Marcelo M. Carvalho

Firefighters must locate victims reliably to carry out rescue operations within burning structures during urban firefighting events. Low visibility, reduced oxygen levels, weakened structural rigidity, and dense smoke make it difficult to locate victims. In addition to these challenges, victims may be unconscious and unable to report their locations to firefighters. This research work explores the Double Deep Q-Network (Double DQN), Dueling Deep Q-Network (Dueling DQN), and Dueling Double Deep Q-Network (D3QN) agents for an unmanned aerial vehicle (UAV) to navigate around a structure and locate trapped victims within it. The UAV’s position, Light Detection and Ranging (LiDAR), and infrared camera data are utilized as inputs for the Deep Q-Networks. The PER is used to store transitions and sample them according to priority for training. Python’s Pygame library is used in this research to create a simulated environment in which infrared camera and LiDAR data are simulated. The performance of the UAV agent is evaluated using cumulative maximum reward, reward distribution histogram, Temporal Difference (TD) error over time, and number of successful episodes. Among the three DQN UAV agents, the Dueling DQN and Double DQN have potential for real-world applications in firefighting.

LoRaWAN holds immense potential in smart applications for its low-power, long-range communication capabilities and in-built AES-128 encryption for end-to-end security. However, prior research has identified critical security vulnerabilities, most notably its use of AES-128 encryption in ECB mode, which lacks semantic security. Sertainty UXP (Unbreakable Exchange Protocol) technology enhances AES by embedding intelligence directly into the data. Sertainty Corporation’s UXP encryption employs AES-256-GCM, which offers authenticated encryption with integrated access control and policy enforcement at the data level, making it a promising candidate for securing sensitive IoT data. The objective of this study is to evaluate whether Sertainty UXP can operate effectively within the strict payload and performance constraints of LoRaWAN. To benchmark performance and overhead, several encryption algorithms, including AES-256-GCM, ASCON-128, SPECK, and XTEA, were implemented for comparison. For experimentation, smart meter data is encrypted with these algorithms and transmitted over LoRaWAN using the LoRa-E5 development board by Seeed Studio. The system’s performance is evaluated based on latency, payload size, and message integrity. Payloads are strategically split into LoRaWAN-compatible chunks and reassembled upon reception to meet network constraints. The results show that integrating UXP encryption within LoRaWAN is technically feasible, though it introduces additional overhead and latency. Despite this, the ability to embed robust encryption and controls directly within the data object offers significant potential to enhance end-to-end IoT security. The research concludes that Sertainty UXP can offer a viable and forward-looking solution for securing resource-constrained networks, provided implementation strategies carefully manage the trade-offs between security strength and transmission efficiency.

Collaborative inference in mobile edge computing (MEC) networks has recently been adopted as an effective framework to perform inference tasks with low energy consumption while adhering to strict inference latency requirements. Introducing reconfigurable intelligent surfaces (RISs) into the system can further enhance the performance of collaborative inference, particularly by reducing inference latency and energy consumption at Internet of Things (IoT) devices. Indeed, combined with the consideration of other promising technologies such as finite blocklength (FBL) coding in ultra-reliable low-latency communication (URLLC) and simultaneous wireless information and power transfer (SWIPT), collaborative inference in RIS-aided MEC networks presents a new direction for future 6G networks. However, only a few studies investigated the integration of RIS with collaborative inference. In this context, while integrated RIS plays a pivotal role in improving wireless transmission environment as well as minimizing the energy consumption under latency constraints in collaborative inference, RIS-aided MEC networks still face both opportunities and challenges. This article provides an overview of these opportunities and challenges and explores potential directions for advancing next-generation communication and inference systems.

The streaming of 360◦ videos is one of the most bandwidth-demanding virtual reality (VR) applications, as the video must be encoded in ultra-high resolution to ensure an immersive experience. To optimize its transmission, current approaches partition the spherical video into tiles, which are encoded at different bitrates and selectively delivered, based on the viewing direction of the user (viewport). The complexity of this architecture, which involves viewport prediction, tile selection, bit rate adaptation, and handling of parallel streaming, requires new tools to evaluate quality of experience (QoE) and quality of service (QoS), especially due to its interactive nature and low reproducibility. This work introduces py360tools, a Python library to handle tile-based 360◦ video streaming. The library automates key client-side tasks, such as spherical projection reconstruction, viewport extraction, and tile selection, facilitating the playback and simulation of streaming sessions. Furthermore, py360tools offers a flexible architecture, enabling efficient analysis of different projections and tiling strategies.

