An Energy-Efficient Architecture for the Internet of Things (IoT)
Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficient architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.
IoT is a smart technology that interconnects each and every “thing” through a network in one form or another. The term “thing” includes sensors, actuators, hardware, software, and storage spread over multiple disciplines such as healthcare, industry, transport, and home appliances. The sensors collect data from the monitoring area, and their communication hardware sends the collected data to the middleware element. An enormous amount of data received by middleware is processed and analyzed by using various data analysis tools to extract interpretable information. Earlier they provided a clear vision of IoT and listed “energy-efficient sensing” as one of the research challenges. They presented a cloud-centric architecture of IoT and emphasized that it is applicable in various areas such as industry, home, medical systems, and many more. Later, many authors worked toward the integrated application of IoT and cloud computing in industries such as manufacturing , environment monitoring , real-time locating systems , energy saving , cloud manufacturing , , and supply chains . Xu et al. in  presented a survey for the application of IoT in industries. IoT has also been used in various other applications such as those listed in –. Since energy efficiency is a challenging issue in IoT, many authors worked toward this direction. In 2014, Akgul and Canberk  proposed the concept of “Self-Organized Things” (SoT), in which the sensors undergo automatic configuration, optimization, and healing mechanisms to save energy. They explained that the sensors can be put in sleep mode when the coverage area can be compromised for energy saving. In 2014, Zhou et al.  designed an “energy-efficient index tree” (EGF-tree) to save the energy utilized in collecting, querying, and aggregating data from sensors located in multiple regions in IoT.
The SNs are battery powered and have limited amount of energy, which is utilized when an SN is in active mode. An SN is said to be “active” if it is in high energy state and is actively sensing and transmitting data to eGN. The PA allows the SN to save its energy by turning off its transceivers and, hence, switching to a low energy state (also called sleep mode). An SN switches to sleep mode immediately after finishing the data transmission and remains in sleep mode until the eGN sends it a wake-up signal. The eGN can send a wake-up signal in three situations: first, when the sleep interval for the SN has expired; second, on arrival of a query; and third, when some other SN wants to communicate with it. In the first two cases, the SN senses the target environment to fill its buffer, whereas in the third case, the SN receives the incoming data in its buffer. The received data can be used by the SN as a trigger. The SN again switches to sleep mode after completion of the required action. The SN can even be switched off if it is not required to sense the target environment for longer duration of time or when the data sensed by it are useless. Thus, the sensors use their battery power efficiently by switching between active and sleep modes as and when required.
In this paper, architecture for IoT has been proposed, which ensures an energy-efficient utilization of the resources. The architecture is tested by using medical data on Amazon EC2 i2.xlarge instance. The results show that the energy is effectively and efficiently saved by switching the hardware resources of the SCL and the IPL to sleep mode. The key feature of the proposed model is the exchange of energy-related information between the two layers. The sensors switch to sleep mode based upon their available battery power and other factors, such as quality of extracted information, conflict factor, and CoV. Thismechanismenables cloud environment to predict the maximum amount of data that can be received during the next time interval, and hence, resources can be provisioned accordingly. Hence, the PA effectively increased resource utilization of hardware resources of both the SCL and the IPL. In a nutshell, the PA is energy efficient. Moreover, due to the flexile nature of the PA, it can be applied in a large number of IoT networks.
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