Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
3 results
Search Results
Item Restricted Characterizing Human Mobility Patterns in Saudi Arabia Using Cellular Data(Florida Institute of Technology, 2025-05) Alnefaie, Meshal Musharraf E; Ivica, KostanicThe study analyzes human mobility in Saudi Arabia. Using crowd-source data, Riyadh mobility is analyzed to find trends and highlight mobility patterns of individuals in Riyadh. Then, the mobility of Riyadh is compared with that of Jeddah and Dammam in a similar data collection and analysis. Four mobility metrics are utilized: Number of Visited Locations (N LOC ), Number of Unique Locations (N ULOC ), Radius of Gyration (R GYR ), and Distance Traveled (D TRV ). The results show interesting outcomes about individuals in the three cities. Although these cities are far from each other, they observe the same mobility patterns. These findings have the potential to help policymakers understand how people move around these cities.12 0Item Restricted A Deep Learning Framework for Blockage Mitigation in mmWave Wireless(Portland State University, 2024-05-28) Almutairi, Ahmed; Aryafar, EhsanMillimeter-Wave (mmWave) communication is a key technology to enable next generation wireless systems. However, mmWave systems are highly susceptible to blockages, which can lead to a substantial decrease in signal strength at the receiver. Identifying blockages and mitigating them is thus a key challenge to achieve next generation wireless technology goals, such as enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC). This thesis proposes several deep learning (DL) frameworks for mmWave wireless blockage detection, mitigation, and duration prediction. First, we propose a DL framework to address the problem of identifying whether the mmWave wireless channel between two devices (e.g., a base station and a client device) is Lineof- Sight (LoS) or non-Line-of-Sight (nLoS). Specifically, we show that existing beamforming training messages that are exchanged periodically between mmWave wireless devices can also be used in a DL model to solve the channel classification problem with no additional overhead. We extend this DL framework by developing a transfer learning model (t-LNCC) that is trained on simulated data and can successfully solve the channel classification problem on any commercial-off-the-shelf (COTS) mmWave device with/without any real-world labeled data. The second part of the thesis leverages our channel classification mechanism from the first part and introduces new DL frameworks to mitigate the negative impacts of blockages. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. We go beyond those techniques by proposing DL frameworks that address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To do so, we developed two Gated Recurrent Unit (GRU) models that are trained using periodically exchanged messages in mmWave systems. Specifically, we first developed a GRU model that tackled the blockage mitigation problem in single-antenna clients wireless environment. Then, we proposed another GRU model to expand our investigation to cover more complex scenarios where both base stations and clients are equipped with multiple antennas and collaboratively mitigate blockages. Those two models are trained on datasets that are gathered using a commercially available mmWave simulator. Both models achieve outstanding results in selecting the optimal blockage mitigation method with an accuracy higher than 93% and 91% for single-antenna and multiple-antenna clients, respectively. We also show that the proposed methods significantly increases the amount of transferred data compared to several other blockage mitigation policies.17 0Item Restricted Security Countermeasures for Topology and Flooding Attacks in Low Power and Lossy Networks(University of Bristol, 2023-10-06) Algahtani, Fahad Mohammed F; Oikonomou, GeorgeInternet of Things have become an integral part in many industries such as health- care, home automation, automobile, and agriculture. Many applications of IoT use networks of unattended micro battery-operated devices with limited compu- tational power and unreliable communication systems. Such networks are called Low-Power and Lossy Network (LLN) which is based on a stack of protocols de- signed to prolong the life of an application by conserving battery power and mem- ory usage. Most commonly used routing protocol is the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). RPL suffers from vulnerabilities related to routing paths formation, network maintenance, and response to some of its control messages. Specifically, compro- mised nodes can advertise falsified routing information to form sub-optimised paths or trigger network reformations. Furthermore, they can flood a network with join- ing requests to trigger a massive number of replies. No standardised RPL solutions provide the security against such attacks. Moreover, existing literature works are mostly based on using monitoring architectures, public key infrastructure (PKI), or a blacklisting approach. Any monitoring devices must be physically secured and utilising only secure communications which is not easily scaleable. Using PKI in LLNs is still a challenge as certificates management is unsuitable for LLN devices. Blacklisting nodes using their advertise addresses is clearly vulnerable to identity spoofing. Moreover, attacks described in few sentences could miss details which transforms any discussion on impact analysis to be subject to interpretation. Therefore, the aim of this dissertation is to first implement attacks using a developed framework to launch multiple attacks simultaneously on different nodes during specified times. Second, to analyse the strategies of an adversary when launching the aforementioned attacks. Then, the impact of the instigated attacks in each strategy is analysed to establish a baseline for countermeasures evaluation. Finally, security countermeasures for the aforementioned attacks are proposed as well as their performances are evaluated. In countering the attack responsible for forming sub-optimised routing paths, preloading a minimum relative location in each node has filtered out any future attempts to accept false routing metrics. As for the attack causing unnecessary net- work reformations, nodes will only accept cryptographically authenticated routing information to trigger future network rebuilds. Lastly, any faster interarriving join- ing requests will be evaluated against thresholds with hysteresis to adjust RPL’s response to potential floods.30 0