Showing papers by "Nandini Mukherjee published in 2021"

Location-Based Services for Smart Living in Urban Areas

Abstract: Location-Based Services (LBSs) are a set of services designed to provide useful information and services based on the current location of the user. The wide applicability of the LBSs in several aspects of modern-day living that include transport, healthcare, leisure activities, business etc., is the main driving force behind drawing significant attention from not only the researchers but also the mobile network operators as well as the service providers in designing such services for smart living in urban areas. At first, this chapter provides a detailed definition of LBSs and also states its importance in today’s world. Then, it provides a state-of-the-art review of the various LBS infrastructures and systems proposed in the literature. A brief description of the architecture of an LBS infrastructure is also given in this chapter. Finally, it outlines some important research issues in the provisioning of LBSs in urban environments.

Memory Optimized Dynamic Matrix Chain Multiplication Using Shared Memory in GPU

Abstract: Number of multiplications needed for Matrix Chain Multiplication of \( n \) matrices depends not only on the dimensions but also on the order to multiply the chain. The problem is to find the optimal order of multiplication. Dynamic programming takes \( O\left( {n^{3} } \right) \) time, along with \( O\left( {n^{2} } \right) \) space in memory for solving this problem. Now-a-days, Graphics Processing Unit (GPU) is very useful to the developers for parallel programming using CUDA computing architecture. The main contribution of this paper is to recommend a new memory optimized technique to solve the Matrix Chain Multiplication problem in parallel using GPU, mapping diagonals of calculation tables \( m[][] \) and \( s[][] \) into a single combined calculation table of size \( O\left( {n^{2} } \right) \) for better memory coalescing in the device. Besides optimizing the memory requirement, a versatile technique of utilizing Shared Memory in Blocks of threads is suggested to minimize time for accessing dimensions of matrices in GPU. Our experiment shows best ever Speedup as compared to sequential CPU implementation, run on large problem size.

Delay and Disruption Tolerant Networks: A Brief Survey

Abstract: Challenged networks represent a very special class of networks characterized by widely varying network conditions such as intermittent connectivity, a heterogeneous mix of resource-constrained nodes, long and variable message communication time, bidirectional data-rate asymmetries and high failure rate of nodes. In such deployment scenarios, connectivity is not well served either by the standard Internet architecture and protocols or by popular mobile ad hoc network (MANET) and Wireless Sensor Network (WSN) protocols. Such networks are characterized by the Delay/Disruption Tolerant Networking (DTN) model that uses automatic store-and-forward mechanisms to provide assured data delivery under such extreme operating conditions. In this paper, we present a brief survey on DTN architecture, routing, potential applications and future research challenges. We envision that the research outcomes on this fairly recent topic will have a profound impact on various applications including interplanetary Internet, deep space explorations and future Mars missions, Artic Observing Network (AON) to explore biological, physical, and chemical processes/changes in polar regions.

Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure

Abstract: Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are host...

Pruning of Health Data in Mobile-Assisted Remote Healthcare Service Delivery

Abstract: The use of cloud computing and mobile devices is increasing in healthcare service delivery primarily because of the huge storage capacity of cloud, the heterogeneous structure of health data and the user-friendly interfaces on mobile devices. We propose a healthcare delivery scheme where a large knowledge base is stored in the cloud and user responses from mobile devices are input to this knowledge base to reach a preliminary diagnosis of diseases based on patients’ symptoms. However, instead of sending every response to the cloud and getting data from cloud server, it may often be desirable to prune a portion of the knowledge base that is stored in a graph form and download in to the mobile devices. Downloading data from cloud depends on the storage, battery power, processor of a mobile device, wireless network bandwidth and cloud processor capacity. In this paper, we focus on developing mathematical expressions involving the above mentioned criteria and show how these parameters are dependent on each other. The expressions built in this paper can be used in real-life scenarios to take decisions regarding the amount of data to be pruned in order to save energy as well as time.

