Optimised Green IoT Network Architectures

The work in this thesis proposes a number of energy efficient architectures of IoT networks. These proposed architectures are edge computing, Passive Optical Network (PON) and Peer to Peer (P2P) based architectures. A framework was introduced for virtualising edge computing assisted IoT. Two mixed integer linear programming (MILP) models and heuristics were developed to minimise the power consumption and to maximise the number of served IoT processing tasks. Further consideration was also given to the limited IoT processing capabilities and hence the potential of processing task blockage. Two placement scenarios were studied revealing that the optimal distribution of cloudlets achieved 38% power saving compared to placing the cloudlet in the gateway while gateway placement can save up to 47% of the power compared to the optimal placement but blocked 50% of the total IoT object requests. The thesis also investigated the impact of PON deployment on the energy efficiency of IoT networks. A MILP model and a heuristic were developed to optimally minimise the power consumption of the proposed network. The results of this investigation showed that packing most of the VMs in OLT at a low traffic reduction percentage and placing them in relays at high traffic reduction rate saved power Also, the results revealed that utilising energy efficient PONs and serving heterogeneous VMs can save up to 19% of the total power. Finally, the thesis investigated a peer-to-peer (P2P) based architecture for IoT networks with fairness and incentives. It considered three VM placement scenarios and developed MILP models and heuristics to maximise the number of processing tasks served by VMs and to minimise the total power consumption of the proposed network. The results showed that the highest service rate was achieved by the hybrid scenario which consumes the highest amount of power compared to other scenarios

Optimisation Methodologies for the Design and Planning of Water Systems

This thesis addresses current topics of design and planning of water systems from water treatment units to a country-wide resources management schemes. The methodologies proposed are presented as models and solution approaches using mathematical programming, and mixed integer linear (MILP) and non-linear (MINLP) programming techniques. In Part I of the thesis, a synthesis problem for water treatment processes using superstructure optimisation is studied. An MINLP model is developed for the minimisation of water production cost considering physicochemical properties of water and operating conditions of candidate technologies. Next, new alternative path options are introduced to the superstructure. The resulting MINLP model is then partially linearised (plMINLP) and also presented as a mixed integer linear fractional programming (MILFP) model in order to improve the convergence of the optimisation model. Various linearisation and approximation techniques are developed. As a solution procedure to the fractional model, a variation of the Dinkelbach's algorithm is proposed. The models are tested on theoretical examples with industrial data. In Part II, an optimisation approach formulated as a spatially-explicit multi-period MILP model is proposed for the design of planning of water resources at regional and national scales. The optimisation framework encompasses decisions such as installation of new purification plants, capacity expansion, trading schemes among regions and pricing, and water availability under climate change. The objective is to meet water demand while minimising the total cost associated with developing and operating the water supply chain. Additionally, a fair trade-o between the total cost and reliability of the supply chain is incorporated in the model. The solution method is applied based on game theory using the concept of Nash equilibrium. The methodology is implemented on a case study based on Australian water management systems