# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results
### Accomplished Result
However, there is still a loophole in these cost computation processes, as these models can perform cost computations but cannot contribute to proactive cost reduction. This loophole is potentially addressed in the proposed system, where novel and distinct cost modeling is carried out that computes the cost over multiple scenarios and significantly reduces the cost. _Quantified Outcome_: The proposed scheme offers an approximately 27% reduction in cost compared to existing approaches. * **Processing Time**: The proposed system's final performance parameter focuses on analyzing the models' computational efficiency performance. This performance metric was computed by recording the overall time required by the model to start and finish its operations. The learning outcomes of the model, shown in Figure 11, are as follows:  
* _Novelty_: The proposed scheme offers a unique cost model in which one objective function complies with seven constraints to ensure optimal resource utilization. The compliance standard toward satisfying constraints is much higher than that of any existing approach ([PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [37], [PERSON] et al. [38]). The existing system must assess its adherence to a broader consideration of constraints. This scheme reduces operational costs and potential resistance to illegitimate activities within the network. Hence, the proposed scheme presents a proper equilibrium between secure blockchain operations and data transmission with optimal resource management. This phenomenon is suitable for supporting secure data transmission in WSNs or any large-scale distributed networks in the presence of mobility, considering emergencies and standard applications hosted on it. * _Evidential Justification_: Reducing the computational complexity of blockchains in sensor networks is crucial for ensuring scalability and efficiency. The proposed scheme uses a simplified consensus mechanism that minimizes the computational overhead. The proposed scheme also aggregates the sensor data at the edge before sending them to the blockchain at the AP. This reduces the volume of data transmitted and processed on the blockchain, lowering computational complexity. Finally, the proposed system uses a cache that frequently accesses the data or computation results to reduce redundant calculations. The scheme stores the previously computed results, avoiding recomputation when the same inputs occur again, thus decreasing the overall computational complexity and reducing the processing time. However, the processing time for existing systems ([PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [37], [PERSON] et al. [38]) uses complex forms of blockchain operations. Simultaneously, some existing approaches (e.g., [PERSON] et al. [48], [PERSON] et al. [49]) tend to use highly iterative internal operations towards accomplishing their security targets. * _Quantified Outcome_: The processing time of the proposed system was found to offer approximately 38% of the reduced score, in contrast to existing approaches. Therefore, the novel and original results can be summarized as follows:* Without adopting any complex cryptographic approach, unlike the frequently used methods in existing studies, the proposed scheme offers a significant reduction in validation time by 40% in dynamic scenarios. Hence, the supportability for scalable performance in the proposed blockchain is highly ensured for near-world services in IoT, unlike existing approaches. * The latency performance is improved by 28% in the proposed system, which can be attributed to its mechanism to handle optimal resources right from the consensus mechanism until the final stage of assessing the constraint adherence of the model. This is an unconventionally novel outcome, as existing systems are mainly reported to undertake extensive internal security operations without any latency compensation schemes in dynamic environments. * The data transmission performance was improved by a 23% increase in throughput and a 38% reduction in overhead.