Energy-Sustainable Fog System for Mobile Web Services in Infrastructure-Less Environments

Nowadays, critical information services such as emergency management and in-situation navigation rely heavily on the assumption of a reliable networking infrastructure and stable cloud processing, which is ineffective in infrastructure-less environments where disrupted or even no telecommunication connectivity is the norm. On the other hand, fog computing is an extension of cloud computing that is at the physical proximity of end-users to enable local storage, computing, and various forms of communication between devices and users. To this end, we design a fog system spanning hardware, software and networking, thus meeting the requirements of various stakeholders in difficult surroundings. As a complement to a cloud-centred solution, this system is geo-distributed, self-powered, self-managed, location-aware, highly efficient and able to provide situational information services without Internet connectivity. The proposed system has been implemented in national parklands of Australia to achieve a personalized information service, emergency management and in-park navigation for all types of parkland users.

[1]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[2]  V. Agarwal,et al.  A Single-Stage Grid Connected Inverter Topology for Solar PV Systems With Maximum Power Point Tracking , 2007, IEEE Transactions on Power Electronics.

[3]  Jing Xiao,et al.  Optimal Tilt Angle and Orientation of Photovoltaic Modules Using HS Algorithm in Different Climates of China , 2017 .

[4]  Jiong Jin,et al.  Sustainability Analysis for Fog Nodes With Renewable Energy Supplies , 2019, IEEE Internet of Things Journal.

[5]  J. Schirmer,et al.  Wellbeing, resilience and liveability in rural and regional Australia: the 2015 Regional Wellbeing Survey , 2016 .

[6]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[7]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[8]  Jiang Zhu,et al.  Fog Computing: A Platform for Internet of Things and Analytics , 2014, Big Data and Internet of Things.

[9]  S. Calver,et al.  Information Technology in Dorset Tourism Businesses , 2005 .

[10]  V. Michael Bove,et al.  Live objects: A system for infrastructure-less location-based services , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[11]  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..

[12]  Giuseppe Vecchi,et al.  Low Power Computing and Communication System for Critical Environments , 2016, 3PGCIC.

[13]  Teruo Higashino,et al.  ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems , 2019, IEEE Access.

[14]  Mianxiong Dong,et al.  Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing , 2018, IEEE Transactions on Industrial Informatics.

[15]  Gerry Moschopoulos,et al.  Optimal tilt angle determination of photovoltaic panels and comparing of their mathematical model predictions to experimental data in Kerman , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[16]  Jianhua Li,et al.  Fog Computing-Enabled Secure Demand Response for Internet of Energy Against Collusion Attacks Using Consensus and ACE , 2018, IEEE Access.

[17]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[18]  Marimuthu Palaniswami,et al.  An Information Framework for Creating a Smart City Through Internet of Things , 2014, IEEE Internet of Things Journal.

[19]  E. Ortega,et al.  Information at tourism destinations. Importance and cross-cultural differences between international and domestic tourists , 2007 .

[20]  Thomas Magedanz,et al.  Application of the Fog computing paradigm to Smart Factories and cyber‐physical systems , 2018, Trans. Emerg. Telecommun. Technol..

[21]  Akif Karafil,et al.  Calculation of optimum fixed tilt angle of PV panels depending on solar angles and comparison of the results with experimental study conducted in summer in Bilecik, Turkey , 2015, 2015 9th International Conference on Electrical and Electronics Engineering (ELECO).

[22]  Jiong Jin,et al.  Virtual Fog: A Virtualization Enabled Fog Computing Framework for Internet of Things , 2018, IEEE Internet of Things Journal.

[23]  Jianhua Li,et al.  Service Popularity-Based Smart Resources Partitioning for Fog Computing-Enabled Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[24]  D. Srinivasan,et al.  Estimation of solar power generating capacity , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[25]  Wei Liaoliao,et al.  Evaluation of solar irradiance on inclined surfaces models in the short-term photovoltaic power forecasting , 2009 .