independence to each senior resident of a smart home, reduction in time to market (Wohlin and Ahlgren, Softw Qual J 4:189–205, 1995) of appropriate assistive smart home technology becomes an essential consideration. Long term analysis of variability binding can help to analyze that few variants are bound more often over others. Respecting the satisfaction guarantee, the highly demanded variants can be permanently migrated into commonality, advocating the reusability. The improved reusability of features (e.g., sensors) not only enhances economy of scale but also time to market. The migration of a feature from variability to commonality is known as realizing.

On Analyzing the Degree of Coldness in Iowa, a North Central Region, United States: An XML Exploitation in Spatial Databases

State of Iowa is an agricultural rich state in north central region and is divided into 99 counties. NCRA in the United States maintains agricultural databases to facilitate crop and risk analysis, pest management and forecasting. NC94 is one such dataset which is intensively used and is available for public use through many sources to process and analyze to get future predictions about agriculture. In this work we calculate the cumulative degree of coldness in Iowa with spatial granularity as county in last 30 years. To demonstrate the degree of coldness, we choose blue as the base color and counties are rendered with different shades of blue color based on the degree of coldness. Higher intensity of the color reflects the higher coldness whereas the lower intensity corresponds to lower coldness. We expect that the results of this research provide direct benefits to farmers and will attract the attention of agricultural/ computational scientific community.

Four-Layer Architecture Model for Energy Conservation in Wireless Sensor Networks

We propose a Four-Layer Architecture Model (FLAM) to obtain the potential energy savings for sensor nodes in large scale wireless sensor networks. The very first layer, the bottom layer is a network of a resource-constrained sensor nodes deployed in the field. The second layer is ad hoc network formed by resource-rich dynamic sink nodes, also knows as cluster sink nodes. The third layer consists of resource-rich static sink nodes, known as zonal sink also, responsible for data collection from cluster sinks of its zone. The fourth (top) layer posses a resource-rich flying sink. We call this sink as hawk sink and is responsible for data collection from zonal sinks. A cluster sink may be few hops away from a static sensor node and moves to the vicinity of sensor nodes to collect the data and continuously updates its zonal sink by propagating the collected data from the respective cluster. Each zonal sink buffers the collected data and propagates to hawk sink through query- response mechanism when later enters the transmission range of the zonal sink. Hawk sink is the one, responsible for data transmission from network environment to the base station. FLAM approach is scalable, increases sensor life by allowing them to operate with a limited transmission range only and provides reduced delay as cluster sink continuously updates to zonal sink without query.

Critical Analysis of Clustering Algorithms for Wireless Sensor Networks

The scientific and industrial community increased their attention on wireless sensor networks (WSNs) during the past few years. WSNs are used in various critical applications like disaster relief management, combat field reconnaissance, border protection, and security observation. In such applications a huge number of sensors are remotely deployed and have cooperatively worked in unaccompanied environments. The disjoint groups are formed from these sensor nodes and such nonoverlapping groups are known as clusters. Clustering schemes have proven to be effective to support scalability. In this paper, authors have reported a detailed analysis on clustering algorithms and have outlined the clustering schemes in WSNs. We also make a comparative analysis of clustering algorithms on the basis of different parameters like cluster stability, cluster overlapping, convergence time, failure recovery, and support for node mobility. Moreover, we highlight the various issues in clustering of WSNs.

Towards XML Interoperability

Now a day’s distributed computing has become a common phenomenon. As the system designed from the bottom up with networking in mind, distributed computing makes it very easy for computers to cooperate and today, scientific world is nourishing the benefits provided by distributing computing under the broad umbrella of client server architecture. Information is requested and used at various distant physical or logical locations as per the requirement by the users. This is very unlikely that all the engaged users use the same computing environment. XML (Extensive Markup Language) technology has emerged as an efficient medium for information transfer and attempting to offer similar kind of information environment up to an extent by using its potential property called, interoperability. Interoperability is a kind of ability of software and hardware on different machines from different vendors to share data. In this paper we employ a few criteria to evaluate interoperability of XML in heterogeneous distributed computing environment.

Three-layer architecture model (TLAM) for energy conservation in wireless sensor networks

We propose a Three-layer architecture model (TLAM) for wireless sensor networks. The first layer (bottom layer) consists of a network of resource-constrained sensor nodes deployed in the field. The second layer contains a set of resource-rich dynamic sink nodes forming an ad hoc network. The sinks at layer second are responsible to monitor and collect data from a region, called cluster. So the sinks at second layer are also knows as cluster sinks. At the third layer, there is a resource-rich static sink, known as zonal sink also, responsible for data collection from cluster sinks. A cluster sink may be few hops away from a static sensor node and moves to the vicinity of sensor nodes to collect the data. Each cluster sink continuously updates the zonal sink by propagating the collected data from the respective cluster. The zonal sink buffers the collected data and propagates to base station. Zonal sink is the one, responsible for data transmission from network environment to the base station, where the data is processed for further use. TLAM approach is robust, increases sensor life by allowing them to operate with a limited transmission range only and provides reduced delay as cluster sink continuously updates to zonal sink without query.

On the Calculation of Coldness in Iowa, a North Central Region, United States: A Summary on XML Based Scheme

NC94 dataset is an agricultural dataset which consists of climate, crop, and soil data and is considered as a completely dataset available. The data is organized based on county and is treated as an object. It is adapted in interdisciplinary research and widely used in various research domains and once again has been exploited in our parametric database research. In this research work, we exploit the climate data of NC94 dataset to calculate the cumulative degree of coldness in Iowa in last 30 years. To demonstrate the degree of coldness, an xml based scheme is used considering blue as the base color with the notion that darker shade reflects higher degree of coldness. This work is a baby step towards the generation of climate atlas of North Central Region of USA in one single click.

Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity

Association rule of data mining is known to encompass a wide set of intelligent techniques that intent to unveil and analyze correlations and associations between items in a set. Market basket analysis is one such, possibly the most popular technique in business domain that is used to analyze combinations of items that often are listed together in various transactions. In this paper, the author strives to expand applicability of the same concept to human health under purview of health informatics. The present growing rate of obesity has raised alarming concept to the communities globally. It entails several chronic diseases that may be fatal eventually. This work aims to aid in the ongoing efforts to alleviate the obesity, primarily caused by lack of physical exercise. Concept of association rule of data mining may help regulating mild exercise by associating it with a daily activity, sleeping at night. Mild but regular short exercise just before sleep may help ameliorating individuals health.