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RE: Action item - web of things literature summary

From: White, Jason J <jjwhite@ets.org>
Date: Tue, 12 Dec 2017 21:50:59 +0000
To: Scott Hollier <scott@hollier.info>, "public-rqtf@w3.org" <public-rqtf@w3.org>
Message-ID: <DM5PR07MB29719C34CCA48230F05F22B9AB340@DM5PR07MB2971.namprd07.prod.outlook.com>
I’ve added Scott’s text to the wiki. There are various formatting issues that still need to be addressed; I prioritized saving my partially completed changes before leaving the building this evening so that they would not be lost.

Also, I need the citation of Shadi’s reference so that it can be added.

I look forward to discussing this work further at the meeting tomorrow.

https://www.w3.org/WAI/APA/task-forces/research-questions/wiki/Accessibility_and_the_Web_of_Things_-_Literature_Summary


Regards,

Jason.

From: Scott Hollier [mailto:scott@hollier.info]
Sent: Wednesday, November 22, 2017 4:19 AM
To: public-rqtf@w3.org
Subject: Action item - web of things literature summary

To the RQTF

Hope TPAC went well!

I’ve completed the action item from the last meeting and include a summary of the Web of Things literature review below. Much of the work is based on my Internet of Things report but significantly condensed with a greater focus on research and implications.

I haven’t put in any specific recommendations as yet so that others have a chance to contribute literature before they are progressed.  If someone is able ot find a home for it on a wiki somewhere it’d be great. Content follows.

Scott.



Web of Things and access implications



1.      Introduction


The significance of the Web of Things can be highlighted by its rapid growth.  With an estimated 8.4 billion devices connected online by the end of 2017 – up 31 per cent in 2016 and growing to an estimated 20.4 billion devices by 2020 (Gartner, 2017). this document is designed to consider research and implications in relation to its access for people with disability
2.      Reasons for Web of Things popularity
Connectivity
Increased connectivity options such as fixed, wireless and mobile broadband make it easier for us to engage with Web of Things devices anywhere, anytime. Examples include in our homes, cars and even clothing (G3ICT, 2015).
Specific environmental information
Specific information from our environment can include broad information such as the current weather, specific control over the home such as changing a connected light or specific individualised data collected from a smart hairbrush (Bradshaw & Waters, 2017).
Affordability.
The low buy-in price of the Web of Things makes it relatively affordable to implement its benefits This includes cheap devices such as the Arduino (Cornel, 2015) and the Raspberry Pi range of devices (Traeg, 2015) which can use sensors and actuators to provide monitoring and adjustment of devices such as adjusting the temperature of a heater. In addition, the ubiquitous presence of smartphones as a consumer-friendly method of interaction provides an affordable method of engagement. The recent uptake of digital assistants also provides affordable mechanisms for Web of Things engagement.
Ease of interaction
The conversational nature of digital assistants and associated smarttspeakers has evolved to a point where it is possible to provide commands in a similar way to typical human interaction  (Mitchell, 2016; Dores, Reis, & Vasco Lopes, 2014). As a result, it is now much easier to engage with devices which in turn can monitor or change our environment in real-time with relative ease.
3.      Benefits and Issues
The broad benefits of the Web of Things for consumers can be placed into six categories (Borne, 2014)(Hollier, et. al., 2017) as follows:

§   Tracking behaviour for real-time marketing: the ability to quickly assess and benefit from, the target market. For example, if our connected devices determined it was raining in our current GPS location, advertisements relating to umbrellas and information on the nearest store could be provided so that we could respond to the situation in real-time.

§   Enhanced situational awareness: the ability to understand and make changes to our real-time environment. For example, features such as updates on traffic based on movement and GPS sensors in cars and smartphones allow us to take a quieter route home from work.

§   Sensor-driven decision analytics: the ability to use big data to record lots of information at once which can then be analysed. For example, information collected from telescopes analysing space phenomenon (Lenz, Meisen, Pomp, & Jeschke, 2016).

§   Process optimisation: For example, the use of sensors to monitor the speech rhythm, pitch and tone of a lecturer to determine the optimal requirement for student engagement (Heng, Yi, & Zhong, 2011).

§   Optimised resource consumption: the ability for an electrical appliance to complete a task based on its ability to determine the optimal point at which the costs are cheapest. For example, a smart washing machine assessing the cost of power and water.

§   Instantaneous control and response in complex autonomous systems: For example, a series of sensors monitoring different aspects of a patient in a hospital, adjusting medication and treatment in real-time as sensors assess data sent and received from each other (Chiong, 2017).
Issues
The primary issues include:

·        Privacy: with digital assistants always listening for the activation word, such devices can potentially monitor our environment without permission leading to debates between the benefits of such devices and the trade-off required in terms of privacy implications (Bradshaw & Waters, 2017). Developers in privacy protections are recommended to be  proactive and preventative rather than reactive and remedial (Weinberg, Milne, Andonova, & Hajjat, 2015).

