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.

The Internet of Things (IoT) should be able to react with minimal human intervention and contribute to the Artificial Intelligence (AI) era requiring real-time and scalable operation under heterogeneous network infrastructures. This thesis investigates how cooperation and allocation of resources can contribute to the evolution of future wireless networks supporting the IoT.

First, we examine how to allocate resources to IoT services which run on devices equipped with multiple network interfaces. The resources are heterogeneous and not interchangeable, and their allocation to a service can be split among different interfaces. We formulate an optimization model for this allocation problem, prove its complexity, and derive two heuristic algorithms to approximate the solution in large instances of the problem.

The concept of virtualization is promising towards addressing the heterogeneity of IoT resources by providing an abstraction layer between software and hardware. Network function virtualization (NFV) decouples traditional network operations such a routing from proprietary hardware platforms and implements them as software entities known as virtualized network functions (VNFs). In the second paper, we study how VNF demands can be allocated to Virtual Machines (VMs) by considering the completion-time tolerance of the VNFs. We prove that the problem is NP-complete and devise a subgradient optimization algorithm to provide near-optimal solutions. Our numerical results demonstrate the effectiveness of our algorithm compared to two benchmark algorithms.

Furthermore, we explore the potential of using intermediate nodes, the so-called relays, in IoT networks. In the third paper, we study a multi-user random-access network with a relay node assisting users in transmitting their packets to a destination node. We provide analytical expressions for the performance of the relay's queue and the system throughput. We optimize the relay’s operation parameters to maximize the network-wide throughput while maintaining the relay's queue stability. A stable queue at relay guarantees finite delay for the packets. Furthermore, we study the effect of the wireless links' signal-to-interference-plusnoise ratio (SINR) threshold and the self-interference (SI) cancellation on the per-user and network-wide throughput.

Additionally, caching at the network edge has recently emerged as an encouraging solution to offload cellular traffic and improve several performance metrics of the network such as throughput, delay and energy efficiency. In the fourth paper, we study a wireless network that serves two types of traffic: cacheable and non-cacheable traffic. In the considered system, a wireless user with cache storage requests cacheable content from a data center connected with a wireless base station. The user can be assisted by a pair of wireless helpers that exchange non-cacheable content as well. We devise the system throughput and the delay experienced by the user and provide numerical results that demonstrate how they are affected by the non-cacheable packet arrivals, the availability of caching helpers, the parameters of the caches, and the request rate of the user.

Finally, in the last paper, we consider a time-slotted wireless system that serves both cacheable and non-cacheable traffic with the assistance of a relay node. The latter has storage capabilities to serve both types of traffic. We investigate how allocating the storage capacity to cacheable and non-cacheable traffic affects the system throughput. Our numerical results provide useful insights into the system throughput e.g., that it is not necessarily beneficial to increase the storage capacity for the non-cacheable traffic to realize better throughput at the non-cacheable destination node.