N. K. Suryadevara

Towards the Implementation of IoT for Environmental Condition Monitoring in Homes

In this paper, we have reported an effective implementation for Internet of Things used for monitoring regular domestic conditions by means of low cost ubiquitous sensing system. The description about the integrated network architecture and the interconnecting mechanisms for the reliable measurement of parameters by smart sensors and transmission of data via internet is being presented. The longitudinal learning system was able to provide a self-control mechanism for better operation of the devices in monitoring stage. The framework of the monitoring system is based on a combination of pervasive distributed sensing units, information system for data aggregation, and reasoning and context awareness. Results are encouraging as the reliability of sensing information transmission through the proposed integrated network architecture is 97%. The prototype was tested to generate real-time graphical information rather than a test bed scenario.

Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly

Wireless-sensor-network-based home monitoring system for elderly activity behavior involves functional assessment of daily activities. In this paper, we reported a mechanism for estimation of elderly well-being condition based on usage of house-hold appliances connected through various sensing units. We defined two new wellness functions to determine the status of the elderly on performing essential daily activities. The developed system for monitoring and evaluation of essential daily activities was tested at the homes of four different elderly persons living alone and the results are encouraging in determining wellness of the elderly.

Forecasting the behavior of an elderly using wireless sensors data in a smart home

In this paper, the ability to determine the wellness of an elderly living alone in a smart home using a low-cost, robust, flexible and data driven intelligent system is presented. A framework integrating temporal and spatial contextual information for determining the wellness of an elderly has been modeled. A novel behavior detection process based on the observed sensor data in performing essential daily activities has been designed and developed. The developed prototype is used to forecast the behavior and wellness of the elderly by monitoring the daily usages of appliances in a smart home. Wellness models are tested at various elderly houses, and the experimental results are encouraging. The wellness models are updated based on the time series analysis.

WSN-Based Smart Sensors and Actuator for Power Management in Intelligent Buildings

The design and development of a smart monitoring and controlling system for household electrical appliances in real time has been reported in this paper. The system principally monitors electrical parameters of household appliances such as voltage and current and subsequently calculates the power consumed. The novelty of this system is the implementation of the controlling mechanism of appliances in different ways. The developed system is a low-cost and flexible in operation and thus can save electricity expense of the consumers. The prototype has been extensively tested in real-life situations and experimental results are very encouraging.

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.

Determining Wellness through an Ambient Assisted Living Environment

Here, we present pervasive computing technology to determine the wellness of the elderly living independently in their homes. The framework of the intelligent system consists of monitoring important daily activities through the observation of everyday object usage. The improved wellness indices defined here have helped in reducing false warnings related to the daily activities of elderly living. Time series data processing techniques have been applied to the improved wellness indicators, to take care of the dynamic situation in relation to aging of the elderly and seasonal weather variation. With a minimal number of binary motion sensors, the elderly were tracked in real time to get better information on their physical condition. The developed system was able to recognize 94 percent of the basic daily activities accurately, and at the same time the system was able to assess the wellness activities quantitatively in near real time. The well-being indices of the elderly can be used by the healthcare providers to take preventive measures on deterioration of activities of daily living, and thus consequently reduce cost of healthcare.

Sensor data fusion to determine wellness of an elderly in intelligent home monitoring environment

In this paper, we present a novel mechanism to foresee the well-being of elderly through monitoring and functional assessment of the daily activities with the help of sensor data fusion. Two wellness indices are defined to determine the wellness of the elderly in performing their daily activities. Home monitoring system is targeted for the elderly people to provide a safe, secured, less cost and privacy system in assessing the ability to perform basic behaviours. Developed system was tested at various elderly homes instead of test bed and the results are encouraging.

Intelligent Sensing Systems for Measuring Wellness Indices of the Daily Activities for the Elderly

In this study, we reported integration of Wireless Sensor Network (WSN) based systems for monitoring elderly health perception and daily activity behaviour recognition. The amalgamation of the systems helps in deducing the elderly wellness indices, thereby informing the health care providers about the tendency of unusual behaviour through telecare system. The developed sensing system along with the intelligent software is low cost, flexible, robust and easy to install and monitor elderly living alone.

Towards the smart sensors based human emotion recognition

A smart sensing system, which would help in detecting human emotions based on information from physiological parameters obtained from sensors, has been designed and developed. Sensors continuously monitor the heart rate, skin conductance and skin temperature. The amplified and filtered signals from the sensors are then processed by a microcontroller and transmitted wirelessly using ZigBee technology. The received signals from the system are displayed and stored on the computer where they are analysed visually for obvious patterns. An algorithm is being developed for automatic recognition of emotions using various clustering techniques. The partial developed system has shown good results in monitoring the physiological parameters.

Reliable measurement of Wireless Sensor Network data for forecasting wellness of elderly at smart home

In this paper, we present an engineering system for monitoring and forecasting wellness of an elderly person in relation to performance of daily activities. Complex behavioural changes of daily activities are captured in real time for reliable measurement of wellness operations. These tasks are realized with the sensor status of the household objects in use by the elderly in combination with prediction process of time series data processing algorithm. This will assist in determining the quantitative well-being of an elderly and alert if the daily activity behaviour is irregular.