Empirical Analysis and Ranging Using Environment and Mobility Adaptive RSSI Filter for Patient Localization during Disaster Management

Chandra-Sekaran, Ashok-Kumar; Dheenathayalan, Prabu; Weisser, Pascal; Kunze, Christophe; Stork, Wilhelm

doi:10.1109/icns.2009.63

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…In [15], a wireless localization network able to track the location of patients in an indoor environment and also to monitor their physical status is presented. A location-aware WSN to track patients using an algorithm called REMA (Ranging using Environment and Mobility Adaptive) filter is proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

Integration of UHF RFID and WSN technologies in healthcare systems

Catarinucci

Donno

Mainetti

et al. 2014

2014 IEEE RFID Technology and Applications Conference (RFID-TA)

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This work describes a Smart Hospital System (SHS) based on the integration of Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) and IEEE 802.15.4 Wireless Sensor Network (WSN) technologies. SHS is able to provide patient localization, tracking, and monitoring services within hospitals or nursing institutes through the deployment of a heterogeneous network of RFID-WSN nodes relaying data to a central server. A set of software applications based on RESTful Java and database Push Notification (PN) technologies has been designed, implemented, and installed on the central in order to manage alert events (e.g. patient falls) and promptly inform the nursing staff through an iOS mobile app which has been designed ad hoc for the smart hospital scenario.

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Section: Introductionmentioning

confidence: 99%

Integration of UHF RFID and WSN technologies in healthcare systems

Catarinucci

Donno

Mainetti

et al. 2014

2014 IEEE RFID Technology and Applications Conference (RFID-TA)

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“…Chandra-Sekaran et al [10] propose an empirical analysis and ranging using environment and mobility. The proposed system is adaptive and uses RSSI filter for patient localization during disaster management.…”

Section: Literature Surveymentioning

confidence: 99%

Towards Independency Using LMN4DISABLED System for Disabled

Aly

2015

International Journal of Distributed Sensor Networks

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We propose a wireless based system to localize, monitor, and navigate people with a single type of disability. The proposed system is called LMN4DISABLED. Smartphone devices are used as interfaces for disabled people to communicate with the surrounding environments. This paper studies two types of disabilities (1) blind people and (2) people on wheelchairs with no mental deficiencies. Experiments are performed on a three-floor university building. Sensor nodes and cameras are distributed in all rooms and hallways. Dijkstra routing algorithm is used to select the appropriate route for each profile. New localization algorithm is used in the experiments. Experiments show that LMN4DISABLED outperforms reference experiments that are not using LMN4DISABLED by about 50.8% for different types of disabilities. When separating the performance of the blind disabled people from the performance of the people on wheelchairs, experiments show that the blind performance improved by 55% while wheelchair users improved by 47% when using the LMN4DISABLED system compared to basic reference experiments for the same people that did not use the LMN4DISABLED.

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“…The network can accomplish learning intelligently using the information provided by the inserted DCs. Moreover, the weights of the input layer and the inserted components are determined using multiple discriminant analysis (MDA) [13] in order to maximize the useful information contained in the network. The RF fingerprinting technique also uses RSS values to determine the position of a sensor node.…”

Section: Rf Fingerprintingmentioning

confidence: 99%

Efficient Sensor Localization Method with Classifying Environmental Sensor Data

Eun

2012

International Journal of Distributed Sensor Networks

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Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. However, there is a problem in determining sensor locations from the RSS: radio signal interference occurs because of the presence of indoor obstacles. To avoid this problem, we propose a novel localization method that uses environmental data recorded at each sensor location and a data classification technique to identify the location of sensor nodes. In this study, we used a wireless sensor node to collect data on various environmental parameters-temperature, humidity, sound, and light. We then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.

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Empirical Analysis and Ranging Using Environment and Mobility Adaptive RSSI Filter for Patient Localization during Disaster Management

Cited by 14 publications

References 5 publications

Integration of UHF RFID and WSN technologies in healthcare systems

Integration of UHF RFID and WSN technologies in healthcare systems

Towards Independency Using LMN4DISABLED System for Disabled

Efficient Sensor Localization Method with Classifying Environmental Sensor Data

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