WATS-SN: a wireless asset tracking system using sensor networks

Rajendran, N.; Kamal, Pratibha; Nayak, Dayananda; Rabara, S. Albert

doi:10.1109/icpwc.2005.1431340

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…RFID is the core of the implementation of the ubiquitous environment and will greatly contribute to the development of related industries in the future. An RFID reader is a device that sends and receives tag information and transmits information collected from the tag to the network [12]. Currently, RFID readers have a characteristic that the range of application is limited because the recognition distance and recognition accuracy are affected by the antenna performance and the surrounding environment [13].…”

Section: Rfid (Radio Frequency Identification) Rfid (Radio Frequency Identificationmentioning

confidence: 99%

A Study for Advanced Visualization of Sensing Data & Meta Data based WSN

Ahn¹,

Choi²

2021

IJEAT

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As the 5G environment is established due to the development of network and information and network technologies, wireless sensor network is drawing attention as a technology that will lead the information technology industry in the future. The sensor-based service must collect and process data recognized from various sensors and deliver information to users to understand. However, there is no research to visually express information about sensor resources to make it easier for users to understand. Therefore, this research proposes an efficient visualization model that is both realistic and economical. Then, we designed a scenario applied to real life and built a webpage.

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Section: Rfid (Radio Frequency Identification) Rfid (Radio Frequency Identificationmentioning

confidence: 99%

A Study for Advanced Visualization of Sensing Data & Meta Data based WSN

Ahn¹,

Choi²

2021

IJEAT

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“…With an advancement of computing devices and radio frequency (RF) technologies, the need for locationbased services has begun to extend beyond military use. Today, wireless localization can be found in a wide variety of industries, including security, commercial, healthcare, and tourism, with applications ranging from intruder detection [1,2], asset tracking [3][4][5], and emergency call locating system [6][7][8] to capsule endoscope tracking [9][10][11], geo-fencing [12,13], and automated museum tour [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach to deep‐learning‐based wireless indoor localization

Wisanmongkol

Taparugssanagorn

Tran

et al. 2022

IET Wireless Sensor Systems

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The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their rootmean-square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single-model counterparts.

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“…These nodes have the characteristic that their positions rarely change. Such systems are used in applications, such as environmental monitoring [ 1 , 2 ], structural health monitoring [ 3 , 4 , 5 ], industrial asset monitoring [ 6 ], building automation [ 7 , 8 ], static asset management [ 9 ] and traffic monitoring applications [ 10 ]. On the other hand, mobile sensor networks are composed of many mobile nodes.…”

Section: Introductionmentioning

confidence: 99%

Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems †

Kim

Jin

2016

Sensors

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Medical asset tracking systems track a medical device with a mobile node and determine its status as either in or out, because it can leave a monitoring area. Due to a failed node, this system may decide that a mobile asset is outside the area, even though it is within the area. In this paper, an efficient classification method is proposed to separate mobile nodes disconnected from a wireless sensor network between nodes with faults and a node that actually has left the monitoring region. The proposed scheme uses two trends extracted from the neighboring nodes of a disconnected mobile node. First is the trend in a series of the neighbor counts; the second is that of the ratios of the boundary nodes included in the neighbors. Based on such trends, the proposed method separates failed nodes from mobile nodes that are disconnected from a wireless sensor network without failures. The proposed method is evaluated using both real data generated from a medical asset tracking system and also using simulations with the network simulator (ns-2). The experimental results show that the proposed method correctly differentiates between failed nodes and nodes that are no longer in the monitoring region, including the cases that the conventional methods fail to detect.

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WATS-SN: a wireless asset tracking system using sensor networks

Cited by 15 publications

References 7 publications

A Study for Advanced Visualization of Sensing Data & Meta Data based WSN

A Study for Advanced Visualization of Sensing Data & Meta Data based WSN

An ensemble approach to deep‐learning‐based wireless indoor localization

Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems †

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