Showing papers by "Sudeep Tanwar published in 2017"

An advanced Internet of Thing based Security Alert System for Smart Home

Abstract: Before inception of Internet of Things (IoT), personal computers and laptop were used to handle daily tasks of individuals like mail surfing, access to bank portal, observing current temperature, among others. Nowadays, IoT-enabled smart devices like smart mobile phones, PDAs, and tablets are being used by them for such tasks due to rapid growth in IoT. Smart homes have been widely accepted by individuals and organizations world wide due to their many advantages. Home security systems can be defined as monitoring of complete home/some portion of home from a remotely located or centralized location. It allows the user to watch all activities inside the home from a remote location that ultimately gives satisfaction to the owner of the home. Many home security systems exist, but they have some challenging issues like: delay, non-web enabled and difficult to handle during transfer of alerts to user in situation where any unusual event occurred inside the home. If any unusual event encountered inside the home, where security systems deployed, then system must be capable enough to send alert to the user without any delay by phone, text, or email. Cameras and other latest network technologies have enabled us to remotely monitor the home more effectively and efficiently from our smart phone. Hence, considering the above mentioned facts, in this paper, we have proposed an advanced Internet of Thing based Security Alert System for Smart Home in order to detect an intruder or any unusual event at home, when nobody is available there. This low-cost home security system utilizes a small pyroelectric Infrared (PIR) module and raspberry pi for minimizing the delay during process of e-mail alert. This paper also confirms the advantage of Raspberry Pi flexibility and broad probability of its usage. Preliminary analyses have shown encouraging results.

FAAL: Fog computing-based patient monitoring system for ambient assisted living

Abstract: From the last few years, Wireless body area networks (WBANs) have attracted a lot of attention from both academia and industry due to an increase in real-time data capturing and processing for patient monitoring. This has become possible due to the technological advancements in which high computing and communication facilities are available for most of the modern handheld devices. In this environment, computing resources are available close to the proximity of the end users using the most popular technology called as Fog computing (devices used in the fog computing are called as fog devices). Most of the solutions reported in the literature for this purpose have used the traditional cloud-based infrastructure in which there may occur a long delay for getting the response even for data which is not of very huge amount which may cause a performance degradation for most of the implemented solutions (such as for treatment of neurological diseases where a real-time monitoring is required) in this environment. Hence, to cope up these issues, in this paper, we proposed a fog computing based patient monitoring system for ambient assisted living (FAAL). Data traces of the movement of the patients (for neurological diseases) are collected using sensor nodes using body area networks (BANs) and are passed using the fog gateways. To reduce the load on the communication infrastructure, an efficient clustering algorithm for data transmission is also presented in the paper. Performance of the proposed solution has been evaluated using the parameters such as-latency, and data overloading. Results obtained clearly show the superior performance of the proposed scheme as compared to the non-fog computing based environment.

Home-based exercise system for patients using IoT enabled smart speaker

Abstract: Physical therapy has a lot of importance for the well being and a better quality of living for an elderly patient. One integral constituent of any patient regime is the home-based exercise that a patient works on in a much comfortable environment. Although the benefits are well known, there is a big lag between the exercises prescribed by the therapists and the ones actually done by the patient. There is no cost effective and non-complex methods available to quantify the exercises performed by the patient. In this paper, a study was performed to check the validity and efficiency of a system consisting of a Smart IoT enabled speaker, which contains an orchestrator. Which is speech learning unit, an exercise database at the edge, and connected to the cloud, where the generated reports are stored and transferred for further analysis, if required. We report the efficiency of the system compared to the ratings of a physical therapist, a standard currently being used.

Dimensionality Reduction Using PCA and SVD in Big Data: A Comparative Case Study

Abstract: With the advancement in technology, data produced from different sources such as Internet, health care, financial companies, social media, etc. are increases continuously at a rapid rate. Potential growth of this data in terms of volume, variety and velocity coined a new emerging area of research, Big Data (BD). Continuous storage, processing, monitoring (if required), real time analysis are few current challenges of BD. However, these challenges becomes more critical when data can be uncertain, inconsistent and redundant. Hence, to reduce the overall processing time dimensionality reduction (DR) is one of the efficient techniques. Therefore, keeping in view of the above, in this paper, we have used principle component analysis (PCA) and singular value decomposition (SVD) techniques to perform DR over BD. We have compared the performance of both techniques in terms of accuracy and mean square error (MSR). Comparative results shows that for numerical reasons SVD is preferred PCA. Whereas, using PCA to train the data in dimension reduction for an image gives good classification output.

