Maneesha Vinodini Ramesh

Real-Time Wireless Sensor Network for Landslide Detection

Wireless sensor networks are one of the emerging areas which have equipped scientists with the capability of developing real-time monitoring systems. This paper discusses the development of a wireless sensor network(WSN) to detect landslides, which includes the design, development and implementation of a WSN for real time monitoring, the development of the algorithms needed that will enable efficient data collection and data aggregation, and the network requirements of the deployed landslide detection system. The actual deployment of the testbed is in the Idukki district of the Southern state of Kerala, India, a region known for its heavy rainfall, steep slopes, and frequent landslides.

Design, development, and deployment of a wireless sensor network for detection of landslides

Wireless sensor networks are one of the most promising emerging technologies, providing the opportunity for real-time monitoring of geographical regions (remote and hostile) that are prone to disasters. With a focus on landslide detection, this work reaffirms the capability of wireless sensor networks for disaster mitigation. A complete functional system consisting of 50 geological sensors and 20 wireless sensor nodes was deployed in Idukki, a district in the southwestern region of Kerala State, India, a highly landslide prone area. The wireless sensor network system has, for the past three years, gathered vast amounts of data such as correlated sensor data values on rainfall, moisture, pore pressure and movement, along with other geological, hydrological and soil properties, helping to provide a better understanding of the landslide scenario. Using the wireless sensor networks, system was developed an innovative three level landslide warning system (Early, Intermediate and Imminent). This system has proven its validity by delivering a real warning to the local community during heavy rains in the July 2009 monsoon season. The implementation of this system uses novel data aggregation methods for power optimization in the field deployment. A report on unanticipated challenges that were faced in the field deployment of the wireless sensor networks and the novel solutions devised to overcome them are presented here.

Wireless Smart Grid Design for Monitoring and Optimizing Electric Transmission in India

Electricity losses in India during transmission and distribution are extremely high and vary between 30 to 45%. Wireless network based architecture is proposed in this paper, for monitoring and optimizing the electric transmission and distribution system in India. The system consists of multiple smart wireless transformer sensor node, smart controlling station, smart transmission line sensor node, and smart wireless consumer sensor node. The proposed software module also incorporates different data aggregation algorithms needed for the different pathways of the electricity distribution system. This design incorporates effective solutions for multiple problems faced by India’s electricity distribution system such as varying voltage levels experienced due to the varying electrical consumption, power theft, manual billing system, and transmission line fault. The proposed architecture is designed for single phase electricity distribution system, and this design can be implemented for three phase system of electricity distribution with minor modifications. The implementation of this system will save large amount of electricity, and thereby electricity will be available for more number of consumers than earlier, in a highly populated country such as India. The proposed smart grid architecture developed for Indian scenario, delivers continuous real-time monitoring of energy utilization, efficient energy consumption, minimum energy loss, power theft detection, line fault detection, and automated billing.

The deployment of deep-earth sensor probes for landslide detection

In this paper, we present a state-of-the-art wireless sensor network (WSN) of deep-earth probes (DEPs) that has been deployed to monitor an active landslide in the Western Ghats mountain range of South India. While India has one of the highest incidences of landslides and landslide-induced fatalities—primarily in the Himalayas of North India and in the Western Ghats of Central and South India—our study is perhaps the first comprehensive attempt to instrumentally detect landslides in the Western Ghats. Wireless networks have enabled us, since June 2009, to continuously monitor the deployment site in real time and from anywhere around the globe. There have been a few earlier landslide monitoring WSNs using accelerometers in Emilia Romagna Apennines, Italy; global navigation satellite system (GNSS) sensors to monitor the Hornbergl landslide, Austria; and vibrating wire stress sensors to monitor a slope in China. We improved upon these WSN systems by incorporating a variety of sensors—piezometers, dielectric moisture sensors, strain gauges, tiltmeters, a geophone, and a weather station—and installing some of these sensors as deep as 20 m below the ground surface. We present the salient aspects of the field deployment of DEPs: the selection of sensors and their incorporation in DEPs, the methodology we used in embedding these DEPs into the soil, and a few of the key aspects of the wireless sensor network. We also present a description of the deployment site and some of the results of geotechnical investigations carried out on borehole corings. Finally, we present the more interesting field data collected from the monitoring system during a rainy season in July and August 2009.

'Open access' generic method for continuous determination of major human CYP450 probe substrates/metabolites and its application in drug metabolism studies.

1. An ‘open access’ generic high-performance liquid chromatography method was developed for different combination sets each containing specific cytochrome P450 probe substrate and the corresponding metabolite. Method development, optimization and validation were carried out with the following combinations: phenacetin + paracetamol + internal standard (IS, celecoxib), bufuralol + hydroxybufuralol + IS, testosterone + 6β-hydroxytestosterone + IS, chlorzoxazone + 6-hydroxychlorzoxazone + IS, coumarin + 7-hydroxycoumarin + IS, tolbutamide + hydroxytolbutamide + IS, and diazepam + desmethyldiazepam + IS. 2. The assay procedure involved a simple one-step liquid/liquid extraction followed by reverse phase chromatography (Inertsil ODS 3V column) employing a ternary gradient system and the eluate was monitored by a photodiode array/fluorescence detector. The standard curve for each compound, in the concentration range 0.1–10 μg ml−1, in various sets was linear (r2>0.99) and absolute recoveries of all analytes were >90%. The lower limit of quantification was 0.1 μg ml−1. The intraday precision and accuracy in the measurements of quality control were <15% relative standard deviation and <15% deviation from nominal values, respectively. 3. Each combination set was tested with individual chemical inhibitors (furafylline, quinidine, ketoconazole, disulfiram, diethyldithiocarbamate, sulphaphenazole and tranylcypromine) and all analytes were well resolved. Overall, the assay is simple, uses conventional instrumentation and provides a scope to analyse all cytochrome P450 combination sets continuously. The application of the method in the cytochrome P450 liability screen of novel compounds is also presented.