·        Security, generally considered a related issue  to privacy (Bian et al., 2016). With smartphones constantly broadcasting our GPS location to a variety of sources – including the operating system manufacturers, telecommunications providers and others depending on smartphone permissions – there is significant concern about who has access to this data and how it is being used (Lin & Bergmann, 2016). Furthermore, most digital assistant interactions are not restricted to personal use meaning that potentially anyone could interact with them for malicious purposes such as adjusting the temperature of a refrigerator to damage its contents. Furthermore, it is unlikely that most consumers would have the technical knowledge to ensure their environment is secure. (Skarzauskiene & Kalinauskas, 2012; Weber, 2010).

·        Interoperability: most current solutions are ecosystem-specific meaning that typical Web of Things components are limited as to what device they can connect. This places unnecessary restrictions on manufacturers which affects the ease in which solutions can be implemented, raises costs due to manufacturers having to make multiple versions of the same product for different digital ecosystems, and reduces consumer choice (Zhao & Qi, 2014; Lin & Bergmann, 2016).



4.      Disability-related implications


4.1.       Consumer engagement

There are two main benefits to the web of things for people with disabilities: its use as an assistive technology and the power of connectivity (Hollier et. Al., 2017). While the use of the term ‘assistive technology’  is generally used to describe specific hardware and software that provides access to information and communications technologies for people with disabilities, the fact that such technologies have the capacity to provide assistance based on human limitations suggests that Web of Things is, in principle, a form of AT in itself (Hennig, 2016).
The literature points to the importance of connectivity through the use of connected sensor and devices in a number of different scenarios that can support people with disabilities.
[NOTE:  the remainder of this document is an exert from them Hollier, et. Al (2017) report and is used with permission]
The connectivity of sensors and actuators to provide disability-specific monitoring – this can lead to significant improvements to the health and well-being of people with disabilities. An example of this is highlighted in a project created by AT&T and Premorbid in which a wirelessly connected wheelchair has the ability to increase user independence and freedom – the concept uses Web of Things to easily monitor the wheelchair for comfort, performance, maintenance requirements and location, with adjustments made in real-time (AT&T, 2015).
A second example is the ability to assist people with disabilities in the achievement of everyday tasks independently such as going shopping. One example focuses on a system used to help a group of vision impaired people to find their way in a store. The store’s RFID system used software to guide the vision impaired people and assist them with scanning products to determine the relevant item (Domingo, 2011). Another retail example is a pilot system developed to assist wheelchair users to interact with shopping items placed beyond their arm’s length – with the help of augmented reality, Web of Things and RFID technologies, this allowed the user to digitally interact with the physical items on the shelf (Rashid et al., 2016).
However, the primary focus of research in this area relates toe-health, particularly in relation to monitoring the health of the ageing population (G3ICT, 2015) and outpatient medical needs. The focus in this regard is on providing proactive support to people with medical conditions and potentially extending both their quality and length of life (Dores et al., 2014).
Examples of e-health include the ability to provide real-time monitoring of the health of seniors in aged care facilities based on an intelligent monitoring system. This includes the use of sensors and actuators to monitor temperature, and assess vital signs such as heart rate and movement. While care givers are able to respond immediately to any adverse change in conditions, seniors also have the ability to get attention if they are in distress (Huang, 2013).
Another example has been applied to tracking patients in e-health/telehealth applications to monitor patients once they are discharged (Chiong, 2017) . A point of particular interest is that while the monitoring system is similar to the aged care example, the implementation of the model infers that medical staff are able to provide improved individual support to outpatients based on Web of Things feedback such as distance travelled, temperatures in their location, and food intake. As such, the non-intrusive sensors are able to assess if outpatients are following the prescribed treatment and, in addition, identify key factors that may have an impact on their health based on lifestyle patterns.
In all these examples, the use of Web of Things data is used in a largely passive way, either without the individual’s specific awareness in the case of e-health or collated to assist in user choice such as the shopping example. However, the broader benefit of Web of Things for people with disabilities comes in the ability to assess data based on their own needs in their own way and, in this regard, it is necessary to review the applicability of the Web of Things user interface as it relates to people with disabilities in the consumer space.
4.2.       Consumer-based Internet of Things and accessibility
There are essentially three types of user interface common to consumer-based Web of Things products – a built-in interface, or interaction via a mobile device such as a smartphone or a standalone device such as a digital assistant smart speaker. The ability for people with disabilities to interact with Web of Things, and technology in general, depends largely on two factors – the accessibility of the interface and the use of accessible content to work with on this interface.
To make an interface accessible, disability-specific AT generally needs to be built into the product.
With regards to devices that have built-in interfaces such as smart refrigerators, there are currently few that have any such AT features built-in, nor are there mechanisms to add features due to the proprietary nature of the interface. Furthermore, even if devices such as a smart refrigerator were to have an AT such as a screen reader to support people who are blind, it is unlikely that, due to the proprietary operating system of the device, the tool would be familiar. This would therefore mean that it would require the user to learn yet another way to control and interact with the device.
However, there is an initiative that may provide an access solution – the Global Public Inclusive Infrastructure (GPII) created by Raising the Floor (2017). In a Web of Things context, GPII could provide support in that a compatible device with a built-in interface, such as a smart refrigerator, could potentially change its interface based on the user’s profile. For example, the interface could be set up with high contrast and large print for a low vision user, or the touchscreen buttons could be lowered for a person in a wheelchair. However, the concept of GPII remains elusive at this point in time. As previously discussed, privacy and security concerns are also present – people with disabilities would need to share information about their disability-specific needs with unknown third parties, and this raises concerns. In addition, the large-scale network required to support the sheer volume of devices is not currently available (Hollier, 2013).
The use of smartphones and other mobile devices as an alternative user interface for Web of Things is therefore currently the most popular, and the most accessible, option available for this purpose (Apple, 2016; Google, 2016; Hollier, 2016). This is due to the two most popular mobile and tablet operating systems, Apple iOS and Google Android, containing a wealth of accessibility features. As such, interaction between a smartphone and Web of Things device can be achieved via an app or a digital assistant in an accessible manner. Furthermore, there are a number of disability-specific benefits in the use of a smartphone to gather information and interact in real-time. For example, the use of parking sensors in a shopping centre can provide useful information to a smartphone app so that a person that needs a disabled parking bay can quickly identify which ones are available and which one is closest to the shop being visited (Lambrinos & Dosis, 2013).
Another important benefit is affordability. While the affordability of the Web of Things is helpful for everyone, it is of particular benefit to people with disabilities due to the generally high costs associated with disability-specific technology solutions. The Web of things can offer more affordable solutions such as the implementation of home automation.
However, while smartphones and apps are an effective way to engage with Web of Things, much of their success depends on the need to ensure that the content within the apps is accessible. To achieve this, the apps need to be created in compliance with web standards.
4.3.
4.4.       Current W3C WAI work
Current W3C Wai work highlights the following issues of importance in addressing potential accessibility issues:

