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Recently, with advancements in Deep Learning (DL) technology, Radio Frequency (RF) sensing has seen substantial improvements, particularly in outdoor applications. Motivated by these developments, this survey presents a comprehensive review of state-of-the-art RF sensing techniques in challenging outdoor scenarios with practical issues such as fading, interference, and environmental dynamics. We first investigate the characteristics of outdoor environments and explore potential wireless technologies. Then, we study the current trends in applying DL to RF-based systems and highlight its advantages in dealing with large-scale and dynamic outdoor environments. Furthermore, this paper provides a detailed comparison between discriminative and generative DL models in support of RF sensing, offering insights into both the theoretical underpinnings and practical applications of these technologies. Finally, we discuss the research challenges and present future directions of leveraging DL in outdoor RF sensing. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for enhancing human activity recognition through the integration of LoRa wireless RF signal preprocessing and deep learning. We tackle the challenge of extracting features from intricate LoRa signals by scrutinizing the unique propagation process of linearly modulated LoRa signals—a critical aspect for effective feature extraction. Our preprocessing technique involves converting intricate data into real numbers, utilizing Short-Time Fourier Transform (STFT) to generate spectrograms, and incorporating differential signal processing (DSP) techniques to augment activity recognition accuracy. Additionally, we employ frequency-to-image conversion for the purpose of intuitive interpretation. In comprehensive experiments covering activity classification, identity recognition, room identification, and presence detection, our carefully selected deep learning models exhibit outstanding accuracy. Notably, ConvNext attains 96.7% accuracy in activity classification, 97.9% in identity recognition, and 97.3% in room identification. The Vision TF model excels with 98.5% accuracy in presence detection. Through leveraging LoRa signal characteristics and sophisticated preprocessing techniques, our transformative approach significantly enhances feature extraction, ensuring heightened accuracy and reliability in human activity recognition. [ABSTRACT FROM AUTHOR]

Networks are essential components in the surveillance applications of control systems. In unmanned surveillance applications, numerous agents are employed to provide unmanned services. These agents secure large areas and communicate with a control system, checks their status and sends/receives data via multiple networks. These networks need to assign roles based on the application characteristics. In this study, we propose the design of a multiple-network-based control system for large surveillance areas. To this end, an interface for transmitting mission commands to agents needs to be developed because it can allow users to monitor and assign tasks to all agents. The proposed system is developed as a test bed connected to fixed/mobile agents using LoRa, Wi-Fi, Bluetooth, and LTE communication methods; moreover, its usability was tested in a real environment. [ABSTRACT FROM AUTHOR]

The use of telerehabilitation systems has shown a significant growth in the past years, demonstrating their crucial relevance in the time of the COVID-19 pandemic. Many devices and sensors have been proposed to analytically measure parameters for patient assessment, with limitations due to costs or feasibility. In this paper, we present a motor telerehabilitation system with computer vision-assisted markerless measures for patients with Rett syndrome. Twenty-one RTT (Rett syndrome) patients, with ages ranging from age 4 to 31 (Median: 12.50; IQR (interquartile range): 9.50–17.25) were recruited. The study follows a pre-test–post-test design, where the patients were submitted to a pre-test, treatment, post-test 1, treatment, post-test 2 procedure. Progress in patient outcomes was assessed by measuring joint passive range of movement (PRoM). Results show the reliability of our system, and the feasibility of a telerehabilitation treatment for RTT patients, with significant improvements in shoulder mobility and in elbow flexion and extension. Limited results in lower limbs suggest that home treatment should be fostered to reduce sedentary time. [ABSTRACT FROM AUTHOR]

Motor rehabilitation exercises require recurrent repetitions to enhance patients' gestures. However, these repetitive gestures usually decrease the patients' motivation and stress them. Virtual Reality (VR) exer-games (serious games in general) could be an alternative solution to address the problem. This innovative technology encourages patients to train different gestures with less effort since they are totally immersed in an easy to play exer-game. Despite this evolution, patients, with available exer-games, still suffer in performing their gestures correctly without pain. The developed applications do not consider the patients psychological states when playing an exer-game. Therefore, we believe that is necessary to develop personalized and adaptive exer-games that take into consideration the patients' emotions during rehabilitation exercises. This paper proposed a VR-PEER adaptive exer-game system based on emotion recognition. The platform contain three main modules: (1) computing and interpretation module, (2) emotion recognition module, (3) adaptation module. Furthermore, a virtual reality-based serious game is developed as a case study, that uses updated facial expression data and provides dynamically the patient's appropriate game to play during rehabilitation exercises. An experimental study has been conducted on fifteen subjects who expressed the usefulness of the proposed system in motor rehabilitation process. [ABSTRACT FROM AUTHOR]

Several decades of eye related research has shown how valuable eye gaze data are for applications that are essential to human daily life. Eye gaze data in a broad sense has been used in research and systems for eye movements, eye tracking, and eye gaze tracking. Since early 2000, eye gaze tracking systems have emerged as interactive gaze-based systems that could be remotely deployed and operated, known as remote eye gaze tracking (REGT) systems. The drop point of visual attention known as point of gaze (PoG), and the direction of visual attention known as line of sight (LoS), are important tasks of REGT systems. In this paper, we present a comparative evaluation of REGT systems intended for the PoG and LoS estimation tasks regarding past to recent progress. Our literature evaluation presents promising insights on key concepts and changes recorded over time in hardware setup, software process, application, and deployment of REGT systems. In addition, we present current issues in REGT research for future attempts. [ABSTRACT FROM AUTHOR]

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard. [ABSTRACT FROM AUTHOR]

Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using 'authentication code'. The 'authentication code' is an 'n' digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input. [ABSTRACT FROM AUTHOR]

In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the combined use of Software-Defined Networking (SDN) and Network Virtualization (NV) is a promising solution, as such techniques allow to reduce the network management complexity. This work presents the PrioSDN Resource Manager (PrioSDN_RM), a resource management mechanism based on admission control for virtualized SDN-based networks. The proposed combination imposes bounds on the resource utilization for the virtual slices, which therefore share the network links, while maintaining isolation from each other. The presented approach exploits a priority-based runtime bandwidth distribution mechanism to dynamically react to load changes (e.g., due to alarms). The paper describes the design of the approach and provides experimental results obtained on a real testbed. [ABSTRACT FROM AUTHOR]

Low-Power Wide-Area Networks (LPWANs) are emerging as appealing solutions for several Internet of Things (IoT) applications, such as healthcare, smart cities and Industry 4.0, thanks to their ease of deployment, low energy consumption and large coverage range. LoRaWAN is one of the most successful LPWAN standards, as it supports robust long-distance communications using low-cost devices. To comply with the ETSI regulations, LoRaWAN can adopt as medium access control (MAC) layer either a pure ALOHA approach with duty-cycle limitations or a polite spectrum access technique, such as Listen Before Talk (LBT). The two approaches have their pros and cons that need to be carefully evaluated. The studies in the literature that so far have addressed an evaluation of MAC protocols for LoRaWAN refer to a previous and now obsolete version of the ETSI regulations, therefore they do not take into account the current limits on the timing parameters for polite spectrum access, such as that maximum time an end-node is allowed to be transmitting per hour. For this reason, the contribution of this work is two-fold. First, the paper discusses the restrictions that the current ETSI regulations impose on some timing parameters of the two kinds of MAC protocols for LoRaWAN. Second, the paper provides comparative performance assessments of the two protocols through simulations in realistic scenarios under different workload conditions. [ABSTRACT FROM AUTHOR]

Telerehabilitation (TR) has been proven to be an effective tool in improving the adaptive skills of children and young adults with Multiple Disabilities (MDs). The application of a comprehensive set of new technologies reveals new opportunities for both physical and cognitive telerehabilitation, but there is no holistic approach in the case of genetic syndromes. In this paper we present reflections and early results of the TCTRS project that aims at implementing a telerehabilitation system capable of offering complete coverage of rehabilitation needs for people with Rett Syndrome, from both the physical and cognitive points of view. Moreover, the data acquired through the system can also represent a basis for machine learning applications to remotely support therapists and physicians. Our first tests on the system application show the great potential of our approach, in terms of feasibility and applicability, for both rehabilitation centers and families. [ABSTRACT FROM AUTHOR]

Cooperative mobile robot applications enable robots to perform tasks that are more complex than those that each single robot can perform alone. In this application context, communication networks play a very important role, as they have to cope with strict requirements (e.g., in terms of mobility, reliability, and bounded latencies). Recent cooperative robot applications foresee the support of low datarate communication technologies, that provide, among other benefits, lower energy consumption and easy integration with Wireless Sensor Networks (WSNs). Unfortunately, the state-of-the-art solutions either entail high costs and complexity or are not suitable for low data rate communications. Consequently, novel solutions for cooperating robots are required. For this reason, this paper presents RoboMAC, a new MAC protocol for mobile cooperating robots that enables the integration of robots with WSNs, supports mobility and real-time communications, and provides high scalability. The paper also presents a proof-of-concept implementation that proves the feasibility of the RoboMAC protocol on COTS devices. [ABSTRACT FROM AUTHOR]

The Deterministic and Synchronous Multichannel Extension (DSME) of the IEEE 802.15.4 standard was designed to fulfill the requirements of commercial and industrial applications. DSME overcomes the IEEE 802.15.4 limitation on the maximum number of Guaranteed Time Slots (GTS) in a superframe and it also exploits channel diversity to increase the communication reliability. However, DSME suffers from scalability problems, as its multi-superframe structure does not efficiently handle GTS in networks with a high number of nodes and periodic flows. This paper proposes the enhanceD DSME (D-DSME), which consists of two extensions that improve the DSME scalability and reliability exploiting a GTS within the multi-superframe to accommodate multiple flows or multiple retransmissions of the same flow. The paper describes the proposed extensions and the performance results of both OMNeT simulations and experiments with real devices implementing the D-DSME. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is becoming a reality thanks to Industry 4.0, which requires the Internet connection of as many industrial devices as possible. The sharing and storing of a huge amount of data in the Cloud allows the implementation of new analysis algorithms and the delivery of new “services” with added value. From an economical point of view, several factors can decide the success of Industry 4.0 new services but, among others, the “short latency” can be one of the most interesting, especially in the industrial market that is used to the “real-time” concept. For these reasons, this work proposes an experimental methodology to investigate the impact of quality of service parameters on the communication delay from the production line to the Cloud and vice versa, when gateways with OPC UA (Open Platform Communications Unified Architecture) are used for accessing data directly in the production line. In this work, the feasibility of the proposed test methodology has been demonstrated by means of a use case with a Siemens S7 1500 controller exchanging data with the IBM Bluemix platform. The experimental results show that, thanks to the proposed method, the solutions based on OPC UA for the implementation of industrial IoT gateways can be easily evaluated, compared and optimized. For instance, during the 14-day observation period of the considered use case, the great impact on performance of the Quality of Service parameters emerged. Indeed, the average communication delay from the production line to the Cloud may vary from less than 90 ms to about 300 ms. [ABSTRACT FROM AUTHOR]