SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

Fafoutis, Xenofon; Janko, Balazs; Mellios, Evangelos; Hilton, Geoff; Sherratt, R. Simon; Piechocki, Robert J.; Craddock, Ian J

doi:10.1007/978-3-319-49655-9_37

Cited by 22 publications

(33 citation statements)

References 24 publications

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“…(3) approximates a linear behaviour (τ p = 129 us, T = 1.71 s), which is validated by the simulations. For instance, in case of a ±20 ppm drift, a typical worstcase clock drift in IoT-devices [8], 390 us is the minimum guard time length for operation without compromising network reliability due to loss of synchronisation or goodput (Fig. 3b).…”

Section: Simulation Resultsmentioning

confidence: 99%

Guard Time Optimisation for Energy Efficiency in IEEE 802.15.4-2015 TSCH Links

Papadopoulos

Mavromatis

Fafoutis

et al. 2017

Interoperability, Safety and Security in IoT

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Abstract. Time Slotted Channel Hopping (TSCH) is among the Medium Access Control (MAC) schemes defined in the IEEE 802.15.4-2015 standard. TSCH aims to guarantee high-level network reliability by keeping nodes time-synchronised. In order to ensure successful communication between a sender and a receiver, the latter starts listening shortly before the expected time of a MAC layer frame's arrival. The offset between the time a node starts listening and the estimated time of frame arrival is called guard time and it aims to reduce the probability of missed frames due to clock drift. In this paper, we investigate the impact of the guard time length on network performance. We identify that, when using the 6TiSCH minimal schedule, the most significant cause of energy consumption is idle listening during guard time. Therefore, we perform empirical optimisations on the guard time to maximise the energy-efficiency of a TSCH link. Our experiments, conducted using the Contiki OS, show that optimal guard time configuration can reduce energy consumption by up to 40%, without compromising network reliability.

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Section: Simulation Resultsmentioning

confidence: 99%

Guard Time Optimisation for Energy Efficiency in IEEE 802.15.4-2015 TSCH Links

Papadopoulos

Mavromatis

Fafoutis

et al. 2017

Interoperability, Safety and Security in IoT

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“…This metric strongly correlates with the physical activity levels of the user. Using our prototype wearable sensor [14], mounted on the wrist of a user in a real residential environment, we experimentally validate this potential violation of privacy in data sets that include a wide range of Activities of Daily Living (ADL). Moreover, this letter proposes a privacy enhancement algorithm that mitigates this leakage, by introducing artificial randomness in the wireless channel when the user is inactive.…”

Section: Introductionmentioning

confidence: 94%

“…The radio of SPW-1 offers 7 transmission power levels from −20 dBm to +4 dBm with a 4 dB step (i.e. P max = 5 dBm, P step = 4 dB) [14]. Focusing on the first data session, Fig.…”

Section: Algorithm 1 Privacy Enhancement Algorithmmentioning

confidence: 99%

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Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems

Fafoutis

Marchegiani

Papadopoulos

et al. 2017

IEEE Signal Process. Lett.

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Abstract-With the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this paper, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Lastly, we propose a privacy enhancement algorithm that mitigates the leakage of this private information.

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“…1). The environmental sensors are attached to SPES-2 and SPG-2 nodes [3], the on-body sensors: to SPW-2 wearable nodes [4]. All the nodes are based on TI CC2650 System-on-Chip [5], selected because its flexibility: it supports both IEEE 802.15.4 and Bluetooth Low Energy (BLE) communication stacks.…”

Section: System Overviewmentioning

confidence: 99%