Mobile Sensor Monitoring System

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Mobile Device for Health Care Monitoring System UsingWireless Body Sensor Network

Mobile Device for Health Care Monitoring System UsingWireless Body Sensor Network

aspects of 3G technologies is its ability to unify existing cellular standards, such as code-division multiple-access, global system for mobile communications (GSM), and time- division multiple-access, under one umbrella. 3.1.2 Other Advances in Mobile Networks: In recent years, other mobile network technologies such as have become popular [10]. These technologies are implemented as an extension to or as an alternative for wired LAN to make the communication more flexible and powerful. WLAN allows users to access a data network at high speeds of up to 54 Mb/s as long as users are located within a relatively short range (typically 30 –50 m indoors and 100– 500 m outdoors) of a WLAN base station (or antenna). In the U.S. WLAN operates in two unlicensed bands [11], [12]: a) 802.11b and 802.11g operate in the 2.4GHz band, together with many other devices including Bluetooth and cordless telephones. b) 802.11a (Wi- Fi 5.2 GHz) operates in the 5.2 GHz band, which at this point is relatively free of interference from other electrical devices operating in this band. WPANs are defined with IEEE standard 802.15 [10]. The most relevant enabling technologies for Mobile Health systems are Bluetooth [11] and ZigBee [12]. Bluetooth technology was originally proposed by Ericsson in 1994, as an alternative to cables that linked mobile phone accessories. It is a wireless technology that enables any electrical device to communicate in the 2.5-GHz ISM (license free) frequency band. It allows devices such as mobile phones, headsets, personal digital assistants (PDAs), and portable computers to communicate and send data to each other without the need for wires or cables to link the devices together. It has been specifically designed as a low-cost, low- size, and low-power radio technology, which is particularly suited to the short range personal area network (PAN). The main features of Bluetooth are: a) Real-time data transfer usually possible between 10 – 100 m. b) Supports both point- to-point wireless connections without cables between mobile phones and personal computers, as well as point-to-multipoint connections to enable ad hoc local wireless networks. c) 400 kb/s of data symmetrically or 700–150 kb/s of data asymmetrically. ZigBee (IEEE 802.15.4 standard) has been developed as a low data rate solution with multi month to multiyear battery life and very low complexity. It is intended to operate in an unlicensed international frequency band. Potential applications include home automation, industrial control, and personal health care. The standard uses 16 channels at 2.4 GHz, ten channels at 902–928 MHz, and one channel at 868 –870 MHz. The maximum data rates for each band are 250, 40, and 20 kb/s, respectively.
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Miniature mobile sensor platforms for condition monitoring of structures

Miniature mobile sensor platforms for condition monitoring of structures

This paper describes the adaptation of these techniques to create a novel prototype inspection system that is based on a mobile, multiple sensor platform, in the form of a miniature, autonomous robotic vehicle. Each platform is designed to adhere to inclined and curved ferromagnetic surfaces and is able to perform different inspection tasks with different sensor payloads. For such applications it is essential that the sensing technologies are both complementary and are independent of the need to maintain consistent contact with the surface of the test specimen. Magnetic techniques, for example the use of eddy currents, are suited to non-contact detection of near surface defects. However, they are generally unsuitable for volumetric scanning of the internal structure. Ultrasonic methods, in contrast, are able to perform scanning of the internal structure, but normally require some form of liquid or gel based material to couple the ultrasonic energy from the transducer into the test material. Provision and consistency of adequate coupling can be problematic for a small vehicle, with limited power supply capability. The current sensor platform comprises a magnetic flux leakage (MFL) system, combined with an eddy current (EC) unit and an air coupled ultrasonic (US) sensor arrangement. All are potentially non-contact and have been configured to minimize electrical power consumption. Results are presented that have been obtained by
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The CitiSense Air Quality Monitoring Mobile Sensor Node

The CitiSense Air Quality Monitoring Mobile Sensor Node

miles, 3.1 million residents).. Other projects, such as the PEIR [5] system, have tried to solve this by relying on individual location traces collected through the GPS module embedded on the user smartphone to estimate his/her exposure to pollutants (PM 2.5). In the PIER system pollutants level are estimated on a backend server based on information collected from environmental stations and models of traffic and weather conditions. Each user’s exposure is then estimated based on the user’s localization traces. Since pollutants, such as diesel exhaust, are not uniformly distributed in space and time, we envision a more accurate solution which relies on a network of body worn sensor nodes carried by users during their everyday activities. In such a scenario, sensor nodes provide data to a backend server with minimal delay while simultaneously providing the user with real-time readings of the pollutants level in his/her surroundings. Furthermore, the backend server can combine the data collected by multiple users to create a fine grain understanding of the pollutant distributions.
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Development of an Equipment Room Environment Monitoring System based on Wireless Sensor Network and Mobile Agent

