# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results
### Accomplished Result
Although these schemes can  
Figure 11: Comparative assessment of processing time [28; 29; 37; 38; 39; 43; 44; 45; 46; 47; 48; 49]. offer better security results, their validation times increase with an increase in the number of sensors and traffic. Similarly, approaches presented by [PERSON] et al. [46] use big data approaches that can effectively offer better data quality towards effective resource management; however, the complex structure could easily offer sub-optimal scalability performance in the presence of a complex form of request or a higher amount of concurrent request processing. The approaches of [PERSON] et al. [45], [PERSON] et al. [44], [PERSON] et al. [43], and [PERSON] et al. [39] offer extensive encryption usage, which eventually increases the validation time. However, in contrast to existing schemes, the proposed model provides a novel synchronized design between the enrollment process and trust score calculation in the presence of both direct and indirect communication. This implies that the proposed scheme is capable of faster trust calculation without much dependency on the complexities of traffic or topological changes. Furthermore, the inclusion of state-based attributes in trust estimation assists in further considering all optimal possibilities of resource usage while performing data transmission between communicating mobile sensors. The lack of such inclusion in the design aspect of existing approaches proves the novel outcome generated by the proposed scheme. \(\circ\) _Quantified Outcome_: The proposed system offers an approximately 40% reduced validation time compared to the average validation time of existing blockchain-based approaches. \(\bullet\) **Latency**: This performance parameter is responsible for computing the latency between the transmitting and receiving nodes and involves processing the AP and GN during either the N2N or N2S mode of communication. The outcome shown in the previous section (Figure 7) exhibited a reduced latency in the proposed model. The learning outcomes of this part of the analysis are as follows: \(\circ\) _Novelty:_ The primary novelty of this outcome is that the proposed scheme assigns channel capacity in the sequential form of mobile user demands. Such progressive allocation of resources has yet to be witnessed in the existing approaches discussed in the literature ([PERSON] et al. [28], [PERSON] et al. [38], [PERSON] et al. [39], and [PERSON] [47]). Simultaneously, the latency attribute is used to determine the cardinality of the allocated resources. This contributes to a progressive reduction in latency, even under peak traffic conditions. The secondary novelty of this outcome is that the proposed scheme can minimize the latency by adhering to the constraints considered while formulating the objective functions. This will eventually mean that the proposed modeling approach complies with the constraints to be satisfied by the objective function, leading to a greater likelihood of being functional over the real-time traffic of the sensor network. Hence, the reduced latency does not degrade the proposed system's data quality or transmission rate, which must be addressed in existing related studies. \(\circ\) _Evidential Justification_: The proposed system completes the task related to data transmission within permissible latency, \(pl\), by adopting optimal resources. Furthermore, the proposed model can perform both N2N and N2S concurrently, thereby contributing towards minimizing the latency, while the objective function constructed is compliant with the latency reduction phenomenon. However, this is different from the existing approaches. For example, the work of [PERSON] et al. [28] has an extensive trust computation overlooking the latency demands of various tasks to resist varied forms of attack. Such a discrete form of task requirement was not considered. Other studies, such as [PERSON] et al. [38],[PERSON] et al. [39], and [PERSON] [47], are characterized by various forms of internal operations related to the computation of security attributes.