Mark Weiser described his vision for ubiquitous computing in his seminal work entitled The Computer for the 21st Century (Weiser, 1991). In his narrative, Weiser envisaged a world where technology would silently reside in the background or periphery of the user's attention and is available at a glance when needed. Consequently, the allocation of minimum attentional resources would enable peripheral interaction with a system as suggested in the following statement.
The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it” (Weiser, 1991, p. 1).
To this end, ubiquitous computing (UbiComp) aims to enable calm technology (Weiser & Brown, 1997), whereby information is easily transported between the center and periphery of attention. According to Greenfield (2010), ubiquitous computing describes the notion that computers are embedded in everyday objects, yet invisible, to support constant and ‘everywhere’ access to information. Examples include smart buildings, smart televisions, smart clothing, and smartwatches among others.
Ambient intelligence (AmI) is enveloped in the field of UbiComp (Raisinghani et al., 2006) and was drafted in 2001 by ISTAG (Ducatel, Bogdanowicz, Scapolo, Leijten & Burgelman, 2001), an EU advisory group, to demonstrate a novel approach to designing intelligent environments with a human-centered focus on enhancing the quality of life. An excerpt from the ISTAG mantra describes an AmI world where, “People are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects and an environment that is capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way” (Ducatel et al., 2001, p. 1).
Revisiting Weiser’s notion of calm technology (Weiser, 1991), AmI draws inspiration from calm computing to design technologies that are not disruptive to daily life, but instead, are seamlessly interwoven to empower peripheral attention.
Accordingly, AmI aspires to detect people’s state (i.e., using sensors) and adaptively respond to their needs and behaviours through the integration of ubiquitous technologies in their environment (Vasilakos & Pedrycz, 2006). Drawing from disciplines such as artificial intelligence, human-computer interaction, pervasive/ubiquitous computing, and computer networks. According to Acampora et al., (2009) AmI systems can sense, reason, and adapt to offer personalized services based on:
Clearly, from the above description, the internet of things (IOT) and sensing devices are enabling technologies for AmI. Leveraging wireless technologies, IOT is defined by Giusto, Iera, Morabito, and Atzori (2010) as follows:
“the pervasive presence of a variety of “things” or “objects”, such as RFID, sensors, actuators, mobile phones, which, through unique addressing schemes, are also able to interact with each other and cooperate with their neighboring “smart” components to reach common goals” (p. 5).
In essence, the Internet of things is an enabler for AmI, such that internet-connected physical objects are embedded with sensors, software, and other technologies to send and receive information. Examples include but are not limited to the following.
✅Smart air-quality monitors
✅Smart smoke detectors/alarms
✅Smart temperature controls
✅Home security systems
✅Wearable monitoring devices e.g., Fitbit
Zheng and Stahl (2012) characterize AmI as having the following features:
Today, ambient intelligence is widely leveraged in smart homes and is projected to improve healthcare, transportation and mobility, energy efficiency, water and waste management, and many others. In addition, AmI has huge potential for improving our health and well-being, and most importantly it provides an opportunity for the elderly to sustain their independence and by extension, it improves their comfort, safety, and overall quality of life.
Although AmI appears to be beneficial, it is not without limitations. For instance, D. J. Cook et al. (2009) outlines critical concerns of AmI such as it is subject to surveillance issues (“big brother syndrome”), violations of data protection rights and reliability, handling and installation errors as it pertains to sensing devices.
For more information on AmI and its application areas, feel free to read my doctoral thesis, which has largely inspired this article.
1. Acampora, G., Cook, D. J., Rashidi, P. & Vasilakos, A. V. (2013). A survey on ambient intelligence in healthcare. Proceedings of the IEEE, 101(12), 2470–2494.
2. Cook, D. J., Augusto, J. C. & Jakkula, V. R. (2009, August). Review: Ambient intelligence: Technologies, applications, and opportunities. Pervasive Mob. Comput., 5(4), 277– 298.
3. Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J. & Burgelman, J.-C. (2001). Scenarios for ambient intelligence in 2010 (Tech. Rep.). European Commission Information Society Directorate-General.
4. Giusto, D., Iera, A., Morabito, G. & Atzori, L. (Eds.). (2010). The internet of things: 20th Tyrrhenian workshop on digital communications. Springer Science & Business Media.
5. Greenfield, A. (2010). Everyware: The dawning age of ubiquitous computing. New Riders.
6. Vasilakos, A. & Pedrycz, W. (2006). Ambient intelligence, wireless networking, and ubiquitous computing. Artech House, Inc.
7. Weiser, M. & Brown, J. S. (1997). The coming age of calm technology. In Beyond calculation (pp. 75–85). New York, NY: Springer New York.
8. Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 94–104.