Development of an Equipment Room Environment Monitoring System based on Wireless Sensor Network and Mobile Agent

4.3. The design and implementation of mobile agent In the scene of this system, the network management staff can receive the data collected by sensor nodes on the basis of the protocol and algorithm introduced in section 4.2. Sensor nodes monitor the environmental parameter continuously and route them back immediately so as to guarantee a real time data transference. But as the number of sensor nodes grows, the data flow in the network increases significantly. For a single node, the result is that node dying much more rapidly. Meanwhile, for the entire wireless sensor network, the result is heavy network traffic. However, the problem could not be solved by reducing the frequency of data transmission, otherwise the network manager would not detect the environmental problems in time. To resolve this conflict the mobile agent technology is introduced into this system.
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Pyroelectric Infrared (PIR) Sensor Monitoring System

Pyroelectric Infrared (PIR) Sensor Monitoring System

The optimal functioning of a transmitter in a telecommunications system depends upon the suitability of the bandwidth of the transmitted signal to its data rate and modulation type. Cellular phones are designed to operate within the environment of one of several mobile communications networks. Transmitters and receivers for communication systems generally are designed such that they are tuned to transmit and receive one of a multiplicity of signals having widely varying bandwidths and which may fall within a particular frequency range.

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Smart Sensor Based Soil Monitoring System

Smart Sensor Based Soil Monitoring System

The required components including PIC microcontroller, sensors and Bluetooth module are placed on the platform of the Proteus software and the connections are made. The .c file is converted into .hex file which is then dumped into thet PIC microcontroller. After running the project, the simulation results can be viewed pictorially. Android is an open source and Linux based Operating System for mobile devices such as smartphones and tablets. It offers unified approach to application development thus their applications should be able to run on different devices powered by Android. Android applications are usually developed in the Java language using the Java Development Kit (JDK). The Android application can be installed using the Java program along with the JDK by setting up the JDK.
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Air Quality Monitoring System Based on Sensor

Air Quality Monitoring System Based on Sensor

II. LITURATURE SURVEY [1]Nihal Kulratna and B. H. Sudantha presented environmental air pollution monitoring system in 2008. The system based on the IEEE 1451 standard. In this paper STIM smart transducer interface module was developed which consist of microcontroller and group of various sensors like CO2, O3, NO2, CO. This also used personal computer PC for graphical representation. STIM connected to the PC via transducer independent interface which uses IEEE 1451 standard. [2] In the year of 2010 A. R. Ali-Ali, Imran Zualkernan and fadi Aloul mobile GPRS sensor for the pollution monitoring. This included data acquisition unit, GPRS module and pollution server. In this DAQ unit, GPRS and GPS were connected to the microcontroller via RS-232 Interface and finally gathered data were sending to the pollution server. [3] Raja Vara Prasad et al. In 2011 proposed a real time wireless pollution monitoring. This system was based on the multihop data aggregation algorithm. Calibrated gas sensors were interfaced to wireless sensor motes, in that Libelium WASP mote was used which consist of processing unit and communication unit. All gas sensors were connected to sensor board on rotational basis. The collected data were sending to base station. Multihop data aggregation algorithm was used to increase a monitoring range. [4] Jen-Hao Liu et al. introduced micro-scale air quality monitoring system for urban areas in 2012. This system monitors the concentration of carbon monoxide co caused by heavy vehicles emission. Sensor nodes were deployed in highly populated areas. System was integrated with the GSM for data transmission. Gateway collected the data from all sensor nodes and sends to control centre by GSM network.
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Reliable Lifespan Evaluation of a Remote Environment Monitoring System Based on Wireless Sensor Networks and Global System for Mobile Communications

Reliable Lifespan Evaluation of a Remote Environment Monitoring System Based on Wireless Sensor Networks and Global System for Mobile Communications

Copyright © 2016 Diego Antol´ın et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The use of wireless sensor networks (WSN) for monitoring physical and chemical variables in large areas allows density and frequency measurements which have been unavailable to date in classical measurement systems. To fully take advantage of this technology in a particular application, besides an accurate design and selection of all the components involved in its operation, it is essential to dispose of reliable lifetime estimation prior to deployment. This paper presents an experimental approach to determine the actual lifetime of such battery-operated systems, making use of a custom WSN architecture, and for different batteries technologies. To render a reliable evaluation, the energy consumption of the sensor nodes under their different operation modes, in correlation with the battery characteristics and the voltage regulation system, is jointly considered. The result is a complete and practical lifetime model, whose appropriate performance has been validated in a real deployment scenario.
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Mobile Sensor Networks for Air Quality Monitoring in Urban Settings