Suitability of big data analytics in Indian banking sector to increase revenue and profitability

Abstract: Banking Sector over the last few decade has undergone drastic changes, when it comes to the way they operate and provide efficient services. Increasing population worldwide overburden the existing banking infrastructure. This will in turn increases the number of customers, online transactions and also create huge amount of data when dealing with large segment of customers. Banks in United State and other countries are now using Big Data Analytics (BDA) to handle this situation in every day. It find various patterns within their databases and for gaining the profits for their organizations. It is very surprising, yet true that most of the banks in India have actually not utilizing the information they have stored in their own databases due to several issues like connectivity, fetching time etc. Data experts expect an enormous increase in the volume of data, before 2020, i.e., the size of the data is in Petabyte's and Exabyte's. This will be the actual amount in which the data is being stored in our banks in past a decade. To address the above mentioned issues, this paper provide a detailed review on suitability of BDA in Indian banking sector. BDA is a huge step towards the development of banking sector. So, applying BDA in banking sector in India would help banks in generating actionable insights to improve strategic and operational decisions, and to stay on top of business and competition, every bank must be highly rich with technology and Analytics. Big Data is definitely going to make things easier for the banking industry.

Fog-based enhanced safety management system for miners

Abstract: The situation of miners is very dangerous when they work nearly 50 feet under the sea level, with not only the fear of rocks falling but also the production of poisonous gases and high temperatures, which adversely affect their health. In-order to ensure the safety of miners, their current position is important which is very difficult to understand with the existing miner's safety management procedures. Moreover, the data sensed from a lot of sensors when directly sent on the cloud, makes the use of the bandwidth and cloud storage very inefficient. To solve these problems we propose an improved safety system, with an added fog layer which takes the dynamic decision and keeps a check on the data sent on the cloud, making the use of the bandwidth very effective. Proposed architecture combines several gaseous and humidity-temperature sensors, which send the data to a local fog server, which keeps a check on the further processing and work towards the efficient use of the system. This not only provides a high quality of security and reliability but also proves to be a voracious system.

GATA: GPS-Arduino based Tracking and Alarm system for protection of wildlife animals

Abstract: In the present arena, wildlife and forest departments are facing the problem of movement of animals from forest area to residential area. The number of trees has reduced drastically from the forest that creates an unhealthy environment for animals to survive in the forest. This paper proposes a system we call GATA for tracking and alarming for the protection of Wildlife Animals. GATA combined Wireless Sensor Network (WSN) [1] and Global Positioning System (GPS) technologies to solve the above mentioned problem. Wild animals straying out of wildlife sanctuaries and natural parks have been tracked by auto generative location tracking and movement patterns. Automatic location and movement tracking has been implemented using GPS with the accelerometer and the WiFi shield. In the event of straying of a wild animal out of the predefined zone of sanctuary or natural reserve, an alert is sounded on a fixed base station (BS). As a prototype, we have tested this hardware on the cows, which shows that the proposed approach is very efficient in terms of flexibility and cost. This may be acting as a deterrent to various anti social activities poaching, train delays, railway accidents and danger to man due to the straying out of the animals off their habitation zone.

