Energy management solutions in the Internet of Things applications: Technical analysis and new research directions

Abstract

By advancement of Internet of Things (IoT) technology in smart life such as smart city, smart home, smart healthcare and smart transportation, interconnections between smart things are growing that complicate evaluation of efficiency factors on the intelligent systems. Energy consumption as one of the most challenging issues is increasing with the growing IoT devices and existing interconnections between cloud data centers, mobile applications and human activities. Managing energy efficiency and power consumption is one of the important issues in green IoT-enabled technologies. This paper presents an overview on the energy management solutions in the IoT based on Systematic Literature Review (SLR). The main goal of this SLR-based overview is to recognize significant research trends in the field of energy management and power consumption techniques which need additional consideration to highlight more efficient and effective methods in IoT. Also, a taxonomy is proposed to categorize the existing research studies on energy management solutions. A statistical and technical analysis of reviewed existing papers are provided, and evaluation factors and attributes are discussed. We observed that variety of published research papers in smart home have highest percentage to evaluate energy management in the IoT. Also, deep learning and clustering methods are must popular techniques that were applied to evaluate the energy management in IoT case studies. Finally, new challenges and forthcoming issues of the energy management and efficient power consumption methods are presented.

Introduction

Today, Internet of Things (IoT) systems are used for connecting a various collection of smart devices, cloud data centers, fog nodes and mobile applications in many smart environments (Al-Turjman and Baali, 2019, Ahmad, 2020). Also, IoT applications provide an upper boundary of cloud-edge services for improving people’s daily lives by supporting cost-efficient and energy saving solutions on various communication strategies such as device to device, device to application, and device to cloud (Souri, 2019, Yan, 2020). Energy-efficient solutions in some case studies of IoT environments such as smart cities, smart transportations, smart home-care, and smart grids focus on energy saving management and improvement of power consumption in the IoT ethics (Zahmatkesh and Al-Turjman, 2020, Naranjo, 2019).

In the recent years, many research studies applied intelligent techniques, Machine Learning (ML) methods (Qadri, 2020, Deng, 2019), formal methods (Souri and Norouzi, 2019) and meta-heuristic algorithms (Chen et al., 2019, Chen, 2019) to examine the development of energy efficiency management for cloud-edge computing in the IoT environments. Also, there exists the challenge of continually providing optimal Quality of Service (QoS) by guarantying the Service level Agreements (SLAs) in energy consumption solutions (Safara, 2020). Based on increasing human usages to smart devices and IoT applications on smart phones, energy management concept is an important issue for decreasing cost of energy consumption in IoT environments (Safara, 2020, Jesudurai and Senthilkumar, 2019). According to existing review and survey studies (Bedi, 2018, Shrouf et al., 2014, Roselli, 2015, Reka and Dragicevic, 2018, Khajenasiri, 2017, Abdullah, 2016), there are some limitations on the presented discussions on energy efficient overview as follows: (1) Providing energy efficient solutions on just single case study of IoT environment such as healthcare monitoring or industrial equipment; (2) There is no an overview on the energy management solutions in IoT environments; (3) Ignoring methodological taxonomies for categorizing energy efficient solutions based on technical aspects and approaches; (4) Omitting technical analysis on energy efficiency features and opportunities in IoT environments.

To overcome the above mentioned gaps, this paper provides a systematic review towards technical evaluation, challenges and opportunities of the relevant research studies on energy management solutions in IoT environments. Then, an empirical exploration is presented potential efforts for evaluating technical aspects of each relevant study. Finally, this review provides a wide opening perspective on the development and progression of energy management techniques in IoT environments.

The important contributions of this review are presented as follows:

• Providing a relevant collection for existing energy management research studies in IoT environments based on Systematic Literature Review (SLR).

• Presenting evaluation analysis on technical aspects of each research study based on primary research questions.

• Discussing new challenges, open issues and research directions on the energy management techniques in IoT environments.

This paper is organized as follows: Section 2 provides a research selection methodology according to the SLR for presenting energy management approaches in IoT environment. Section 3 illustrates a technical taxonomy for existing energy management solutions and techniques and a technical analysis and summery for existing research studies according to the proposed technical taxonomy. Section 4 presents a discussion according to existing research studies in energy management solutions. Section 5 presents open issues and new challenges on the energy efficiency techniques on the IoT. Finally, the conclusion is illustrated in Section 6.