Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15642
Title: Optimised cloud-based 6LoWPAN network using SDN/NFV concepts for energy-aware IoT applications
Authors: Al-Kaseem, Bilal R.
Advisors: Al-Raweshidy, H
Banitsas, K
Keywords: IPv6 over Low power Wireless Personal Area Networks (6LoWPAN);Internet of Things (IoT);Software-Defined Networking (SDN);Network Function Virtualisation (NFV);Cloud computing
Issue Date: 2017
Publisher: Brunel University London
Abstract: The Internet of Things (IoT) concept has been realised with the advent of Machineto-Machine (M2M) communication through which the vision of future Internet has been revolutionised. IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) provides feasible IPv6 connectivity to previously isolated environments, e.g. wireless M2M sensors and actuator networks. This thesis’s contributions include a novel mathematical model, energy-efficient algorithms, and a centralised software controller for dynamic consolidation of programmability features in cloud-based M2M networks. A new generalised joint mathematical model has been proposed for performance analysis of the 6LoWPAN MAC and PHY layers. The proposed model differs from existing analytical models as it precisely adopts the 6LoWPAN specifications introduced by the Internet Engineering Task Force (IETF) working group. The proposed approach is based on Markov chain modelling and validated through Monte-Carlo simulation. In addition, an intelligent mechanism has been proposed for optimal 6LoWPAN MAC layer parameters set selection. The proposed mechanism depends on Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particles Swarm Optimisation (PSO). Simulation results show that utilising the optimal MAC parameters improve the 6LoWPAN network throughput by 52-63% and reduce end-to-end delay by 54-65%. This thesis focuses on energy-efficient data extraction and dissemination in a wireless M2M sensor network based on 6LoWPAN. A new scalable and self-organised clustering technique with a smart sleep scheduler has been proposed for prolonging M2M network’s lifetime and enhancing network connectivity. These solutions succeed in overcoming performance degradation and unbalanced energy consumption problems in homogeneous and heterogeneous sensor networks. Simulation results show that by adopting the proposed schemes in multiple mobile sink sensory field will improve the total aggregated packets by 38-167% and extend network lifetime by 30-78%. Proof-of-concept real-time hardware testbed experiments are used to verify the effectiveness of Software-Defined Networking (SDN), Network Function Virtualisation (NFV) and cloud computing on a 6LoWPAN network. The implemented testbed is based on open standards development boards (i.e. Arduino), with one sink, which is the M2M 6LoWPAN gateway, where the network coordinator and the customised SDN controller operated. Experimental results indicate that the proposed approach reduces network discovery time by 60% and extends the node lifetime by 65% in comparison with the traditional 6LoWPAN network. Finally, the thesis is concluded with an overall picture of the research conducted and some suggestions for future work.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London
URI: http://bura.brunel.ac.uk/handle/2438/15642
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

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