IoT Networking Resource Allocation and Cooperation

Sammanfattning: The Internet of Things (IoT) promises that "anything that can be connected, will be connected". It comprises of Information and Communication Technologies that interconnect billions of physical and visual things with some "basic" intelligence. The emerging IoT services will be able to react with minimal human intervention and further contribute to the big data era that requires real-time, ultrareliable, ubiquitous, scalable, and heterogeneous operation.This thesis is the result of our investigations on problems dealing with the evolution of such technologies. First, we explore the potential of using relay i.e., intermediate, nodes that assist users to transmit their packets in a a cellular network. Paper I provides insights into how adapting the cooperation of the relay's receiver and transmitter optimizes the network-wide throughput while the relay's queue stability is guaranteed.The next part of the thesis copes with the resource allocation of services on IoT devices equipped with multiple network interfaces. The resources are heterogeneous and can be split among dierent interfaces. Additionally, they are not interchangeable. In paper II, we develop optimization models for this resource allocation problem, prove the complexity of the models, and derive results that give intuition into the problems. Moreover, we propose algorithms that approximate the optimal solution and show under which circumstances this is possible.Finally, in paper III, we present a resource allocation problem specically for smart cities services. In comparison to the previous problem denition, resources are of one type but the IoT network device can oer capacities that vary over time. Furthermore, services have a tolerance regarding their preferred scheduling, namely, their allocation over time. We parametrize each service with a pricing function to indicate its tolerance to be served at the beginning of the scheduling window. We prove that the problem is computationally hard and provide numerical results to gain insight into how different pricing weight functions impact the allocations' distribution within the scheduling window.