Data storage in the Industrial Internet-of-Things scenario presents critical aspects related to the necessity of bringing storage devices closer to the point where data are captured. Concerns on storage temperature are to be considered especially when Solid State Drives (SSD) based on 3D NAND Flash technology are part of edge gateway architectures. Indeed, self-heating effects caused by oppressive storage demands combined with harsh environmental conditions call for proper handling at multiple abstraction levels to minimize severe performance slow downs and reliability threats. In this work, with the help of a SSD co-simulation environment that is stimulated within a realistic Industrial Internet-of-Things (IIoT) workload, we explore a methodology orthogonal to performance throttling that can be applied in synergy with the operating system of the host. Results evidenced that by leveraging on the SSD micro-architectural parameters of the queuing system it is possible to reduce the Input/Output operations Per Second (IOPS) penalty due to temperature protection mechanisms with minimum effort by the system. The methodology presented in this work opens further optimization tasks and algorithmic refinements for SSD and system designers not only in the IIoT market segment, but generally in all areas where storage power consumption is a concern.