Cooperation And Resource Allocation In Wireless Networking Towards The Iot
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Cooperation and Resource Allocation in Wireless Networking towards the IoT
Author: Ioannis M. Avgouleas
language: en
Publisher: Linköping University Electronic Press
Release Date: 2019-11-08
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.
Resource Management for Internet of Things
This book investigates the pressing issue of resource management for Internet of Things (IoT). The unique IoT ecosystem poses new challenges and calls for unique and bespoke solutions to deal with these challenges. Using a holistic approach, the authors present a thorough study into the allocation of the resources available within IoT systems to accommodate application requirements. This is done by investigating different functionalities and architectural approaches involved in a basic workflow for managing the lifecycle of resources in an IoT system. Resource Management for the Internet of Things will be of interest to researchers and students as well as professional developers interested in studying the IoT paradigm from data acquisition to the delivery of value-added services for the end user.
Microscopic Traffic Simulation of Automated Driving
Author: Ivan Postigo
language: en
Publisher: Linköping University Electronic Press
Release Date: 2025-02-25
The introduction of automated driving systems (ADSs) in road transportation systems will affect the traffic flow characteristics, and have ripple effects which will lead to larger societal implications. The traffic flow is characterized by speed, density, and vehicular throughput, which determine the road capacity and the traffic performance in terms of, among others, travel times and delays. A tool used to study traffic flow dynamics and analyze traffic performance is microscopic traffic simulation, which works by describing the interactions between road users to simulate observed traffic phenomena. To use microscopic traffic simulation to evaluate the impact of ADSs on traffic performance, driving models need to be able to simulate driving decisions and behavioral patterns of ADSs. Driving models have been proposed specifically for ADSs, however, it remains to be validated whether these driving models when used in combination with traditional human driving models adequately simulate mixed traffic that includes human drivers and ADSs. Ideally, a clear interpretation of the behavioral assumptions for each type of vehicle should be possible, as these determine the simulation results. However, it is challenging to compare behavioral assumptions when using different driving models to describe different vehicle types. Empirical research has validated that some driving models, such as the intelligent driver car-following model (IDM), are well-suited for describing both human or automated driving when calibrated with the proper data. The aim of this thesis is two fold: to further develop microscopic traffic simulation for the study of mixed traffic, and to evaluate the effects of mixed traffic on motorway traffic performance. To enhance the modeling of mixed traffic, a model for perception is proposed which allows the explicit inclusion of perception errors in driving decisions. Its use, in combination with driving models capable of describing both human and automated driving, enables to make distinctions between human drivers and ADSs both in perception capabilities and in driving behavior. This modeling approach focuses on describing essential differences to simulate mixed traffic and removes risks involved in using different driving models. Simulation experiments are conducted using state-of-the-art tools to evaluate the modeling of perception errors on traffic flow dynamics and to evaluate the effects of mixed traffic on motorway traffic performance.