One of the most promising approaches to address the mismatch between computation- intensive applications and computation-limited end devices is multi-access edge computing (MEC). To overcome the rapid increase in traffic volume and offload the traffic from macrocells, a massive number of small cells have been deployed, so-called heterogeneous networks (HetNets). Strongly motivated by the close integration of MEC and HetNets, in this paper, we propose an envisioned architecture of MEC-empowered HetNets, where both wireless and wired backhaul solutions are supported, flying base stations (BSs) can be equipped with MEC servers, and mobile users (MUs) need both communication and computation resources for their computationally heavy tasks. Subsequently, we provide the research progress summary of task offloading and resource allocation in the proposed MEC-empowered unmanned aerial vehicle (UAV)-assisted heterogeneous networks. We complete this article by spotlighting key challenges and open future directives for researches.

Long range (LoRa) has gained popularity in realizing Internet of Things applications by facilitating long-range communication with low-power consumption. However, LoRa faces scalability issues due to its unsophisticated random access (RA) protocol, Aloha, which is vulnerable to collision and not scalable in dense network scenarios. In contrast, distributed queuing (DQ), a collision-free contention-based RA protocol, is a promising candidate for replacing Aloha because of its near-optimal performance and independence from the traffic load and pattern. However, DQ is not compatible with LoRa, as it was initially designed to operate on CableTV that supports the full-duplex (FDX) bidirectional link. In FDX, the contention feedback overhead is negligible. Most wireless networks, such as LoRa, support the half-duplex, for which the feedback overhead is considerable. Furthermore, a reduction in efficiency is aggravated under a multichannel environment because of an unbalanced traffic load. This study proposes a joint distributed queueing protocol to consider contention for LoRa that minimizes control overhead and achieves interchannel load balancing. Our analysis shows that the proposed protocol reduces the control overhead by up to 70% compared to DQ. The protocol performance demonstrates near-optimum throughput and access delay, irrespective of the number of arrivals.

DASH-based multiview video allows users to freely navigate between available viewpoints. However, achieving seamless transition during viewpoint switching is still challenging, especially in free-viewpoint video streaming, where user navigation is unpredictable. When bandwidth is limited, delivering all viewpoints in high quality is nearly impossible. The DASH system needs to prioritize high video quality for the selected viewpoint. The problem arises when the switching event occurs because the new video may not yet be available in the buffer. This paper introduces multiview video quality of experience (QoE) then discusses how viewpoint transition delay and viewpoint quality delay affect the overall user experience. We propose quality-based adaptive bitrate (ABR) for multiview video by specifying viewpoint priority and a target quality of the main viewpoint, namely multiview video priority (MVP). Although it select a lower bitrate level than rate-based ABR, it performs better in perceptual video quality, especially when the viewpoint changes frequently. The simulation results show that the MVP algorithm effectively allocates available bandwidth to the required viewpoints. Hence, it can eliminate transition delay and decrease viewpoint quality delay. As a result, the user gets better video quality, smoothness, continuity, and seamless transition during video switching.

A substantial amount of money and time is required to optimize resources in a massive Wi-Fi network in a real-world environment. Therefore, to reduce cost, proposed algorithms are first verified through simulations before implementing them in a real-world environment. A traffic model is essential to describe user traffic for simulations. Existing traffic models are statistical models based on a discrete-time random process and combine a spatiotemporal characteristic model with the varying parameters, such as average and variance, of a statistical model. The spatiotemporal characteristic model has a mathematically strict assumption that the access points (APs) have approximately similar traffic patterns that increase during day times and decrease at night. The mathematical assumption ensures a homogeneous representation of the network traffic. It does not include heterogeneous characteristics, such as the fact that lecture buildings on campus have a high traffic during lectures, while restaurants have a high traffic only during mealtimes. Therefore, it is difficult to represent heterogeneous traffic using this mathematical model. Deep learning can be used to represent heterogeneous patterns. This study proposes a generative model for Wi-Fi traffic that considers spatiotemporal characteristics using deep learning. The proposed model learns the heterogeneous traffic patterns from the AP-level measurement data without any assumptions and generates similar traffic patterns based on the data. The result shows that the difference between the sample generated by the proposed model and the collected data is up to 72.1% less than that reported in previous studies.