System modelling and performance evaluation of a three-tier Cloud of Things

Abstract The emergent paradigm of fog computing advocates that the computational resources can be extended to the edge of the network, so that the transmission latency and bandwidth burden caused by cloud computing can be effectively reduced. Moreover, fog computing can support and facilitate some kinds of applications that do not cope well with some features of cloud computing, for instance, applications that require low and predictable latency, and geographically distributed applications. However, fog computing is not a substitute but instead a powerful complement to the cloud computing. This paper focuses on studying the interplay and cooperation between the edge (fog) and the core (cloud) in the context of the Internet of Things (IoT). We first propose a three-tier system architecture and mathematically characterize each tier in terms of energy consumption and latency. After that, simulations are performed to evaluate the system performance with and without the fog involvement. The simulation results show that the three-tier system outperforms the two-tier system in terms of the assessed metrics.

[1]  H. T. Mouftah,et al.  Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[2]  Laurence T. Yang,et al.  A holistic energy optimization framework for cloud-assisted mobile computing , 2015, IEEE Wireless Communications.

[3]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[4]  Yue-Shan Chang,et al.  Mobile cloud-based depression diagnosis using an ontology and a Bayesian network , 2015, Future Gener. Comput. Syst..

[5]  Rongxing Lu,et al.  Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing , 2015, 2015 IEEE International Conference on Communications (ICC).

[6]  Antonio Puliafito,et al.  Enabling the Cloud of Things , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[7]  Adam Dunkels,et al.  Contiki - a lightweight and flexible operating system for tiny networked sensors , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[8]  Mohammad S. Obaidat,et al.  On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network , 2017, IEEE Systems Journal.

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

[10]  Yasir Saleem,et al.  Resource Management in Mobile Sink Based Wireless Sensor Networks through Cloud Computing , 2014 .

[11]  Eui-nam Huh,et al.  Fog Computing and Smart Gateway Based Communication for Cloud of Things , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[12]  B. B. P. Rao,et al.  Cloud computing for Internet of Things & sensing based applications , 2012, 2012 Sixth International Conference on Sensing Technology (ICST).

[13]  Cody Bunch,et al.  OpenStack Cloud Computing Cookbook , 2012 .

[14]  Ejaz Ahmed,et al.  A survey on mobile edge computing , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[15]  Seng Wai Loke,et al.  Supporting ubiquitous sensor-cloudlets and context-cloudlets: Programming compositions of context-aware systems for mobile users , 2012, Future Gener. Comput. Syst..

[16]  Graeme M. Bragg,et al.  868MHz 6LoWPAN with ContikiMAC for an Internet of Things environmental sensor network , 2016, 2016 SAI Computing Conference (SAI).

[17]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[18]  Mubashir Husain Rehmani,et al.  An efficient trajectory design for mobile sink in a wireless sensor network , 2014, Comput. Electr. Eng..

[19]  Joy Laskar,et al.  60GHz single-chip CMOS digital radios and phased array solutions for gaming and connectivity , 2009, IEEE Journal on Selected Areas in Communications.

[20]  Luís Veiga,et al.  Energy Efficiency Dilemma: P2P-cloud vs. Datacenter , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[21]  Feng Xia,et al.  Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges , 2016, J. Netw. Comput. Appl..

[22]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

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

[24]  Hannu Tenhunen,et al.  Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems , 2015, 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC).

[25]  Madoka Yuriyama,et al.  Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing , 2010, 2010 13th International Conference on Network-Based Information Systems.

[26]  Yi Xie,et al.  An energy-efficient task scheduling for mobile devices based on cloud assistant , 2016, Future Gener. Comput. Syst..

[27]  Arkady B. Zaslavsky,et al.  Sensing as a Service and Big Data , 2013, ArXiv.

[28]  Yasir Faheem,et al.  Cognitive Radio Sensor Networks: Applications, Architectures, and Challenges , 2014 .

[29]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[30]  Roy Want,et al.  International Symposium on Wearable Computing (ISWC) 2008 , 2009, IEEE Pervasive Computing.

[31]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[32]  Rajkumar Buyya,et al.  Seamless application execution in mobile cloud computing: Motivation, taxonomy, and open challenges , 2015, J. Netw. Comput. Appl..

[33]  Ingrid Moerman,et al.  Sensor Function Virtualization to Support Distributed Intelligence in the Internet of Things , 2015, Wirel. Pers. Commun..

[34]  Hai Le Vu,et al.  An estimation of sensor energy consumption , 2009 .

[35]  Ivan Stojmenovic,et al.  Fog computing: A cloud to the ground support for smart things and machine-to-machine networks , 2014, 2014 Australasian Telecommunication Networks and Applications Conference (ATNAC).

[36]  Mubashir Husain Rehmani,et al.  Emerging Communication Technologies Based on Wireless Sensor Networks : Current Research and Future Applications , 2016 .

[37]  Feng Xia,et al.  Application optimization in mobile cloud computing: Motivation, taxonomies, and open challenges , 2015, J. Netw. Comput. Appl..

[38]  Sudip Misra,et al.  Assessment of the Suitability of Fog Computing in the Context of Internet of Things , 2018, IEEE Transactions on Cloud Computing.

[39]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[40]  Teruo Higashino,et al.  Edge-centric Computing: Vision and Challenges , 2015, CCRV.

[41]  Eui-Nam Huh,et al.  Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved , 2014, Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pakistan, 14th - 18th January, 2014.

[42]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[43]  Zhengping Qian,et al.  TimeStream: reliable stream computation in the cloud , 2013, EuroSys '13.