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.

随着物联网（IoT）技术在诸如智能城市，智能家居，智能医疗保健和智能交通等智能生活中的发展，智能事物之间的互连正在不断增长，这使对智能系统效率因素的评估变得复杂。随着物联网设备的增长以及云数据中心，移动应用程序和人类活动之间的现有互连，能源消耗成为最具挑战性的问题之一。管理能源效率和功耗是绿色物联网支持技术中的重要问题之一。本文概述了基于系统文献回顾（SLR）的物联网中的能源管理解决方案。此基于SLR的概述的主要目标是认识到能源管理和功耗技术领域的重要研究趋势，需要进一步考虑以突出物联网中更有效的方法。此外，还提出了一种分类法，以对能源管理解决方案的现有研究进行分类。提供了对已审查的现有论文的统计和技术分析，并讨论了评估因素和属性。我们观察到，智能家居中各种已发表的研究论文在评估物联网中的能源管理方面具有最高的百分比。此外，深度学习和集群方法必须是流行的技术，用于评估IoT案例研究中的能源管理。最后，