The IOT – More than just the sum of the parts
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One of the earliest manifestations of the power of the Internet of Things (IOT) and analytics to upend established business models has been its role in the smart grid transformation now taking place in the energy sector. This transformation perhaps suggests how other smart city systems might also evolve.
Smart Grid as a Paradigm
Smart grid has been enabled by the IOT, in this case in the form of networked meters and sensors, coinciding with the revolution in energy generation and storage technologies. Transmission and distribution systems can be monitored and managed more effectively. But most importantly from a city point of view, distributed energy resources (DER) such as wind and solar or energy from waste, perhaps integrated with energy storage on microgrids, and perhaps supported by demand response (DR), are augmenting or replacing traditional energy sources from central generation plants. These newer sources are often on a neighborhood, city block or single property scale, and are poised to grow even faster once electric vehicle batteries are added into the mix. The IOT has enabled the near real-time control and management required for these innovations, as well as more granular monitoring of consumption (or generation) by consumers combined with billing and supporting data, through AMI.
Smart cities are embracing smart grid because, as the old monolithic, centralized model has broken down, the new technologies collectively allow the energy system to do more. Cities now use urban smart grids to become more efficient and sustainable, and less polluting; or to generate economic activity; or to enhance social equity; or with redundancy built in, to become more resilient. Their ultimate aim will presumably be to have more and more of their total energy needs met from renewable/distributed energy sources, while still maintaining an integrated whole. While this is still hard to do on a city-wide scale, because it requires significant investment and incorporates multiple suppliers including the public, it is the discernible direction of travel.
In short, while smart grid is causing the old monolithic energy supply model to break down, the real benefit will come when it is built up again – when multiple energy sources are aggregated – into some new form, with new potential. What happens when this aggregation model is applied to other smart city systems?
There are multiple possible modes of network connectivity, and ordinarily these would remain separate and dedicated to various purposes – public phone/cellphone and internet, or private networks for controlling energy, water, lighting, traffic management and other systems. But perhaps the smart city will need to think through how these networks can fulfil their owners’ requirements, while also enabling aggregation to further wider city goals, such as pooling connectivity to enable redundancy in times of emergency, or how, say, a network for advanced energy metering can be used also to bring other city services into homes that may not have internet access. One could imagine an arrangement to aggregate a single city-wide networking resource where communication system owners agree to make some of the bandwidth their networks available for specified services, or in response to specified events, and receive payment for this.
Following the model of DER, perhaps we might have distributed communications resources, or DCR! Cyber security concerns will clearly be an issue – but even here, there is scope to envisage as different future and I come back to this below.
Turning to computation, cloud and “fog” computing (the term is Cisco’s), and the ubiquity of mobile devices means that the old concept of a computing platform as a set of processing machinery in a single dedicated computer room in city hall is obsolete. Cloud computing means that processing can happen anywhere; smart water pumps and smart traffic signals may allocate at least some processing of the data they collect to the device itself at the edge of the network (“fog” computing). Further if, say, my smartphone is sharing air pollution data from my home sensor with my neighbor, or creating a 311 request to the city, then, the phone is also in effect co-opted as part of the city’s computing platform, holistically conceived.
A smart city will be able to optimize processing and data integration across all of these modes of computing, taking into account the type of data, its origins and ownership, security and privacy concerns. In so doing, it will aggregate – in effect – a single “virtual” computation platform.
Today, each smart system in a city will have its own sensors and meters, and the data these collect will typically be retained by the system’s owner. However, data from sensors may have value beyond the system to which it immediately relates. One well-established example is where cell-phone location signals are used to detect traffic; another example might be where smart meters are used to detect the track of tornados (the track will be represented by the meters you don’t hear from when you ping them). Today, these ancillary uses are essentially serendipitous and are negotiated separately. But, given that they were not foreseen when the sensors were first created or installed – what if the city tried to maximize the level of serendipity that might arise?
As an example, San Diego is deploying of clusters of sensors integrated with smart street lights to create a city sensing “fabric” – a uniform coverage of selected data capture devices over large parts of its area. While there are target applications for the data, the city appears to be expected other uses to arise that are not yet foreseen. Several European cities encourage citizen sensing of noise and air pollution levels, although to my knowledge that data is not integrated by anyone with other sources. Suppose cities went a step further, and tried to include 3rd parties’ sensor data, via agreements with the sensors’ owners, for which they would be paid? Those agreements could provide for a permanent connection, or an “as needed” connection, as for example when police forces access private security data to investigate crimes.
In such an arrangement, the city would need to include the software tools for receiving, validating and distributing that data, and to offer a variety of connectivity and data standards. The overall goal might be an integrated data ingestion and distribution capability for the whole city, allowing new combinations of sensors and data to be created at will (subject obviously to privacy and compliance requirements), with the sensors’ owners reimbursed according to the data received; and users charged according to usage. Data exchanges are increasingly mooted in other industries – why not with smart cities too?
Because systems in a smart city are linked with each other, a cyber-weakness in one may cause another to which it is linked also to be compromised, even when it is otherwise secure. Increasingly, therefore, the entire smart city will only be as secure as its weakest link. This suggests that over time, smart city system owners might need to agree to aggregate their cyber security activities into a city-wide cyber-security apparatus and set of monitoring tools that address the interfaces between their systems. As an example, at least one energy company to my knowledge is looking at enabling a cyber security service covering all of the major city systems with which it interfaces – so creating revenue for itself while also making its own systems more secure.
The IOT-enabled smart grid phenomenon, in particular the growing extent to which multiple, new, more localized energy source are re-integrated into a coherent city-wide supply, suggests a process which may also play out in other areas of city activity. To misquote Bill Gates (but only be a little!), “the IOT changes everything”.
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