Real-Time Monitoring and Detection of "Heart Attack" Using Wireless Sensor Networks

The main purpose of this research work is to develop a wireless sensor network system that can continuously monitor and detect cardiovascular disease experienced in patients at remote areas. A wearable wireless sensor system (WWSS) is designed to continuously capture and transmit the ECG signals to the patient’s mobile phone. The fastest alert will be issued to doctors, relatives, and hospitals, using the proposed data processing algorithm implemented in the patients mobile phone. The complete data from WWSS will also be transmitted to a central station, which provides a service to the doctor to view his patient’s record and provide his prescription remotely, on his request. A heterogeneous wireless network design is also proposed for the continuous transmission from WWSS to a Central Data Center (CDC). The proposed system is integrated with a dynamic data collection algorithm that collects the ECG signals at regular intervals, according to the health risk perceived in each patient. Employment of this system will contribute in reducing heart diseases, leading to death of a patient, and also act as an effective health care service to patients in rural area. This continuous monitoring system will provide effective, efficient, and fast health care service to patients at risk, even if the doctor, relatives are not near the patient and also during the non availability of the ellular network.

Context aware ad hoc network for mitigation of crowd disasters

Abstract Our research works focuses on the design and implementation of a novel ubiquitous multi context-aware mobile phone sensing network for mitigation of crowd disasters using machine-to-machine (M2M) communications. A mobile sensor network system integrated with wireless multimedia sensor networks (WMSNs) was designed for effective prediction of a stampede during crowd disasters. This proposed sensor network consists of mobile devices that are used as crowd monitoring participant nodes that employ light sensors, accelerometers, as well as audio and video sensors to collect the relevant data. Real-time crowd dynamics modeling and real-time activity modeling have been achieved by implementing the algorithms developed for Context Acquisition and multi-context fusion. Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus. The implementation of the proposed architecture in Android smartphone provides light-weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation and generation of an in time alert to take measures to avoid the occurrence of a stampede. The system has been tested and illustrated within a group of people for stampede prediction by using empirically collected data.

A Low Cost Remote Cardiac Monitoring Framework for Rural Regions

Cardiovascular diseases in rural developing countries take a large toll of human lives, due to inadequate quality health care facilities and their limited reach to the patients. The burgeoning population of developing nations make the linear organic scaling of health care facilities impractical to cater the diverse rural geography. Hence it is imperative to scale the health care facilities through wireless communication technologies in an affordable manner. Timely analysis of ECG data is critical for early diagnosis and treatment of several cardiovascular diseases. With this aim, a wearable wireless ECG monitoring framework, named as Amrita-spandanam was designed. This framework consist of a patient wearable device and a patient smart phone with amrita-spandanam application, enabling a doctor/hospital to monitor the remote patient through his internet connected mobile phone or web browser. The framework does the post analysis of the ECG signal using a backend server to disseminate warnings to the doctor and the patient. Several de noising algorithms were applied to the acquired ECG signal prior to this post analysis. The framework was implemented successfully enabling real time remote monitoring of the cardiac patients in rural villages.

Intruder tracking using wireless sensor network

Nowadays, almost all the countries are facing threats from terrorists and intruders from their border areas, challenging the internal security of the country in those areas. So many civilian and military applications require locating an intruder in a secured area. Target tracking, data processing and analysis play a major role in this type of applications. The proposed system is to develop a centralized computer application that needs to identify moving objects in a specific area using sensors. The system will be basically designed to detect human intruders. The objective is to design and implement an object tracking system using a wireless sensor network. This application is able to detect and track objects, and report direction and speed of the intruder to a central base station. The human intruder is detected using a passive infrared (PIR) sensor. The sensor is connected to a MICAz sensor node. The PIR sensor is able to detect the humans and provide information about the direction of the movement. The gathered information from the sensor network is to be given to the base station for processing. The proposed system provides an environment for easy deployment and which does not require any existing infrastructure or constant monitoring by humans.

Security Enhancement in Wireless Sensor Networks Using Machine Learning

Ensuring the security of wireless sensor networks (WSNs) is vital for monitoring real-time systems. One of the major security flaws experienced by WSNs is denial of service (DoS) which can even lead to the breakdown of the complete system or to wrong decisions being made by the system that can cause adverse results. This research work focuses on two techniques for detecting a DoS attack at a medium access control (MAC) layer. Our research compares and evaluates the performance of two major machine learning techniques: neural network (NN) and support vector machine (SVM). Vanderbilt Prowler is used for simulating the scenarios. In the simulations, normalized critical parameters and their corresponding probabilities of DoS attack are computed in 50 trial runs. These normalized critical parameters and their corresponding probabilities of DoS attack are used as training inputs in NN and SVM approaches. The simulation results clearly show that SVM provides better accuracy compared to NN, 97% accuracy by SVM and 91% accuracy by NN. The simulation also shows that SVM takes much less time to detect and determine the probability of a DoS attack, 0.25 seconds by SVM and 0.75 seconds by NN. All these results clearly show that SVM performs better than NN when used for detecting the probability of DoS attack in WSNs.