Electronic Thesis and Dissertation Repository

Thesis Format



Master of Engineering Science


Electrical and Computer Engineering

Collaborative Specialization

Artificial Intelligence


Wang, Xianbin


Accurate onsite wireless environment information is crucial for effective heterogeneous small cell network (HetNet) operations, especially due to strong signal attenuation in millimeter wave (mmWave) compromising communication quality. Opportunistic mobile crowdsensing (OCS) offers a cost-effective solution by opportunistically recruiting ubiquitous active mobile devices for onsite wireless environment sensing. This thesis introduces two tailored OCS scheduling mechanisms: optimizing information quality and enhancing communication throughput.

The first study addresses the complex challenge of collecting real-time high-quality spectrum information for effective UAV network operation. Existing OCS approaches, due to a lack of sensing and transmission coordination, failed to gather spectrum information with sufficient quality in the data acquisition process, leading to deteriorated UAV network operation performance. To address this, we propose a novel dynamic scheduling mechanism that maximizes the quality of information (QoI) in the location-aware OCS scheme. Our proposed approach jointly allocates sensing and communication resources to maximize the collected information quality and reduce the OCS communication overhead, marking an improvement over existing approaches.

The subsequent study tackles the issue of onsite real-time interference information collection for successful mmWave-empowered SC network deployment. Conventional OCS methods follow a routine schedule for updating interference levels for each user at fixed time intervals, leading to unnecessary sensing overhead and degrading system performance. To resolve this, we propose a novel low-overhead situation-dependent OCS scheme that maximizes the system throughput in the indoor location-aware SC communication system by leveraging the interference spatial correlation to reduce the sensing frequency. Our proposed mechanism jointly optimizes the sensing scheduling and time allocation to maximize the total throughput in the SC network system, offering a more advanced solution than conventional approaches.

All algorithms presented in this thesis are demonstrated using MATLAB simulations that mirror real-world conditions and are compared against existing schemes.

Summary for Lay Audience

Opportunistic mobile crowdsensing (OCS) leverages the capabilities of commonplace mobile devices, like smartphones, turning them into tools for gathering information about wireless environments, thereby optimizing wireless network operations. This thesis delves into two notable research areas within this domain.

The first study addresses the challenge of obtaining high-quality wireless spectrum data for effective UAV network operations. Existing OCS methods, due to uncoordinated sensing and transmission, often fail to provide adequate spectrum data quality, compromising UAV network efficiency. In response, our research introduces a dynamic scheduling mechanism for a location-aware OCS system aimed at enhancing data quality. By coordinating sensing and transmission resources, we reduce both communication and sensing overhead, leading to optimized information quality. This system streamlines the way devices detect their environment and share that information, minimizing overhead. Comparative simulations with other crowdsensing approaches underscore the effectiveness and efficiency of our proposed solution.

The subsequent study tackles the issue of on-site interference information collection, which is vital for effective Small Cell (SC) network deployment. Conventional OCS methods routinely update interference levels at fixed intervals, causing excessive sensing overhead and diminished system performance. Addressing this, we utilize interference spatial correlation to devise a low-overhead, situation-dependent OCS scheme for indoor location-aware SC systems. Specifically, we schedule a subset of users to perform interference sensing and leverage interference spatial correlation to predict the interference conditions of adjacent users, thus reducing the sensing overhead. Comparative evaluations with baseline methods affirm the superior performance of our proposed approach.

These research efforts indicate a new era for wireless networks, where everyday mobile devices play a pivotal role. This move promises more robust and flexible networks and highlights the transformative power of leveraging standard technology for substantial improvements in the field.

Available for download on Wednesday, October 01, 2025