Figure 2 - uploaded by Melanie Swan
Content may be subject to copyright.
![Disruptive Computing Paradigms: Mainframe, PC, Internet, Social-Mobile, Connected World. Expanded from O’Reilly Radar (by Mark Sigal) [18].](profile/Melanie-Swan-2/publication/272399412/figure/fig2/AS:294846246998031@1447308231541/Disruptive-Computing-Paradigms-Mainframe-PC-Internet-Social-Mobile-Connected-World.png)
Disruptive Computing Paradigms: Mainframe, PC, Internet, Social-Mobile, Connected World. Expanded from O’Reilly Radar (by Mark Sigal) [18].
Source publication
The automotive industry could be facing a situation of profound change and opportunity in the coming decades. There are a number of influencing factors such as increasing urban and aging populations, self-driving cars, 3D parts printing, energy innovation, and new models of transportation service delivery (Zipcar, Uber). The connected car means tha...
Context in source publication
Context 1
... and global reach. Specifically here, QS-auto sensor applications that link quantified-self sensors (that measure the personal biometrics of individuals like heart rate) and automotive sensors (that measure driver and passenger biometrics or quantitative automotive performance metrics like speed and braking activity) are discussed. This work is intended as an exploration of some of the kinds of applications that connected world data flows make possible. It is a look at the development of the Connected Car concept, and at some of the features and functionality of potential integrated QS-auto sensor applications, and their future possibilities and implications, and does not support, advocate, or offer any advice or prediction as to the direction and viability of these types of solutions. The objective is to provide an overview of the nature, scope, and type of activity that is occurring in the area of QS-auto sensors, and envision its wide-ranging potential application. The account may necessarily omit, misstate, understate, or otherwise misrepresent activity in the sector, either already-launched solutions or those in development. This is a general outline and wide survey of potentiality, not a comprehensive review of existing developments. It is intended as a conceptual articulation of potential future. This paper is structured to first introduce the automotive industry in its current context of potential dramatic change in the upcoming decades, and how the connected world of continuous computing may be interrelated. The concept of the quantified self and wearable sensors is discussed. Second, five potential killer applications of linked QS-auto sensors are presented in the areas of fatigue detection, real-time assistance for parking and accidents, anger management and stress reduction, keyless authentication and digital identity verification, and DIYdiagnostics. Third, potential limitations are considered, together with a conclusion about future possibilities and implications. One model of understanding the world is through computing paradigms, where a revolutionary new organizing paradigm has arisen in the order of one per decade (Figure 2). First, there were the mainframe and PC (personal computer) paradigms, and then the Internet revolutionized everything. Mobile and social networking has been the most recent paradigm. The current model in development is the Connected World, perhaps starting first and foremost with the Connected Car. A Connected Car is a vehicle that is equipped with Internet access, and is also usually within a wireless local area network which allows the car to receive and communicate information, and share Internet access with other devices both inside and outside the vehicle. A Connected Car made after 2010 may have an infotainment head unit, essentially an in-dashboard system with a screen from which operations can be seen or managed by the driver by touch or voice command. The types of operations that may be possible include music or audio playing, smartphone apps, navigation, roadside assistance, voice commands, contextual help and offers, parking applications, engine controls, and car diagnosis. The Connected Car is one part of the larger Connected World of computing currently in development. Modern reality is increasingly becoming a seamlessly connected world of multi-device computing with wearables, Internet-of-Things (IOT) sensors, smartphones, tablets, laptops, quantified self-tracking devices ( i.e. , Fitbit), personal robotics, smarthome, smartcar, and smartcity sensors often linked through big data deep-learning algorithms crunching in the background to make predictive recommendations. Connected world research firm GSMA estimates that 100% of cars will be connected to a ...
Similar publications
Blockchain builds a distributed point-to-point system, which is widely used in the fields of financial economy, Internet of Things (IoT), big data, cloud computing and edge computing. Meanwhile, edge artificial intelligence (AI) computing refers to the emergence of swarm intelligence AI computing model for edge network application scenarios. Althou...
Citations
... Through this practice, people and organizations can temporarily access a large vehicle fleet [101] for various uses, such as on-demand services, corporate fleet management, or personal transportation. Renters used to frequently have restricted access to thorough and accessible information about the vehicle's history [102], such as maintenance logs, accident histories, and prior lease behaviour. Renters found it challenging to make wise selections because of this lack of information. ...
In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.
... On the other hand, there are industries where the cost of data production are relatively low because they are a by-product of another production process, with virtually no additional production cost for the data producer (Duch-Brown et al., 2017;Hugenholtz, 2016). For instance, car manufacturers generate data about the emotional responses of their drivers with smart car systems with relative low effort (Swan, 2015). Another example is eBay's market price data being a by-product of its auction activities. ...