§   Interoperability: for example, a connected television can be controlled by a smartphone with a screen reader.

§   Accessibility support: for example, a connected projector provides access to the presentation data in addition to the video output.

§   Configuration: for example, a profile with preferences, such as large text, could be sent from one device to another.

§   Privacy: for example, a connected refrigerator suggests shopping lists but does not share specific dietary and health needs.

§   Security and safety: for example, a connected pacemaker is safe from manipulation and failure.


5.      References

AT&T. (2015). AT&T and Permobil unveil the connected wheelchair proof of concept at CTIA. AT&T Newsroom. Retrieved from http://about.att.com/story/att_permobil_unveils_connected_wheelchair.html


Apple. (2016). iOS accessibility.

Bian, J., Yoshigoe, K., Hicks, A., Yuan, J., He, Z., Xie, M., Guo, Y., Prosperi, M., Salloum, R., & Modave, F. (2016) Mining Twitter to assess the public perception of the “Internet of Things”. PLoS ONE 11(7), e0158450. http://dx.doi.org/10.1371/journal.pone.0158450


Bradshaw, T., & Waters, R. (2017). The dash to connect the consumer. Financial Times. https://www.ft.com/content/67a08388-d3f8-11e6-9341-7393bb2e1b51?mhq5j=e5


Cornel, C. E. (2015). The role of Internet of Things for a continuous improvement in education. Hyperion Economic Journal, 3(2), 24-31.

