## 1 Introduction  
A WSN is a network made up of small, resource-limited devices that communicate wirelessly in a network. Each device, called a sensor node, operates with other devices in the network to perform certain operations, such as monitoring the environment (temperature, sound, pressure, etc.). Over the past ten years, there has been an explosive growth in the number of Internet of Things (IoT) devices [1]. From the car to the mobile phone, to watches and televisions, connected devices are omnipresent in everyday life, and continue to expand their footprint [2]. Wireless sensor networks (WSNs) are an important part of this trend with numerous applications (home automation, industrial automation, video games, health monitoring, etc.) [3]. The Time Slotted Channel Hopping (TSCH) [4] protocol defined in the IEEE 802.15.4-2015 version of the IEEE 802.15.4 standard orchestrates the medium access for WSNs according to a time-frequency communication schedule. MAC protocols play a crucial role to meet the energy and latency requirements of these networks. As the number of connected objects increases, the volume of data generated by the Internet of Things greatly increases. A large amount of data passes through these nodes and raises challenges. Among these, the critical issue in WSN is energy consumption and latency especially for industrial applications [5, 6]. An important problem with wireless communications is the minimization of the energy consumption while ensuring latency and that every bit of information transmittedover the network is not lost. Energy consumption is one of the biggest concerns as a sensor must be able to operate without intervention or battery replacement for months or even years. Indeed, users generally choose to deploy these sensor nodes randomly in a targeted area, often difficult to access, in large numbers (hundreds or even thousands of sensor nodes), so their replacement is practically impossible or a very expensive task. Latency in TSCH networks is also crucial in many applications such as industrial automation, and reliability is important so that all data are received at the final destination. The TSCH MAC layer plays a crucial role in meeting power consumption and latency requirements. More specifically, the cell scheduling algorithms that are responsible for node behavior at each timeslot are at the center of these parameters. A certain number of studies, which aims at the development of these algorithms, have emerged to try to answer these different challenges. These scheduling algorithms can be classified either as centralized [7; 8; 9], distributed [10; 11; 12; 13], or autonomous [14; 15; 16; 17]. Centralized scheduling allows a central entity collect all the information of the network, then it calculates the link schedules. However, when the network topology changes, the update of the network information is done more slowly by the root, which becomes overloaded, and therefore can be inefficient.The distributed approach is characterized by the fact that the schedule is built through negotiations between neighboring nodes. However, this negotiation may cause a scheduling delay due to additional overhead. Autonomous scheduling is an attractive alternative as it avoids these problems by allowing each node to autonomously choose its own schedule without any signaling for schedule creation or modification. Orchestra [14] is the pioneer of this autonomous approach. However, despite several strengths such as the use of simple scheduling rules and the high delivery ratio, Orchestra determines the TSCH schedule for each node regardless of its traffic load, which can significantly affect communication delay. The demand of each node is not the same and varies depending on the location of the node in the network. For example, nodes close to the root are constantly in demand and have heavy convergecast traffic because all of the nodes transmit packets to this destination.