A software-defined networking framework for IoT
AffiliationElectrical and Electronic Engineering
Document TypePhD thesis
Access StatusThis item is embargoed and will be available on 2022-09-28. This item is currently available to University of Melbourne staff and students only, login required.
© 2020 Fabian Fernando Jurado Lasso
In recent years, we have witnessed a shift from traditional internet networks interconnecting computers based on well-established standards, towards a pervasive network of networks that provides internet connectivity even to the smallest physical objects. This Internet of Things (IoT) network is an enabling technology to the next industrial revolution (aka Industry 4.0) where the operational technology meets the information technology or computer-based world. The creation of new IoT applications across special context such as smart cities, smart homes, smart agriculture, etc., are realised upon sensors and actuators. The networking of sensors and actuators has extended the scope of networked sensing technologies such as Wireless Sensor Networks (WSNs). However, the networking of wireless sensor devices, or sensor nodes, imposes several challenges due to their inherent resource limitations such as computational capabilities, energy, memory, and communication bandwidth. The management of the limited resources of WSNs becomes challenging and its complexity increases as the network size grows. Thus, the current state of WSNs would not be able to meet the IoT requirements unless appropriate solutions to the aforementioned challenges are found. The focus of this thesis is to investigate the challenges and benefits of Software- Defined Wireless Sensor Networks (SDWSNs) as a solution to flexible resource management and reconfiguration of WSNs. In short, the contributions of this thesis are as follows. (i) the feasibility and practicability, of SDWSNs, to perform network and resource management was demonstrated. This research work shows the ease of managing: the network topology and the transmission power of sensor nodes, using a centralized controller without any firmware modification. (ii) The previous research work is extended to an SDN-based management system for IP sensor networks and compare it with the Routing Protocol for Low-Power and Lossy Networks (RPL) to show the advantages of removing energy- and processing-intensive functions from sensor nodes. This contribution also presents, for the first time, the control overhead metric of an SDWSN, and compare it against a WSN running RPL. (iii) Next, the effects in network performance when making the WSN reprogrammable were examined by, proposing a model-based characterisation of energy consumption to calculate the energy consumed and control overhead introduced for small, large and ‘pseudo-dynamic’ SDWSNs. (iv) Last, the benefit of SDWSNs to augment the network lifetime, whilst keeping the control overhead low, was demonstrated by, proposing an energy-aware routing protocol for software-defined multihop wireless sensor networks, that seeks to prolong the overall network lifetime of the sensor network while also maintaining a high packet delivery ratio. Extensive simulation and experimental results were carried out, to validate the benefits and impacts in network performance, for all aforesaid research works. This thesis also puts forth SDWSN as a potential pathway to overcome the rigidity in management that currently exists in WSNs.
KeywordsInternet of Things; Wireless Sensor Networks; Software-Defined Networking; Software-Defined Wireless Sensor Networks; IoT; WSNs; SDN; SDWSNs; Resource management; Centralised routing; Energy-efficient; Energy-efficient routing
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