MIST: Mobility-inspired software-defined fog system

Softwarization approaches in networks, storages, M2M, services, and smart things aim to optimize costs and processes and bring new infrastructure definitions and functional values. A recent integration of wireless and mobile cyber physical systems with the dramatically growing smart sensors enables a new type of pervasive smart and mobile urban surveillance infrastructures, which opens up new opportunities for boosting the accuracy, efficiency, and productivity of uninterrupted target tracking and situation awareness. In this paper, we present a design and prototype of a mobility-inspired efficient and effective fog system using software-defined control over a mobile and wireless environment (MIST). Fog Computing, a recently proposed extension and complement for cloud computing, enables computing at the network edge in a smart device without outsourcing jobs to a remote cloud. We investigated an effective softwarization approach in the Fog environment for dynamic big data driven, real-time urban surveillance tasks of uninterrupted target tracking. We address key technical challenges of node mobility to improve the system awareness. We have built a preliminary proof-of-concept MIST architecture on both Android and Linux based smart devices and tested various collaboration scenarios among the mobile sensors.

[1]  Luis Javier García Villalba,et al.  Advances on Software Defined Sensor, Mobile, and Fixed Networks , 2016, Int. J. Distributed Sens. Networks.

[2]  Song Guo,et al.  Evolution of Software-Defined Sensor Networks , 2013, 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks.

[3]  Syed Faraz Hasan,et al.  A discussion on software-defined handovers in Hierarchical MIPv6 networks , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).

[4]  Stefano Basagni,et al.  Mobile Ad Hoc Networking , 2010 .

[5]  Gregory M. P. O'Hare,et al.  Radio Sleep Mode Optimization in Wireless Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[6]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[7]  Patrick Doherty,et al.  Use of Geo-referenced Images with Unmanned Aerial Systems , 2008 .

[8]  Anja Feldmann,et al.  Logically centralized?: state distribution trade-offs in software defined networks , 2012, HotSDN '12.

[9]  Zonghua Zhang,et al.  Enabling security functions with SDN: A feasibility study , 2015, Comput. Networks.

[10]  Vincent Lenders,et al.  A software-defined sensor architecture for large-scale wideband spectrum monitoring , 2015, IPSN.

[11]  Jian Liu,et al.  A dddams-based UAV and UGV team formation approach for surveillance and crowd control , 2014, Proceedings of the Winter Simulation Conference 2014.

[12]  Milind Dawande,et al.  Energy efficient schemes for wireless sensor networks with multiple mobile base stations , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[13]  Dan Romascanu,et al.  Auto-attach using LLDP with IEEE 802.1aq SPBM networks , 2016 .

[14]  Qun Li,et al.  Fog Computing: Platform and Applications , 2015, 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb).

[15]  Ivan Stojmenovic,et al.  Mobile Ad Hoc Networking: Basagni/Ad Hoc Networking , 2004 .

[16]  Andreas Timm-Giel,et al.  Mobile Networks and Management: 7th International Conference, MONAMI 2015, Santander, Spain, September 16-18, 2015, Revised Selected Papers , 2016 .

[17]  Ming Liu,et al.  Software defined networking for distributed mobility management , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[18]  Mourad Debbabi,et al.  A Survey and a Layered Taxonomy of Software-Defined Networking , 2014, IEEE Communications Surveys & Tutorials.

[19]  F.C. Jabour,et al.  Mobility Support for Wireless Sensor Networks , 2008, 2008 International Conference on Computer and Electrical Engineering.

[20]  Luis M. Contreras,et al.  Software-Defined Mobility Management: Architecture Proposal and Future Directions , 2016, Mob. Networks Appl..

[21]  Vivek Ashok Bohara,et al.  Measurement results for cooperative device-to-device communication in cellular networks , 2016 .

[22]  Vincent Lenders,et al.  A low-cost sensor platform for large-scale wideband spectrum monitoring , 2015, IPSN.

[23]  R G Driggers,et al.  Sensor performance conversions for infrared target acquisition and intelligence-surveillance-reconnaissance imaging sensors. , 1999, Applied optics.