360-degree videos introduce unique challenges to storytelling, especially if filmmakers want to draw the user’s attention to specific regions of the surrounding sphere to ensure the proper flow of narrative at any given time. For instance, at scene cuts, a user might lose track of a story if he/she looks toward a direction that prevents an intended Region of Interest (RoI) to appear in the viewport of the user’s Head-mounted Display (HMD). Because of that, new editing techniques for 360-degree videos are being investigated, and the use of dynamic (i.e., real time) alignment of intended RoI across scene cuts is receiving growing attention. To support research on this topic, we introduce 360EAVP, an open source web-based application for streaming and visualization of 360-degree edited videos on HMDs. The 360EAVP is built on top of the VR DASH Tile Player, which is based on cube mapping projection, and adds new capabilities to handle dynamic video editing. Specifically, 360EAVP introduces (1) real-time track of a user’s viewport direction on the HMD; (2) support for dynamic editing via “snap-change” or “fade-rotation” effects; (3) visibility mapping of the user’s Field of View (FoV) with respect to the player’s cube mapping projection (for purposes of video tile requests during streaming); (4) incorporation of editing timing information into the operation of an ABR algorithm; (5) viewport prediction module based on linear regression or ridge regression models; and (6) video playback data collection and log module. To showcase the use of 360EAVP, we present the results of a small-scale subjective experiment that aimed to evaluate the impact of the “snap-change” and “fade-rotation” editing techniques on user’s Quality of Experience (QoE), comfort, and head movement. Our findings indicate that these editing techniques do not compromise the overall QoE; in fact, in certain scenarios, an improvement is observed. In addition, some subjects significantly reduced their head movements with the editing techniques implemented.

This paper presents the resources utilized for and gathered from a subjective QoE experiment conducted with 45 participants who watched 360° videos processed with offline alignment edits. The aim of these edits is to redirect the assumed field of view to a specified region of interest by rotating the 360° video around the horizon. The user experiment involved alignment edits employing both gradual and instant rotation. We employed the Double Stimulus method, whereby participants evaluated each original-processed video pair, resulting in a dataset with 5400 comfort and sense of presence ratings. During video consumption, head motion was recorded from the Meta Quest 2 device. The resulting dataset, containing the original and processed videos, is made publicly accessible. The accompanying web application, developed for the execution of the experiment, is released alongside scripts for the evaluation of head rotation data in a public repository. A cross-analysis of QoE and HM behavior provides insight into the efficacy of alignment edits for attention alignment within the same scene. This comprehensive set of experimental resources establishes a foundation for further research of 360° videos processed with alignment edits.

The challenge posed by the escalating energy requirements demanded by the proliferation of Internet of Things (IoT) devices, associated with their deployment in remote and hard-to-reach locations, creates a logistical problem for maintenance and battery replacements. In response to this, simultaneous wireless information and power transfer (SWIPT) emerges as a compelling solution, allowing devices to be powered by received RF signals intended for information transfer. In spite of its potential, research on SWIPT has mostly focused on physical layer issues or have dealt with generic networks or single-link scenarios. To help advance research in this area, this paper introduces an ns-3 extension for simulation of power-splitting SWIPT in IoT networks based on the IEEE 802.11ah standard. We describe the adopted SWIPT model, its implementation in the ns-3, key parameters and features. To showcase its use, we present simulation results of a SWIPT-based IEEE 802.11ah network targeting a specific IoT application, and investigate the effect of the power-splitting parameter on the energy-sustainable operation of sensors at different distances from the access point.

Optimizing user quality of experience (QoE) for 360° videos faces two major roadblocks: inaccurate viewport prediction and viewers missing the plot of a story. To tackle these issues simultaneously, alignment edits have emerged as a promising solution. These edits, also known as "re-targeting edits," work by aligning the user’s field of view with a specific region of interest in the video content. Despite their potential benefits, there is limited knowledge about the actual impacts of alignment edits on user experience (UX). Therefore, we conducted subjective experiments following ITU-T P.919 methodology to explore their effects on QoE. We proposed an alignment edit based on gradual rotation of the 360° frame, aiming to replicate natural viewing behavior. We tested this approach under various conditions and thoroughly analyzed its impacts using both head motion data and feedback from observers, focusing on their sense of presence, comfort, and perceived experience. The results of our experiments are encouraging. Our proposed gradual alignment technique achieved a level of comfort and presence comparable to that of instant edits. Furthermore, all alignment edits tested led to a noticeable reduction in head movement speed after the edit, affirming their potential utility for on-demand video streaming. Notably, the gradual edits, in particular, induced a significant reduction of approximately 8% in head movement speeds when compared to the instant alignment technique. These findings shed light on the positive effects of alignment edits on user experience and firmly establish the viability of the proposed gradual alignment technique to enhance QoE during video consumption.