An OFDMA-based Hybrid MAC Protocol for IEEE 802.11ax

Two types of MAC mechanisms i.e., random access and reservation could be adopted for OFDMA-based wireless LANs. Reservation-based MAC is more appropriate than random access MAC for connection-oriented applications as connectionoriented applications provide strict requirements of traffic demands. On the other hand, random access mechanism is a preferred choice for bursty traffic i.e., data packets which have no fixed pattern and rate. As OFDMA-based wireless networks promise to support heterogeneous applications, researchers assume that applications with and without traffic specifications will coexist. Eventually, OFDMA-based wireless LAN will deploy hybrid MAC mechanisms inheriting traits from random access and reservation. In this article, we design a new MAC protocol which employs one kind of hybrid mechanism that will provide high throughput of data as well as maintains improved fair access policy to the medium among the terminals. The protocol works in two steps, where at step 1 sub-channels are approximately evenly distributed to the terminals and at step 2 terminals within in a subchannel will contend for medium randomly if the total number of terminals of the system is larger than the number of sub-channels. The details of the protocol is illustrated in the paper and we analyze the performance of our OFDMA-based multi-channel hybrid protocol using comprehensive computer simulations. Simulation results validate that our proposed protocol is more robust than the conventional CSMA/CA protocol in terms of throughput, collision reduction and fair access. In addition, the theoretical analysis of the saturation throughput of the protocol is also evaluated using an existing comprehensive model

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Revisiting the Feasibility of Public Key Cryptography in Light of IIoT Communications

Digital certificates are regarded as the most secure and scalable way of implementing authentication services in the Internet today. They are used by most popular security protocols, including Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). The lifecycle management of digital certificates relies on centralized Certification Authority (CA)-based Public Key Infrastructures (PKIs). However, the implementation of PKIs and certificate lifecycle management procedures in Industrial Internet of Things (IIoT) environments presents some challenges, mainly due to the high resource consumption that they imply and the lack of trust in the centralized CAs. This paper identifies and describes the main challenges to implement certificate-based public key cryptography in IIoT environments and it surveys the alternative approaches proposed so far in the literature to address these challenges. Most proposals rely on the introduction of a Trusted Third Party to aid the IIoT devices in tasks that exceed their capacity. The proposed alternatives are complementary and their application depends on the specific challenge to solve, the application scenario, and the capacities of the involved IIoT devices. This paper revisits all these alternatives in light of industrial communication models, identifying their strengths and weaknesses, and providing an in-depth comparative analysis.This work was financially supported by the European commission through ECSEL-JU 2018 program under the COMP4DRONES project (grant agreement N∘ 826610), with national financing from France, Spain, Italy, Netherlands, Austria, Czech, Belgium and Latvia. It was also partially supported by the Ayudas Cervera para Centros Tecnológicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) under the project EGIDA (CER-20191012), and in part by the Department of Economic Development and Competitiveness of the Basque Government through the project TRUSTIND—Creating Trust in the Industrial Digital Transformation (KK-2020/00054)

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

Using age-progression facial morphing technology to encourage smoking cessation in women and the role of the stress response

Background: Women are at increased risk from smoking and experience specific barriers to smoking cessation. Age-progression interventions that demonstrate the ageing effect of smoking to the face, appear to be effective in changing smoking intentions and behaviour in women. One underlying theme of age-progression research is a shock reaction that is thought to create stress reactivity. The impact of this shock response on efficacy of the intervention has yet to be understood. Aim: The research within this thesis aimed to investigate the effectiveness of an ageprogression intervention for smoking cessation in women aged 18-55 years, and the role of the stress response elicited by the intervention on smoking outcomes. Methods: A systematic review updated and synthesised information regarding the effectiveness of appearance based interventions. A mixed methods approach was used in a pilot study, to develop aspects of research design, including the use of physiological stress measurement and intervention instruction types (Neutral and Reassuring) to influence levels of stress. A qualitative investigation also explored the experiences of women who received the intervention. Findings from the pilot were implemented in a randomised controlled trial that assessed the impact of psychological and physiological stress induced by the intervention and its impact on the long-term smoking outcomes. Results: Qualitative study indicated the age-progression technique continues to create shock, with more instances of accounts of shock reported by women that received the Reassuring instructions. The quantitative study showed this response was accompanied by an increase in subjective and physiological stress. Lastly, findings from the randomised controlled trial indicated the age-progression intervention delivered using Reassuring instructions produced changes in smoking intentions and abstinence. Importantly, stress elicited by the intervention, positively moderated intentions to quit. Conclusions: The synthesised findings from this thesis conclude that age-progression interventions for smoking cessation can reduce smoking behaviour in women. Additionally, when administered via Reassuring instructions, high levels of shortterm stress can increase the effectiveness of the intervention. Future research should ii focus on identifying the optimal stress levels induced by smoking cessation interventions that increase successful smoking cessation