We study the incentives and welfare properties of industrial data sharing taking into account the data (economy) readiness of companies. We differentiate between two regulatory settings. First, there is no compulsion for companies to provide data. Companies, which also use the data for other corporate purposes, decide whether to share their data voluntarily. Second, there is a regulatory requirement on the minimum amount of data to be shared by the data provider. We assume that data sharing affects the data provider’s value of the data. The magnitude and sign of this effect have an impact on the optimal investment level of data generation and overall welfare in the different cases under study. Our results suggest that the implementation of a data-sharing policy has ambiguous welfare properties. It has positive welfare properties if (a) the data receiving firm does not pay too much for the data, (b) the data receiving firm benefits enough from the data provider’s data generating effort, and (c) the intensified competition due to data sharing is not too harmful to the data provider. In contrast, it will always have negative welfare properties if the data provider’s minimum amount of data to be shared under the policy is prohibitively high such that no data is created in the first place. Our results also suggest that a positive effect of data sharing on the data-generating company’s value of the data and its data economy readiness positively affect the incentives to share data. Finally, we find that data sharing under a data-sharing policy leads to a lower data quality if the data economy readiness of the data-generating company is too low.
... HMI strategies that effectively keep the driver engaged include various approaches such as emotion regulation, digital nudges, Quantified Self, and utilizing voice interaction, lights, and music [2], [4], [6], [8], [18], [20]. ...
... The core idea behind these strategies is to enhance the driver's awareness of their own state through visualization, self-tracking, and self-monitoring. This heightened awareness enables them to practice self-regulation [18], ensuring they remain in-the-loop. Moreover, in instances where the driver falls OOTL, these strategies facilitate reengagement through recovery strategies that leverage nudges and emotion regulation techniques [2], [4], [6], [8], [20]. ...
... The driver state section has been designed not only to serve as one of the channels for implementing recovery strategies but also to maintain the driver's awareness of their driving state, encouraging them to stay in the loop. To achieve this, a continuous type of visualization has been utilized, providing the driver with a constant display of their state for visualization and self-monitoring, which aligns with the principles of Quantified Self [3], [18] and digital nudges [4]. Furthermore, the aim was to make the reasons behind an unsafe driving state more explicit, enabling the driver to intervene without overwhelming them with excessive information. ...
Leveraging the driver state classification performed by state-of-the-art intelligent driver monitoring systems, new multimodal Human Machine Interfaces (HMIs) strategies can be designed to support safe driving. With the purpose of engaging drivers in safe driving behaviors by keeping them aware of their driving state, visual nudges, voice interaction, ambient lights, and music have been exploited to design an HMI prototype of that kind. This study presents the results of a focus group performed with daily drivers to assess the proposed HMI approach in terms of its perceived usefulness, engagement and explainability in partial autonomous driving scenarios. The feedback gathered shows that the proposed solution matches participants' expectations in supporting emotion regulation and attention to keep a safe driving state. Furthermore, it highlights the need for the design of different types of explanations of the driver complex state as well as engagement techniques to adapt to personality traits and mood.
... On the other hand, there are industries where the cost of data production are relatively low because they are a by-product of another production process, with virtually no additional production cost for the data producer (Duch-Brown, Martens, and Mueller-Langer 2017;Hugenholtz 2016). For instance, car manufacturers generate data about the emotional responses of their drivers with smart car systems with relative low effort (Swan 2015). Another example is eBay's market price data being a by-product of its auction activities. ...
We study the incentives and welfare properties of industrial data sharing taking into account the data (economy) readiness of companies. We differentiate between two regulatory settings. First, there is no compulsion for companies to provide data.
... The continuous HMI, on the other hand, was designed following theories related to digital nudge [6] and Quantified Self applied in automotive [2,30]. The concept behind it is that the driver, by visualizing and self-monitoring their driving state, may be able to better understand why and to what extent their driving state is unsafe and, with this increased awareness, be able to return to a safe zone. ...
Dangerous driver behavior can arise from different factors: distraction, sleepiness, and emotional states like anger, anxiety, boredom, or happiness. The Driver Monitoring Systems (DMS) collect data on driver behavior and emotional states, which can help design safer driving systems. Human-machine interfaces (HMIs) can leverage the detection of altered states and foster a safe driving style. To this end, we presents two visual HMI prototypes designed to assist drivers in countering distraction conditions and emotional states of too high or too low activation. The HMI prototypes combine voice assistance, ambient lighting, and visual displays. The HMI visual strategies are designed to indicate the dangerous conditions to the driver and to provide the driver with additional information about the type of dangerous state detected. This work provides details on the design and of the methodology applied to evaluate the two HMI prototypes and presents the results of a user assessment with 26 participants, showing insights into user attitudes and helping to identify future design directions.KeywordsDriver Monitoring SystemHuman Machine InterfaceUser testNudgeEmotion
... On the one hand, the existence of transportation is needed by humans, but on the other hand, it also raises crucial problems, especially related to traffic accidents (Swan, 2015). A traffic accident is an unexpected and accidental road event involving vehicles or other road users resulting in human casualties and property loss (Lubis, 2022). ...
