The communication networks of today are evolving to support a large number of heterogeneous Internet-connected devices. Several emerging applications and services rely on the growing amount of device-generated data; however, such applications place diverse requirements on the network. For instance, interactive applications such as augmented reality demand very low latency for a satisfactory user experience. On the other hand, automotive applications require very reliable transmission and processing of data to ensure that accidents do not occur. To this end, new technologies have been proposed in the communication network to address these diverse connectivity and computational requirements. In the radio access networks, secondary access technologies comprising WiFi and white space are used to supplement cellular connectivity. New classes of radio technologies, such as long range (LoRa), have emerged for connecting resource-constrained devices. Additionally, processing and storage resources are being placed closer to the end-devices to efficiently process their data under the edge computing paradigm. This dissertation investigates the scalability of communication networks through intelligent network design, analysis, and management. In particular, it proposes novel solutions to manage different components in a network. The overall goal is to ensure that communication networks efficiently support both the connectivity and the computing requirements of a large number of heterogeneous devices. First, we investigate the role of secondary access networks in providing scalable connectivity to devices. Specifically, we propose new algorithms to maximize the traffic that is offloaded to white space and WiFi, thereby resulting in significantly more capacity in the cellular spectrum. Next, we investigate Long Range (LoRa) communications to enable large-scale connectivity for resource-constrained and battery-powered devices. In particular, we propose novel optimization models to manage the LoRa communication parameters to support reliable communications from massive densities of such devices in urban areas. Finally, we investigate the deployment of a communication infrastructure with edge computing capabilities to efficiently process large volumes of device-generated data. In particular, we experimentally characterize the impact of edge computing in supporting data-intensive applications. Additionally, we present a novel approach to optimally place edge devices in an urban environment to support both connectivity of cars and reliable processing of their data.
|Translated title of the contribution||Scalable networked systems: analysis and optimization|
|Publication status||Published - 2020|
|MoE publication type||G5 Doctoral dissertation (article)|
- wireless communications
- edge computing