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