Jayavardhana Gubbi

Internet of Things (IoT): A vision, architectural elements, and future directions

Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fueled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public Clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.

An Information Framework for Creating a Smart City Through Internet of Things

Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services.

Network architecture and QoS issues in the internet of things for a smart city

The emerging Internet of Things (IoT) that effectively integrates cyber-physical space to create smart environments will undoubtedly have a plethora of applications in the near future. Meanwhile, it is also the key technological enabler to create smart cities, which will provide great benefits to our society. In this paper, four different IoT network architectures spanning various smart city applications are presented and their corresponding network Quality of Service (QoS) requirements are defined. Furthermore, as the beneficiary of smart city, we have the responsibility to actively participate in its development as well. A new network paradigm, participatory sensing, is thus discussed as a special case to highlight the way people may be involved in the information acquisition-transmission-interpretation-action loop.

Lip reading using optical flow and support vector machines

This paper presents a lip reading technique to classify the discrete utterances without evaluating the acoustic signals. The reported technique analysis the video data of lip motions by computing the optical flow (OF). The statistical properties of the vertical OF component were used to form the feature vectors for training the support vector machines (SVM) classifier. The impact of the variation in speed/velocity of speaking on the performance of the system was minimized by removing the zero energy frames and normalizing the number of frames by interpolation. The resulting system is an efficient visual viseme classifier with high accuracy (95.9%), specificity (98.1%) and sensitivity (66.4%). The results of the experiments demonstrate the developed technique is insensitive to inter speaker variations.

Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures.

A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in‐patient video–electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time‐frequency mapping of data from a movement‐recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES.

A Pilot Study of Automatic Lung Tumor Segmentation from Positron Emission Tomography Images using Standard Uptake Values

Positron emission tomography (PET) is a medical imaging procedure that shows the physiological function of an organ or tissue. The role of PET during the past decade has evolved rapidly in the detection of lung tumors but the research on quantitative evaluation of PET images is still in its infancy. PET commonly involves scanning the patient after administration of a radioactive analogue of glucose called fluorodeoxy-glucose (FDG). Tumor cells metabolise more glucose than most normal cells. In PET lung images the heart is often visible and because of its constant pumping of blood it requires more glucose and hence both the tumor and the heart appear brighter than the rest in the PET image. In this paper we present a novel segmentation scheme for detecting the tumor alone in lung PET images using standard uptake values (SUV) and connected component analysis. We perform the segmentation in two steps. In coarse segmentation, a non linear scaling of SUV values is performed and then a threshold is chosen adaptively to convert the gray image into the binary image. Fine segmentation is performed on the coarse segmented data in order to narrow down the region of interest using connected component labeling. To our knowledge no one has used connected component analysis for segmenting PET images. We compare our proposed scheme with several commonly used medical image segmentation techniques like threshold, Sobel edge detector, Laplacian of Gaussian (LoG) edge detector, region growing and SUV based segmentation (applied only to PET as SUV is specific to PET). One of the problems in lung tumor detection is the presence of the heart in the image which accumulates activity and often gets recognized as a hot spot (a probable tumor). All the other segmentation schemes detected both the heart and the tumor as hot spots while our segmentation scheme detected the tumor alone as the hot spot. The preliminary study of the proposed scheme has yielded very promising results and will be studied for more lung tumor detection scenarios in future

Motor recovery monitoring using acceleration measurements in post acute stroke patients.

BackgroundStroke is one of the major causes of morbidity and mortality. Its recovery and treatment depends on close clinical monitoring by a clinician especially during the first few hours after the onset of stroke. Patients who do not exhibit early motor recovery post thrombolysis may benefit from more aggressive treatment.MethodA novel approach for monitoring stroke during the first few hours after the onset of stroke using a wireless accelerometer based motor activity monitoring system is developed. It monitors the motor activity by measuring the acceleration of the arms in three axes. In the presented proof of concept study, the measured acceleration data is transferred wirelessly using iMote2 platform to the base station that is equipped with an online algorithm capable of calculating an index equivalent to the National Institute of Health Stroke Score (NIHSS) motor index. The system is developed by collecting data from 15 patients.ResultsWe have successfully demonstrated an end-to-end stroke monitoring system reporting an accuracy of calculating stroke index of more than 80%, highest Cohen’s overall agreement of 0.91 (with excellent κ coefficient of 0.76).ConclusionA wireless accelerometer based ‘hot stroke’ monitoring system is developed to monitor the motor recovery in acute-stroke patients. It has been shown to monitor stroke patients continuously, which has not been possible so far with high reliability.

Design of low-cost autonomous water quality monitoring system

Good water quality is essential for the health of our aquatic ecosystems. Continuous water quality monitoring is an important tool for catchment management authorities, providing real-time data for environmental protection and tracking pollution sources; however, continuous water quality monitoring at high temporal and spatial resolution remains prohibitively expensive. An affordable wireless aquatic monitoring system will enable cost-effective water quality data collection, assisting catchment managers to maintain the health of aquatic ecosystems. In this paper, a low-cost wireless water physiochemistry sensing system is presented. The results indicate that with appropriate calibration, a reliable monitoring system can be established. This will allow catchment managers to continuously monitoring the quality of the water at higher spatial resolution than has previously been feasible, and to maintain this surveillance over an extended period of time. In addition, it helps to understand the behavior of aquatic animals relative to water pollution using data analysis.

Analyzing temporal variability of standard descriptors of Poincaré plots

The Poincaré map is a visual technique to recognize the hidden correlation patterns of a time series signal. The standard descriptors of the Poincaré map are used to quantify the plot that measures the gross variability of the time series data. However, the problem lies in capturing temporal information of the plot quantitatively. In this article, we propose a new formulation for calculating the standard descriptors SD1 and SD2 from localized measures SD1^(w) and SD2^(w). To justify the importance of the temporal measure, SD1^(w), SD2^(w) are calculated for the 2 case studies (normal sinus rhythm [NSR] vs congestive heart failure and NSR vs arrhythmia) and are compared with the performance using the overall measures (SD1, SD2). Using overall SD1, receiver operating characteristic areas of 0.72 and 0.86 were obtained for NSR vs congestive heart failure and NSR vs arrhythmia, and using the proposed method resulted in 0.82 and 0.89. Because we have shown that the overall SD1 and SD2 are functions of the respective localized measures SD1^(w) and SD2^(w), we can conclude that use of localized measure provides equal or higher performance in pathology detection compared with the overall SD1 or SD2.

Estimation of crowd density by clustering motion cues

Understanding crowd behavior using automated video analytics is a relevant research problem in recent times due to complex challenges in monitoring large gatherings. From an automated video surveillance perspective, estimation of crowd density in particular regions of the video scene is an indispensable tool in understanding crowd behavior. Crowd density estimation provides the measure of number of people in a given region at a specified time. While most of the existing computer vision methods use supervised training to arrive at density estimates, we propose an approach to estimate crowd density using motion cues and hierarchical clustering. The proposed method incorporates optical flow for motion estimation, contour analysis for crowd silhouette detection, and clustering to derive the crowd density. The proposed approach has been tested on a dataset collected at the Melbourne Cricket Ground (MCG) and two publicly available crowd datasets—Performance Evaluation of Tracking and Surveillance (PETS) 2009 and University of California, San Diego (UCSD) Pedestrian Traffic Database—with different crowd densities (medium- to high-density crowds) and in varied environmental conditions (in the presence of partial occlusions). We show that the proposed approach results in accurate estimates of crowd density. While the maximum mean error of