Innovative and disruptive technologies to prescribe, encourage, and evaluate physical exercise in healthy adults: a protocol of exploratory study followed by a noninferiority, investigator-blinded randomized clinical trial.

BACKGROUND: Cardiovascular diseases are a leading cause of mortality worldwide. A significant contributing factor to this mortality is the lack of engagement in preventive activities. Consequently, strategies for enhancing adherence to and duration of physical activity (PA) have become pivotal. This project aims to create and validate innovative, disruptive, and secure technologies that ensure appropriate exercise intensity, bolster adherence to PA, and monitor health biomarker responses pre-, during, and post-physical activity. METHODS: This exploratory study, followed by a noninferiority, investigator-blinded randomized clinical trial, will be divided into three phases: (1) development and validation of a sensor for real-time biofeedback during a functional assessment test; (2) integration of biofeedback and gamification into an app for the structured prescription of physical training within a controlled setting; and (3) implementation of biofeedback and gamification into an app for the prescription and monitoring of physical training in an uncontrolled setting. Phase 1 entails a validation test of a biosensor-monitoring heart rate (HR) and steps-during a modified shuttle walk test. In phase 2, the biosensor interfaces with a gamified smartphone application. The training regimen spans 6 weeks, 5 days weekly, with each session lasting 60 min: a five-min warm-up involving stationary gait, followed by 50 min of training at the target HR on the step and concluding with a five-min cool-down at a stationary pace. After 6 weeks of training, a new functional capacity test is conducted. Phase 3 involves an investigator-blinded, randomized clinical trial to demonstrate noninferiority. Participants are randomly assigned to either the intervention group (IG) or the control group (CG). IG participants practice exercise using the gamified application in an uncontrolled environment according to the prescribed method outlined in phase 2. CG participants receive PA practice guidelines exclusively. DISCUSSION: Anticipated outcomes include improved exercise adherence through the gamified application, better maintenance of prescribed exercise intensity, and enhanced health biomarkers. The results of this study will inform health-related decision-making. TRIAL REGISTRATION: The study protocol received approval from the Ethics Committee of Universidade Federal de Ciências da Saúde de Porto Alegre (54,492,221.80000.5345) and has been registered with the Brazilian Registry of Clinical Trials (ReBEC, RBR-359p69v).

Adaptive Parameters for LoRa-Based Networks Physical-Layer.

Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.

Drone Swarms as Networked Control Systems by Integration of Networking and Computing.

The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

Hybrid Routing, Modulation, Spectrum and Core Allocation Based on Mapping Scheme.

With the persistently growing popularity of internet traffic, telecom operators are forced to provide high-capacity, cost-efficient, and performance-adaptive connectivity solutions to fulfill the requirements and increase their returns. However, optical networks that make up the core of the Internet gradually reached physical transmission limits. In an attempt to provide new solutions emerged, the Space-Division Multiplexing Elastic Optical Network emerged as one of the best ways to deal with the network depletion. However, it is necessary to establish lightpaths using routing, modulation, spectrum, and core allocation (RMSCA) algorithms to establish connections in these networks. This article proposes a crosstalk-aware RMSCA algorithm that uses a multi-path and mapping scheme for improving resource allocation. The results show that the proposed algorithm decreases the blocking ratio by up to four orders of magnitude compared with other RMSCA algorithms in the literature.

RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing.

Technological advancement is currently focused on the miniaturization of devices, and integrated circuits allow us to observe the increase in the number of Internet of Things (IoT) devices. Most IoT services and devices require an Internet connection, which needs to provide the minimum processing, storage and networking requirements to best serve a requested service. One of the main goals of 5G networks is to comply with the user's various Quality of Service (QoS) requirements in different application scenarios. Fifth-generation networks use Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) concepts to achieve these QoS requirements. However, the computational resource allocation mechanisms required by the services are considered very complex. Thus, in this paper, we propose an allocation and management resources mechanism for 5G networks that uses MEC and simple mathematical methods to reduce the model complexity. The mechanism decides to allocate the resource in MEC to meet the requirements requested by the user. The simulation results show that the proposed mechanism provides a larger amount of services, leading to a reduction in the service lock number and as a reduction in the blocking ratio of services due to the accuracy of the approach and its load balancing in the process of resource allocation.

LoRaWAN Gateway Placement Model for Dynamic Internet of Things Scenarios.

