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Resource Allocation in Software Defined Internet of Things InfrastructureAuthor: Rhishi Pratap Singh Date: 2018-06-23 Report no: IIIT/TH/2018/25 Advisor:Garimella Ramamurthy AbstractIn recent years, Internet connected devices have increased signicantly. Technologies such as Internet of Things and Cloud Computing are enabling more and more devices to connect to the Internet. With rise in number of Internet connected devices, new computational paradigms such as Edge computing and Fog Computing are emerging. People are even voluntarily providing their Internet connected devices for computation and storage. These connected devices are not only communicating with the central cloud resource, but also with other connected devices using variety of protocols. All these changes are making the network infrastructure very complex, dense and heterogeneous. In this dynamically altering and growing scenario, existing traditional network infrastructures are inadequate. To fulfill the growing data requirements, the network service providers need to update the infrastructure hardware and software parameters dynamically. They need to manage co-operation, co-ordination and co-existence among diverse network types. For this, novel self configuring resource management techniques are required. Networks built on foundations of software defined paradigm provide the solution for above mentioned challenges. In this direction, we have presented novel methods for allocating resources at different levels of network infrastructure. In the rst part, computational resources optimization for IoT devices has been done. IoT device density is increasing and current philosophy of processing requests at cloud is not appropriate for emerging IoT domains such as health care and real time control. We have considered to use variety of devices available at the network access layer. This includes the devices voluntarily given by users, dedicated edge servers and cloud infrastructure. The proposed system learns the optimal operating parameters during initial runs. Using the knowledge acquired in the learning phase, an integer linear programming problem is formulated to minimize the mean time to complete the request for all the IoT nodes. The solution of the formulated problem provides fair resource allocation for all the IoT nodes. Later, considering the unreliable nature of the voluntary devices, the learning and formulation has been extended to incorporate probability of failure of these devices. A multi-objective optimization problem has been formulated and solved using Genetic algorithm. Second part covers the economic way to configure the physical infrastructure of a software defined wireless network (SDWN). In a SDWN, the radio units can be configured dynamically. This feature gives the flexibility to change the operating parameters on the go. Resources can be allocated dynamically as per operating conditions. Utilizing these features, cost effective way to configure access layer of modern network is presented. An integer linear programming problem, with objective of cost minimization and indirect quality of service constraints, has been formulated and solved. In the third part, we have looked into the problems related to time optimization in spectrum sensing. As it is expected to have dense populated wireless IoT devices, spectrum must be utilized effectively. In a SDWN, the signal processing happens in software. It gives an opportunity to perform spectrum sensing in software using innovative ways. In our approach, the historical occupancy records of the channels are considered for sensing time allocation. The problem is formulated and solved using integer linear programming with special practical constraints. The problem is also formulated using quadratic programming method and interesting observations have been presented. Full thesis: pdf Centre for Communications |
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