· This study focuses on the first problem of solving traffic accident criminal cases that have been applied in law enforcement and how to reconstruct the ideal traffic accident crime resolution based on a restorative justice approach. The method used in this study is normative juridical research with a research approach in the form of a normative approach. The result of this study is that the settlement of criminal cases of traffic violations in Indonesia to date (ius constitutum), basically still prioritizes conventional legal approaches and criminal sanctions. Cases of traffic accident violations are processed and resolved within the framework of the criminal justice system normally, starting from the police, prosecutor's office, and court levels, according to the criminal procedure law, (2) Reconstruction of the settlement of traffic accident violations is very likely to be applied in Indonesia with several non-litigation models (mediation), namely the independent perpetrator-victim mediation approach and perpetrator-victim mediation involving law enforcement (restorative justice agencies). Settlement efforts through the courts can only be pursued if restorative efforts fail to reach an agreement. With these results, it is suggested: (1) The police, prosecutors, and courts should be more intensive to socialize and provide education related to the application of restorative justice, especially for traffic accident crimes, (2) The principle of restorative justice needs to be realized in the main legal form, namely a law that comprehensively and integratively covers the regulation of all components of the criminal justice system.
... Self-tracking has transformed into elaborate interconnected practices that have significant social, cultural, and political implications (Lupton, 2016). The digital technologies contributing to the growth of selfquantification are manifold, including such diverse tools as spreadsheets (Swan, 2013), networked vehicles (Swan, 2015), smart phones (Almalki et al., 2015), and the Internet of Things 4 (IoT; Menychtas et al., 2017;Swan, 2012), but the movement has been most tied to the emergence of wearable computing devices (Baker, 2020;Gilmore, 2016). ...
... More broadly, the phenomenon of the quantified-self-in-community could provide distinct benefits for consumer-brand interactions. For example, in the context of autonomous vehicles using quantified self sensors (measuring biometrics like heart rate) (Swan, 2015), companies can use the power of communities to enhance consumers' awareness, interest, desire, purchase intention, and post-purchase satisfaction. Even proposed limitations of these products (e.g. ...
... While the use of these products is ostensibly voluntary, practically speaking, consumers may have no alternative. A prominent example of such 'inevitable IoT' is the car: while increasingly connected [80], it is also often perceived indispensable, e.g., for daily commuting. Privacy implications of using connected cars are investigated in the field of usable privacy [Blank for review, accepted for publication]. ...
Data protection risks play a major role in data protection laws and have shown to be suitable means for accountability in designing for usable privacy. Especially in the legal realm, risks are typically collected heuristically or deductively, e.g., by referring to fundamental right violations. Following a user-centered design credo, research on usable privacy has shown that a user-perspective on privacy risks can enhance system intelligibility and accountability. However, research on mapping the landscape of user-perceived privacy risks is still in its infancy. To extend the corpus of privacy risks as users perceive them in their daily use of technology, we conducted 9 workshops collecting 91 risks in the fields of web browsing, voice assistants and connected mobility. The body of risks was then categorized by 11 experts from the legal and HCI-domain. We find that, while existing taxonomies generally fit well, a societal dimension of risks is not yet represented. Discussing our empirically backed taxonomy including the full list of 91 risks, we demonstrate roads to use user-perceived risks as a mechanism to foster accountability for usable privacy in connected devices.
... In this sense, the introduction of the personal computer (PC) in the field of domestic use could be considered the first step and, subsequently, the internet revolution was the second. Today's world is hyper-connected, and the car is no exception [4]. As Swan [4] also indicates, the greatest limitation of a connected car is data privacy management. ...
... Today's world is hyper-connected, and the car is no exception [4]. As Swan [4] also indicates, the greatest limitation of a connected car is data privacy management. In the same context, all the humongous amounts of data could be used to generate different business models [10]. ...
Connected cars have often been defined as vehicles that can provide some services and information without human intervention. Several scholars have examined the factors that promote the purchase or adoption of such augmented vehicles. However, little emphasis has been placed on the determinants for reducing car expenditures when a driver owns a car with an Internet of Things (IoT) device or a smart assistant in the context of smart mobility. Therefore, this article analyzes whether emerging technology such as IoT plays a key factor for a driver concerning the expenses related to the car (e.g., insurance, maintenance, and repairs). To this extent, a methodology based on exploratory (i) and confirmatory analysis (ii) was carried out. The authors initially conducted an exploratory phase by means of a Delphi method in which a group of vehicle experts (n = 25) were recruited to give their opinions and reach an agreement defining the determinants that they believed affected vehicle expenditures the most. Secondly, and taking into consideration that the salient determinant from the Delphi method was the use of technology and the warnings and alerts it triggers, a questionnaire was delivered to 556 drivers to analyze the everyday spending on their cars. Specifically, the survey aimed to compare the responses of people who own connected cars or have any kind of built-in IoT infrastructure (n = 302) with those of people with non-connected cars (n = 254). The main conclusion obtained for this latter approach was that drivers with a connected car have remarkably lower car expenses than those driving a conventional car.
... We defined two scenario categories focusing on two different aspects of CAV features: enhancing driving safety/security and convenience [25]. We referred to the use cases in the IoT and CAV literature [8], [10], [31], [47], [59] and created four scenarios in each category describing the collection and use of various types of data (e.g., audio [69], visual [32], and biometric [75]). Each safety/security-related scenario focused on a specific CAV function or feature and listed the relevant data flow and information usage. ...