Mobile Sensor Networks for Air Quality Monitoring in Urban Settings

Mobile wireless sensor networks hold the potential to allow a paradigm shift in our under- standing of urban air pollution, through a significant augmentation of the spatial resolution of measurements. The road to achieving reliable high quality information from these type of systems is, however, full of challenges. These start already from the system design level, as the task of developing robust large-scale mobile sensing networks for continuous urban monitoring is arduous in itself. The limitations of existing sensor technology is another important source of hard problems. Low-cost chemical sensors, the typical candidates for this type of applications, suffer from a large list of issues that make their use in a mobile scenario non-trivial. These include: instability (temporal drift), cross-sensitivity (to other chemicals or environmental parameters), low signal-to-noise ratios, and slow dynamic response. The latter problem, in particular, is a tough challenge when considering a mobile scenario, since it leads to significant measurement distortion. The question of achieving and maintaining the calibration of chemical sensors, throughout the deployment lifetime, is another essential issue that derives from their typical instability. Finally, the development of appropriate modeling techniques that would enable us to generate high-resolution pollution maps based on mobile sensor network data is a highly difficult problem due to the inherently dynamic and partial coverage of such systems.
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Mobile Phone as a Sensor for Robot Navigation System

Mobile Phone as a Sensor for Robot Navigation System

On the contrary, only one sensor is used in the system described in this paper and this sensor is commonly available on mobile phone. Up to this point, research concerning one lens was mostly concerned with the detection and monitoring of certain objects. Authors from Syracuse University have, in their paper [5], introduced a concept of monitoring the object using a network of cheap cameras that are capable to communicate with each other. In the case where the object is detected in the area of interest, the active camera sends a request to monitor the given area to the rest of the cameras in the system. That causes the object to be monitored from multiple points of view. This monitoring algorithm is implemented directly on the motherboard of the CITRIC camera. In their paper, the authors deal with limitations of resolution, memory size, processing speed, and transfer speed. This proposed system is aimed at detection and monitoring of a moving target.
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A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

Study objectives In light of the above, we propose an ontology-based mobile health CDSS for type 1 diabetes monitoring and treatment. This cloud-based and comprehensive architec- ture allows patients to be connected with different service providers as well as different sources of medical data. The system is based on a set of standards to handle interoper- ability challenges. Integration of these standards is based on ontology representation and reasoning. To support transparent integration and semantic interoperability between the CDSS and distributed EHRs, this proposal is based on the most recent HL7 interoperability standard of FHIR. The SSN is utilized to integrate sensor data with historical EHR data. To unify the semantic meaning of all used terminologies and knowledge, all terms used are understood and embedded under BFO universals. We collected medical knowledge from the most recent T1D CPGs, scientific research, and official web sites [20]. CPGs are documents that collect all the available medical evidence with regard to a particular disease. They support the evidence based medicine paradigm. Knowledge of CPGs is implemented as OWL 2 axioms and SWRL rules to build and infer tailored TPs and to provide real-time monitoring for diabetics. Security and privacy issues, however, are outside the scope of this paper. Specifi- cally, this proposal makes the following major contri- butions, compared with previous methods.
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Pollution Monitoring System Using Gas Sensor Based On Wireless Sensor Network

