Buildings and Structures: The Foundation of Intelligence
Pivot3 Smart City Blog Part 3
One of the most important parts of a city are the buildings that give it its identity, and deliver the services that keep it running and its inhabitants happy. These critical structures — hospitals, schools and universities, libraries, shopping malls, police and fire stations — are all integral parts of a city and all provide specific (and important) functions that are designed to contribute to the well-being and success of the region.
Not only are these operations critical to the vitality of a city but there are critical to the daily lives of its inhabitants. Location and frequency are key; a larger metropolis’; require multiple police stations, for example, and it must be in close proximity to local leadership and other first responders. Quality is also important because it differentiates an area and can mean the deciding factor between in choosing a residence. How often have you heard parent’s say they chose a home in a specific region simply because the school district is better?
What I’ve outlined above about city structures is very similar to how modern-day IoT deployments need to be considered and implemented. It is critical to outline what services, quality, location and impact of the IoT services your city requires. You need to deploy the sensors most suited to achieving the outcome of your IoT project.
Here’s a great example of “macro-analytics” on a city-wide scale — where there is going to be a large volume of data created that will be analyzed over a long period of time to give trends and long-term models. If your city is monitoring air pollution levels, a multi-contaminant sensor can look for airborne particulates – but the location of those sensors is just as important to meeting your goal as the sensors themselves. If you are looking to crack down on pollution, deploy the devices in concentric rings moving away from the industrial area. This will enable operators to gather data on how the particulates are traveling and will allow city leaders to make better and more informed decisions on the allocation of funding for pollution prevention.
Let’s take Toronto as another example and a potential application of macro-analytics using the the city’s innovative PATH system, which serves as an underground pedestrian area, where citizens can move around the downtown district without the travails of traffic or weather. We all know that cold, wet or windy weather will drive people into PATH, but is there a specific temperature threshold that encourages a mass migration of people underground? 40 degrees, 32 degrees or 20 degrees? Or is a curve that increases steadily as temperature drops? Allying this meteorological data with pedestrian counting at PATH ingress points would give a useful set of metrics that could influence how often the stairs are gritted to prevent slip and falls, or whether pedestrian flow control measures are put in place to direct traffic along certain routes to avoid congestion. And also, do these patterns change over the course of the years?
Macro-analytics doesn’t always have an applicable use – take a fire alarm for example; you require an immediate response in that scenario to prevent wider damage and enable first responders to manage the incident before it gets out of control. Sending data from that fire alarm back to a datacenter for analysis of particulates and temperature variations isn’t going to help in preventing your building burning down. It’s also important to manage the locations and distribution of your fire alarms throughout your building or city, targeting potential problem areas with a higher density of alarms, in the same way that you’d ideally have a greater police presence in known crime spots — think about it, a lot of IoT boils down to simple common sense.
The long and the short of IoT sensor placement is to understand the data you are trying to collect and the goals you are trying to achieve. Immediate responses require a local sensor, with limited stimuli and analysis taking place on the sensor. Macro-analytics require a wide range of data and data sources collected over an extended time frame and can be used to spot patterns that you may not previously have considered. Make sure that you use the right sensor and analytics time frame for the problem at hand.
It’s important to not only select the correct sensors based on overarching goals and the correct location for deployment but select the correct location for data analysis based on the outcome you’re looking to achieve.
Join us for our final chapter, where we are going to be looking at some real-examples of services that IoT can deliver to improve the security, safety and engagement with your citizens.
Mike Beevor is the technical director at Pivot3, where he leads the company's safe city and smart city strategy. A 15-year industry veteran, Mike has held a number of technical roles across a wide range of startups, both in the field and in marketing organizations. He regularly presents at Pivot3 industry events and participates in industry panels, as well as performing the technical evangelist role for EMEA and APAC. A keen technologist, he has a specific interest in all of the ways that IoT and analytics can be combined to build the ultimate smart city.