Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject to constraints on transmit power of mobile users (MUs), system latency caused by transmission and computation. The problem is confirmed to be a challenging time-series mixed-integer non-convex programming problem, and we propose a joint UAV Movement control, MU Association and MU Power control (UMAP) algorithm to solve it effectively, where three sub-problems are optimized iteratively. Specifically, UAV movement and MU association are optimized utilizing deep reinforcement learning (DRL) to decrease the energy consumption and system latency. Next, a closed-form solution of the MU transmit power is derived. Finally, simulation results show that the UMAP algorithm can significantly decrease the system latency and energy consumption and increase the coverage rate compared with benchmark algorithms.

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios such as vehicular adhoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based Over-The-Top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both endusers and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.

Positioning is envisioned as an essential enabler of future fifth generation (5G) mobile networks due to the massive number of use cases that would benefit from knowing users’ positions. In this work, we propose a particle filter-based reinforcement learning (PFRL) approach for the robust wireless indoor positioning system. Our algorithm integrates information of indoor zone prediction, inertial measurement units, wireless radio-based ranging, and floor plan into an particle filter. The zone prediction method is designed with an ensemble learning algorithm by integrating individual discriminative learning methods and Hidden Markov Models. Further, we integrate the particle filter approach with a reinforcement learning-based resampling method to provide robustness against localization failure problems such as the kidnapping robot problem. The PFRL approach is validated on a two-tier architecture, in which distributed machine learning tasks are hosted at client and edge layer. Experiment results show that our system outperforms traditional terminal-based approaches in both stability and accuracy.

Traditional WiFi positioning relies on a predefined radio map, which is labor-intensive and time-consuming for professionals. Recently, crowdsourcing has emerged as a promising solution for facilitating WiFi positioning. To crowdsense a radio map, traces collected from normal users are merged to recover the original walking paths. In this work, we design a robust iterative trace merging algorithm called WiFi-RITA based on WiFi access points as signal-marks. The algorithm formulates the trace merging problem as an optimization problem in which each trace is translated and rotated to minimize the limitation of distances among traces defined by WiFi access points. WiFi-RITA is further enhanced by removing outliers. WiFi-RITA is robust to the rotation errors of traces and efficient for a large number of short traces. According to the crowdsensed radio map, a sensor fusion approach based on particle filter by fusing inertial sensors and a multivariate Gaussian fingerprinting is proposed to enhance the accuracy of crowdsourcing indoor positioning. The experiment results in two large-scale environments demonstrate that WiFi-RITA positioning with zero-effort calibration achieves high positioning accuracy, which outperforms Pedestrian Dead Reckoning (PDR) and fingerprinting with K Nearest Neighbor.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for enhancing mobile edge computing (MEC) networks. However, the integration of UAVs into MEC networks poses unique challenges, such as the presence of dynamic devices and complex resource allocation. This research investigates the problem of task offloading in a distributed MEC network with multiple ground and aerial base stations (UAV base stations). With a focus on the cost-sensitive nature of Internet of Things devices (IoTDs), our objective is to maximize the quality of experience (QoE) in terms of average task response time and cache queue length in IoTDs by jointly optimizing device association, offloading decision, and UAV trajectory planning. To address the combinatorial and non-convex nature of the problem, we propose an artificial intelligence (AI) based optimization scheme. Firstly, the association between IoTDs and stations is determined using a recursive selection and replacement transmission-rate-based (RSRT) algorithm. Subsequently, the offloading problem is formulated as a 0-1 Backpack Problem with variable value, for which we present a backtracking task offloading (BTO) algorithm. Additionally, we employ a multi-agent deep deterministic policy gradient (MADDPG) approach to determine the trajectory planning of UAVs. Numerical results demonstrate the effectiveness of the proposed scheme in terms of reduction in average response time, and cache queue length in IoTDs within the MEC system when compared to benchmark schemes.