Sensors and sensing

Elliot and Urry (2010) outline a digital networks future as a course of action that could be undertaken to address the challenges posed by climate change, increasing populations and energy descent (Elliot and Urry 2010, p.138). It is a post-crisis or post-shock response comprising of a suite of products and services produced by ‘low carbon’ corporations aimed achieving sustainability. In this context much of what we do, including how we get around or movement in the city is optimised and integrated into a network system enabled by a slew of sensors and technology that do everything from navigation to payment collection (Elliot and Urry 2010, p.147). Andrejevic and Burdon (2015) call this the “sensor society” and they argue that this is not a future prospect, but a world we are already living within. Taking the examples of a smart phone and web browser, they argue that the interactive devices and applications that populate our digital information environment also double as sensors — sensors being anything that automatically captures and records data that can then be transmitted, stored, and analysed. (Andrejevic and Burdon, 2014, p.7)

Drawing on Wood et al (2006) Andrejevic and Burdon (2014, p.5)  argue that the sensor society “reconfigures received categories of privacy, surveillance and sense-making” because unlike traditional forms of surveillance, which may be focused on identifiable persons, the goal of sensor-based forms of surveillance is much more of a comprehensive capture of data about a particular population or environment, from which, more systematic forms of targeting occur (Andrejevic and Burdon, 2014, p.5). This is described as the monitoring of “dimensions of a population, environment or ecosystem or registers of activity” for comparison and for the identification of patterns. Returning to Elliot and Urry’s (2010) digital networks future the pervasiveness of sensing and sensors (both obvious and as a byproduct of our interactions) may well be a useful tool in terms of sustainability, however because (amongst other attributes) data is, as Powell (2014) describes it, “indeterminate” and what it reveals can change depending on how different dimensions are brought together. It is the case that we will increasingly find it difficult to comprehend or predict what our data might reveal about us. (Andrejevic and Burdon, 2014, p16).

Andrejevic, M., and Burdon, M., 2014. Defining the Sensor Society. [online] Available at: <http://cccs.uq.edu.au/sensor-society> [Accessed 3 May 2014].

Elliot, A., and Urry, J., 2010. Mobile Lives. Routledge: Oxon.

Powell, A., 2014. Making data bleed, [Lecture to Information Experience Design symposium]. Royal College of Art. 5 May 2014.

Links:

Image source: http://www.engadget.com/2014/01/07/intel-smart-baby-onesie/

Invisible Cities – visualisation of geo-social data: https://www.schemadesign.com/invisiblecities/

Detection devices: how a ‘sensor society’ quietly takes over
http://theconversation.com/detection-devices-how-a-sensor-society-quietly-takes-over-26089

Human Microchips
http://media.theage.com.au/technology/tech-talk/human-microchips-5354618.html

Affective computing
http://affect.media.mit.edu/projects.phphttp://edition.cnn.com/2014/02/04/tech/innovation/this-new-tech-can-detect-your-mood/index.html

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