Extended Range Wide Area Network (LoRaWAN) has recently gained a lot of attention from the industrial and research community for dynamic Internet of Things (IoT) applications. IoT devices broadcast messages for neighbor gateways that deliver the message to the application server through an IP network. Hence, it is required to deploy LoRaWAN gateways, i.e., network planning, and optimization, in an environment while considering Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) along with Quality of Service (QoS) requirements. In this article, we introduced a LoRaWAN gateway placement model for dynamic IoT applications called DPLACE. It divides the IoT devices into groups with some degree of similarity between them to allow for the placement of LoRaWAN gateways that can serve these devices in the best possible way. Specifically, DPLACE computes the number of LoRaWAN gateways based on the Gap statistics method. Afterward, DPLACE uses K-Means and Fuzzy C-means algorithms to calculate the LoRaWAN gateway placement. The simulations' results proved the benefits of DPLACE compared to state-of-the-art LoRaWAN gateway placement models in terms of OPEX, CAPEX, and QoS.

Data Improvement Model Based on ECG Biometric for User Authentication and Identification.

The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG's data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram-DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.

A multi-tier fog content orchestrator mechanism with quality of experience support.

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network.

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to establish a Flying Ad-hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapidly deployable systems. In this context, Software-Defined Networking FANET (SDN-FANET ) separates the control and data plane and provides network programmability, which considers a centralized controller to perform all FANET control functions based on global UAV context information, such as UAV positions, movement trajectories, residual energy, and others. However, control message dissemination in an SDN-FANET with low overhead and high performance is not a trivial task due to FANET particular characteristics, i.e., high mobility, failures in UAV to UAV communication, and short communication range. With this in mind, it is essential to predict UAV information for control message dissemination as well as consider hierarchical network architecture, reducing bandwidth consumption and signaling overhead. In this article, we present a Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NEtwork, called CAPONE. Based on UAV contextual information, the controller can predict UAV information without control message transmission. In addition, CAPONE divides the FANET into groups by computing the number of clusters using the Gap statistics method, which is input for a Fuzzy C-means method to determine the group leader and members. In this way, CAPONE reduces the bandwidth consumption and signaling overhead, while guaranteeing the control message delivering in FANET scenarios. Extensive simulations are used to show the gains of the CAPONE in terms of Packet Delivery Ratio, overhead, and energy compared to existing SDN-FANET architectures.

Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency.

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to set up a Flying Ad Hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapid deployable systems. The user experience on watching videos transmitted over FANETs should always be satisfactory even under influence of topology changes caused by the energy consumption of UAVs. In addition, the FANET must keep the UAVs cooperating as much as possible during a mission. However, one of the main challenges in FANET is how to mitigate the impact of limited energy resources of UAVs on the FANET operation in order to monitor the environment for a long period of time. In this sense, UAV replacement is required in order to avoid the premature death of nodes, network disconnections, route failures, void areas, and low-quality video transmissions. In addition, decision-making must take into account energy consumption associated with UAV movements, since they are generally quite energy-intensive. This article proposes a cooperative UAV scheme for enhancing video transmission and global energy efficiency called VOEI. The main goal of VOEI is to maintain the video with QoE support while supporting the nodes with a good connectivity quality level and flying for a long period of time. Based on an Software Defined Network (SDN) paradigm, the VOEI assumes the existence of a centrailized controller node to compute reliable and energy-efficiency routes, as well as detects the appropriate moment for UAV replacement by considering global FANET context information to provide energy-efficiency operations. Based on simulation results, we conclude that VOEI can effectively mitigate the energy challenges of FANET, since it provides energy-efficiency operations, avoiding network death, route failure, and void area, as well as network partitioning compared to state-of-the-art algorithm. In addition, VOEI delivers videos with suitable Quality of Experience (QoE) to end-users at any time, which is not achieved by the state-of-the-art algorithm.

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends.

A routing protocol based on energy and link quality for Internet of Things applications.

The Internet of Things (IoT) is attracting considerable attention from the universities, industries, citizens and governments for applications, such as healthcare, environmental monitoring and smart buildings. IoT enables network connectivity between smart devices at all times, everywhere, and about everything. In this context, Wireless Sensor Networks (WSNs) play an important role in increasing the ubiquity of networks with smart devices that are low-cost and easy to deploy. However, sensor nodes are restricted in terms of energy, processing and memory. Additionally, low-power radios are very sensitive to noise, interference and multipath distortions. In this context, this article proposes a routing protocol based on Routing by Energy and Link quality (REL) for IoT applications. To increase reliability and energy-efficiency, REL selects routes on the basis of a proposed end-to-end link quality estimator mechanism, residual energy and hop count. Furthermore, REL proposes an event-driven mechanism to provide load balancing and avoid the premature energy depletion of nodes/networks. Performance evaluations were carried out using simulation and testbed experiments to show the impact and benefits of REL in small and large-scale networks. The results show that REL increases the network lifetime and services availability, as well as the quality of service of IoT applications. It also provides an even distribution of scarce network resources and reduces the packet loss rate, compared with the performance of well-known protocols.