Currently, there is lack of implementation of practical power-saving schemes in most of the batteries powered by IoT smart city applications available in the market that can extend the battery lifetime, even though numerous researches have been carried to reduce the average power consumption. This is because electronics consume similar amounts of power during the idling state as compared to the active state, resulting in low power efficiency of the application. Thus, power consumption is affected by the modes of the electronic operations. Different electronics also have their own types of settings that can be configured to reduce the power consumption, and this will be further investigated in this study. This chapter will address the issue of how to create a power-saving IoT application by applying power-saving schemes and creating an accurate model to predict the battery lifetime of the IoT application.
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Power consumption has and will always be an important factor in the IoT Smart City applications as they are required to operate continuously and for long periods of time. A non-negligible amount of power is drawn from the supply because there are usually multiple types of sensors used in these systems. Although numerous researches had been carried to reduce the sensor nodes’ average power consumption, but there is still improvement that can be made in reducing the power wastage, especially during the idling state of the sensor nodes. During the idling state, similar amount of power is consumed even though there are no outputs from the sensor. As a result, power is wasted, and this will in turn affect the power efficiency of the applications.
An IoT Smart City application to monitor the ambient temperature is taken as a case study to investigate the system’s power consumption. The application consists of sensor nodes and a sink node, as shown in Figure 1 (only one sensor node is shown). Each sensor node connects multiple sensors to collect different types of environmental data. In order to get an accurate ambient temperature, the sensor nodes are required to be located in multiple indoor and outdoor locations. Information collected from the sensor node will be transmitted to the sink node which act as a data collector using wireless transmission. Then, the sink node will upload the information to the Cloud for monitoring purposes. However, it is not feasible to power the sensor nodes through power points, especially in outdoor locations. Thus, using batteries is the most preferred solution because of its simplicity. There is the risk of application failure which can extend for many hours because batteries can be easily depleted from high power consumption. Worse still, not knowing when the batteries will be depleted, users will need to always monitor their application from time to time. Therefore, power saving schemes are necessary in order to extend the applications’ system lifetime. With the help of power saving schemes, users can reduce the power consumption and increase the operating time of their applications. Furthermore, this will also help them to reduce the operation cost especially if the users plan for mass deployment.
Figure 1. IoT Smart City Application
To develop the power saving schemes, each component used in the application is investigated individually to find the suitable configurations for reduced power consumption. In order to achieve this, the low power modes as specified by the manufacturer’s datasheet will be explored. The schemes that provide the highest power saving will be chosen and implemented. This is followed by testing to verify the amount of power saved. And since there are multiple types of components, power consumption of each components is very much different because each consumes different amount of power based on its operation modes. It is important to know the components’ power characteristics in order to be able to estimate the battery’s and hence the application system’s lifetime. The power consumption of each component in the system needs to be monitored so that rectification can be done immediately if the system does not meet the expected power level. Power measurement equipment are also used in this project to measure the power consumption in order to obtain accurate results.
After the power consumption of every component has been measured and recorded, a model will be developed to predict the battery’s lifetime based on the given battery capacity. Then, both the model and experimental battery lifetimes will be compared to validate the accuracy of the model created. With the help of the model created from this study, users are able to estimate the battery lifetime of similar systems with different types of components before deployment.
This proposed book chapter describes the design and implementation of power saving schemes for extending the lifetime of a novel IoT Smart City Application. The book chapter outline includes the introduction, literature review, methodology, results and discussion and conclusion. From this chapter, the readers can benefit from understanding the need for power saving for smart city applications, the current state-of-the-art related development in the area of power saving, the methodology used in incorporating power saving in the system design, component selections and finally the data collection and analysis. Then, they can apply a similar power saving design methodology to their own application domain.