  Internet of Things is an ecosystem of interconnected devices that are
accessible through the internet. The recent research focuses on adding more
smartness and intelligence to these edge devices. This makes them susceptible
to various kinds of security threats. These edge devices rely on cryptographic
techniques to encrypt the pre-processed data collected from the sensors
deployed in the field. In this regard, block cipher has been one of the most
reliable options through which data security is accomplished. The strength of
block encryption algorithms against different attacks is dependent on its
nonlinear primitive which is called Substitution Boxes. For the design of
S-boxes mainly algebraic and chaos-based techniques are used but researchers
also found various weaknesses in these techniques. On the other side,
literature endorse the true random numbers for information security due to the
reason that, true random numbers are purely non-deterministic. In this paper
firstly a natural dynamical phenomenon is utilized for the generation of true
random numbers based S-boxes. Secondly, a systematic literature review was
conducted to know which metaheuristic optimization technique is highly adopted
in the current decade for the optimization of S-boxes. Based on the outcome of
Systematic Literature Review (SLR), genetic algorithm is chosen for the
optimization of s-boxes. The results of our method validate that the proposed
dynamic S-boxes are effective for the block ciphers. Moreover, our results
showed that the proposed substitution boxes achieve better
