Assignment of sensing tasks to IoT devices: Exploitation of a Social Network of Objects

Luigi Atzori;Roberto Girau;Virginia Pilloni;Marco Uras
2019-01-01

Abstract

The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to implement the following processes: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoT-based Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches.
2019
2018
Inglese
6
2
8478361
2679
2692
14
http://ieeexplore.ieee.org/servlet/opac?punumber=6488907
https://ieeexplore.ieee.org/document/8478361
Esperti anonimi
internazionale
scientifica
Mobile Crowd Sensing; resource allocation; Social Internet of Things; Signal Processing; Information Systems; Hardware and Architecture; Computer Science Applications1707 Computer Vision and Pattern Recognition; Computer Networks and Communications
no
Atzori, Luigi; Girau, Roberto; Pilloni, Virginia; Uras, Marco
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
4
open
File in questo prodotto:
File Dimensione Formato  
main.pdf

Open Access dal 02/10/2019

Tipologia: versione post-print
Dimensione 4.32 MB
Formato Adobe PDF
4.32 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Questionario e social

Condividi su:
Impostazioni cookie