Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications

The unprecedented proliferation of smart devices together with novel communication, computing, and control technologies have paved the way for the Advanced Internet of Things~(A-IoT). This development involves new categories of capable devices, such as high-end wearables, smart vehicles, and consumer drones aiming to enable efficient and collaborative utilization within the Smart City paradigm. While massive deployments of these objects may enrich people's lives, unauthorized access to the said equipment is potentially dangerous. Hence, highly-secure human authentication mechanisms have to be designed. At the same time, human beings desire comfortable interaction with their owned devices on a daily basis, thus demanding the authentication procedures to be seamless and user-friendly, mindful of the contemporary urban dynamics. In response to these unique challenges, this work advocates for the adoption of multi-factor authentication for A-IoT, such that multiple heterogeneous methods - both well-established and emerging - are combined intelligently to grant or deny access reliably. We thus discuss the pros and cons of various solutions as well as introduce tools to combine the authentication factors, with an emphasis on challenging Smart City environments. We finally outline the open questions to shape future research efforts in this emerging field.Comment: 7 pages, 4 figures, 2 tables. The work has been accepted for publication in IEEE Network, 2019. Copyright may be transferred without notice, after which this version may no longer be accessibl

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Continuously Healthy, Continuously Used? – A Thematic Analysis of User Perceptions on Consumer Health Wearables

Along with the miniaturization of digital devices, consumer health wearables (CHWs) further decrease the distance between users and devices, allowing users to continuously track their personal health information (PHI). While this provides more control to users, history has shown that users’ potential concerns (e.g. privacy) can lead to devices not meeting users’ expectations and failing market diffusion. The existing literature has mostly focused on particular aspects that could foster or hinder adoption of CHWs but the big picture is still missing. Drawing upon the previous literature, we use a rigorous iterative thematic analysis to provide a comprehensive picture of any potential benefits and deficiencies that users associate with CHWs. We take the example of fitness trackers and conduct 16 semi-structured interviews that help understand the determinants on which users assess the benefits and deficiencies of CHWs related to their continuous usage. We identify 11 subthemes that we can attribute to three main user determinants (perceived benefit, deficiency, and privacy). Our results not only show the failure to meet privacy expectations as a particular potential hindrance factor, we further propose a new theoretical construct (perceived relativity) as well as a novel tracking motive (social tracking), both of which can benefit future research on PHI disclosure. We enable both researchers and practitioners to uncover and visualize user perceptions of fitness trackers, on which future design decisions can be oriented and user expectations be better met. Available at: https://aisel.aisnet.org/pajais/vol11/iss1/5

From Palm to Arm

The number of people diagnosed with diabetes is increasing at an alarming rate. However, strong evidence shows that health information technology has improved medical outcomes, especially within the field of diabetes research. This thesis investigates how to motivate people with diabetes to perform self-management activities with the help of a smartwatch application. The work is grounded in a literature review, discovering how people manage diabetes with smartwatches today and the lack of existing motivational features on existing solutions. As a result, a system design of a smartwatch application is presented, including a graphical user interface (UI). The system aims to manage and monitor the essential diabetes metrics: nutrition, blood glucose, and physical activity while generating motivation through goal setting. In addition, the presented system is oriented on a standalone architecture, removing the need to pair a smartphone to the smartwatch and introducing novel features for smartwatch diabetes management. Finally, a proof of concept is implemented using Android studio to solidify the systems requirements. Furthermore, a descriptive analysis of a survey presents that among people with diabetes, simplicity is the most crucial factor in smartwatch applications. Based on this, the presented UI is evaluated according to the simplicity of other systems and the impact the motivational features have on the system’s complexity. Then, the potential of a standalone architecture for diabetes management is discussed. Finally, it is concluded that goal-setting features should be more widely used among smartwatch applications due to their low impact on the application. The future work of the thesis is to test the system on people with diabetes. Both to evaluate the system useability scale and observe the impact goal-setting has on performing diabetes self-management. Furthermore, in this thesis, it is assumed that there is a communication channel between diabetes devices and the smartwatch. This must be further investigated with the next generation of diabetes devices

