Reserachers Bokolo Anthony Jnr and Sobah Abbas Petersen, from the Department of Computer Science, Norwegian University of Science and Technology, NTNU, Norway, Dirk Ahlers from the Department of Architecture and Planning, Norwegian University of Science and Technology, NTNU, Norway and John Krogstie from the Department of Computer Science, Norwegian University of Science and Technology, NTNU, Norway published an article on Emerald Publishing Limited regarding Big data driven multi-tier architecture for electric mobility. The article provides an up to date review on this issue. Here are some of the key issues.
The increase in urban population and transport infrastructure in cities has resulted in an increase in the number of cars in urban environments that leads to pollution, which poses a serious threat to citizens’ health. electric vehicles (EVs) play a key role in improving sustainable transportation and reduction of carbon dioxide.
Electric mobility as a service (eMaaS) is suggested as a possible solution to ease transportation and lessen environmental issues by providing a collaborative transport sharing infrastructure that is based on EVs such as electric cars, electric bicycles and so on.
eMaaS in smart cities requires the integration of data from different sources and processed into useful information deployed through services, used by citizens and businesses. This challenge entails the collection of enormous amounts of data aggregated in various formats, where the analysis of these data is important to deduce useful information. Moreover, the absence of a common and standardized platform that supports the provision and dissemination of such data severely limits the prospects of services that can be derived to benefit citizens.
This results in the increasing need for efficient and practical big data analytics tools to process the data by converting, analyzing and using the data toward realizing beneficial information to improve mobility services.
API (Application programming interface)
The study points out that the data sets are mostly not interoperable, as each data has been produced by different systems, people and time, etc. APIs create technical gateways for achieving a data-driven economy and have been acknowledged as a key enabler of interoperability of services in smart cities.
Additionally, the researchers indicate that APIs aids interoperability toward unifying different mobility systems to create open innovation platforms that provide a repository of metadata and data sources that provides light-weight access to information without e-mobility service providers having to worry about database size as data is only retrieved on demand.
API provides free access to mobility data to offer services to citizens and businesses in creating open ecosystems of mobility data sources, mobility data providers and mobility application and/or service developers in making transport predictions, detecting anomalies in EV fleets for early warning and for producing recommendations to eMaaS operators.
The authors stress that the interoperable approach is required to foster the development of open data ecosystems, to unlock the commercial potential of big data in providing an approach to expose and access e mobility data in a secure and efficient way
Accordingly, API can be deployed as data adapters, which can be used for establishing connections to CSV, external databases, files, spreadsheets, global positioning system (GPS) data and real-time mobility data. Therefore, there is a need for an architecture that integrates APIs that can deal with interoperability of data collected during eMaaS operation in smart cities.
The proposed multi-tier architecture
The architecture integrates APIs to enable interoperability between different infrastructures required for eMaaS and aids multiple partners to exchange and share data for making decisions regarding e-mobility services.
The proposed multi-tier architecture, which comprises of seven layers, are:
context, service, business, application, data space, technology and physical infrastructure layers. Thus, each of the layers are discussed below.
-Context layer
The context layer is concerned with the main feature or capability to be provided, which in the context of the study is eMaaS to citizens. Moreover, the context layer captures the needs and requirements of all stakeholders involved in eMaaS. Hence, this layer depends on all other layers in the architecture to be actionable.
-Service layer
The services layer refers to operations required to accomplish eMaaS business processes. This layer involves all individual services that works together in ensuring that the mobility services are provided to citizens and stakeholders.
-Business layer
This layer entails enterprises that collaborate virtually to create eMaaS to citizens in smart cities. Thus, business layer involves businesses’ strategies used by each enterprise to meet their goals as relates to sustainable transportation
-Application and data processing layer
The application and data processing involve the software programs and APIs used to provide eMaaS solutions to citizens. Thus, this layer integrates APIs to process, provide and manage mobility related data from various sources to ensure that transport services are provided to citizens.
-Data space layer
This layer is the center of the architecture as it comprises of types and sources of data required to facilitate eMaaS operations in smart cities
-Technologies layer
This layer describes the software and hardware infrastructure that supports the deployment of eMaaS operations in smart cities. The technology layer comprises the essential computing, telecommunications networks and physical hardware
-Physical infrastructures layer
This layer includes the generation of real-time mobility data from EVs, charging stations, buses, taxis, bikes and other physical devices related to transportation services in smart cities. This layer produces massive real-time data collected in aggregate from physical sources that are transferred to the technology layer for big data processing, analysis and storage.
Finding suggest that the multi-tier architecture is applicable in managing eMaaS that are entirely based on real-time data in creating an ecosystem of open mobility data that uses APIs to provide interoperable access to mobility metadata (that catalog and describes the data) and data sources (that point to internal open and external data resources).
Data collected from four participants in a technology infrastructure company in Norway confirmed the applicability of the multi-tier architecture. Although qualitative data was collected to verify each layer of the architecture, there is need to use quantitative data either from survey or experiment to statistically test the applicability of the architecture. Similarly, data was collected from a single company in Norway, hence, there is a need to test the architecture with real case data collected from other transport service companies.
Conclusions
The researchers underline that the multi-tier architecture integrates and exploits e-mobility data collected from transport company open data, private data from mobility operators and personal data generated from citizens.
Therefore, the study proposes a multi-tier architecture that stores, processes, analyzes and provides data and related services to improve e-mobility within smart cities. The multi tier architecture aims to support and increase eMaaS operation of EVs toward improving transportation services for city transport operators and citizens moving within the city to provide solutions for sustainable transport and e-mobility services.