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A development approach for collective opportunistic Edge-of-Things services

Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Wilma Russo, Claudio Savaglio, Mirko Viroli
Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains. The requirements of such IoT applications, however, pose significant non/functional challenges to engineering efforts, mitigated by emerging computing paradigms. On the infrastructure side, Cloud, Fog, and Edge Computing provide virtualised, on-demand, elastic resource provisioning – at the distant data centres, Network core and Edge – supporting the abstraction and scalability needs of IoT settings while also altogether giving options for QoS-driven trade-offs. However, despite intense research in these fields, there is still a gap of approaches supporting the engineering of dynamic, heterogeneous smart environments, such as those involving “collectives” of devices coordinating in a complex fashion to provide “global” services. In this paper, we integrate the Aggregate Computing and Opportunistic IoT Service models and propose a full-fledged approach for the engineering – from analysis to simulation – of complex “Edge of Things” applications. We compare by simulation two deployment targets for the same collective application: one centralised/Cloud-based, and the other decentralised/Edge-based. We discuss the trade-offs each one introduces, and we draw recommendations on application-driven choices of the appropriate deployment.
Keywords: Internet of Things, Edge computing, Smart city, Opportunistic services, Aggregate computing
Information Sciences 498, pp. 154--169, 2019, Elsevier
@article{CFPRSV-INFSCI2019,
title = {A development approach for collective opportunistic {Edge-of-Things} services},
journal = {Information Sciences},
publisher = {Elsevier},
volume = {498},
pages = {154--169},
year = {2019},
issn = {0020-0255},
doi = {10.1016/j.ins.2019.05.058},
url = {http://www.sciencedirect.com/science/article/pii/S002002551930461X},
author = {Casadei, Roberto  and Fortino, Giancarlo  and Pianini, Danilo  and Russo, Wilma  and Savaglio, Claudio  and Viroli, Mirko},
keywords = {Internet of Things, Edge computing, Smart city, Opportunistic services, Aggregate computing},
abstract = {Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains. The requirements of such IoT applications, however, pose significant non/functional challenges to engineering efforts, mitigated by emerging computing paradigms. On the infrastructure side, Cloud, Fog, and Edge Computing provide virtualised, on-demand, elastic resource provisioning – at the distant data centres, Network core and Edge – supporting the abstraction and scalability needs of IoT settings while also altogether giving options for QoS-driven trade-offs. However, despite intense research in these fields, there is still a gap of approaches supporting the engineering of dynamic, heterogeneous smart environments, such as those involving “collectives” of devices coordinating in a complex fashion to provide “global” services. In this paper, we integrate the Aggregate Computing and Opportunistic IoT Service models and propose a full-fledged approach for the engineering – from analysis to simulation – of complex “Edge of Things” applications. We compare by simulation two deployment targets for the same collective application: one centralised/Cloud-based, and the other decentralised/Edge-based. We discuss the trade-offs each one introduces, and we draw recommendations on application-driven choices of the appropriate deployment.}
}