‘Bintel’ is an innovative urban waste management system that aims to improve the liveability, attractiveness, efficiency, and environmental management of the city and its services. Developed in line with a re-imagination of urban areas’ pre-existing infrastructure, ‘Bintel’ provides potential administrations with an opportunity to increase their influence throughout the city more efficiently and cost-effectively through the use of low-cost sensors.

Full Description

Re-invigorating one of urban administrations most resource-intensive initiatives, ‘Bintel’ aims to provide decision makers with context and location specific information in real time analysis of city life. ‘Bintel’ is another component of an increasingly wider constellation of instrumented devices, often developed under the remit of the wider smart city agenda. The embedding of low cost sensors into previously ‘unconnected’ devices aims to provide councils with additional sources of big data. Utilising both the hardware and software provided by both Intel and IBM at this event, this service provides prospective councils with an opportunity to govern in a dynamic manner while simultaneously seeks to make cities cleaner, safer, efficient, and more connected. By alerting prospective councils’ attention to imminent environmental and infrastructural concerns this form of technology aims to optimise the management of waste collection routes. Moreover, this managerial optimisation seeks to reduce both fuel and labour costs along more environmentally supportive rationales. Accordingly, this data may be correlated with the city’s various other datasets in order to create a more contextually contingent visualisation of the city from a wider sociotechnical perspective.

The primary aim of ‘Bintel’ is to address existing issues by retrofitting already existing mediums like the vast array of bins that are dotted around the city’s various neighbourhoods. In the digitally enhanced streetscapes of modern cities this initiative seeks to provide self-powered, energy efficient, ubiquitous, and low cost sensors in comparison to contemporary private models whose pricing plans differ dramatically.


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Alejandro Ricatti
Neeku Shamekhi
Garry Hester
Jules Fitzsimons
Darach Mac Donncha


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