Abstract
Implementing a wireless sensor and actuator network (WSAN) in Internet of Things (IoT) applications is a complex task. The need to establish the number of nodes, sensors, and actuators, and their location and characteristics, requires a tool that allows the preliminary determination of this information. Additionally, in IoT scenarios where a large number of sensors and actuators are present, such as in a smart city, it is necessary to analyze the scalability of these systems. Modeling and simulation can help to conduct an early study and reduce development and deployment times in environments such as a smart city. The design-time verification of the system through a network simulation tool is useful for the most complex and expensive part of the system formed by a WSAN. However, the use of real components for other parts of the IoT system is feasible by using cloud computing infrastructure. Although there are cloud computing simulators, the cloud layer is poorly developed for the requirements of IoT applications. Technologies around cloud computing can be used for the rapid deployment of some parts of the IoT application and software services using containers. With this framework, it is possible to accelerate the development of the real system, facilitate the rapid deployment of a prototype, and provide more realistic simulations. This article proposes an approach for the modeling and simulation of IoT systems and services in a smart city leveraged in a WSAN simulator and technologies of cloud computing. Our approach was verified through experiments with two use cases. (1) A model of sensor and actuator networks as an integral part of an IoT application to monitor and control parks in a city. Through this use case, we analyze the scalability of a system whose sensors constantly emit data. (2) A model for cloud-based IoT reactive parking lot systems for a city. Through our approach, we have created an IoT parking system simulation model. The model contains an M/M/c/N queuing system to simulate service requests from users. In this use case, the model replication through hierarchical modeling and scalability of a distributed parking reservation service were evaluated. This last use case showed how the simulation model could provide information to size the system through probability distribution variables related to the queuing system. The experimental results show that the use of simulation techniques for this type of application makes it possible to analyze scalability in a more realistic way.
Highlights
The paradigm of pervasive systems permits to connect devices that may have some intelligence capacity
The first use case was the construction of a wireless sensor and actuator network (WSAN) model of an Internet of Things (IoT) system for the monitoring and control of a city park
We need to have the technology layers that satisfy the requirements of an IoT application that handles cloud-based services
Summary
The paradigm of pervasive systems permits to connect devices that may have some intelligence capacity. The first use case was the construction of a WSAN model of an IoT system for the monitoring and control of a city park This WSAN was used to simulate the data collection of certain physical variables. This model was extended by replication using Ptolemy’s hierarchical modeling capacity to model an IoT application for the control and monitoring of multiple parks in a city. Through this use case, we analyze the hierarchical modeling and scalability of the system whose sensors constantly emit data.
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