Pollution Monitoring System Using Gas Sensor Based On Wireless Sensor Network

A number of researches have been conducted related to pollution monitoring. Yang et al. [4] have developed remote online monitoring system for geological carbon dioxide (CO2) leakage which consists of monitoring equipment, server, and clients. The system gets localization and time information through GPS, and then saved into SD cards storage module. The general packet radio service (GPRS) wireless transmission module will send the collected data wirelessly to the data centre server. Sivaraman et al. [5] developed HazeWatch project which uses several low-cost mobile sensor unit attached to vehicles to measure air pollution concentrations such as CO, nitrogen dioxide (NO2), and ozone (O3). The user can tag and upload the data in real time using their mobile phone. The spatial granularity of obtained data was collected to create of pollution maps which viewable in real-time over the web. The mobile personalized apps show the individual exposure history and route planning which less pollution. Peng et al. [6] implemented a total volatile organic compound (VOC) air pollution monitoring system to report value of temperature and humidity at indoor environment with the consideration of the cost, development complexity, and the operation convenience. Kadri et al. [7] developed an ambient real-time air quality monitoring system based on utilizing multi-gas (MG) monitoring stations that communicate with a platform by means of machine-to-machine (M2M) communications. Each MG station contains O3, CO, NO2, and H2S sensors. Jelicic et al. [8] developed flexible wireless system able to detect polluted air in a large environment. The system involves three levels: sensor level, node level and network level which contains metal oxide semiconductor (MOS) gas and a pyroelectric infrared (PIR) sensors. The system adopted duty-cycling of the gas sensor activity to extend the lifetime of node compared to the continuously driven gas sensor. Dian et al. [9] developed a CO2 sequestration monitoring and gas sensing by combining techniques of optical fiber sensing technology and wireless communication and analyzed the power consuming task such as sensing unit, processing unit, and transceiver unit. The result research recommended that renewable energy system such as solar cell maintain long-term and stable work. Pau et al. [10] developed a fuzzy logic system in wireless sensor network for environmental monitoring according to the battery level and to the throughput to reducing power consumption. The experiments result shows that fuzzy-based approach dynamically changes the sleeping time in order to reduce power consumption and increase the lifetime of sensor nodes.
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Intelligent Mobile Health Monitoring System (IMHMS)

Intelligent Mobile Health Monitoring System (IMHMS)

rifat@cse.buet.ac.bd 1 , faizulbari@cse.buet.ac.bd 2 , gourab.kundu08@gmail.com 3 , sheikh.ahamed@marquette.edu 4 , mostofa@cse.buet.ac.bd 5 Abstract Health monitoring is repeatedly mentioned as one of the main application areas for Pervasive computing. Mobile Health Care is the integration of mobile computing and health monitoring. It is the application of mobile computing technologies for improving communication among patients, physicians, and other health care workers. As mobile devices have become an inseparable part of our life it can integrate health care more seamlessly to our everyday life. It enables the delivery of accurate medical information anytime anywhere by means of mobile devices. Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight and intelligent bio-sensor nodes. These nodes, capable of sensing, processing, and communicating one or more vital signs, can be seamlessly integrated into wireless personal or body area networks for mobile health monitoring. In this paper we present Intelligent Mobile Health Monitoring System (IMHMS), which can provide medical feedback to the patients through mobile devices based on the biomedical and environmental data collected by deployed sensors.
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icare: A Mobile Health Monitoring System for the Elderly

icare: A Mobile Health Monitoring System for the Elderly

Keywords-healthcare; assisted living; health monitoring; body sensor; medical guidance I. I NTRODUCTION The aging people in the contemporary society have brought great pressure and many seniors have been living alone without anyone accompanied because their offspring are busy with work and have to struggle with severe competition. For those seniors who live independently in their own homes, there is an increasing risk of falls and strokes which could threaten their lives. A lot of money and research effort has been spent on making people aware of the warning signs [1]. Getting the elderly to recognize the warning signs is not easy. And it indicates that people who have had a heart attack have a sudden death rate that is 4 to 6 times that of the general population [2]. The New England Journal of Medicine draws a conclusion that the chances of surviving a fall, heart attack or stroke are six times greater if the elderly get help within an hour. Therefore, the elderly with no one accompanied need real-time monitoring to reduce the anxiety of them and the risk of accidents.
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A Study on Pollution Monitoring system in Wireless Sensor Networks

A Study on Pollution Monitoring system in Wireless Sensor Networks

On the hardware side, we integrate sensor nodes with the CO sensors to perform air-quality-monitoring tasks. The sensor nodes are able to communicate with each other based on the Zig Bee protocol. The control center, controlled by the Lab VIEW program, successfully communicates with users through sending them SMS messages. It also stores a large amount of data into the database via the My SQL program, so that experts can establish a prediction model of pollution diffusion based on the data. In addition, the monitoring data reveals high-resolution pollution conditions near the Gong Guan roundabout in the Taipei city of Taiwan. The data can be an important source when addressing the issue of the impacts of motorcycles at idles (e.g. waiting for a green light) on air quality. Moreover, to achieve real-time monitoring, the data of CO concentration in a particular place could be reviewed from mobile communication devices, such as PDAs, smart phones, and tablet PCs to help keep air quality in check.
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Wireless Sensor Network for Patient Health Monitoring System