Efficient NoSQL Graph Database for Storage and Access of Health Data

Abstract: Graph data models are mostly used for storage and access of large, unstructured real-life data generated from real-life applications. The graph data model can be implemented by NoSQL graph databases. In this paper, eight well-known NoSQL graph databases are compared to study their properties. After rigorous review of different research works which are focused on different parametric measures of storage and access, only the best three NoSQL graph databases, Neo4j, OrientDB and ArangoDB, are chosen. The efficiency of these three graph databases is compared based on searching or traversing and querying operations on the databases for storage and access. A particular type of graph, i.e., disease-symptom graph database has been used for this purpose.

A Brief Survey on Placement of Access Points in Ultra-Dense Heterogeneous 5G Networks

Abstract: Ultra-dense heterogeneous networks involving LTE, Wi-Fi, and D2D communications appear to be the dominant technologies for many more years to come for achieving the distinct capabilities that 5G or higher generation networks are expected to provide. LTE and Wi-Fi devices follow different protocols in grabbing the communication channel. Because of this heterogeneity and also the proximity of various types of devices due to ultra densification, there will be increased channel interferences among the communicating devices which often degrade the overall performance of the network. Judicious placement of the access points (APs) or base stations (BSs) to cover a given geographical region in such an ultra-dense network taking the transmitting powers and interfering distances into account constitutes an important research problem of current interest. We present here a brief survey on the various techniques for the placement of APs or BSs under such a scenario of ultra-dense heterogeneous networks.

Designing a NoSQL Database for Efficient Storage and Retrieval of Health Data

Abstract: The role of Cloud to store and retrieve health data is felt more than ever before in this pandemic. In remote places, and in emergency situations, there is scarcity of medical personnel and proper medical infrastructure. Hand held devices like smart phones and other devices having sensing capabilities can be used to store health data in cloud. This health data containing audio, video, images, sensor data as well as text data can be easily stored in a cloud. A static data model does not have the capability to cope with changes in the requirements of a health care application. Ontology comes as a solution to incorporate the dynamic requirements of such an application. Also, there are multiple complicated health data standards to follow or to be interoperable with. Ontology has the capacity to bring different health data standards and relate these standards. Health data being Big data by its nature is stored in MongoDB cloud in this work. Designing a data model that stores health data so that queries can run in less time- is a challenge. An algorithm is used to generate the data model design for a set of queries. This data model design is used to store data in this work. The experience of storing and running some queries is discussed here.

MICR:Mobile-Assisted Immunization of Child in Remote Healthcare

Abstract: Immunization is an effective way to prevent or resist in infectious diseases in children. Proper tracking of vaccination of children in remote areas and remembering vaccination time schedules by new mothers or caregivers are two real challenges for immunization system in India. To alleviate the challenges in immunization system, a mobile based child immunization system is implemented. An android-based mobile app is developed to send Push Message alerts to the registered mobile numbers of new mothers or caregivers for reminder of vaccination of each child within a given time schedule.

Handling Errors in eHealth Sensors Using Interval Mapping and Fuzzy Modeling

Abstract: EHealth sensors are connected through an Internet of Things infrastructure and the vital sign measurements are sent to cloud for real-time diagnosis by a remotely located physician. We use a cloud-based virtual sensing technology for collecting the vital signs of the patients remotely for smart diagnosis. Here the challenge is that the uncertainty generated from underlying physical sensor layer is propagated to the virtual sensors in cloud inflicting the diagnosis process. This article focuses on handling this challenge by introducing a new approach, Interval Mapping Technique (IMT), which is used to indicate the presence of any inaccuracy of devices and random errors at real-time. The novelty of this technique lies in the consideration that the Systolic and Diastolic BP of a person fall within certain ranges and the knowledge about these two ranges or intervals together can be used to get rid of random errors. A fuzzy modeling based approach is also proposed in this article. Fuzzy linguistic terms are used to determine the systolic and diastolic intervals of a person and a rule base is developed to find any real time error. Primarily, the techniques are used for error reduction in blood pressure measurements of Hypertensive, Pre-hypertensive and Normotensive patients. However, these methodologies can be extended for other vitals as well. These two techniques are compared with other standard techniques, like linear regression and random forest. Results show that IMT generates 3.69% less error for Systolic and 7.24% less error for Diastolic BP compared to linear regression model, 1.91% less error for Systolic and 7.18% less error for Diastolic BP compared to random forest model.