A methodology for deployment of IoT application in fog

The foreseen increase of IoT connected to the Internet is worrying the ICT community because of its impact on network Infrastructure when the number of requesters become larger and larger. Moreover also reliability of network connection and real-time constraints can affect the effectiveness of the Cloud Computing paradigm for developing IoT solutions. The necessity of an intermediate layer in the whole IoT architecture that works as a middle ground between the local physical memories and Cloud is proposed by the Fog paradigm. In this paper we define and use a methodology that supports the developer to address the Fog Service Placement Problem, which consists of finding the optimal mapping between IoT applications and computational resources. We exploited and extended a Fog Application model from the related work to apply the proposed methodology in order to investigate the optimal deployment of IoT application. The case study is an IoT application in the Smart Energy domain. In particular, we extended a software platform, which was developed, and released open source by the CoSSMic European project, with advanced functionalities. The new functionalities provide capabilities for automatic learning of energy profiles and lighten the platform utilization by users, but they introduce new requirements, also in terms of computational resources. Experimental results are presented to demonstrate the usage and the effectiveness of the proposed methodology at deployment stage.

[1]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[2]  Quang Tran Minh,et al.  Toward service placement on Fog computing landscape , 2017, 2017 4th NAFOSTED Conference on Information and Computer Science.

[3]  Philipp Leitner,et al.  Optimized IoT service placement in the fog , 2017, Service Oriented Computing and Applications.

[4]  Salvatore Venticinque,et al.  A distributed agent-based system for coordinating smart solar-powered microgrids , 2016, 2016 SAI Computing Conference (SAI).

[5]  Nobuo Funabiki,et al.  Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system , 2017, Int. J. Space Based Situated Comput..

[6]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[7]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[8]  Ling Chen,et al.  Cooperation forwarding data gathering strategy of wireless sensor networks , 2017, Int. J. Grid Util. Comput..

[9]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[10]  Bin Cheng,et al.  Real-time data reduction at the network edge of Internet-of-Things systems , 2015, 2015 11th International Conference on Network and Service Management (CNSM).

[11]  Salvatore Venticinque,et al.  Software Agents for Collaborating Smart Solar-Powered Micro-Grids , 2014 .

[12]  Rajkumar Buyya,et al.  Fog Computing: Principles, Architectures, and Applications , 2016, ArXiv.

[13]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[14]  Roberto Nardone,et al.  Cost-energy modelling and profiling of smart domestic grids , 2016 .

[15]  Mingzhe Jiang,et al.  Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[16]  Athanasios V. Vasilakos,et al.  Fog Computing for Sustainable Smart Cities , 2017, ArXiv.

[17]  Salvatore Venticinque,et al.  A Distributed System for Smart Energy Negotiation , 2014, IDCS.