A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

doi:10.3390/s16081043

Cited by 51 publications

(38 citation statements)

References 73 publications

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“…The outdoor healthcare monitoring system consumed more power when it continuously monitored for subject fall and position. The system was suitable for outdoor applications; however, GPS would not work in indoor environments [26].…”

Section: Related Workmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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Section: Related Workmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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“…The study done by Gharghan et al in [5], the authors constructed an ANFIS (Adaptive Neuro Fuzzy Inference System) trained Mamdani fuzzy system with three, five and seven membership functions. For the purpose of training, a ratio of deployed nodes was put in training set and rest of the nodes in testing set.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Fuzzy System Based Cooperative Scalable and Secured Localization Scheme For Wireless Sensor Networks

Kumar¹

2018

Preprint

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“…In recent years, soft techniques have been used to develop both range-based and range-free localization estimation methods 11,12,13,14,15,16 22,23,26 . While calculating the location of a sensor of unknown location using nodes with known locations, the weight parameter is used to increase the impact of the node with the highest RSSI value on location determination.…”

Section: Fig1 Localization Algorithmsmentioning

confidence: 99%

Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm

Tuncer¹

2017

IJCIS

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For many of the applications in which wireless sensor networks are used, it is important to know from which nodes or what location useful information is acquired. The Global Positioning System (GPS) is conventionally used to determine location. However, GPS systems are not ideal for many applications due to their excessive power consumption and high cost. As an alternative to GPS, distance and location can be estimated through the usage of at least 3 nodes with known locations. Received Signal Strength Indication (RSSI) is the simplest and most inexpensive technique used to determine distance and location, and is a standard feature on every sensor. However, RSSI can be affected by noise and environmental obstacles. For this reason, it is difficult to set up a mathematical model for RSSI. This paper presents a conversion of the Centroid Localization (CL) method in determining the location of a sensor of unknown location to the Intelligent Centroid Localization (ICL) Method. Fuzzy logic and genetic algorithm are employed in the ICL method. RSSI values measured by anchor nodes are applied as inputs to the fuzzy system in the ICL developed. Anchor nodes have been assigned weight values to increase the effect of high-value RSSI nodes in positioning. Therefore the fuzzy system's output is defined as weight (w). The base values of the fuzzy system's output membership functions are adjusted using genetic algorithm to minimize location error. Toward observing the performance of the proposed ICL, comparisons with the both Centroid Localization method and APIT (Approximate Point In Triangle) algorithm have been provided. The localization error has been reduced to minimum levels.

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A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

Cited by 51 publications

References 73 publications

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

A Hybrid Fuzzy System Based Cooperative Scalable and Secured Localization Scheme For Wireless Sensor Networks

Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm

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