Factors Influencing the purchase intention of Smart wearable technology

A Research Report Submitted to the Faculty of Commerce, Law and Management, Witwatersrand University School of Economics and Business Sciences, In partial fulfilment of the requirements of a Master Degree in Marketing, May 2017The consumer market of Smart wearable technology has shown a massive growth, therefore convincing that Smart wearable technology will be the next great thing, with market analysts forecasting its market to be worth over $30 billion by 2020. However this belief is mainly driven by major new technology manufacturers to produce Smart wearable devices that commoditise cellphones, tablets, and portable computers to influence consumer purchase intention. Consumers purchase intention is crucial for every business survival, therefore cannot be overemphasised. With the increasing number of Smart wearable technology brands on the electronics market, South African consumers have to make a choice on which brands to purchase. This study examines the factors influencing the purchase intention of Smart wearable technology in South Africa, with a special focus on product quality, design, price, and consumer attitude. From the academic side, the study makes a significant contribution by exploring the impact of product price and consumer attitude on consumer purchase intention. As a result, manufacturers in the wearable technology industry may apply this study information to develop proper strategies that will help influence more people to purchase wearable devices and ensure Smart wearable technology market growth. The study data were collected through the aid of a self-administered hardcopy questionnaire, which was circulated by the researcher in the University of the Witwatersrand Johannesburg. The research findings show that both consumer attitude and product price have a significant positive effect on the intention to purchase Smart wearable devices. Nevertheless, to be more precise, the effect of consumer’s attitude on purchase intention goes through the positive effect of a product design on consumer’s attitude. Both product quality and price are found to extend the effect of positivity of consumer’s attitude toward the product or brand, and the price tag of the product. These scenarios are fully supported in hypotheses one, two, and three. Although both quality and design positively influence product price, Product design is found to have an enlarging effect on product price. Generally, it can be stated that the design of a product successfully influence the price set for product.XL201

A Data Science approach to behavioural change: large scale interventions on physical activity and weight loss

This PhD thesis is a quantitative investigation combining Behaviour Change Science with a Data Science approach in search of more effective large scale, multi-component behavioural interventions for health and well-being. There is limited evidence about how technology-based interventions (including those using wearable physical activity monitors and apps) are efficacious for increasing physical activity and nutrition. The relevance of this research is the systematic approach to overcome previous studies’ limitations in method and measurement: restricted research about multi-component interventions, limited analysis about the impact of social networking, the inclusion of components without sufficient evidence about the components’ effectiveness, the absence of a control group(s), small sample sizes, subjective physical activity reporting, among other limitations. The research was done in conjunction with Tictrac Ltd as the industrial partner, and the UCL Centre for Behaviour Change. Tictrac Ltd builds platforms for the collection and aggregation of personal data generated by the users’ devices and mobile apps. The collaboration with the UCL Centre for Behaviour Change has been instrumental to design, implement, evaluate and analyse behaviour change interventions that impact wellbeing and health. The thesis comprises three areas of research: 1. Computational platforms for large scale behavioural interventions. To support this research, computational platforms were designed, built, deployed and used for randomised behavioural interventions with control groups. The interventions were implemented as experiments related to the behavioural impact on physical activity, weight loss and change in diet. / 2. Behaviour change experiments. The two experiments use the Behaviour Change Wheel framework for behaviour change, intervention design and evaluation. A Data Science approach was used to test hypotheses, determine and quantify the effect of the fundamental intervention components and their interactions. The effective use of tracking devices and apps was determined by comparing the results of ‘structured intervention’ –vs- those of the control group. / Experiment 1: Large scale intervention in a corporate wellness setting. Multi-component behavioural intervention with: control group, self-defined goals, choice architecture and personal dashboards for physical activity and weight loss. The analysis covers network effects of social interactions, the role of being explicit about a type of goal, the impact of making part of team, among other relevant outcomes. / Experiment 2: Identification of critical factors of a technology-based intervention. Multi-component behavioural intervention with simultaneous target behaviours related to weight loss and physical activity, inspired by factorial design for the determination of critical factors and effective components. The analysis comprises: components’ interactions (coach, challenge, team, action plans, forum), non-linear relationships (BMI, change in diet habit), five personality traits, among other relevant results. / 3. Frameworks for future large scale interventions in behaviour change. The implementation of both experiments required an applied use of theoretical and practical principles for the design of the experimental computational platforms. As a result, two frameworks were suggested for future interventions: an implementation framework and a data strategy framework

A proposal to improve wearables development time and performance : software and hardware approaches.

Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.Wearable devices are a trending topic in both commercial and academic areas. Increasing demand for innovation has raised the number of research and products, addressing brandnew challenges, and creating profitable opportunities. Current wearable devices can be employed in solving problems in a wide variety of areas. Such coverage generates a relevant number of requirements and variables that influences solutions performance. It is common to build specific wearable versions to fit each targeting application niche, what requires time and resources. Currently, the related literature does not present ways to treat the hardware/software in a generic way enough to allow both parts reuse. This manuscript presents the proposal of two components focused on hardware/software, respectively, allowing the reuse of di?erent parts of a wearable solution. A platform for wearables development is outlined as a viable way to recycle an existing organization and architecture. The platform use was proven through the creation of a wearable device that was enabled to be used in di?erent contexts of the mining industry. In the software side, a development and customization tool for specific operating systems is demonstrated. This tool aims not only to reuse standard software components but also to provide improved performance simultaneously. A real prototype was designed and created as a manner to validate the concepts. In the results, the comparison between the operating system generated by the tool versus a conventional operating system allows quantifying the improvement rate. The former operating system showed approximate performance gains of 100% in processing tasks, 150% in memory consumption and I/O operations, and approximately 20% of reduction in energy consumption. In the end, performance analysis allows inferring that the proposals presented here contribute to this area, easing the development and reuse of wearable solutions as a whole

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design

Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de Andalucía PI-0010-2013Junta de Andalucía PI-0041-2014Junta de Andalucía PIN-0394-201