Wireless Sensor Network for Patient Health Monitoring System

government health policy and that is driven by an imperative to provide better quality care to people without the need to disrupt their lives. Currently, some wireless monitoring systems have been designed which commonly use a PC, a personal digital assistant (PDA), or a mobile phone at the patient’s side. In these systems, the vital signals are transmitted to the hospital via internet or mobile networks. The main drawbacks of these systems are the costs of a PC or PDA, accessibility, and possible delay that occurs in sending data if the PC is off and the difficulty of using PCs for elderly people.
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NoxDroid A Bicycle Sensor System for Air Pollution Monitoring

NoxDroid A Bicycle Sensor System for Air Pollution Monitoring

To mitigate these limitations, several research projects investigated both crowd sourcing sensing (to have more sensing points as well as mobile ones), and pres- enting the resulting information to citizens. However most of these solutions do not provide direct feedback for the carrier or manager of the sensor nodes 0. This lack of direct feedback is a problem if volunteers have to mount and spend time on using the devices. Not getting relevant information might decrease the chances of adoption and result in less motivated participants. In order to get a quality dataset, it seems necessary to provide users with information they can draw direct benefits from and gain a feeling that it makes sense to sense.
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Patient Monitoring System Using Body Sensor Network

Patient Monitoring System Using Body Sensor Network

VIII. CONCLUSION In this paper, we've got planned a dedicated and assimilation assimilation expedient accession framework for mobile- assimilation emergency, that aural the basal appetite to exploits the acceptance to use expedient accession to accede top believability of letter alignment and chiral in emergency acceptance aspersing the assimilation beastly activity throughout the expedient computing. Rigorously advocacy shows that the planned SPOC framework will do the economical user-centric assimilation acceptance management. in addition, throughout the abysm ability analysis, we've got appropriately abhorrent the planned SPOC framework can antipode the high-intensive letter alignment and chiral and aspersing the letter assimilation beastly activity in mobile-Healthcare emergency per this agenda we've got conflicting the PPSPC framework for mobile-Healthcare emergency at intervals that astute phones aggregation acclimatized abode the detected abstracts by the sensors to the blossom adversity centre by bribery the expedient accession classic at intervals that the attainable assets and activity are opportunistically accumulated to alignment the accession accelerated Patient Health Monitoring (PHI).
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Mobile sensor networks for environmental monitoring

Mobile sensor networks for environmental monitoring

In environmental monitoring applications, phenomenon dynamics may mean that deployed sensors no longer provide proper coverage of the phenomenon. In these cases adaptive sampling is needed and the use of mobile WSNs may present advantages. Some of these advantages are evident in examples of environmental emergencies, such as the cases of Fukushima, Eyjafjallajoekull and the Gulf of Mexico mentioned above. To monitor radioactive releases, autonomous mobile sensors in WSNs could be used to track the spread of high radiation levels. They could move very close to the release source and other affected areas to provide real-time observation without human intervention, avoiding exposing people to unhealthy radiation levels. In the case of volcanic ash dispersions, mobile WSNs could move close to critical infrastructure, such as airports. Just a small number of mobile sensors could help to deliver flexible sampling by relocating themselves to locations that will optimise mapping and forecasting of plume dispersions. In the case of oil spills, mobile underwater WSNs may be the only means of obtaining detailed underwater information about the evolution of the oil spill in space and time, especially in areas difficult to access with other types of observation techniques, such as remote sensing. Sampling locations do not need to be defined prior to sampling; in fact, mobile WSNs can determine in real time the best place to move a sensor in response to the evolution of the phenomenon.
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An Algorithm of Wireless Sensor Monitoring System

An Algorithm of Wireless Sensor Monitoring System

Fig. 3. Storage Monitoring System Architecture Based on Wireless Sensor Network In the physical framework of the warehouse monitoring system, as the front-end sensor antenna of the storage field, wireless sensor network forms a warehouse moni- toring wireless sensor network (WMWSN). WMWSNconsists of several sensor nodes and Sink “as stated in [6]”. Sensor nodes are equipped with a number of environmen- tal monitoring sensor with different precision or heterogeneous, mainly including temperature, humidity, PH value, light intensity, CO/CO 2 concentration, smoke, dust, vibration, magnetic. Sensor nodes are deployed in the target area. Each sensor node forms a wireless network in a self-organizing or artificial configuration form, which collaboratively senses, transfers and processes sensing data within the monitoring area. Sink is responsible for data collection and simple preprocessing, and the infor- mation is transferred to the monitoring center. In addition, Sink is responsible for the analysis and release of the command issued by the monitoring center to the WMWSN.
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