Hemant Ghayvat

WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings

Our research approach is to design and develop reliable, efficient, flexible, economical, real-time and realistic wellness sensor networks for smart home systems. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed into the home environment. These nodes generate real-time data related to the object usage and movement inside the home, to forecast the wellness of an individual. Here, wellness stands for how efficiently someone stays fit in the home environment and performs his or her daily routine in order to live a long and healthy life. We initiate the research with the development of the smart home approach and implement it in different home conditions (different houses) to monitor the activity of an inhabitant for wellness detection. Additionally, our research extends the smart home system to smart buildings and models the design issues related to the smart building environment; these design issues are linked with system performance and reliability. This research paper also discusses and illustrates the possible mitigation to handle the ISM band interference and attenuation losses without compromising optimum system performance.

Wellness Sensor Networks: A Proposal and Implementation for Smart Home for Assisted Living

In recent times, wireless sensor networks (WSNs) have become the backbone of many systems. Smart homes based on WSN protocols are used to provide an assisted living environment to humans. The currently reported smart home systems based on generalized WSN protocols suffer from complexity, large data handling, and data transmission delay. This paper has reported a new protocol especially developed to address smart homes for assisted living. The whole purpose of a smart home is to provide a safe environment for the well-being of its inhabitants. Ergo, the protocol is named as wellness sensor networks. The developed protocol has been used in an old home built in 1938, which was converted into a smart home with the use of sensing technologies.

Context-aware low power intelligent SmartHome based on the Internet of things

Constructing a smart home is not a task without intricate challenges due to involvement of various tools and technologies. Therefore, this research work presents a concept of context-aware low power intelligent SmartHome (CLPiSmartHome). For CLPiSmartHome, we propose a communication model, which provides a common medium for communication, i.e., same communication language. Moreover, an architecture is also proposed that welcomes all the electronic devices to communicate with each other using a single platform service. The proposed architecture describes the application, analysis and visualization aspects of the CLPiSmartHome. Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS coreTMi5 machine with 3.2GHz processor and 4 GB memory. Sample medical sensory data sets and fire detection datasets are tested on the proposed system. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets and triggers actions to provide safety measurements for elderly age people, patients, and others.

A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.

Activity and anomaly detection in smart home : a survey

Activity recognition is a popular research area with a number of applications, particularly in the smart home environment. The unique features of smart home sensors have challenged traditional data analysis methods. However, the recognition of anomalous activities is still immature in the smart home when compared with other domains such as computer security, manufacturing defect detection, medical image processing, etc. This chapter reviews smart home’s dense sensing approaches, an extensive review from sensors, data, analysis, algorithms, prompting reminder system, to the recent development of anomaly activity detection.

Sensing Technologies for Intelligent Environments: A Review

Sensors are fundamental components for making any environment intelligent. Depending on the applications, different sensors are required to implement specific objectives. This chapter will review different applications and consequently the requirements for different sensors and sensing technologies used in intelligent environment with a special emphasis on smart homes.

Introducing Intel Galileo as a development platform of smart sensor: Evolution, opportunities and challenges

An Intel Galileo based wireless smart sensor platform targeted for monitoring, control, maintenance and automation are presented. Intel Galileo standard processing platform with XBee RF chip supports the hardware interface and communication needs of sensors, controller and actuators. Sensor monitors and collects the real world information; actuator and controller react upon that information. Although the current research is at an initial stage, but the research outcome is encouraging to develop Intelligent and smart self-management wireless sensor node.

Accelerometer based human activities and posture recognition

The quantity of elderly people like to live in their homes, secluded, in their brilliant age is expanding exponentially. This is not a perfect path for an elderly individual to live. However, the urbanization and resultant change of the social and social conduct makes it a more regular event. Falls are a noteworthy reason for death and horribleness in more established grown-ups. In this way, it has turn into an opportune need to create mechanized look after the elderly. The first end, purpose of such fall computer looking-glass is to ready caregivers of the fall event, which can then start an earlier process. In the present study, we amplify the use of wearable inertial sensors for fall identification and information of human posture and activities, by creating and assessing the precision of a sensor framework for identifying the same. We found that our system could discover fall events and monitor human activities with at least 95% accuracy.

Deep convolutional neural networks for motion instability identification using kinect

Evaluating the execution style of human motion can give insight into the performance and behaviour exhibited by the participant. This could enable support in developing personalised rehabilitation programmes by providing better understanding of motion mechanics and contextual behaviour. However, performing analyses, generating statistical representations and models which are free from external bins, repeatable and robust is a difficult task. In this work, we propose a framework which evaluates clinically valid motions to identify unstable behaviour during performance using Deep Convolutional Neural Networks. The framework is composed of two parts; 1) Instead of using the whole skeleton as input, we divide the human skeleton into five joint groups. For each group, feature encoding is used to represent spatial and temporal domains to permit high-level abstraction and to remove noise these are then represented using distance matrices. 2) The encoded representations are labelled using an automatic labelling method and evaluated using deep learning. Experimental results demonstrates the ability to correctly classify data compared to classical approaches.

Sensors and instrumentation to measure Sap flow in small stem plants

Heat-based sap flow sensors are widely used in research to analyse plant-water relationships and to study water flow dynamics of plants to determine the water requirements for optimum growth. To date, the majority of field studies of sap flow have been conducted in large plants, with only a few methods being suitable for small stems of less than 10 mm in diameter. This paper discusses the suitability of sensors and electronic designs for a heat-based method to monitor sap flows over the velocity range 2-150 cm/hr, in a small plant stems of 5 to 10 mm in diameter. Three heat-pulse methods (Compensated Heat Pulse, Heat Ratio and Tmax) were tested using both internal and external sensor designs. Based on preliminary findings, two alternative methods of analysis were developed and tested in the laboratory. A short field experiment was carried out to compare heat pulse velocity and global solar radiation. We found the external sensor was not suitable for small stems (<; 20 mm diam.) with thick (> 3mm) bark. A small internal sensor showed more promising results for resolving a wider range of sap flows and tracking the influence of global solar radiation.