Towards Transitions between Role Assignment Schemes: Enabling Adaptive Offloading in Challenged Networks

The ever increasing number of mobile devices and their heterogeneity requires an efficient utilization of the cellular communication infrastructure. To this end, different offloading approaches have been proposed in the literature. All approaches rely on schemes that assign roles to mobile users, with the respective assignment procedure being realized either centrally or distributed. However, current role assignment schemes are limited to a specific utility function by design (e.g., minimizing the energy consumption), and are unable to adapt to dynamic network conditions and target utilities. This substantially limits the applicability of offloading approaches in dynamic and challenged networks. In this paper, we propose the execution of transitions between role assignment schemes to adapt offloading approaches to challenged networks. We propose and discuss a framework that enables the integration of centralized and decentralized role assignment schemes and the execution of transitions between the respective schemes. Based on an initial evaluation of the coordination aspects of our framework, we identify future research directions and challenges towards the adaptive utilization of offloading schemes in challenged networks.

[1]  Charalampos Konstantopoulos,et al.  Clustering in mobile ad hoc networks through neighborhood stability-based mobility prediction , 2008, Comput. Networks.

[2]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[3]  Ralf Steinmetz,et al.  The human factor: A simulation environment for networked mobile social applications , 2017, 2017 International Conference on Networked Systems (NetSys).

[4]  Sajal K. Das,et al.  WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks , 2002, Cluster Computing.

[5]  Marco Conti,et al.  Data Offloading Techniques in Cellular Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[6]  Injong Rhee,et al.  Mobile data offloading: how much can WiFi deliver? , 2013, TNET.

[7]  Sally Floyd,et al.  ns-3 project goals , 2006 .

[8]  Pascal Lorenz,et al.  Connectivity, Energy and Mobility Driven Clustering Algorithm for Mobile Ad Hoc Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[9]  Shobha Venkataraman,et al.  A first look at cellular network performance during crowded events , 2013, SIGMETRICS '13.

[10]  Ralf Steinmetz,et al.  Buddies, not enemies: Fairness and performance in cellular offloading , 2016, 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[11]  Kyunghan Lee,et al.  Mobile Data Offloading: How Much Can WiFi Deliver? , 2013, IEEE/ACM Transactions on Networking.

[12]  R. Biswas,et al.  ALEACH: Advanced LEACH routing protocol for wireless microsensor networks , 2008, 2008 International Conference on Electrical and Computer Engineering.

[13]  S. Yousef,et al.  A flexible weighted clustering algorithm based on battery power for Mobile Ad hoc Networks , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[14]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[15]  QingLi,et al.  Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks , 2006 .

[16]  Ralf Steinmetz,et al.  Limiting the Footprint of Monitoring in Dynamic Scenarios through Multi-Dimensional Offloading , 2016, 2016 25th International Conference on Computer Communication and Networks (ICCCN).

[17]  Aravind Srinivasan,et al.  Mobile data offloading in metropolitan area networks , 2010, MOCO.

[18]  Santanu Kumar Rath,et al.  A Survey on One-Hop Clustering Algorithms in Mobile Ad Hoc Networks , 2009, Journal of Network and Systems Management.

[19]  P.H.J. Chong,et al.  A survey of clustering schemes for mobile ad hoc networks , 2005, IEEE Communications Surveys & Tutorials.

[20]  Justin Manweiler,et al.  Predicting length of stay at WiFi hotspots , 2013, 2013 Proceedings IEEE INFOCOM.

[21]  Li Qing,et al.  Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks , 2006, Comput. Commun..

[22]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[23]  Arun Venkataramani,et al.  Augmenting mobile 3G using WiFi , 2010, MobiSys '10.

[24]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[25]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[26]  Alan Page Fiske,et al.  Structures of social life : the four elementary forms of human relations : communal sharing, authority ranking, equality matching, market pricing : with a new epilogue , 1991 .