Smart cities are a typical large-scale deployment of the Internet of Things. From air and water quality to street lights in the urban transportation network, one can imagine that almost everything in the city of tomorrow will be connected into a continuous stream of data that would monitored and analyzed by a platform with little or no human intervention.
A myriad of large-scale projects emerged everywhere but the main challenge now is to integrate technology in a global strategy – not only different local initiatives must be aggregated for better coordinate and avoid duplication, but it is also necessary to broaden the perspectives of action. The city of tomorrow will be a player of a national and global network of smart cities interconnected.
Needs for smart cities
The needs of smart cities:
- Connectivity and Communications – LAN proximity or wireless Internet connection
- Analysis and Decision – Analysis of operational data and use approaches such as Machine Learning to predictive maintenance of urban infrastructure
- Time – Incident hot spots detection in real-time and storage capacity for massive data
- Action – Offer key performance indicators to support actions or recommendation
The IoT platforms are able to meet the diversity of these recurrent needs for smart cities. According to Gartner, an IoT platform is a software ( SaaS, Software as a Service ) or a Cloud service ( PaaS Platform as a Service) that facilitates transactions involving IoT actors (sensors, devices, networks), the Cloud and enterprise resources. The platform collects the stream of events, allows for specialized analysis and to develop applications, and involves IT back-end systems. The IoT platform can be developed on existing infrastructure (on Premise) or in a public or private cloud (in the company’s IT department).
Offers from IoT platforms
Identifying these needs enables us to list the features that must be incorporated into the architecture of the IoT platform, which can be associated with data processing:
- Data reception – Managing multiple sources of incoming data; must be extensive and fault tolerant
- Data storage – High capacity storage and low cost of the incoming data stream; in the long term, the system will be able to permanently store large amounts of data without worrying about costs
- Data analysis “off-line” – The potentials of processing complex and deep analysis with a large amount of historical data
- Real-time data analysis – Real-time processing of incoming data, with rapid availability of results
- Data visualization – Effectively display data analysis outputs offline or in real-time
- Processing tasks – Treat the output information of the real-time components through an Application Programming Interface (API) to act and make decisions automatically or semi-automatically
- Action – Take actions to the connected objects using the data analysis output
The bet of IoT platforms is to build a foundation upon which a system can be expanded and configured to adapt better to the use case. This issue is central to the smart cities initiative, which use cases are particularly diverse, constantly changing, and for the sake of a comprehensive strategy as mentioned at the beginning of this post. The goal is to rapidly expand features offered by IoT without being limited by technology and technical problems. Some examples are:
- Decoupled components – more components and modules are independent, the easier it is to maintain, update or even change them (whether to adapt to changing use cases)
- Asynchronous communication – the various points of entry and exit must be capable of recording/read data at any time without being restricted by technology
- Fault tolerance – needed to ensure high availability and manage a potential loss of data or hardware