Nowadays, the popularity of the unmanned aerial vehicles (UAVs) is high, and it is expected that, in the next years, the implementation of UAVs in day-to-day service will be even greater. These new implementations make use of novel technologies encompassed under the term Internet of Things (IoT). One example of these technologies is Long-Range (LoRa), classified as a Low-Power Wide-Area Network (LPWAN) with low-cost, low-power consumption, large coverage area, and the possibility of a high number of connected devices. One fundamental part of a proper UAV-based IoT service deployment is performance evaluation. However, there is no standardized methodology for assessing the performance in these scenarios. This article presents a case study of an integrated UAV-LoRa system employed for air-quality monitoring. Each UAV is equipped with a set of sensors to measure several indicators of air pollution. In addition, each UAV also incorporates an embedded LoRa node for communication purposes. Given that mobility is key when evaluating the performance of these types of systems, we study eight different mobility models, focusing on the effect that the number of UAVs and their flying speed have on system performance. Through extensive simulations, performance is evaluated via multiple quality dimensions, encompassing the whole process from data acquisition to user experience. Results show that our performance evaluation methodology allows a complete understanding of the operation, and for this specific case study, the mobility model with the best performance is Pathway because the LoRa nodes are distributed and move orderly throughout the coverage area.
Monitoring the Quality of user Experience is a challenge for video streaming services. Models for Quality of User Experience (QoE) evaluation such as the ITU-T Rec. P.1203 are very promising. Among the input data that they require are the occurrence and duration of stalling events. A stalling even5 is an interruption in the playback of multimedia content, and its negative impact on QoE is immense. Given the idiosyncrasy of this type of event, to count it and its duration is a complex task to be automated, i.e., without the participation of the user who visualizes the events or without direct access to the final device. In this work, we propose two methods to overcome these limitations in video streaming using the DASH framework. The first method is intended to detect stalling events. For simplicity, it is based on the behavior of the transport layer data and is able to classify an IP packet as belonging (or not) to a stalling event. The second method aims to predict if the next IP packet of a multimedia stream will belong to a stalling event (or not), using a recurrent neural network with a variant of the Long Short–Term Memory (LSTM). Our results show that the detection model is able to spot the occurrence of a stalling event before being experienced by the user, and the prediction model is able to forecast if the next packet will belong to a stalling event with an error rate of 10.83%, achieving an F1 score of 0.923.
Web Content Management Systems (WCMS) play an increasingly important role in the Internet's evolution. They are software platforms that facilitate the implementation of a web site or an e-commerce and are gaining popularity due to its flexibility and ease of use. In this work, we explain from a tutorial perspective how to manage WCMS and what can be achieved by using them. With this aim, we select the most popular open-source WCMS; namely, Joomla!, WordPress, and Drupal. Then, we implement three websites that are equal in terms of requirements, visual aspect, and functionality, one for each WCMS. Through a qualitative comparative analysis, we show the advantages and drawbacks of each solution, and the complexity associated. On the other hand, security concerns can arise if WCMS are not appropriately used. Due to the key position that they occupy in today's Internet, we perform a basic security analysis of the three implement websites in the second part of this work. Specifically, we explain vulnerabilities, security enhancements, which errors should not be done, and which WCMS is initially safer.
Summary The deployment of Internet of Things (IoT) solutions in smart cities or industrial environments (Industrial IoT, IIoT) demands careful consideration in terms of user‐centric or system‐centric target metrics. A better monitoring system able to transform performance outputs into decision‐making and intelligent actions requires less restrictive performance evaluation methods. Classic approaches to performance evaluation in telecommunication networks rely on Quality of Service (QoS) and/or Quality of user Experience (QoE) assessment models. However, the new IoT paradigm establishes a completely different scenario, where, for instance, consumers might no longer be users but machines. In this paper, we propose the evaluation of the performance of IoT services and applications comprising the combination of four quality measures, namely, Quality of Data (QoD), Quality of Information (QoI), Quality of user Experience (QoE), and Quality Cost (QC). The proposal is analyzed using computing simulations. Specifically, we improve the simulation tool FLoRa (Framework for LoRa) incorporating additional LoRa Wide Area Network (LoRaWAN) features. LoRaWAN is a Low‐Power Wide Area Network (LPWAN) technology that operates on the LoRa modulation scheme. Long coverage ranges, low power consumption, and support for a massive number of IoT devices using a limited infrastructure are the main assets of LoRaWAN, establishing it as one of the top IoT technologies. Results show that it is easier and more efficient to disassociate metrics into different dimensions in order to provide a clear vision of the performance of IoT services. This performance evaluation method can be customized and applied to different IoT markets.
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