Evaluating a TDMA MAC for body area networks using a space-time dependent channel model

Maman, Mickael; Dehmas, François; D’Errico, Raffaele; Ouvry, Laurent

doi:10.1109/pimrc.2009.5449852

Cited by 46 publications

(29 citation statements)

References 8 publications

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“…Since these wireless networks are implanted or body worn, they require specific investigations and optimizations to limit radiated power (and consequently electromagnetic field absorption by the user), consumed power, device size and interferences with coexistent wireless networks. In particular, detailed knowledge of the BAN propagation channel is required to analyze and design properly systems at the PHY, MAC and Networking levels [1].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Model for On-Body Dynamic Channels

D’Errico

Ouvry

2010

Int J Wireless Inf Networks

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In this paper we present a dynamic on-body channel model based on a time-variant measurement campaign at 2.45 GHz and in the 3-5 GHz band. Three different human body movements for seven human subjects were considered to assess the influence of human activity on the channel behavior, both in an anechoic chamber and an indoor scenario. Two Body Area Network (BAN) star topologies have been addressed. An analysis on mean channel gain, slow fading and shadowing correlation is presented with emphasis on the differences given by the human body variability and the movement condition.

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Section: Introductionmentioning

confidence: 99%

A Statistical Model for On-Body Dynamic Channels

D’Errico

Ouvry

2010

Int J Wireless Inf Networks

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“…An overview of required transmission distances and latency requirements reported in the overview papers [1] and [2] are given in table 2.4. From the given literature it can be concluded that a typical WBAN network consists of 10 nodes and the transmission distance is less than 10m.…”

Section: Applicationsmentioning

confidence: 99%

“…This is graphically depicted in figure 1.1. While the required bit rate of the different sensors varies from a few kilobits per second up to a few hundred kilobits per second, most applications require a bit rate around 100kbps, see [1] and [2]. Additionally, the average packet rate of a sensor node is very low.…”

Section: Introductionmentioning

confidence: 99%

Wake-up Receiver Based Ultra-Low-Power WBAN

Lont¹

2014

Analog Circuits and Signal Processing

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“…In our evaluation framework, the overall body mobility in its environment is based on a mixed model, like in [10]. First of all, a macro-mobility Reference Point Group Mobility Model (RPGM) accounts for the body barycenter mobility, where the dynamic reference point as a function of time is chosen as a Random Gauss-Markov process [1].…”

Section: A Simulation Scenario and Parametersmentioning

confidence: 99%

Constrained decentralized algorithm for the relative localization of wearable wireless sensor nodes

2012

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Abstract-Motion capture might become one key feature of future Wireless Body Sensor Networks (WBSN), allowing new applications such as home activity monitoring, nomadic postural rehabilitation or sportive gesture recording through standard on-body communications. In this context we present herein a stand-alone solution that enables the localization of wearable wireless nodes relatively to a body-strapped Local Coordinate System (LCS). In particular, we consider adapting a Distributed Weighted Multi-Dimensional Scaling (DWMDS) algorithm fed by cooperative inter-node range measurements obtained through e.g., Time Of Arrival (TOA) estimation, where estimated nodes' locations are asynchronously updated based on their neighborhood information. Exploiting further the presence of constantlength radio links, a Constrained solution (CDWMDS) is thus proposed to improve localization accuracy, while reducing traffic and power consumption. Another novelty lies in the initialization step, which somehow benefits from the space-time correlation of nodes' locations under body mobility. Relying on a realistic biomechanical model, we provide preliminary simulation results to illustrate the relative gains observed in comparison with a nominal algorithm setting.

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Evaluating a TDMA MAC for body area networks using a space-time dependent channel model

Cited by 46 publications

References 8 publications

A Statistical Model for On-Body Dynamic Channels

A Statistical Model for On-Body Dynamic Channels

Wake-up Receiver Based Ultra-Low-Power WBAN

Constrained decentralized algorithm for the relative localization of wearable wireless sensor nodes

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