To exploit the LoRaWAN (Long-Range Wide Area Network), it is essential to design suitable allocation schemes for the wireless resources. To this aim, strategies for a fair allocation of Spreading Factors (SF) among the network devices have been presented. These strategies greatly outperform the basic Adaptive Data Rate (ADR) scheme. Within these techniques, EXPLoRa-AT yields so far the best results exploiting an “ordered water-filling” approach which aims to equalize the Air-Time channel usage for each group of devices using the same SF. This paper proposes two innovative schemes based on the former one, named EXPLoRa-KM (K-means) and EXPLoRa-TS (Time Symbol). Both schemes exploit the “ordered water-filling” approach, and apply further heuristics based on network traffic knowledge. EXPLoRa-KM aims to relieve critical regions, characterized by a significant number of collisions, computing suitable adjustments on the SF allocation using K-means. Conversely, and with incremented complexity, EXPLoRa-TS performs an equalization of the traffic load (measured in symbol times) among the SF channels. The latter takes into account the fact that each device, according to its application, transmits a variable amount of data at a different sending rate. Thus, different traffic types (more or less aggressive) can be recognized. Simulation results show how both heuristics give significant performance improvements when different traffic loads are generated around a LoRaWAN Gateway. Taking into account the traffic behavior, the techniques provided in this paper contribute as promising kick-off strategies for enhancing the network performance in order to come up with the ultimate goal of scalability on a LoRaWAN network for heterogeneous IoT scenarios.