City as a Platform
Utilising Sensors to Help Create Successful City Network Management
- Imagine a dynamic technical infrastructure across the city. Could this be done reliably, securely and on demand?
- Recently there has been a move away from sparse high-precision city sensing to embedding highly dense networks, however deployment issues remain.
- Sensing placement is problematic and political so alternatives are required.
- A solution would be to view ‘city sensing’ as a combination of fixed, mobile and soft-sensors (crowd sourced), that come together to provide the sensed information.
- We need to have metrics that communicate notions that represent data provenance, its source, precision and potential degree of trustworthiness.
- Need to incentivise both private companies and the general public to share resources, interact and collaborate.
The city-wide computer is viewed as a protected yet shared asset; this is beyond today’s notion of cloud computing. Imagine a dynamic technical infrastructure across the city, some parts manage the water network, some belong to transport or the city’s cafes etc. The networks are owner managed and individual. However, what is an accident caused some parts of the city’s management networks to fail? Could we build a cross-city membrane that allows data to be related via internet cafes, or be gathered by transport systems or even by motivated citizens? Can we do this reliably, securely and on demand?
Recently there has been a move away from spares high-precision city sensing to embedding highly dense networks to cost effectively monitor city eco systems, which feed data to models in a near-real-time fashion, but from lower quality sensing systems. However, though the cost issues are being resolved to a degree, deployment issues remain. Sensing placement s problematic and political so alternatives to solely fixed deployments are required.
A solution would be to view ‘city sensing’ as a combination of fixed, mobile and soft-sensors (crowd sourced), that come together to provide the sensed information. Data about atmospheric conditions can be monitored but also can other city data. For example, a logistics company travelling the city delivering (e.g. food) will require real-time traffic updates to minimise the delivery time, costs and carbon foot-print. These routes will be calculated on demand from traffic reports (many such companies have their own Sat Navigation systems). They also carry some sensors – what if this array of sensors was extended and the data shared? Instead of requiring many fixed sensing and base-station units, mobility can be exploited. This can be combined with data coming in from sensor augmented phones and other soft-sensing sources.
There are the obvious issues of reliability, in terms of how reliable the data is and how reliable the platform is. To this end we need to have metrics that communicate notions that represent data provenance, its source, precision and potential degree of trustworthiness. Further, what guarantees can be made when one interest is relying on another interest’s hardware computing infrastructure when these infrastructures are assumed to be composed from dynamic, low-end, heterogeneous components? Finally, what are the incentivisation schemes that can be used to drive this diverse market of computing services, the interaction and collaboration? Such schemes should not only incentivise companies to share resources, but the public also.