Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities

Abstract The digital world is expanding rapidly and advances in networking technologies such as 4G long-term evolution (LTE), wireless broadband (WiBro), low-power wide area networks (LPWAN), 5G, LiFi, and so on, all of which are paving the way for the emergence of sophisticated services. The number of online applications is increasing along with more computation, communication, and intelligent capabilities. Although current devices in use today are also getting more powerful in terms of features and capabilities, but they are still incapable of executing smart, autonomous, and intelligent tasks such as those often required for smart healthcare, ambient assisted living (AAL), virtual reality, augmented reality, intelligent vehicular communication, as well as in many services related to smart cities, Internet of Things (IoT), Tactile Internet, Internet of Vehicles (IoV), and so on. For many of these applications, we need another entity to execute tasks on behalf of the user’s device and return the results - a technique often called offloading, where tasks are outsourced and the involved entities work in tandem to achieve the ultimate goal of the application. Task offloading is attractive for emerging IoT and cloud computing applications. It can occur between IoT nodes, sensors, edge devices, or fog nodes. Offloading can be performed based on different factors that include computational requirements of an application, load balancing, energy management, latency management, and so on. We present a taxonomy of recent offloading schemes that have been proposed for domains such as fog, cloud computing, and IoT. We also discuss the middleware technologies that enable offloading in a cloud-IoT cases and the factors that are important for offloading in a particular scenario. We also present research opportunities concerning offloading in fog and edge computing.

[1]  Hongke Zhang,et al.  Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach , 2017, Comput. Networks.

[2]  Simona Halunga,et al.  Implementation of Fog computing for reliable E-health applications , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[3]  Qi Han,et al.  Virtual Sensor Networks - A Resource Efficient Approach for Concurrent Applications , 2007, Fourth International Conference on Information Technology (ITNG'07).

[4]  Xiaohui Zhao,et al.  An Energy Consumption Oriented Offloading Algorithm for Fog Computing , 2016, QSHINE.

[5]  Khaled A. Harras,et al.  On practical device-to-device wireless communication: A measurement driven study , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[6]  Hsiao-Hwa Chen,et al.  An Integrated Architecture for Software Defined and Virtualized Radio Access Networks with Fog Computing , 2017, IEEE Network.

[7]  Stephen Farrell,et al.  DTN: an architectural retrospective , 2008, IEEE Journal on Selected Areas in Communications.

[8]  Ke Zhang,et al.  Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks , 2016, IEEE Access.

[9]  Dongbin Zhao,et al.  Computational Intelligence in Urban Traffic Signal Control: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Khaled A. Harras,et al.  The Hive: An Edge-based Middleware Solution for Resource Sharing in the Internet of Things , 2017, SmartObjects@MobiCom.

[11]  Winfried Lamersdorf,et al.  Computing at the Mobile Edge: Designing Elastic Android Applications for Computation Offloading , 2015, 2015 8th IFIP Wireless and Mobile Networking Conference (WMNC).

[12]  Khaled A. Harras,et al.  Femto Clouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[13]  Laurent Massoulié,et al.  Greening the internet with nano data centers , 2009, CoNEXT '09.

[14]  Eui-nam Huh,et al.  E-HAMC: Leveraging Fog computing for emergency alert service , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[15]  Mohamed Ibrahim,et al.  Over-The-Air TV Detection Using Mobile Devices , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[16]  Hai Jin,et al.  CLOUDLET: towards mapreduce implementation on virtual machines , 2009, HPDC '09.

[17]  Xavier Masip-Bruin,et al.  Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems , 2016, IEEE Wireless Communications.

[18]  David Zhang,et al.  Moving Vehicle Detection for Automatic Traffic Monitoring , 2007, IEEE Transactions on Vehicular Technology.

[19]  Khaled A. Harras,et al.  Cumulus: A distributed and flexible computing testbed for edge cloud computational offloading , 2016, 2016 Cloudification of the Internet of Things (CIoT).

[20]  Vaskar Raychoudhury,et al.  A survey of routing and data dissemination in Delay Tolerant Networks , 2016, J. Netw. Comput. Appl..

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

[22]  Cheng Huang,et al.  Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges , 2017, IEEE Communications Magazine.

[23]  Khaled A. Harras,et al.  Towards Mobile Opportunistic Computing , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[24]  Sanjay Madria,et al.  Sensor Cloud: A Cloud of Virtual Sensors , 2014, IEEE Software.

[25]  Eui-nam Huh,et al.  Dynamic resource provisioning through Fog micro datacenter , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[26]  Philippe Robert,et al.  Analysis of an Offloading Scheme for Data Centers in the Framework of Fog Computing , 2015, ACM Trans. Model. Perform. Evaluation Comput. Syst..

[27]  Khaled A. Harras,et al.  Adaptive forwarding of mHealth data in challenged networks , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[28]  Khaled A. Harras,et al.  Vision: The Case for Symbiosis in the Internet of Things , 2015, MCS '15.

[29]  Khaled A. Harras,et al.  Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[30]  Khaled A. Harras,et al.  Towards Intelligent Edge Storage Management: Determining and Predicting Mobile File Popularity , 2018, 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).

[31]  Sherali Zeadally,et al.  Internet of Vehicles: Architecture, Protocols, and Security , 2018, IEEE Internet of Things Journal.

[32]  Khaled A. Harras,et al.  UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator , 2015, MobiHoc.

[33]  Rongxing Lu,et al.  From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework , 2017, IEEE Access.

[34]  Imran Khan,et al.  Wireless sensor network virtualization: A survey , 2015, IEEE Communications Surveys & Tutorials.

[35]  Khaled A. Harras,et al.  MagBoard: Magnetic-Based Ubiquitous Homomorphic Off-the-Shelf Keyboard , 2016, 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[36]  Min Sheng,et al.  Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling , 2016, IEEE Transactions on Communications.

[37]  Choong Seon Hong,et al.  An architecture of IPTV service based on PVR-Micro data center and PMIPv6 in cloud computing , 2016, Multimedia Tools and Applications.

[38]  Sherali Zeadally,et al.  Vehicular ad hoc networks (VANETS): status, results, and challenges , 2010, Telecommunication Systems.

[39]  Xu Chen,et al.  D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration , 2016, IEEE Journal on Selected Areas in Communications.

[40]  Khaled A. Harras,et al.  Argus: Realistic Target Coverage by Drones , 2017, 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[41]  Mahmoud Al-Ayyoub,et al.  The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing , 2016, 2016 23rd International Conference on Telecommunications (ICT).

[42]  Khaled A. Harras,et al.  WiGest: A ubiquitous WiFi-based gesture recognition system , 2014, 2015 IEEE Conference on Computer Communications (INFOCOM).

[43]  Max Mühlhäuser,et al.  Decision Support for Computational Offloading by Probing Unknown Services , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[44]  D.S. Hedin,et al.  Smartphone based face recognition tool for the blind , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[45]  Ragib Hasan,et al.  Aura: An incentive-driven ad-hoc IoT cloud framework for proximal mobile computation offloading , 2017, Future Gener. Comput. Syst..

[46]  Christine Julien,et al.  Virtual sensors: abstracting data from physical sensors , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).