Background: Sleep is essential for human health. Considerable effort has been made into academic and industrial research and development on wireless body area networks for sleep monitoring in terms of non-intrusiveness, portability and autonomy. Thanks to rapid advances in smart sensing and communication technologies, various sleep monitoring systems (SMS) have been developed with advantages such as low-cost, accessible, discreet, contactless, unmanned and suitable for long-term monitoring. Objective: The objective of this paper is to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods: This review investigates the use of various common sensors in the hardware implementation of current SMS, as well as the types of parameters collected, the positions they are on the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different works about SMS and their results are presented. This review is not limited to the study of laboratory research, but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages and disadvantages. In particular, we categorized existing research on SMS based on how the sensor is used, including the number and type of sensors, and the preferred positions on the body. In addition to focusing on a specific system, issues concerning SMS such as privacy, economic and social impact are also included. Finally, we present an original SMS solution developed in our laboratory. Results: Through retrieving large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence and data mining have not been widely applied to the sleep monitoring research area. Accelerometer is the most commonly used sensor in SMS. Most of commercial sleep monitoring products can’t provide performance evaluation based on gold standard PSG. Conclusions: The combination of hotspot techniques such as big data, machine learning, artificial intelligence and data mining with sleep monitoring may be a promising research direction and attracts more and more researchers in the future. How to balance user acceptance and monitoring performance is the biggest challenge in SMS research.