Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier Data Collection in Sensor-cloud: A Systematic Literature Review IHSAN ALI 1 , ISMAIL AHMEDY 1 , ABDULLAH GANI 1,2 ,Senior Member, IEEE, MUHAMMAD TALHA 3 , MUHAMMAD AHSAN RAZA 4 , MOHAMMAD HOSSEIN ANISI 5 Senior Member, IEEE 1 Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Malaysia 2 Faculty of Computing and Informatics, University Malaysia Sabah, Labuan 88400, Malaysia. 3 Deanship of Scientific Research, King Saud University, Riyadh 11543, Saudi Arabia. 4 Department of Information Technology, Bahauddin Zakariya University, Multan, Pakistan. 5 School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom. Corresponding authors: Ihsan Ali,Ismail Ahmedy, Abdullah Gani (e-mail:ihsanalichd@siswa.um.edu.my,ismailahmedy@um.edu.my,abdullahgani@ums.edu.my/ abdullah@um.edu.my ) This research work was partially supported by the Faculty of Computer Science and Information Technology, University of Malaya under Postgraduate Research Grant (PG035-2016A) and LR003-2019.The authors are grateful to the Deanship of Scientific Research, King Saud University for funding this research project through Vice Deanship of Scientific Research Chairs (DSRVCH). ABSTRACT The integration of cloud computing and Wireless Sensor Networks (WSNs) to create Sensor- cloud helps in extending the data processing capability and storage capability of WSNs. Knowing how weak WSNs are with regards to communication ability, how to collect and upload sensory data to the cloud in limited time has become an issue in Sensor-cloud. In the last decade, with increasing interest by researchers in the domain, a considerable amount of research works have been conducted and published in the research domain. The main objective of this study is to systematically review the current research on data collection in Sensor-cloud. Hence, the study also aims at identifying, categorizing, and synthesizing important studies in the field of study. Accordingly, an evidence-based methodology is utilized in this study. By doing so,43 relevant studies were identified and retrieved to answer the formulated research questions. The systematic methodology offers a methodical and rigorous study selection and evaluation process that is repeatable and precise. The result shows that research on data collection in Sensor-cloud is relatively consistent with stable output in the last five years. Ten proposal contributions were identified with System, Framework, and Algorithm being the most used by the selected studies. In conclusion, key research challenges and future research directions were identified and discussed for researchers to propose effective solutions to the existing challenges. Although research on data collection in Sensor-cloud is gaining some traction in recent years, the works in the domain are not sufficient and concrete proposals are needed to improve data collection. INDEX TERMS Data Collection,Sensor Cloud, Internet of Things(IoT) Wireless Sensor Networks (WSN), Systematic Literature Review (SLR) I. INTRODUCTION R ECENTLY,Wireless Sensor Networks (WSNs) were mostly deployed in many applications, such as forest fire detection [1], agriculture [2], health monitoring [3], and so on. Hence, WSNs used for these applications nor- mally generate a vast amount of data that necessitates to be collected and processed in a minimal time period with relatively low delay. However, sensors are known to have a limited battery with limited computing capability and storage capability to support huge data transmission and processing. This constraint frequently leads to a small network lifetime. As a solution, the data processing and storage abilities of WSNs can be extended using cloud computing [4]. With cloud computing, WSNs performance can be improved, such as service quality, computation latency, energy consumption, and so on. Therefore, the integration of WSNs and cloud computing is termed as Sensor-cloud. The last 10 years have seen quite a number of works on data collection in Sensor- cloud by proposing different solutions on ways to enhance the efficiency and effectiveness of data collection. In recent years, many surveys and review papers were published on data collection of sensory data from sensor devices in the research domain (see Section II). In a study by Khan et al., the authors presented a taxonomy of numerous data collection VOLUME 4, 2016 1