Domingo, M. C. (2011). An overview of the Internet of Things for people with disabilities. Journal of Network and Computer Applications. 35(2), 584-596. http://dx.doi.org/10.1016/j.jnca.2011.10.015


Dores, C., Reis, L., & Vasco Lopes, N., (2014). Internet of things and cloud computing. 9th Iberian Conference on Information Systems and Technologies (CISTI), 18-21 June, 2014. http://ieeexplore.ieee.org/document/6877071/?reload=true


G3ICT. (2015). Internet of Things: New Promises for Persons with Disabilities. Global Initiative for Inclusive Information and Communications Technology.  http://g3ict.org/resource_center/publications_and_reports/p/productCategory_books/subCat_2/id_335


Gartner. (2017). Gartner says 8.4 billion connected “things” will be in use in 2017, up 31 percent from 2016. Retrieved from http://www.gartner.com/newsroom/id/3598917


Google. (2016). Android accessibility – Overview. Retrieved from https://support.google.com/accessibility/android/answer/6006564?hl=en


Heng, Z., Yi, C. D., & Zhong, L. J. (2011). Study of classroom teaching aids system based on wearable computing and centralized sensor network technique. 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing, Dalian, 624-628.

Hennig, N. (2016). Natural user interfaces and accessibility. Library Technology Reports, 52(3), 5-17. https://journals.ala.org/index.php/ltr/article/view/5969/7598


Hollier, S. (2013). The accessibility of cloud computing – current and future trends. Media Access Australia. https://mediaaccess.org.au/audio-description-on-radio/current-and-future-trends-of-cloud-computing-accessibility


Hollier, S. (2016). Affordable access. Retrieved from http://www.affordableaccess.com.au


Hollier, S., et. al (2017), Internet of Things (IoT) Education:Implications for Students with Disabilities. Curtin University.

Huang, J. (2013). Research on application of Internet of Things in nursing home. Applied Mechanics and Materials, 303-306, 2153. http://dx.doi.org/10.4028/www.scientific.net/AMM.303-306.2153


Lenz, L., Meisen, T., Pomp, A., & Jeschke, S. (2016). How will the Internet of Things and big data analytics impact the education of learning-disabled students? A Concept Paper. 3rd MEC International Conference on Big Data and Smart City (ICBDSC) 15-16 March.

Lin, H., & Bergmann, N. (2016). Web of Things privacy and security challenges for smart Home environments. Information, 7(3), 44. http://dx.doi.org/10.3390/info7030044


LogMeIn. (2013). Xively brings the Internet of Things to the classroom. Press Release. https://globenewswire.com/news-release/2013/08/21/568300/10045697/en/Xively-Brings-the-Internet-of-Things-to-the-Classroom.html


Mitchell, N. (2016). The 2016 state of the speech technology industry. Speech Technology, 21(1), 29-41.

Raising the Floor. (2017). Global Public Inclusive Infrastructure (GPII). Retrieved from http://gpii.net


Rashid, Z., Melià-Seguí, J., Pous, R., & Peig, E. (2016). Using augmented reality and Internet of Things to improve accessibility of people with motor disabilities in the context of smart cities. Future Generation Computer Systems. http://dx.doi.org/10.1016/j.future.2016.11.030


Roby, J. (2016). Intelligent new products in home automation. Air Conditioning, Heating & Refrigeration News, 257(12), 12-16.

Skarzauskiene, A., & Kalinauskas, M. (2012). The future potential of Internet of Things. Socialinės technologijos: mokslo darbai, 1(2), 102-113. http://www.mruni.eu/lt/mokslo_darbai/st/archyvas/dwn.php?id=326522<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.mruni.eu%2Flt%2Fmokslo_darbai%2Fst%2Farchyvas%2Fdwn.php%3Fid%3D326522&data=02%7C01%7Cjjwhite%40ets.org%7C351b1a41737a4ce5bdca08d5318a2fae%7C0ba6e9b760b34fae92f37e6ddd9e9b65%7C0%7C0%7C636469391938748743&sdata=bqBnwNlVetz14yQyiGgTRNUPxOw5cr%2BNEo%2BY60hc49M%3D&reserved=0>

Traeg, P. (2015). Web of Things projects: Raspberry Pi vs Arduino. Retrieved from https://www.universalmind.com/blog/raspberry-pi-vs-arduino-when-to-use-which/


Weber, R. H. (2010). Internet of Things – New security and privacy challenges. Computer Law and Security Review: The International Journal of Technology and Practice, 26(1), 23-30. http://dx.doi.org/10.1016/j.clsr.2009.11.008


Weinberg, B. D., Milne, G. R., Andonova, Y. G., & Hajjat, F. M. (2015). Internet of Things: Convenience vs. privacy and secrecy. Business Horizons, 58(6), 615-624. http://dx.doi.org/10.1016/j.bushor.2015.06.005


Zhao, G., & Qi, B. (2014). Application of the WEB OF THINGS technology in the intelligent management of university multimedia classrooms. Applied Mechanics and Materials, 513-517, 2050-2053. http://dx.doi.org/10.4028/www.scientific.net/AMM.513-517.2050





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Received on Tuesday, 12 December 2017 21:51:28 UTC

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