Exploring Analytical Models for Proactive Resource Management in Highly Mobile Environments

In order to provide ubiquitous communication, seamless connectivity is now required in all environments including highly mobile networks. By using vertical handover techniques it is possible to provide uninterrupted communication as connections are dynamically switched between wireless networks as users move around. However, in a highly mobile environment, traditional reactive approaches to handover are inadequate. Therefore, proactive handover techniques, in which mobile nodes attempt to determine the best time and place to handover to local networks, are actively being investigated in the context of next-generation mobile networks. Using this approach, it is possible to enhance channel allocation and resource management by using probabilistic mechanisms; because, it is possible to explicitly detect contention for resources. This paper presents a proactive approach for resource allocation in highly mobile networks and analyzed the user contention for common resources such as radio channels in highly mobile wireless networks. The proposed approach uses an analytical modelling approach to model the contention and results are obtained showing enhanced system performance. Based on these results an operational space has been explored and are shown to be useful for emerging future networks such as 5G by allowing base stations to calculate the probability of contention based on the demand for network resources. This study indicates that the proactive model enhances handover and resource allocation for highly mobile networks. This paper analyzed the effects of and alpha and beta, in effect, how these parameters affect the proactive resource allocation requests in the contention queue has been modelled for any given scenario from the conference paper "Exploring analytical models to maintain quality-of-service for resource management using a proactive approach in highly mobile environments".

[1]  Apollinaire Nadembega,et al.  A Destination and Mobility Path Prediction Scheme for Mobile Networks , 2015, IEEE Transactions on Vehicular Technology.

[2]  Qian Huang,et al.  Loss Performance Modeling for Hierarchical Heterogeneous Wireless Networks With Speed-Sensitive Call Admission Control , 2011, IEEE Transactions on Vehicular Technology.

[3]  Navrati Saxena,et al.  Experimental framework of proactive handover with QoS over WLANs , 2009 .

[4]  Jordi Vilaplana,et al.  A queuing theory model for cloud computing , 2014, The Journal of Supercomputing.

[5]  Thilo Sauter,et al.  Seamless handover in industrial WLAN using IEEE 802.11k , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[6]  Antonio F. Gómez-Skarmeta,et al.  Towards seamless inter-technology handovers in vehicular IPv6 communications , 2017, Comput. Stand. Interfaces.

[7]  Minming Ni,et al.  Seamless handover for high mobility environments , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[8]  Lotfi Kamoun,et al.  Multi-Attribute Decision Making Handover Algorithm for Wireless Body Area Networks , 2016, Comput. Commun..

[9]  F. Shaikh,et al.  Proactive Policy Management using TBVH Mechanism in Heterogeneous Networks , 2007, The 2007 International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST 2007).

[10]  Dharma P. Agrawal,et al.  Modeling of handoffs and performance analysis of wireless data networks , 2001, Proceedings International Conference on Parallel Processing Workshops.

[11]  Hua Zhou,et al.  Proactive unnecessary handover avoidance scheme in LTE-A small cells , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Lei Ding,et al.  A location aware based handoff algorithm in V2I system of railway environment , 2014, 2014 Global Information Infrastructure and Networking Symposium (GIIS).

[13]  Mahdi Aiash,et al.  Exploiting Location and Contextual Information to Develop a Comprehensive Framework for Proactive Handover in Heterogeneous Environments , 2012, J. Comput. Networks Commun..

[14]  Yonal Kirsal,et al.  Analytical Modelling of a New Handover Algorithm to Improve Allocation of Resources in Highly Mobile Environments , 2016, Int. J. Comput. Commun. Control.

[15]  Kishor S. Trivedi,et al.  Analytic modeling of handoffs in wireless cellular networks , 2002, Inf. Sci..

[16]  Shih Jung Wu An intelligent handover decision mechanism for heterogeneous wireless networks , 2010, The 6th International Conference on Networked Computing and Advanced Information Management.

[17]  Frank Stajano,et al.  Autonomic system for mobility support in 4G networks , 2005, IEEE Journal on Selected Areas in Communications.

[18]  Huan Xuan Nguyen,et al.  Exploring a New Proactive Algorithm for Resource Management and Its Application to Wireless Mobile Environments , 2017, 2017 IEEE 42nd Conference on Local Computer Networks (LCN).

[19]  Tarek Bejaoui QoS-Oriented High Dynamic Resource Allocation in Vehicular Communication Networks , 2014, TheScientificWorldJournal.

[20]  Li Zhang,et al.  A Trade-off Between Unnecessary Handover and Handover Failure for Heterogeneous Networks , 2017 .

[21]  Arindam Ghosh,et al.  Building a Prototype VANET Testbed to Explore Communication Dynamics in Highly Mobile Environments , 2016, TRIDENTCOM.

[22]  Azzedine Boukerche,et al.  Design of a Fast Location-Based Handoff Scheme for IEEE 802.11 Vehicular Networks , 2014, IEEE Transactions on Vehicular Technology.

[23]  Jon Crowcroft,et al.  AN ARCHITECTURAL FRAMEWORK FOR HETEROGENEOUS NETWORKING , 2018 .

[24]  Yonal Kirsal,et al.  Exploring analytical models to maintain quality-of-service for resource management using a proactive approach in highly mobile environments , 2018, 2018 7th International Conference on Computers Communications and Control (ICCCC).

[25]  Lazaros F. Merakos,et al.  Handover decision for small cells: Algorithms, lessons learned and simulation study , 2016, Comput. Networks.