Efficient energy for the Internet of Things

Abstract

The Internet of Things is an intelligent technology that connects everything anywhere and anytime. The nature of the Internet of Things requires that resources be drained of energy. Therefore, energy efficiency from Internet of Things resources is an important issue in the research field. In this paper, an energy-efficient architecture for the Internet of Things is proposed, which consists of three layers, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of the sensors based on their remaining battery level, their previous usage history, and the quality of information required for a specific application. The predicted value can be used to increase the utilization of cloud resources by reallocating resources when the corresponding sensor node is in sleep mode. This mechanism is the efficient use of energy in all Internet of Things resources.

Introduction

With the emergence of a new era in computing, the Internet of Things [1] was used as the main structure of pervasive computing [2]. The Internet of Things is an intelligent technology that connects every "thing" through a network. The term "thing" includes sensors, actuators, hardware, software, and storage in fields such as healthcare, industry, transportation, and home appliances. The main goal of the Internet of Things is to maximize the communication of hardware objects with the physical world to transform the data of these objects into useful information without any human assistance. The Internet of Things consists of three elements: hardware, middleware, and presentation. The hardware element consists of sensors embedded in the battery, disk and communication systems. These sensors collect data from the monitoring area and their communication hardware sends the collected data to the middleware element. A significant amount of data received by the middleware is processed using various data analysis tools to extract interpretive information. The presentation element in IoT is responsible for visualizing the processed data and results in a readable form. It also receives the user's requirements and sends them to the middleware element to perform the necessary tasks. Figure 1 shows the elements and data transfer in IoT systems.

The limited energy of hardware elements is consumed while collecting and transmitting data. Most of the collected data is analyzed and carefully extracted information but, at any time, they consume energy. Due to the energy limitation, there is a need to maintain a balance between the quality of extracted information and the energy consumed by Internet of Things systems. In addition, the lifetime of any resource in the Internet of Things depends on the availability of energy. Loss of energy affects the entire environment. Therefore, there is an outstanding need to reduce energy consumption to increase resource lifetime and effective implementation of IoT systems.

past works

In 2013, Gubbi and his colleagues [1] presented a clear vision of the Internet of Things, which was challenged as "efficient energy metering" in one of the researches. They presented a cloud-centric architecture of the Internet of Things and emphasized that it is applicable in various areas including industry, home, medical systems and many other areas. After that, many authors turned to integrated software from Internet of Things and cloud computing in industries such as manufacturing [3], environmental monitoring [4], real-time systems [5], energy saving [6], cloud manufacturing [7]. , [8] and supply chain [9]. Xu and colleagues in [10] presented a survey for the use of IoT in industries. IoT was also used in various other applications such as those mentioned in [11] - [18].

Pa

the shape. 2 shows that PA consists of three layers, Sensing and Control Layer (SCL), Information Processing Layer (IPL) and Application Layer (AL), along with the issues employed by each layer. The SCL collects data from the target environment in an energy-efficient manner and sends it to the IPL. AL uses the information collected by IPL in various areas such as health monitoring, smart city, smart transportation, etc. These three layers are described in more detail below.

SCL

SCL consists of hardware elements of an Internet of Things system. which collects raw data in large volume and sends them for data analysis and analysis. The three main components of this layer are sensor nodes (SNS), energy-efficient gateway nodes (eGNs), and an energy-efficient base station (called an evolved node or eNode). Each of these components is described below.

Theoretical analysis

Before presenting the theoretical analysis of the system, first the method of calculating the energy level of a node is explained. It is observed that the energy consumption of a particular node in the system is inversely proportional to its sleep interval, which in turn depends on various factors including remaining battery level, contention factor, data quality, and COV. The longer the sleep interval, the lower the energy consumption and vice versa. In addition, each node consumes a certain amount of energy in the active state (EA) and in the sleep state (ES). The amount of energy consumed in active and sleep mode depends on the node. Therefore, by calculating the sleep distance and using different factors, the energy level of a node can be determined using the following relationship

Energy consumed = Ts ∗ Es + (T − Ts) ∗ Ea.

Here, Ts is the sum of all sleep intervals and T is the total elapsed time. Hence, (T - TS) represents the total duration for the node in the active state.

Experimental setup and performance analysis

This section discusses the empirical analysis of PA. The test setup is divided into two parts: 1) initialization in SCL and 2) data transfer to the cloud environment.

Initialization in SCL

the shape. 4 shows the experimental setup used to evaluate PA in the university. Here, five university volunteers were attached to three sensors to monitor blood pressure (BP), heart rate (HR) and respiratory rate (RR). These sensors for monitoring blood pressure, heart rate, and RR of these people are: 1) Omron 10 upper arm BP monitor Model BP785 BP785 [45], 2) polar RS 300 × HR monitor [46] and 3) content management system 50F OLED wrist RR monitor [47]. Tables 3 and 4 describe the details of subjects and sensors.

analyze

In this section, we move towards the comparison of PA performance in the following three cases: 1) performance comparison with related techniques; 2) experimental comparison with related technique; and 3) compare PA performance under different system states and configurations. The first two compare PA with some of the energy efficiency methods discussed in the second section. The methods used for comparison are SOT [19], EGF tree [20], ECH tree [21] and in object group localization (OGL) [22]. The third case highlights the suitability of PA in different scenarios.

Performance Comparison

Table 5 shows the comparison of PA performance with other methods. It can be seen that 1) energy efficiency from cloud resources is only considered in PA and 2) SN energy efficiency is a good idea in SOT, ECF, and PA, while EGF and OGL do not act on this issue.

Conclusion

In this paper, an architecture for the Internet of Things is proposed, which ensures energy efficient use of resources. The architecture is tested using medical data on Amazon EC2 i2.xlarge. The results show that energy goes to sleep mode effectively and efficiently by switching the resource storage hardware from SCL and IPL. The key feature of the presented model is energy-based information exchange between two layers. The sensor goes into sleep mode based on its battery power and other factors, such as the quality of the extracted data, contrast factor, and COV. This mechanism prepares the cloud environment to predict the maximum amount of data that can be received during the next time interval. Therefore, PA increases the utilization of hardware resources in SCL and IPL. In summary, TPA is energy efficient. Furthermore, due to the flexible nature of PA, it can be applied to a large number of IoT networks.