WLAN-Based Real-Time Asset Tracking System in Healthcare Environments

Youna, Jong-Hoon; Ali, Hesham; Sharif, Hamid; Deogun, Jitender S.; Uher, J.; Hinrichs, Saba

doi:10.1109/wimob.2007.4390865

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…Techniques for tracking the whereabouts of IEEE 802.11 standard compliant (Wi-Fi) mobile devices can be employed for several benign use cases, such as positioning [1][2][3][4], asset tracking [5,6], travel time estimation [7], and behavioral modeling [8]. These use cases inspired the creation of numerous startups and commercial products such as Skyhook (http://www.skyhookwireless.com/), Wifarer (http://www.wifarer.com/), YFind, and Nomi (http://www.nomi.com/), as well as a substantial amount of academic research.…”

Section: Introductionmentioning

confidence: 99%

Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices

Robyns

Bonné

Quax

et al. 2017

Security and Communication Networks

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We present two novel noncooperative MAC layer fingerprinting and tracking techniques for Wi-Fi (802.11) enabled mobile devices. Our first technique demonstrates how a per-bit entropy analysis of a single captured frame allows an adversary to construct a fingerprint of the transmitter that is 80.0 to 67.6 percent unique for 50 to 100 observed devices and 33.0 to 15.1 percent unique for 1,000 to 10,000 observed devices. We show how existing mitigation strategies such as MAC address randomization can be circumvented using only this fingerprint and temporal information. Our second technique leverages peer-to-peer 802.11u Generic Advertisement Service (GAS) requests and 802.11e Block Acknowledgement (BA) requests to instigate transmissions on demand from devices that support these protocols. We validate these techniques using two datasets, one of which was recorded at a music festival containing 28,048 unique devices and the other at our research lab containing 138 unique devices. Finally, we discuss a number of countermeasures that can be put in place by mobile device vendors in order to prevent noncooperative tracking through the discussed techniques.

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Section: Introductionmentioning

confidence: 99%

Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices

Robyns

Bonné

Quax

et al. 2017

Security and Communication Networks

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“…Real-world applications for an indoor LBS aim to detect the location of targets in various service domains, such as medical personnel or equipment in a hospital [ 14 – 17 ], museum guide [ 18 ], stored inventories in a warehouse [ 19 ], or tagged products in a supply chain [ 20 ]. In particular, many manufacturers like AeroScout [ 2 ], Ekahau [ 3 ], or Honeywell [ 21 ] support various commercial products for mobile asset management in a real-time location system (RTLS).…”

Section: Related Workmentioning

confidence: 99%

Fully Distributed Monitoring Architecture Supporting Multiple Trackees and Trackers in Indoor Mobile Asset Management Application

Jeong

Kang

2014

Sensors

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A tracking service like asset management is essential in a dynamic hospital environment consisting of numerous mobile assets (e.g., wheelchairs or infusion pumps) that are continuously relocated throughout a hospital. The tracking service is accomplished based on the key technologies of an indoor location-based service (LBS), such as locating and monitoring multiple mobile targets inside a building in real time. An indoor LBS such as a tracking service entails numerous resource lookups being requested concurrently and frequently from several locations, as well as a network infrastructure requiring support for high scalability in indoor environments. A traditional centralized architecture needs to maintain a geographic map of the entire building or complex in its central server, which can cause low scalability and traffic congestion. This paper presents a self-organizing and fully distributed indoor mobile asset management (MAM) platform, and proposes an architecture for multiple trackees (such as mobile assets) and trackers based on the proposed distributed platform in real time. In order to verify the suggested platform, scalability performance according to increases in the number of concurrent lookups was evaluated in a real test bed. Tracking latency and traffic load ratio in the proposed tracking architecture was also evaluated.

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“…In these networks, one or more mobile nodes are attached to a mobile object to monitor its movement and current location in real time. Such systems can be applied to healthcare systems [ 11 , 12 , 13 ], mobile asset tracking [ 14 , 15 , 16 , 17 ] and human monitoring applications [ 18 ].…”

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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WLAN-Based Real-Time Asset Tracking System in Healthcare Environments

Cited by 16 publications

References 14 publications

Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices

Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices

Fully Distributed Monitoring Architecture Supporting Multiple Trackees and Trackers in Indoor Mobile Asset Management Application

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

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