Two-level mapping to mitigate congestion in machine to machine (M2M) cloud

This work focuses on mitigation of the network congestion in a Machine to Machine (M2M) cloud. Since the inception of M2M cloud communication technology, the number of M2M devices has radically increased. This has consequently increased the overall traffic of the network which, in turn, makes the entire network congestion prone. The work assumes an underlying clustered network topology of the M2M devices. We propose a hierarchical congestion control scheme in which we distinctly manage the network traffic by a two-level mapping - i) from the cluster heads (CHs) to the sink nodes and ii) from the sink nodes to the cloud gateways. To ensure "fairness", the mapping is based on the Theory of Social Choice. Results are demonstrated at the end which provide fair allocation of CHs to sink nodes and sink nodes to cloud gateways.

[1]  Henri Prade,et al.  Decision-Making Process: Concepts and Methods , 2009 .

[2]  Choong Seon Hong,et al.  Congestion Control Protocol for Wireless Sensor Networks Handling Prioritized Heterogeneous Traffic , 2008, MobiQuitous.

[3]  Awais Ahmad,et al.  Mobility Aware Energy Efficient Congestion Control in Mobile Wireless Sensor Network , 2014, Int. J. Distributed Sens. Networks.

[4]  Didier Dubois,et al.  Decision-making Process , 2009 .

[5]  Shin-ichi Kuribayashi,et al.  Congestion control method with fair resource allocation for cloud computing environments , 2011, Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.

[6]  Joel J. P. C. Rodrigues,et al.  Mapping of sensor nodes with servers in a mobile Health-Cloud environment , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[7]  Mohammad S. Obaidat,et al.  QoS-Guaranteed Bandwidth Shifting and Redistribution in Mobile Cloud Environment , 2014, IEEE Transactions on Cloud Computing.

[8]  Fabio Ricciato,et al.  A Novel Approach to Web of Things: M2M and Enhanced Javascript Technologies , 2012, 2012 IEEE International Conference on Green Computing and Communications.

[9]  Yuhua Liu,et al.  A Clustering Algorithm Based on Communication Facility in WSN , 2009, 2009 WRI International Conference on Communications and Mobile Computing.

[10]  Sudip Misra,et al.  Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization , 2014, Inf. Sci..

[11]  Robert Birke,et al.  Delay-Based Cloud Congestion Control , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[12]  Sudip Misra,et al.  Target Tracking Using Sensor-Cloud: Sensor-Target Mapping in Presence of Overlapping Coverage , 2014, IEEE Communications Letters.

[13]  V. Kalogeraki,et al.  Cluster-based congestion control for supporting multiple classes of traffic in sensor networks , 2005, The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II..

[14]  D. De,et al.  Cluster Based Energy Efficient Lifetime Improvement Mechanism for WSN with Multiple Mobile Sink and Single Static Sink , 2012, 2012 Third International Conference on Computer and Communication Technology.