Abstract Long Range Wide Area Network (LoRaWAN) is an emerging wireless technology that is expected to be widely deployed and implemented in several applications, especially with the promising widespread use of the Internet of Things (IoT) and its potential applications within the Fifth Generation (5G) communication technology. LoRaWAN consists of a number of nodes that monitors and senses the environment to collect specific data, and then sends the collected data to a remote monitoring device for further processing and decision-making. Energy consumption and security assurance are two vital factors needed to be optimized to ensure an efficient and reliable network operation and performance. To achieve that, each of LoRaWAN nodes can be configured by five transmission parameters, which are the spreading factor, carrier frequency, bandwidth, coding rate and transmission power. Choosing the best values of these parameters leads to enhancing the network deployment. In this paper, we shed the light to the security aspect in LoRaWAN network. Then, we introduced an algorithm that depends on the reinforcement learning approach to enable each node in the network to choose the best values of spreading factor and transmission power such that it leads to a lower energy consumption and higher packets’ delivery rate. The results of the simulation experiments of our proposed technique showed a valuable increase in the packet reception rate at the gateway and a significant decrease in the total consumed energy at the end nodes compared with the most related work in literature.

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[26] Zhou Q., Zheng K., Xing J., and Xu R., “Design and Implementation of Open LoRa for IoT,” IEEE Access, vol. 7, pp. 100649-100657, 2019. Ala’ Khalifeh received the PhD degree in Electrical and Computer Engineering from the University of California, Irvine -USA in 2010. He is currently an Associate Professor in the Communication Engineering department at the German Jordanian University and the department chair. His research is in communications technology, and networking with particular emphasis on optimal resource allocations for multimedia transmission over wired and wireless networks, Quality of Service, Internet of Things and Wireless Sensor Networks. Khaled Aldahdouh is a researcher in the Communication Engineering department at the German Jordanian University. He received the MSc degree in the Computer Engineering from the German Jordanian University in 2020, and the BA degree in the communication engineering from Philadelphia University in Jordan in 2017. His research is in wireless communications, 5G system, Internet of Things, and Wireless Sensor Networks based on LoRa technology. Sahel Alouneh is a full professor of electrical and computer engineering. Currently, he is a member of the Cybersecurity program in Engineering College, Al Ain University, Abu Dhabi campus, in the UAE. His posts before joining Al Ain University, Prof. Alouneh held the post of the dean of Scientific Research at German Jordanian University (GJU) from October 2019 to August 2020. In addition, he served as the dean of the faculty of electrical engineering and information technology at the German Jordanian University (GJU) from September 2015 and September 2019. Prof. Alouneh also served as the vice dean of the deanship of graduate studies and scientific research at GJU from March 2014 to March 2015. He also served as the director of computer center at GJU from July 2009 to January 2012.