Sensor Cloud Based Measurement to Management System for Precise Irrigation

Abstract: With the widespread popularity of low cost sensors, the data collection and processing on real-time from different geographical regions becomes easy. Agriculture is one such areas where sensors can be deployed to get real-time data of different regions. In agriculture field, temperature monitoring, soil moisture monitoring and plants growth monitoring can be obtained using sensor nodes. Obtaining the maximum growth in agricultural domain under optimized resources availability through Precise Irrigation (PI) is called as Precision Agriculture (PA). In this paper, we propose "PI-Cloud," which is a sensorcloud based measurement to management (M2M) system, which can be used in an agricultural field to maintain the moisture level of soil above a predefined threshold in real-time. We used a cluster based hierarchical architecture of sensor-cloud, where deployed sensors, called as mobile sensor robot (MSR) can be recharged/replaced as and when required. In order to maintain the real-time monitoring of moisture level of soil, replacement mechanism of energy depleted MSR and cluster heads is also proposed. Management to maintain the PI is done using the control action matrix prepared from sensor cloud. Two novel parameters have been selected as, number of replacements required (NRR) for energy depleted MSR and first replacement analysis (FRA) are used for validation of proposed scheme. The results obtained show that with the availability of energy and recharging of MSR, PI can be done on real-time basis with PICloud based architecture system.

Risk of chronic liver disease in post-menopausal women due to body mass index, alcohol and their interaction: a prospective nested cohort study within the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

Abstract: We investigated the risk of chronic liver disease (CLD) due to alcohol consumption and body mass index (BMI) and the effects of their interaction in a prospective cohort study of women recruited to the UKCTOCS trial. 95,126 post-menopausal women without documented CLD were stratified into 12 groups defined by combinations of BMI (normal, overweight, obese) and alcohol consumption (none, <1–15, 16–20 and ≥21 units/week), and followed for an average of 5.1 years. Hazard ratios (HR) were calculated for incident liver-related events (LRE). First LREs were reported in 325 (0.34%) participants. Compared to women with normal BMI, HR = 1.44 (95% CI; 1.10–1.87) in the overweight group and HR = 2.25 (95% CI; 1.70–2.97) in the obese group, adjusted for alcohol and potential confounders. Compared to those abstinent from alcohol, HR = 0.70 (95% CI; 0.55–0.88) for <1–15 units/week, 0.93 (95% CI; 0.50–1.73) for 16–20 units/week and 1.82 (95% CI; 0.97–3.39) for ≥21 units/week adjusted for BMI and potential confounders. Compared to women with normal BMI drinking no alcohol, HR for LRE in obese women consuming ≥21 units/week was 2.86 (95% CI; 0.67–12.42), 1.58 (95% CI; 0.96–2.61) for obese women drinking <1–15 units/week and 1.93 (95% CI; 0.66–5.62) in those with normal BMI consuming ≥21 units/week after adjustment for potential confounders. We found no significant interaction between BMI and alcohol. High BMI and alcohol consumption and abstinence are risk factors for CLD in post-menopausal women. However, BMI and alcohol do not demonstrate significant interaction in this group. UKCTOCS is registered as an International Standard Randomised Controlled Trial, number ISRCTN22488978 . Registered 06/04/2000.

The Enhanced Liver Fibrosis (ELF) Panel: Analyte Stability Under Common Sample Storage Conditions Used in Clinical Practice.

Abstract: Background: The enhanced liver fibrosis (ELF) blood test has recently been recommended by the National Institute for Health and Care Excellence to test for advanced fibrosis in nonalcoholic fatty liver disease. The ELF test involves calculating a score from the concentrations of serum biomarkers: tissue inhibitor of matrix metalloproteinases-1 (TIMP-1), aminoterminal propeptide of procollagen type III (P3NP), and hyaluronic acid (HA). Blood samples for the ELF score are often acquired in primary care and may be stored before analysis. However, the effect of preanalytical storage on the ELF test is not known. Methods: We conducted experiments to assess the stabilities of the ELF score, P3NP, HA, and TIMP-1 under medium- to long-term storage at −80 °C, repeated freeze-thawing, and refrigeration at 4 °C for days. Results: Mean TIMP-1 concentrations increased during medium- to long-term storage (+16.5%) and refrigeration (+4.9%), but were stable during freeze-thawing. Mean P3NP concentrations were stable under medium- to long-term storage, but increased during refrigeration (+7.4%) and freeze-thawing (+9.3%). Mean HA concentrations decreased during medium- to long-term storage (−12.3%) but were stable during refrigeration and freeze-thawing. Despite changes in biomarker concentrations, the changes in the mean ELF score were not clinically significant and not >0.1 U (0.7%). Conclusions: The ELF score was stable, with no clinically significant changes under common storage conditions. These findings demonstrate that the ELF score is robust in situations where analysis may be delayed.