It’s easy to see the value of sensor data that enables acting in time. Just picture driving as the scene of a massive traffic accident unfolds and ambulances race to the rescue…what a difference a few seconds can make. What if past traffic patterns could help city operators predict and manage traffic flow at critical times? Real-time insights generated by sensor data today allow city operators to detect and manage traffic incidents faster, thereby improving public safety and accelerating emergency services response.
IoT-enabled smart city technology is transforming the way emergency services respond. For example, the ability to access information from various databases and then share that with other responders has become a game-changer for first responders. Overlaying not just location information, but also data gathered from multiple sources, also gives dispatchers and first responders higher situational awareness. Dispatchers and responders can view the same Geographic Information System (GIS) maps displaying incident response locations, road closures and building floor plans. When city agencies can instantly share real-time information through one integrated platform, decision-making and emergency response become more efficient.
IoT solution for smart city management and public safety
Smart city technology that enables first responders to act faster is in demand. Enter Worldsensing, a global IoT pioneer and creator of OneMind. OneMind is an end-to-end, integrated IoT real-time platform featuring real-time visibility of existing systems to conduct data-driven urban planning. It is used to monitor and manage mobility, parking, traffic and security in over 60 cities around the world. Worldsensing’s connected operational intelligence and monitoring solutions enable better insights and improved decision-making for city operators.
Why a time series database for storing sensor data
Worldsensing realized that they needed to bring together data from multiple sources — data from their sensors (parking, road traffic, security, air quality), city information (events information that includes dates, times, locations, expected crowds), weather reports, CCTV, and citizen reports (incidents reported) in order build a real-time platform capable of making predictions that guide first responders to the best path to the incident.
Worldsensing chose the InfluxDB open source time series database because it met those criteria, allowing their product, OneMind, to use InfluxDB to ingest and store a city’s critical time series data from multiple sources.
Enabling smart city traffic monitoring, management and action
OneMind enables directors of operations to know what is happening in real time and make decisions based on real-time data and actionable insights. Integrated in a city’s control room, Worldsensing’s OneMind provides city authorities with a comprehensive real-time mobility management system. This allows operators to manage incidents and communicate with each other through one single system, to share information with city stakeholders and act faster.
Worldsensing works with cities around the globe to reduce the time first responders need to arrive at an incident by 4 minutes or more and cut response times to anomalies by 15%.
How time series data fits in OneMind Platform
The use of the InfluxDB platform at Worldsensing progressed from infrastructure monitoring to using it to store, process, manage and visualize all the time series data collected from their sensors. One of the ways that this time series data is presented to the user is with a map.
The map is central to the OneMind user interface because most of the information is geo-localized. Operators want to know where things are happening. Different information is layered on the map such as traffic flow information to show its evolution over time and KPIs and metrics about traffic flow so the user can quickly understand the traffic situation. Variances in traffic information can be seasonal (time of day, day of week, time of year), planned (events, construction), or unplanned (accidents).
Generic time series data
To show generic time series data in their popups, Worldsensing integrated InfluxDB in their systems. They added time series to their Custom Object Service — COS (a service that allows them to insert static and generic data in OneMind in order to show geolocated data on their map and relate a popup visualization with current sensor data).
OneMind also uses InfluxDB for alerting. Saving historical data allowed Worldsensing to create alerting as a feature. They automatically detect anomalies using this data and trigger alerts based on thresholds. To configure thresholds, they used the task templates of Kapacitor, the native data processing engine for InfluxDB.
Once they detect the alert, they send it to a queue and store it in Postgres database for later visualization in OneMind.
Below is a sample anomaly detection dashboard through which Worldsensing informs operators whether a given city system is functioning normally. The indicator shown below is a percentage close to 100%, indicating proper performance. An indicator in the red area would mean problems potentially requiring corrective action. Worldsensing can automate such anomaly detection using the previously mentioned alert system.
As city systems are often interconnected and interdependent, and with transportation and public safety depending on the simultaneous proper functioning of multiple systems, IoT monitoring takes further forms with Worldsensing’s other products. These include geotechnical sensors (such as those used to monitor Florence’s famous Ponte Vecchio bridge after it had collapsed in 2016) and parking sensors to enable real-time monitoring and prediction of parking behavior.
Learn more about how Worldsensing is leveraging the InfluxDB platform to make cities smarter and safer.