Page:Catalyzing the Internet of Things and Smart Cities ： Global City Teams Challenge.pdf/2

 Many distributed IoT-based control systems employ a relatively small-scale Data Analytics layer. An example of a small-scale layer can be found in a smart thermostat that could also function as a local decision maker within the home network.

On the other hand, many IoT solutions deployed at a citywide scale may require a big centralized data repository and more powerful processors to handle a larger amount of data from multiple sectors and applications. An example of such a system could be a city’s disaster command center that is designed to provide simultaneous visibility into different departments (e.g., water, energy, transportation, healthcare, etc.).

The main function of the Data Analytics layer is to collect data from the lower layers and extract useful information from the set of data. Note that the set of data itself may not have significant value and may not be very useful to the user. The information extracted from the data, however, could be valuable in taking actions and achieving a desired end result.

The top layer is the Service layer. This layer is where intelligence resides and decisions are made. This layer receives information from the Data Analytics layer, and then makes decisions on next steps. The next steps could include displaying the information on a monitor screen or operating and controlling actuators. The Service layer is important because it is in the position in the architecture to create the highest value for the users of the system. Many business decisions are made in this layer, including human-in-the-loop actions. The human-machine interface can be an important factor in this layer.

Once the decision of the next step is made at the Service layer, sometimes (but not always) information starts flowing in the reverse manner (i.e., from Service layer down to the Hardware layer). This is especially true for systems based on some type of autonomous control. On the other hand, it is sometimes a human being who makes the decision and executes it. In either case, the end result is some type of action that closes the loop of the information flow. A similar representation of IoT data flow was proposed in another article .

Many developers consider IoT to be the combination of just the two bottom layers (Hardware and Communications). It is important to note, however, that these two layers are merely a part of the whole IoT architecture. In many cases, the top two layers (Data Analytics and Service) play more important roles in defining and producing the real value from the system. Also in many cases, the design and implementation of the top two layers may be more complex and unclear than the bottom two layers. In many cases, the top two layers are heavily coupled with business cases that are important factors in determining sustainability and replicability of the solutions.

In the case of smart city applications, it is often easier to conceptualize the architecture as two groups of layers— Infrastructure and Applications. “Infrastructure” typically refers to the bottom two layers of the IoT architecture, and “Applications” refers to the top two layers. In some cases, however, the Data Analytics layer could belong to the infrastructure group, depending on the nature of its functionality. Many solutions/products that belong to the application group have more flexibility in deployments than the ones belonging to the infrastructure group. This simple IoT architecture can serve as an initial template to map different smart city solutions to build consensus on their technical interoperability, which is essential in addressing the challenges in accelerating the market momentum for IoT and smart cities.

Smart cities use smart technologies such as IoT and CPS to improve the quality of life of the residents and citizens. Although progress in deploying IoT solutions has been quite impressive, the IoT market still suffers from the issue of “fragmentation, ” and the smart city market shares similar concerns. Many smart city solution projects are isolated and heavily rely on custom-solution developments. Naturally, many of them are “one-off” projects with heavy emphasis on customization and inadequate consideration for future upgradability and extensibility. As a result, these deployments are isolated and do not enjoy economies of scale. Although many cities share the same issues (i.e., parking problems, traffic jams, air pollution, etc.), they often do not share best practices and end up reinventing the wheel. In this landscape, it is very difficult to create common standards for development and deployment of interoperable solutions.

To address this issue, the National Institute of Standards and Technology (NIST) has teamed up with US-Ignite and private sector partners to create the Global City Teams Challenge (GCTC) program. The goal of GCTC is to establish and demonstrate replicable, scalable, and sustainable models for incubation and deployment of interoperable, standards-based IoT solutions and to demonstrate measurable benefits in smart communities/cities. “Replicability” means that the solutions should be designed to operate in more than one city or community with minimal customization. “Scalability” means that the solution should be functional regardless of the size and volume of the deployment. “Sustainability” means that the project should be designed to last beyond its initial funding stage. In other words, the deployed solution must either (1) create its own revenue to support the operational cost or (2) provide enough tangible benefits that the municipal governments are willing to cover the operation cost using their budgets. Many of today’s smart city deployments lack one or more of these characteristics. GCTC places significant emphasis on the ability to measure tangible benefits for residents and citizens, thus empowering leaders within communities to demonstrate the benefits of adoption.

A. Approach

To achieve the goal of GCTC, the program was designed to create a voluntary environment for multi-stakeholder collaboration. As can be seen in Figure 2, multiple cities and technology innovators are brought into the program and asked