Wearable Computing by Giancarlo Fortino Raffaele Gravina Stefano Galzarano & Raffaele Gravina & Stefano Galzarano

Wearable Computing by Giancarlo Fortino Raffaele Gravina Stefano Galzarano & Raffaele Gravina & Stefano Galzarano

Author:Giancarlo Fortino,Raffaele Gravina,Stefano Galzarano & Raffaele Gravina & Stefano Galzarano
Language: eng
Format: epub
ISBN: 9781119078838
Publisher: John Wiley & Sons, Inc.
Published: 2018-05-08T00:00:00+00:00


Such a three‐layer architecture lacks fundamental capabilities to support inter‐BSN communication and collaborative, distributed processing functionality, which are needed to successfully support the Multiple Bodies–Multiple BS configuration.

Hence, the novel reference architecture for CBSNs proposed in this chapter has been purposely conceived to fully adhere to all the possible BSN configurations. Such a general architecture has been later exploited as a guideline for implementing a supporting framework aimed at facilitating the development of collaborative BSN applications. In particular, the need for a CBSN infrastructure can be better motivated by the fact that it is capable of easily enabling new services allowing single BSNs to interact with each other (not addressed in the other BSN configurations):

Client/Server services: a pair of BSNs can interact in a standard client/server communication paradigm, where a server BSN (e.g. the monitored individual) provides services to let the client BSN issue (i) a continuous monitoring request or (ii) a single data request. In the former, the server BSN continuously pushes information to the client, whereas the latter works as a more typical single‐reply‐upon‐request model.

Broadcast services: BSNs can broadcast (push) information without being queried about (i) the individuals’ worn sensors or (ii) alarm/events triggered by the individuals’ conditions (e.g. a critical status like a fall or a heartbreak).

Collaborative services: aimed at performing specific tasks upon direct interactions between BSNs and based on a peer‐to‐peer model to exchange information. They usually detect and recognize group activities and relevant events based on the implicit or explicit multiuser interactions.



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