Text, radiological pictures, audio notes, video, and other types of multimedia healthcare data are all generated by today's smart healthcare system [1].The evolution of COVID-19 has resulted in an incremental rise in current healthcare data.The study of multimodal healthcare data on such a big scale has revealed both obstacles and potential.Thanks to artificial intelligence (AI) and, more specifically, deep learning (DL) algorithms, which have been widely used by researchers for handling massive amounts of epidemic data, predicting live epidemic crises, and initiating new research directions in the analysis of healthcare multimedia data [2].As a result, deep learning for multimedia healthcare data analysis is becoming a hot topic in multimedia and computer vision research.The call for papers attracted 54 submissions and after a rigorous review, 20 papers have been accepted for this special issue.A brief summary of papers in this special issue is presented in the following:The paper titled "A Novel Study for Automatic Twoclass Covid-19 Diagnosis (between Covid-19 and Healthy, Pneumonia) on X-ray Images using Texture Analysis and 2-D/3-D Convolutional Neural Networks" aims to diagnose COVID-19 early using X-ray images, automatic two-class classification was carried out in four different titles: COVID-19/Healthy, COVID-19 Pneumonia/Bacterial Pneumonia, COVID-19 Pneumonia/Viral Pneumonia, and COVID-19 Pneumonia/Other Pneumonia.In the study, besides using

introduction Share on Special Section on Edge-AI for Connected Living Editors: M. Shamim Hossain King Saud University, Saudi Arabia King Saud University, Saudi ArabiaView Profile , Changsheng Xu Chinese Academy of Sciences, China Chinese Academy of Sciences, ChinaView Profile , Josu Bilbao IKERLAN, Spain IKERLAN, SpainView Profile , Md. Abdur Rahman University of Prince Mugrin, KSA University of Prince Mugrin, KSAView Profile , Abdulmotaleb El Saddik University of Ottawa, Canada University of Ottawa, CanadaView Profile , Mohamed Bin Zayed University of Artificial Intelligence, UAE & University of Ottawa, Canada University of Artificial Intelligence, UAE & University of Ottawa, CanadaView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 3August 2022 Article No.: 55epp 1–3https://doi.org/10.1145/3514196Published:14 March 2022Publication History 0citation176DownloadsMetricsTotal Citations0Total Downloads176Last 12 Months176Last 6 weeks24 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We study the performance of the Message Queuing Telemetry Transport Protocol (MQTT) over QUIC. QUIC has been recently proposed as a new transport protocol, and it is gaining relevance at a very fast pace, favored by the support of key players, such as Google. It overcomes some of the limitations of the more widespread alternative, TCP, especially regarding the overhead of connection establishment. However, its use for Internet of Things (IoT) scenarios is still under consideration. In this paper we integrate a GO-based implementation of the QUIC protocol with MQTT, and we compare the performance of this combination with that exhibited by the more traditional MQTT/TLS/TCP approach. We use Linux Containers and we emulate various wireless network technologies by means of the ns3 simulator. The results of an extensive measurement campaign, show that QUIC protocol can indeed yield good performances for typical IoT use cases.

Cloud computing technologies such as virtualization and orchestration have revolutionized a great number of different domains, from the industry to healthcare. However, there are still certain applications that can not take advantage of this paradigm due to stricter requirements such as end-to-end response times. A new paradigm called Fog computing is arising to answer the needs of these applications. Fog nodes have lower hardware resources that their Cloud counterparts and are distributed over a wider geographical area. Porting technologies designed for the Cloud, specifically orchestration, to this new paradigm requires to handle these peculiarities. A novel orchestration architecture is proposed evolving on current ones, directly focusing Fog characteristics. Multiple schedulers are dynamically deployed in order to achieve a better scheduling throughput and spread the resources required by the scheduler among multiple lower-resource nodes. It uses an application-centric approach at the scheduler level in order to enable new scheduling parameters. The scheduling throughput of this architecture is compared to the most common Cloud orchestration architecture.

The presence of IoT in current networking scenarios is more relevant every day. IoT covers a wide range of applications, ranging from wearable devices to vehicular communications. With the consolidation of Industry 4.0, IIoT (Industrial IoT) environments are becoming more common. Communications in these scenarios are mostly wireless, and due to the lossy nature of wireless communications, the loss of information becomes an intrinsic problem. However, loss recovery schemes increase the delay that characterizes any communication. On the other hand, both reliability (robustness) and low delay are crucial requirements for some applications in IIoT. An interesting strategy to improve both of them is the use of Network Coding techniques, which have shown promising results, in terms of increasing reliability and performance. This work focuses on a possible new coding approach, based on systematic network coding scheme with overlapping generations. We perform a thorough analysis of its behavior. Based on the results, we draw out a number of conclusions for practical implementations in wireless networks, focusing our interest in IIoT environments.

Electrical substations are crucial elements of Smart Grids (SGs), where they are mainly responsible for voltage transformations. However, due to the integration of distributed energy resources in the grid, substations now have to provide additional grid management capabilities which in turn require supervision and automation solutions for large low-voltage grids. A recurring challenge in such deployments are siloed systems that are due to noninteroperable communication protocols across substations: although most substations' communication is based on the International Electrotechnical Commission (IEC) 61850 standard, deployed legacy protocols lag behind modern communication technologies in terms of performance, hindering the full transition to lightweight protocols. This paper demonstrates that IEC 61850 can be fully mapped to the Constrained Application Protocol (CoAP) in combination with the Concise Binary Object Representation (CBOR) format while improving system performance compared to existing alternatives [e.g., WS-SOAP and Hypertext Transfer Protocol (HTTP)]. On average, CoAP+CBOR needs 44% and 18% of the message size and 71% and 85% of the time compared to systems based on HTTP and WS-* Web services, respectively-this is especially relevant for resource-constrained devices and networks in electrical grids. In addition, CoAP is based on the Representational State Transfer (REST) architectural style, which supports system integration and interoperability through uniform identification and interaction. This approach fosters the standard-compliant integration of legacy platforms with modern substations as well as current IoT systems in neighboring domains, such as building management and infrastructure automation systems.

The wide availability of power distribution cables provides an interesting no-new-wires communication channel. However, its electrical characteristics make it a harsh environment for the data transmission purpose and prevent the deployment of services with high reliability requirements. This paper proposes and implements an OSI-Layer2 network coding-based cooperative scheme with the aim of improving communication reliability in indoor narrowband powerline channels. The proposed scheme uses random linear network coding with a sliding window and relaying. We use network coding to replace the retransmissions triggered by legacy subsequent repeat request (ARQ) schemes. We evaluate the performance of our approach in terms of throughput and delay. Regarding the throughput achieved in harsh environments, we show that our scheme often more than doubles the throughput of existing legacy ARQ schemes. At the same time, and even under the large variation of traffic characteristics, it is shown that the delay is likely to be upper bounded by a few seconds, a bound that cannot be guaranteed in other existing transmission techniques.

In this paper, we describe an innovative queue management mechanism in order to achieve an optimal use of shared communication resources. The target scenario contemplates different data flows sharing the same network resources at the same time. The main challenge over this scenario is to achieve the required QoS (Quality of Service) to redistribute real-time and high constraints services. The described queue management algorithm, proposes an innovative queue utilization method based on the combination of the data flows to be redistributed, providing a more efficient use of the network resources, and inducing important improvements in the caused delay and jitter. Present work describes the design of a mechanism based on Network Coding that achieves new QoS up-bound limits and reduces the queue occupancy that cannot be achieved with store-and-forward mechanisms. Finally, we describe the very promising preliminary results obtained by the simulation of a still open study, and discover a new potential future research field.