Advances in the Future Internet and Industry 4.0 are increasing the requirements, especially for Wireless Sensor Networks due to their constrained nature. The IEEE 802.15.4e-TSCH mode has been proposed to meet the needs of such networks and has been largely investigated in the literature to fully exploit its advantages for a wide range of applications, mainly in the industrial automation and process control domains. However, scheduling TSCH transmissions in the time and frequency dimensions is left to implementers. A plethora of TSCH schedulers are proposed in the literature and are developed either in a centralized or a decentralized fashion. Our work aims at giving an overview of the most relevant decentralized TSCH schedulers which we classified into : autonomous, distributed, and Reinforcement Learning based protocols. Then, an evaluation of a selection of these schedulers is carried out in a scenario considering highly heterogeneous traffic, i.e, considering the coexistence of very high and very low data rates in the network. These traffic conditions can be faced in current and future IoT applications, especially surveillance applications. Based on the experiment results, a set of conclusions is drawn for each of the considered schedulers to gain insights into their relevance for applications that need to handle highly heterogeneous traffic.